feat: implement frontend for calendar integration (Phase 3 & 4)

## Frontend Implementation ### Meeting Selection & Management - Created MeetingSelection component for choosing between multiple active meetings - Shows both active meetings and upcoming calendar events (30 min ahead) - Displays meeting metadata with privacy controls (owner-only details) - Supports creation of unscheduled meetings alongside calendar meetings ### Waiting Room - Added waiting page for users joining before scheduled start time - Shows countdown timer until meeting begins - Auto-transitions to meeting when calendar event becomes active - Handles early joining with proper routing ### Meeting Info Panel - Created collapsible info panel showing meeting details - Displays calendar metadata (title, description, attendees) - Shows participant count and duration - Privacy-aware: sensitive info only visible to room owners ### ICS Configuration UI - Integrated ICS settings into room configuration dialog - Test connection functionality with immediate feedback - Manual sync trigger with detailed results - Shows last sync time and ETag for monitoring - Configurable sync intervals (1 min to 1 hour) ### Routing & Navigation - New /room/{roomName} route for meeting selection - Waiting room at /room/{roomName}/wait?eventId={id} - Classic room page at /{roomName} with meeting info - Uses sessionStorage to pass selected meeting between pages ### API Integration - Added new endpoints for active/upcoming meetings - Regenerated TypeScript client with latest OpenAPI spec - Proper error handling and loading states - Auto-refresh every 30 seconds for live updates ### UI/UX Improvements - Color-coded badges for meeting status - Attendee status indicators (accepted/declined/tentative) - Responsive design with Chakra UI components - Clear visual hierarchy between active and upcoming meetings - Smart truncation for long attendee lists This completes the frontend implementation for calendar integration, enabling users to seamlessly join scheduled meetings from their calendar applications.
feat: implement Phase 2 - Multiple active meetings per room with grace period
2025-12-20 20:29:06 +00:00 · 2025-08-18 19:29:56 -06:00 · 2025-08-18 19:03:41 -06:00 · 2025-08-18 17:22:41 -06:00 · 2025-08-18 17:03:23 -06:00 · 2025-08-18 16:51:30 -06:00
430 changed files with 14212 additions and 77872 deletions
--- a/.github/workflows/db_migrations.yml
+++ b/.github/workflows/db_migrations.yml
@@ -2,8 +2,6 @@ name: Test Database Migrations

 on:
  push:
-    branches:
-      - main
    paths:
      - "server/migrations/**"
      - "server/reflector/db/**"
@@ -19,9 +17,6 @@ on:
 jobs:
  test-migrations:
    runs-on: ubuntu-latest
-    concurrency:
-      group: db-ubuntu-latest-${{ github.ref }}
-      cancel-in-progress: true
    services:
      postgres:
        image: postgres:17
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -0,0 +1,47 @@
+name: Deploy to Amazon ECS
+
+on: [workflow_dispatch]
+
+env:
+  # 950402358378.dkr.ecr.us-east-1.amazonaws.com/reflector
+  AWS_REGION: us-east-1
+  ECR_REPOSITORY: reflector
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    permissions:
+      deployments: write
+      contents: read
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@0e613a0980cbf65ed5b322eb7a1e075d28913a83
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@62f4f872db3836360b72999f4b87f1ff13310f3a
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+
+      - name: Build and push
+        id: docker_build
+        uses: docker/build-push-action@v4
+        with:
+          context: server
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
--- a/.github/workflows/dockerhub-backend.yml
+++ b/.github/workflows/dockerhub-backend.yml
@@ -1,53 +0,0 @@
-name: Build and Push Backend Docker Image (Docker Hub)
-
-on:
-  push:
-    tags:
-      - "v*"
-  workflow_dispatch:
-
-env:
-  REGISTRY: docker.io
-  IMAGE_NAME: monadicalsas/reflector-backend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=ref,event=tag
-            type=raw,value=latest,enable={{is_default_branch}}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v5
-        with:
-          context: ./server
-          file: ./server/Dockerfile
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
--- a/.github/workflows/dockerhub-frontend.yml
+++ b/.github/workflows/dockerhub-frontend.yml
@@ -1,70 +0,0 @@
-name: Build and Push Frontend Docker Image
-
-on:
-  push:
-    tags:
-      - "v*"
-  workflow_dispatch:
-
-env:
-  REGISTRY: docker.io
-  IMAGE_NAME: monadicalsas/reflector-frontend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: monadicalsas
-          password: ${{ secrets.DOCKERHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=ref,event=tag
-            type=raw,value=latest,enable={{is_default_branch}}
-          github-token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push Docker image
-        uses: docker/build-push-action@v5
-        with:
-          context: ./www
-          file: ./www/Dockerfile
-          push: true
-          tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-          platforms: linux/amd64,linux/arm64
-
-  deploy:
-    needs: build-and-push
-    runs-on: ubuntu-latest
-    if: success()
-    strategy:
-      matrix:
-        environment: [reflector-monadical, reflector-media]
-    environment: ${{ matrix.environment }}
-    steps:
-      - name: Trigger Coolify deployment
-        run: |
-          curl -X POST "${{ secrets.COOLIFY_WEBHOOK_URL }}" \
-            -H "Content-Type: application/json" \
-            -H "Authorization: Bearer ${{ secrets.COOLIFY_WEBHOOK_TOKEN }}" \
-            -f || (echo "Failed to trigger Coolify deployment for ${{ matrix.environment }}" && exit 1)
--- a/.github/workflows/test_next_server.yml
+++ b/.github/workflows/test_next_server.yml
@@ -1,45 +0,0 @@
-name: Test Next Server
-
-on:
-  pull_request:
-    paths:
-      - "www/**"
-  push:
-    branches:
-      - main
-    paths:
-      - "www/**"
-
-jobs:
-  test-next-server:
-    runs-on: ubuntu-latest
-
-    defaults:
-      run:
-        working-directory: ./www
-
-    steps:
-    - uses: actions/checkout@v4
-
-    - name: Setup Node.js
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-
-    - name: Install pnpm
-      uses: pnpm/action-setup@v4
-      with:
-        version: 8
-
-    - name: Setup Node.js cache
-      uses: actions/setup-node@v4
-      with:
-        node-version: '20'
-        cache: 'pnpm'
-        cache-dependency-path: './www/pnpm-lock.yaml'
-
-    - name: Install dependencies
-      run: pnpm install
-
-    - name: Run tests
-      run: pnpm test
--- a/.github/workflows/test_server.yml
+++ b/.github/workflows/test_server.yml
@@ -5,17 +5,12 @@ on:
    paths:
      - "server/**"
  push:
-    branches:
-      - main
    paths:
      - "server/**"

 jobs:
  pytest:
    runs-on: ubuntu-latest
-    concurrency:
-      group: pytest-${{ github.ref }}
-      cancel-in-progress: true
    services:
      redis:
        image: redis:6
@@ -24,47 +19,29 @@ jobs:
    steps:
      - uses: actions/checkout@v4
      - name: Install uv
-        uses: astral-sh/setup-uv@v6
+        uses: astral-sh/setup-uv@v3
        with:
          enable-cache: true
          working-directory: server
+
      - name: Tests
        run: |
          cd server
          uv run -m pytest -v tests

-  docker-amd64:
-    runs-on: linux-amd64
-    concurrency:
-      group: docker-amd64-${{ github.ref }}
-      cancel-in-progress: true
+  docker:
+    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Build AMD64
-        uses: docker/build-push-action@v6
+        uses: docker/setup-buildx-action@v2
+      - name: Build and push
+        id: docker_build
+        uses: docker/build-push-action@v4
        with:
          context: server
-          platforms: linux/amd64
-          cache-from: type=gha,scope=amd64
-          cache-to: type=gha,mode=max,scope=amd64
-          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
-
-  docker-arm64:
-    runs-on: linux-arm64
-    concurrency:
-      group: docker-arm64-${{ github.ref }}
-      cancel-in-progress: true
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      - name: Build ARM64
-        uses: docker/build-push-action@v6
-        with:
-          context: server
-          platforms: linux/arm64
-          cache-from: type=gha,scope=arm64
-          cache-to: type=gha,mode=max,scope=arm64
-          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
+          platforms: linux/amd64,linux/arm64
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 .DS_Store
 server/.env
 .env
-Caddyfile
 server/exportdanswer
 .vercel
 .env*.local
@@ -15,9 +14,4 @@ data/
 www/REFACTOR.md
 www/reload-frontend
 server/test.sqlite
-CLAUDE.local.md
-www/.env.development
-www/.env.production
-.playwright-mcp
-docs/pnpm-lock.yaml
-.secrets
+CLAUDE.local.md
--- a/.gitleaksignore
+++ b/.gitleaksignore
@@ -1,5 +0,0 @@
-b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15
-docs/docs/installation/auth-setup.md:curl-auth-header:250
-docs/docs/installation/daily-setup.md:curl-auth-header:277
-gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
-gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -27,8 +27,3 @@ repos:
        files: ^server/
      - id: ruff-format
        files: ^server/
-
-  - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.28.0
-    hooks:
-      - id: gitleaks
--- a/.secrets.example
+++ b/.secrets.example
@@ -1,24 +0,0 @@
-# Example secrets file for GitHub Actions workflows
-# Copy this to .secrets and fill in your values
-# These secrets should be configured in GitHub repository settings:
-# Settings > Secrets and variables > Actions
-
-# DockerHub Configuration (required for frontend and backend deployment)
-# Create a Docker Hub access token at https://hub.docker.com/settings/security
-# Username: monadicalsas
-DOCKERHUB_TOKEN=your-dockerhub-access-token
-
-# GitHub Token (required for frontend and backend deployment)
-# Used by docker/metadata-action for extracting image metadata
-# Can use the default GITHUB_TOKEN or create a personal access token
-GITHUB_TOKEN=your-github-token-or-use-default-GITHUB_TOKEN
-
-# Coolify Deployment Webhook (required for frontend deployment)
-# Used to trigger automatic deployment after image push
-# Configure these secrets in GitHub Environments:
-# Each environment should have:
-#   - COOLIFY_WEBHOOK_URL: The webhook URL for that specific deployment
-#   - COOLIFY_WEBHOOK_TOKEN: The webhook token (can be the same for both if using same token)
-
-# Optional: GitHub Actions Cache Token (for local testing with act)
-GHA_CACHE_TOKEN=your-github-token-or-empty
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,307 +1,5 @@
 # Changelog

-## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
-
-
-### Bug Fixes
-
-* build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
-
-## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
-
-
-### Bug Fixes
-
-* populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
-
-## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
-
-
-### Features
-
-* dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
-* llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
-
-
-### Bug Fixes
-
-* celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
-* deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
-* hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
-* return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
-
-## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
-
-
-### Bug Fixes
-
-* Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
-
-## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
-
-
-### Bug Fixes
-
-* align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
-
-## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
-
-
-### Bug Fixes
-
-* daily auto refresh fix ([#755](https://github.com/Monadical-SAS/reflector/issues/755)) ([fe47c46](https://github.com/Monadical-SAS/reflector/commit/fe47c46489c5aa0cc538109f7559cc9accb35c01))
-* Skip mixdown for multitrack ([#760](https://github.com/Monadical-SAS/reflector/issues/760)) ([b51b7aa](https://github.com/Monadical-SAS/reflector/commit/b51b7aa9176c1a53ba57ad99f5e976c804a1e80c))
-
-## [0.22.1](https://github.com/Monadical-SAS/reflector/compare/v0.22.0...v0.22.1) (2025-11-27)
-
-
-### Bug Fixes
-
-* participants update from daily ([#749](https://github.com/Monadical-SAS/reflector/issues/749)) ([7f0b728](https://github.com/Monadical-SAS/reflector/commit/7f0b728991c1b9f9aae702c96297eae63b561ef5))
-
-## [0.22.0](https://github.com/Monadical-SAS/reflector/compare/v0.21.0...v0.22.0) (2025-11-26)
-
-
-### Features
-
-* Multitrack segmentation ([#747](https://github.com/Monadical-SAS/reflector/issues/747)) ([d63040e](https://github.com/Monadical-SAS/reflector/commit/d63040e2fdc07e7b272e85a39eb2411cd6a14798))
-
-## [0.21.0](https://github.com/Monadical-SAS/reflector/compare/v0.20.0...v0.21.0) (2025-11-26)
-
-
-### Features
-
-* add transcript format parameter to GET endpoint ([#709](https://github.com/Monadical-SAS/reflector/issues/709)) ([f6ca075](https://github.com/Monadical-SAS/reflector/commit/f6ca07505f34483b02270a2ef3bd809e9d2e1045))
-
-## [0.20.0](https://github.com/Monadical-SAS/reflector/compare/v0.19.0...v0.20.0) (2025-11-25)
-
-
-### Features
-
-* link transcript participants ([#737](https://github.com/Monadical-SAS/reflector/issues/737)) ([9bec398](https://github.com/Monadical-SAS/reflector/commit/9bec39808fc6322612d8b87e922a6f7901fc01c1))
-* transcript restart script ([#742](https://github.com/Monadical-SAS/reflector/issues/742)) ([86d5e26](https://github.com/Monadical-SAS/reflector/commit/86d5e26224bb55a0f1cc785aeda52065bb92ee6f))
-
-## [0.19.0](https://github.com/Monadical-SAS/reflector/compare/v0.18.0...v0.19.0) (2025-11-25)
-
-
-### Features
-
-* dailyco api module ([#725](https://github.com/Monadical-SAS/reflector/issues/725)) ([4287f8b](https://github.com/Monadical-SAS/reflector/commit/4287f8b8aeee60e51db7539f4dcbda5f6e696bd8))
-* dailyco poll ([#730](https://github.com/Monadical-SAS/reflector/issues/730)) ([8e438ca](https://github.com/Monadical-SAS/reflector/commit/8e438ca285152bd48fdc42767e706fb448d3525c))
-* multitrack cli ([#735](https://github.com/Monadical-SAS/reflector/issues/735)) ([11731c9](https://github.com/Monadical-SAS/reflector/commit/11731c9d38439b04e93b1c3afbd7090bad11a11f))
-
-
-### Bug Fixes
-
-* default platform fix ([#736](https://github.com/Monadical-SAS/reflector/issues/736)) ([c442a62](https://github.com/Monadical-SAS/reflector/commit/c442a627873ca667656eeaefb63e54ab10b8d19e))
-* parakeet vad not getting the end timestamp ([#728](https://github.com/Monadical-SAS/reflector/issues/728)) ([18ed713](https://github.com/Monadical-SAS/reflector/commit/18ed7133693653ef4ddac6c659a8c14b320d1657))
-* start raw tracks recording ([#729](https://github.com/Monadical-SAS/reflector/issues/729)) ([3e47c2c](https://github.com/Monadical-SAS/reflector/commit/3e47c2c0573504858e0d2e1798b6ed31f16b4a5d))
-
-## [0.18.0](https://github.com/Monadical-SAS/reflector/compare/v0.17.0...v0.18.0) (2025-11-14)
-
-
-### Features
-
-* daily QOL: participants dictionary ([#721](https://github.com/Monadical-SAS/reflector/issues/721)) ([b20cad7](https://github.com/Monadical-SAS/reflector/commit/b20cad76e69fb6a76405af299a005f1ddcf60eae))
-
-
-### Bug Fixes
-
-* add proccessing page to file upload and reprocessing ([#650](https://github.com/Monadical-SAS/reflector/issues/650)) ([28a7258](https://github.com/Monadical-SAS/reflector/commit/28a7258e45317b78e60e6397be2bc503647eaace))
-* copy transcript ([#674](https://github.com/Monadical-SAS/reflector/issues/674)) ([a9a4f32](https://github.com/Monadical-SAS/reflector/commit/a9a4f32324f66c838e081eee42bb9502f38c1db1))
-
-## [0.17.0](https://github.com/Monadical-SAS/reflector/compare/v0.16.0...v0.17.0) (2025-11-13)
-
-
-### Features
-
-* add API key management UI ([#716](https://github.com/Monadical-SAS/reflector/issues/716)) ([372202b](https://github.com/Monadical-SAS/reflector/commit/372202b0e1a86823900b0aa77be1bfbc2893d8a1))
-* daily.co support as alternative to whereby ([#691](https://github.com/Monadical-SAS/reflector/issues/691)) ([1473fd8](https://github.com/Monadical-SAS/reflector/commit/1473fd82dc472c394cbaa2987212ad662a74bcac))
-
-## [0.16.0](https://github.com/Monadical-SAS/reflector/compare/v0.15.0...v0.16.0) (2025-10-24)
-
-
-### Features
-
-* search date filter ([#710](https://github.com/Monadical-SAS/reflector/issues/710)) ([962c40e](https://github.com/Monadical-SAS/reflector/commit/962c40e2b6428ac42fd10aea926782d7a6f3f902))
-
-## [0.15.0](https://github.com/Monadical-SAS/reflector/compare/v0.14.0...v0.15.0) (2025-10-20)
-
-
-### Features
-
-* api tokens  ([#705](https://github.com/Monadical-SAS/reflector/issues/705)) ([9a258ab](https://github.com/Monadical-SAS/reflector/commit/9a258abc0209b0ac3799532a507ea6a9125d703a))
-
-## [0.14.0](https://github.com/Monadical-SAS/reflector/compare/v0.13.1...v0.14.0) (2025-10-08)
-
-
-### Features
-
-* Add calendar event data to transcript webhook payload ([#689](https://github.com/Monadical-SAS/reflector/issues/689)) ([5f6910e](https://github.com/Monadical-SAS/reflector/commit/5f6910e5131b7f28f86c9ecdcc57fed8412ee3cd))
-* container build for www / github ([#672](https://github.com/Monadical-SAS/reflector/issues/672)) ([969bd84](https://github.com/Monadical-SAS/reflector/commit/969bd84fcc14851d1a101412a0ba115f1b7cde82))
-* docker-compose for production frontend ([#664](https://github.com/Monadical-SAS/reflector/issues/664)) ([5bf64b5](https://github.com/Monadical-SAS/reflector/commit/5bf64b5a41f64535e22849b4bb11734d4dbb4aae))
-
-
-### Bug Fixes
-
-* restore feature boolean logic ([#671](https://github.com/Monadical-SAS/reflector/issues/671)) ([3660884](https://github.com/Monadical-SAS/reflector/commit/36608849ec64e953e3be456172502762e3c33df9))
-* security review ([#656](https://github.com/Monadical-SAS/reflector/issues/656)) ([5d98754](https://github.com/Monadical-SAS/reflector/commit/5d98754305c6c540dd194dda268544f6d88bfaf8))
-* update transcript list on reprocess ([#676](https://github.com/Monadical-SAS/reflector/issues/676)) ([9a71af1](https://github.com/Monadical-SAS/reflector/commit/9a71af145ee9b833078c78d0c684590ab12e9f0e))
-* upgrade nemo toolkit ([#678](https://github.com/Monadical-SAS/reflector/issues/678)) ([eef6dc3](https://github.com/Monadical-SAS/reflector/commit/eef6dc39037329b65804297786d852dddb0557f9))
-
-## [0.13.1](https://github.com/Monadical-SAS/reflector/compare/v0.13.0...v0.13.1) (2025-09-22)
-
-
-### Bug Fixes
-
-* TypeError on not all arguments converted during string formatting in logger ([#667](https://github.com/Monadical-SAS/reflector/issues/667)) ([565a629](https://github.com/Monadical-SAS/reflector/commit/565a62900f5a02fc946b68f9269a42190ed70ab6))
-
-## [0.13.0](https://github.com/Monadical-SAS/reflector/compare/v0.12.1...v0.13.0) (2025-09-19)
-
-
-### Features
-
-* room form edit with enter ([#662](https://github.com/Monadical-SAS/reflector/issues/662)) ([47716f6](https://github.com/Monadical-SAS/reflector/commit/47716f6e5ddee952609d2fa0ffabdfa865286796))
-
-
-### Bug Fixes
-
-* invalid cleanup call ([#660](https://github.com/Monadical-SAS/reflector/issues/660)) ([0abcebf](https://github.com/Monadical-SAS/reflector/commit/0abcebfc9491f87f605f21faa3e53996fafedd9a))
-
-## [0.12.1](https://github.com/Monadical-SAS/reflector/compare/v0.12.0...v0.12.1) (2025-09-17)
-
-
-### Bug Fixes
-
-* production blocked because having existing meeting with room_id null ([#657](https://github.com/Monadical-SAS/reflector/issues/657)) ([870e860](https://github.com/Monadical-SAS/reflector/commit/870e8605171a27155a9cbee215eeccb9a8d6c0a2))
-
-## [0.12.0](https://github.com/Monadical-SAS/reflector/compare/v0.11.0...v0.12.0) (2025-09-17)
-
-
-### Features
-
-* calendar integration ([#608](https://github.com/Monadical-SAS/reflector/issues/608)) ([6f680b5](https://github.com/Monadical-SAS/reflector/commit/6f680b57954c688882c4ed49f40f161c52a00a24))
-* self-hosted gpu api ([#636](https://github.com/Monadical-SAS/reflector/issues/636)) ([ab859d6](https://github.com/Monadical-SAS/reflector/commit/ab859d65a6bded904133a163a081a651b3938d42))
-
-
-### Bug Fixes
-
-* ignore player hotkeys for text inputs ([#646](https://github.com/Monadical-SAS/reflector/issues/646)) ([fa049e8](https://github.com/Monadical-SAS/reflector/commit/fa049e8d068190ce7ea015fd9fcccb8543f54a3f))
-
-## [0.11.0](https://github.com/Monadical-SAS/reflector/compare/v0.10.0...v0.11.0) (2025-09-16)
-
-
-### Features
-
-* remove profanity filter that was there for conference ([#652](https://github.com/Monadical-SAS/reflector/issues/652)) ([b42f7cf](https://github.com/Monadical-SAS/reflector/commit/b42f7cfc606783afcee792590efcc78b507468ab))
-
-
-### Bug Fixes
-
-* zulip and consent handler on the file pipeline ([#645](https://github.com/Monadical-SAS/reflector/issues/645)) ([5f143fe](https://github.com/Monadical-SAS/reflector/commit/5f143fe3640875dcb56c26694254a93189281d17))
-* zulip stream and topic selection in share dialog ([#644](https://github.com/Monadical-SAS/reflector/issues/644)) ([c546e69](https://github.com/Monadical-SAS/reflector/commit/c546e69739e68bb74fbc877eb62609928e5b8de6))
-
-## [0.10.0](https://github.com/Monadical-SAS/reflector/compare/v0.9.0...v0.10.0) (2025-09-11)
-
-
-### Features
-
-* replace nextjs-config with environment variables ([#632](https://github.com/Monadical-SAS/reflector/issues/632)) ([369ecdf](https://github.com/Monadical-SAS/reflector/commit/369ecdff13f3862d926a9c0b87df52c9d94c4dde))
-
-
-### Bug Fixes
-
-* anonymous users transcript permissions ([#621](https://github.com/Monadical-SAS/reflector/issues/621)) ([f81fe99](https://github.com/Monadical-SAS/reflector/commit/f81fe9948a9237b3e0001b2d8ca84f54d76878f9))
-* auth post ([#624](https://github.com/Monadical-SAS/reflector/issues/624)) ([cde99ca](https://github.com/Monadical-SAS/reflector/commit/cde99ca2716f84ba26798f289047732f0448742e))
-* auth post ([#626](https://github.com/Monadical-SAS/reflector/issues/626)) ([3b85ff3](https://github.com/Monadical-SAS/reflector/commit/3b85ff3bdf4fb053b103070646811bc990c0e70a))
-* auth post ([#627](https://github.com/Monadical-SAS/reflector/issues/627)) ([962038e](https://github.com/Monadical-SAS/reflector/commit/962038ee3f2a555dc3c03856be0e4409456e0996))
-* missing follow_redirects=True on modal endpoint ([#630](https://github.com/Monadical-SAS/reflector/issues/630)) ([fc363bd](https://github.com/Monadical-SAS/reflector/commit/fc363bd49b17b075e64f9186e5e0185abc325ea7))
-* sync backend and frontend token refresh logic ([#614](https://github.com/Monadical-SAS/reflector/issues/614)) ([5a5b323](https://github.com/Monadical-SAS/reflector/commit/5a5b3233820df9536da75e87ce6184a983d4713a))
-
-## [0.9.0](https://github.com/Monadical-SAS/reflector/compare/v0.8.2...v0.9.0) (2025-09-06)
-
-
-### Features
-
-* frontend openapi react query ([#606](https://github.com/Monadical-SAS/reflector/issues/606)) ([c4d2825](https://github.com/Monadical-SAS/reflector/commit/c4d2825c81f81ad8835629fbf6ea8c7383f8c31b))
-
-
-### Bug Fixes
-
-* align whisper transcriber api with parakeet ([#602](https://github.com/Monadical-SAS/reflector/issues/602)) ([0663700](https://github.com/Monadical-SAS/reflector/commit/0663700a615a4af69a03c96c410f049e23ec9443))
-* kv use tls explicit ([#610](https://github.com/Monadical-SAS/reflector/issues/610)) ([08d88ec](https://github.com/Monadical-SAS/reflector/commit/08d88ec349f38b0d13e0fa4cb73486c8dfd31836))
-* source kind for file processing ([#601](https://github.com/Monadical-SAS/reflector/issues/601)) ([dc82f8b](https://github.com/Monadical-SAS/reflector/commit/dc82f8bb3bdf3ab3d4088e592a30fd63907319e1))
-* token refresh locking ([#613](https://github.com/Monadical-SAS/reflector/issues/613)) ([7f5a4c9](https://github.com/Monadical-SAS/reflector/commit/7f5a4c9ddc7fd098860c8bdda2ca3b57f63ded2f))
-
-## [0.8.2](https://github.com/Monadical-SAS/reflector/compare/v0.8.1...v0.8.2) (2025-08-29)
-
-
-### Bug Fixes
-
-* search-logspam ([#593](https://github.com/Monadical-SAS/reflector/issues/593)) ([695d1a9](https://github.com/Monadical-SAS/reflector/commit/695d1a957d4cd862753049f9beed88836cabd5ab))
-
-## [0.8.1](https://github.com/Monadical-SAS/reflector/compare/v0.8.0...v0.8.1) (2025-08-29)
-
-
-### Bug Fixes
-
-* make webhook secret/url allowing null ([#590](https://github.com/Monadical-SAS/reflector/issues/590)) ([84a3812](https://github.com/Monadical-SAS/reflector/commit/84a381220bc606231d08d6f71d4babc818fa3c75))
-
-## [0.8.0](https://github.com/Monadical-SAS/reflector/compare/v0.7.3...v0.8.0) (2025-08-29)
-
-
-### Features
-
-* **cleanup:** add automatic data retention for public instances ([#574](https://github.com/Monadical-SAS/reflector/issues/574)) ([6f0c7c1](https://github.com/Monadical-SAS/reflector/commit/6f0c7c1a5e751713366886c8e764c2009e12ba72))
-* **rooms:** add webhook for transcript completion ([#578](https://github.com/Monadical-SAS/reflector/issues/578)) ([88ed7cf](https://github.com/Monadical-SAS/reflector/commit/88ed7cfa7804794b9b54cad4c3facc8a98cf85fd))
-
-
-### Bug Fixes
-
-* file pipeline status reporting and websocket updates ([#589](https://github.com/Monadical-SAS/reflector/issues/589)) ([9dfd769](https://github.com/Monadical-SAS/reflector/commit/9dfd76996f851cc52be54feea078adbc0816dc57))
-* Igor/evaluation ([#575](https://github.com/Monadical-SAS/reflector/issues/575)) ([124ce03](https://github.com/Monadical-SAS/reflector/commit/124ce03bf86044c18313d27228a25da4bc20c9c5))
-* optimize parakeet transcription batching algorithm ([#577](https://github.com/Monadical-SAS/reflector/issues/577)) ([7030e0f](https://github.com/Monadical-SAS/reflector/commit/7030e0f23649a8cf6c1eb6d5889684a41ce849ec))
-
-## [0.7.3](https://github.com/Monadical-SAS/reflector/compare/v0.7.2...v0.7.3) (2025-08-22)
-
-
-### Bug Fixes
-
-* cleaned repo, and get git-leaks clean ([359280d](https://github.com/Monadical-SAS/reflector/commit/359280dd340433ba4402ed69034094884c825e67))
-* restore previous behavior on live pipeline + audio downscaler ([#561](https://github.com/Monadical-SAS/reflector/issues/561)) ([9265d20](https://github.com/Monadical-SAS/reflector/commit/9265d201b590d23c628c5f19251b70f473859043))
-
-## [0.7.2](https://github.com/Monadical-SAS/reflector/compare/v0.7.1...v0.7.2) (2025-08-21)
-
-
-### Bug Fixes
-
-* docker image not loading libgomp.so.1 for torch ([#560](https://github.com/Monadical-SAS/reflector/issues/560)) ([773fccd](https://github.com/Monadical-SAS/reflector/commit/773fccd93e887c3493abc2e4a4864dddce610177))
-* include shared rooms to search ([#558](https://github.com/Monadical-SAS/reflector/issues/558)) ([499eced](https://github.com/Monadical-SAS/reflector/commit/499eced3360b84fb3a90e1c8a3b554290d21adc2))
-
-## [0.7.1](https://github.com/Monadical-SAS/reflector/compare/v0.7.0...v0.7.1) (2025-08-21)
-
-
-### Bug Fixes
-
-* webvtt db null expectation mismatch ([#556](https://github.com/Monadical-SAS/reflector/issues/556)) ([e67ad1a](https://github.com/Monadical-SAS/reflector/commit/e67ad1a4a2054467bfeb1e0258fbac5868aaaf21))
-
-## [0.7.0](https://github.com/Monadical-SAS/reflector/compare/v0.6.1...v0.7.0) (2025-08-21)
-
-
-### Features
-
-* delete recording with transcript ([#547](https://github.com/Monadical-SAS/reflector/issues/547)) ([99cc984](https://github.com/Monadical-SAS/reflector/commit/99cc9840b3f5de01e0adfbfae93234042d706d13))
-* pipeline improvement with file processing, parakeet, silero-vad ([#540](https://github.com/Monadical-SAS/reflector/issues/540)) ([bcc29c9](https://github.com/Monadical-SAS/reflector/commit/bcc29c9e0050ae215f89d460e9d645aaf6a5e486))
-* postgresql migration and removal of sqlite in pytest ([#546](https://github.com/Monadical-SAS/reflector/issues/546)) ([cd1990f](https://github.com/Monadical-SAS/reflector/commit/cd1990f8f0fe1503ef5069512f33777a73a93d7f))
-* search backend ([#537](https://github.com/Monadical-SAS/reflector/issues/537)) ([5f9b892](https://github.com/Monadical-SAS/reflector/commit/5f9b89260c9ef7f3c921319719467df22830453f))
-* search frontend  ([#551](https://github.com/Monadical-SAS/reflector/issues/551)) ([3657242](https://github.com/Monadical-SAS/reflector/commit/365724271ca6e615e3425125a69ae2b46ce39285))
-
-
-### Bug Fixes
-
-* evaluation cli event wrap ([#536](https://github.com/Monadical-SAS/reflector/issues/536)) ([941c3db](https://github.com/Monadical-SAS/reflector/commit/941c3db0bdacc7b61fea412f3746cc5a7cb67836))
-* use structlog not logging ([#550](https://github.com/Monadical-SAS/reflector/issues/550)) ([27e2f81](https://github.com/Monadical-SAS/reflector/commit/27e2f81fda5232e53edc729d3e99c5ef03adbfe9))
-
 ## [0.6.1](https://github.com/Monadical-SAS/reflector/compare/v0.6.0...v0.6.1) (2025-08-06)


--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -66,6 +66,7 @@ pnpm install

 # Copy configuration templates
 cp .env_template .env
+cp config-template.ts config.ts
 ```

 **Development:**
@@ -151,7 +152,7 @@ All endpoints prefixed `/v1/`:

 **Frontend** (`www/.env`):
 - `NEXTAUTH_URL`, `NEXTAUTH_SECRET` - Authentication configuration
- `REFLECTOR_API_URL` - Backend API endpoint
+- `NEXT_PUBLIC_REFLECTOR_API_URL` - Backend API endpoint
 - `REFLECTOR_DOMAIN_CONFIG` - Feature flags and domain settings

 ## Testing Strategy
--- a/Caddyfile.example
+++ b/Caddyfile.example
@@ -1,22 +0,0 @@
-# Reflector Caddyfile
-# Replace example.com with your actual domains
-# CORS is handled by the backend - Caddy just proxies
-#
-# For environment variable substitution, set:
-#   FRONTEND_DOMAIN=app.example.com
-#   API_DOMAIN=api.example.com
-#   AUTHENTIK_DOMAIN=authentik.example.com  (optional, for authentication)
-# Or edit this file directly with your domains.
-
-{$FRONTEND_DOMAIN:app.example.com} {
-	reverse_proxy web:3000
-}
-
-{$API_DOMAIN:api.example.com} {
-	reverse_proxy server:1250
-}
-
-# Uncomment if using Authentik for authentication (see auth-setup.md)
-# {$AUTHENTIK_DOMAIN:authentik.example.com} {
-# 	reverse_proxy authentik-server-1:9000
-# }
--- a/ICS_IMPLEMENTATION.md
+++ b/ICS_IMPLEMENTATION.md
@@ -0,0 +1,497 @@
+# ICS Calendar Integration - Implementation Guide
+
+## Overview
+This document provides detailed implementation guidance for integrating ICS calendar feeds with Reflector rooms. Unlike CalDAV which requires complex authentication and protocol handling, ICS integration uses simple HTTP(S) fetching of calendar files.
+
+## Key Differences from CalDAV Approach
+
+| Aspect | CalDAV | ICS |
+|--------|--------|-----|
+| Protocol | WebDAV extension | HTTP/HTTPS GET |
+| Authentication | Username/password, OAuth | Tokens embedded in URL |
+| Data Access | Selective event queries | Full calendar download |
+| Implementation | Complex (caldav library) | Simple (requests + icalendar) |
+| Real-time Updates | Supported | Polling only |
+| Write Access | Yes | No (read-only) |
+
+## Technical Architecture
+
+### 1. ICS Fetching Service
+
+```python
+# reflector/services/ics_sync.py
+
+import requests
+from icalendar import Calendar
+from typing import List, Optional
+from datetime import datetime, timedelta
+
+class ICSFetchService:
+    def __init__(self):
+        self.session = requests.Session()
+        self.session.headers.update({'User-Agent': 'Reflector/1.0'})
+
+    def fetch_ics(self, url: str) -> str:
+        """Fetch ICS file from URL (authentication via URL token if needed)."""
+        response = self.session.get(url, timeout=30)
+        response.raise_for_status()
+        return response.text
+
+    def parse_ics(self, ics_content: str) -> Calendar:
+        """Parse ICS content into calendar object."""
+        return Calendar.from_ical(ics_content)
+
+    def extract_room_events(self, calendar: Calendar, room_url: str) -> List[dict]:
+        """Extract events that match the room URL."""
+        events = []
+
+        for component in calendar.walk():
+            if component.name == "VEVENT":
+                # Check if event matches this room
+                if self._event_matches_room(component, room_url):
+                    events.append(self._parse_event(component))
+
+        return events
+
+    def _event_matches_room(self, event, room_url: str) -> bool:
+        """Check if event location or description contains room URL."""
+        location = str(event.get('LOCATION', ''))
+        description = str(event.get('DESCRIPTION', ''))
+
+        # Support various URL formats
+        patterns = [
+            room_url,
+            room_url.replace('https://', ''),
+            room_url.split('/')[-1],  # Just room name
+        ]
+
+        for pattern in patterns:
+            if pattern in location or pattern in description:
+                return True
+
+        return False
+```
+
+### 2. Database Schema
+
+```sql
+-- Modify room table
+ALTER TABLE room ADD COLUMN ics_url TEXT;  -- encrypted to protect embedded tokens
+ALTER TABLE room ADD COLUMN ics_fetch_interval INTEGER DEFAULT 300;  -- seconds
+ALTER TABLE room ADD COLUMN ics_enabled BOOLEAN DEFAULT FALSE;
+ALTER TABLE room ADD COLUMN ics_last_sync TIMESTAMP;
+ALTER TABLE room ADD COLUMN ics_last_etag TEXT;  -- for caching
+
+-- Calendar events table
+CREATE TABLE calendar_event (
+    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    room_id UUID REFERENCES room(id) ON DELETE CASCADE,
+    external_id TEXT NOT NULL,  -- ICS UID
+    title TEXT,
+    description TEXT,
+    start_time TIMESTAMP NOT NULL,
+    end_time TIMESTAMP NOT NULL,
+    attendees JSONB,
+    location TEXT,
+    ics_raw_data TEXT,  -- Store raw VEVENT for reference
+    last_synced TIMESTAMP DEFAULT NOW(),
+    is_deleted BOOLEAN DEFAULT FALSE,
+    created_at TIMESTAMP DEFAULT NOW(),
+    updated_at TIMESTAMP DEFAULT NOW(),
+    UNIQUE(room_id, external_id)
+);
+
+-- Index for efficient queries
+CREATE INDEX idx_calendar_event_room_start ON calendar_event(room_id, start_time);
+CREATE INDEX idx_calendar_event_deleted ON calendar_event(is_deleted) WHERE NOT is_deleted;
+```
+
+### 3. Background Tasks
+
+```python
+# reflector/worker/tasks/ics_sync.py
+
+from celery import shared_task
+from datetime import datetime, timedelta
+import hashlib
+
+@shared_task
+def sync_ics_calendars():
+    """Sync all enabled ICS calendars based on their fetch intervals."""
+    rooms = Room.query.filter_by(ics_enabled=True).all()
+
+    for room in rooms:
+        # Check if it's time to sync based on fetch interval
+        if should_sync(room):
+            sync_room_calendar.delay(room.id)
+
+@shared_task
+def sync_room_calendar(room_id: str):
+    """Sync calendar for a specific room."""
+    room = Room.query.get(room_id)
+    if not room or not room.ics_enabled:
+        return
+
+    try:
+        # Fetch ICS file (decrypt URL first)
+        service = ICSFetchService()
+        decrypted_url = decrypt_ics_url(room.ics_url)
+        ics_content = service.fetch_ics(decrypted_url)
+
+        # Check if content changed (using ETag or hash)
+        content_hash = hashlib.md5(ics_content.encode()).hexdigest()
+        if room.ics_last_etag == content_hash:
+            logger.info(f"No changes in ICS for room {room_id}")
+            return
+
+        # Parse and extract events
+        calendar = service.parse_ics(ics_content)
+        events = service.extract_room_events(calendar, room.url)
+
+        # Update database
+        sync_events_to_database(room_id, events)
+
+        # Update sync metadata
+        room.ics_last_sync = datetime.utcnow()
+        room.ics_last_etag = content_hash
+        db.session.commit()
+
+    except Exception as e:
+        logger.error(f"Failed to sync ICS for room {room_id}: {e}")
+
+def should_sync(room) -> bool:
+    """Check if room calendar should be synced."""
+    if not room.ics_last_sync:
+        return True
+
+    time_since_sync = datetime.utcnow() - room.ics_last_sync
+    return time_since_sync.total_seconds() >= room.ics_fetch_interval
+```
+
+### 4. Celery Beat Schedule
+
+```python
+# reflector/worker/celeryconfig.py
+
+from celery.schedules import crontab
+
+beat_schedule = {
+    'sync-ics-calendars': {
+        'task': 'reflector.worker.tasks.ics_sync.sync_ics_calendars',
+        'schedule': 60.0,  # Check every minute which calendars need syncing
+    },
+    'pre-create-meetings': {
+        'task': 'reflector.worker.tasks.ics_sync.pre_create_calendar_meetings',
+        'schedule': 60.0,  # Check every minute for upcoming meetings
+    },
+}
+```
+
+## API Endpoints
+
+### Room ICS Configuration
+
+```python
+# PATCH /v1/rooms/{room_id}
+{
+    "ics_url": "https://calendar.google.com/calendar/ical/.../private-token/basic.ics",
+    "ics_fetch_interval": 300,  # seconds
+    "ics_enabled": true
+    # URL will be encrypted in database to protect embedded tokens
+}
+```
+
+### Manual Sync Trigger
+
+```python
+# POST /v1/rooms/{room_name}/ics/sync
+# Response:
+{
+    "status": "syncing",
+    "last_sync": "2024-01-15T10:30:00Z",
+    "events_found": 5
+}
+```
+
+### ICS Status
+
+```python
+# GET /v1/rooms/{room_name}/ics/status
+# Response:
+{
+    "enabled": true,
+    "last_sync": "2024-01-15T10:30:00Z",
+    "next_sync": "2024-01-15T10:35:00Z",
+    "fetch_interval": 300,
+    "events_count": 12,
+    "upcoming_events": 3
+}
+```
+
+## ICS Parsing Details
+
+### Event Field Mapping
+
+| ICS Field | Database Field | Notes |
+|-----------|---------------|-------|
+| UID | external_id | Unique identifier |
+| SUMMARY | title | Event title |
+| DESCRIPTION | description | Full description |
+| DTSTART | start_time | Convert to UTC |
+| DTEND | end_time | Convert to UTC |
+| LOCATION | location | Check for room URL |
+| ATTENDEE | attendees | Parse into JSON |
+| ORGANIZER | attendees | Add as organizer |
+| STATUS | (internal) | Filter cancelled events |
+
+### Handling Recurring Events
+
+```python
+def expand_recurring_events(event, start_date, end_date):
+    """Expand recurring events into individual occurrences."""
+    from dateutil.rrule import rrulestr
+
+    if 'RRULE' not in event:
+        return [event]
+
+    # Parse recurrence rule
+    rrule_str = event['RRULE'].to_ical().decode()
+    dtstart = event['DTSTART'].dt
+
+    # Generate occurrences
+    rrule = rrulestr(rrule_str, dtstart=dtstart)
+    occurrences = []
+
+    for dt in rrule.between(start_date, end_date):
+        # Clone event with new date
+        occurrence = event.copy()
+        occurrence['DTSTART'].dt = dt
+        if 'DTEND' in event:
+            duration = event['DTEND'].dt - event['DTSTART'].dt
+            occurrence['DTEND'].dt = dt + duration
+
+        # Unique ID for each occurrence
+        occurrence['UID'] = f"{event['UID']}_{dt.isoformat()}"
+        occurrences.append(occurrence)
+
+    return occurrences
+```
+
+### Timezone Handling
+
+```python
+def normalize_datetime(dt):
+    """Convert various datetime formats to UTC."""
+    import pytz
+    from datetime import datetime
+
+    if hasattr(dt, 'dt'):  # icalendar property
+        dt = dt.dt
+
+    if isinstance(dt, datetime):
+        if dt.tzinfo is None:
+            # Assume local timezone if naive
+            dt = pytz.timezone('UTC').localize(dt)
+        else:
+            # Convert to UTC
+            dt = dt.astimezone(pytz.UTC)
+
+    return dt
+```
+
+## Security Considerations
+
+### 1. URL Validation
+
+```python
+def validate_ics_url(url: str) -> bool:
+    """Validate ICS URL for security."""
+    from urllib.parse import urlparse
+
+    parsed = urlparse(url)
+
+    # Must be HTTPS in production
+    if not settings.DEBUG and parsed.scheme != 'https':
+        return False
+
+    # Prevent local file access
+    if parsed.scheme in ('file', 'ftp'):
+        return False
+
+    # Prevent internal network access
+    if is_internal_ip(parsed.hostname):
+        return False
+
+    return True
+```
+
+### 2. Rate Limiting
+
+```python
+# Implement per-room rate limiting
+RATE_LIMITS = {
+    'min_fetch_interval': 60,  # Minimum 1 minute between fetches
+    'max_requests_per_hour': 60,  # Max 60 requests per hour per room
+    'max_file_size': 10 * 1024 * 1024,  # Max 10MB ICS file
+}
+```
+
+### 3. ICS URL Encryption
+
+```python
+from cryptography.fernet import Fernet
+
+class URLEncryption:
+    def __init__(self):
+        self.cipher = Fernet(settings.ENCRYPTION_KEY)
+
+    def encrypt_url(self, url: str) -> str:
+        """Encrypt ICS URL to protect embedded tokens."""
+        return self.cipher.encrypt(url.encode()).decode()
+
+    def decrypt_url(self, encrypted: str) -> str:
+        """Decrypt ICS URL for fetching."""
+        return self.cipher.decrypt(encrypted.encode()).decode()
+
+    def mask_url(self, url: str) -> str:
+        """Mask sensitive parts of URL for display."""
+        from urllib.parse import urlparse, urlunparse
+
+        parsed = urlparse(url)
+        # Keep scheme, host, and path structure but mask tokens
+        if '/private-' in parsed.path:
+            # Google Calendar format
+            parts = parsed.path.split('/private-')
+            masked_path = parts[0] + '/private-***' + parts[1].split('/')[-1]
+        elif 'token=' in url:
+            # Query parameter token
+            masked_path = parsed.path
+            parsed = parsed._replace(query='token=***')
+        else:
+            # Generic masking of path segments that look like tokens
+            import re
+            masked_path = re.sub(r'/[a-zA-Z0-9]{20,}/', '/***/', parsed.path)
+
+        return urlunparse(parsed._replace(path=masked_path))
+```
+
+## Testing Strategy
+
+### 1. Unit Tests
+
+```python
+# tests/test_ics_sync.py
+
+def test_ics_parsing():
+    """Test ICS file parsing."""
+    ics_content = """BEGIN:VCALENDAR
+VERSION:2.0
+BEGIN:VEVENT
+UID:test-123
+SUMMARY:Team Meeting
+LOCATION:https://reflector.monadical.com/engineering
+DTSTART:20240115T100000Z
+DTEND:20240115T110000Z
+END:VEVENT
+END:VCALENDAR"""
+
+    service = ICSFetchService()
+    calendar = service.parse_ics(ics_content)
+    events = service.extract_room_events(
+        calendar,
+        "https://reflector.monadical.com/engineering"
+    )
+
+    assert len(events) == 1
+    assert events[0]['title'] == 'Team Meeting'
+```
+
+### 2. Integration Tests
+
+```python
+def test_full_sync_flow():
+    """Test complete sync workflow."""
+    # Create room with ICS URL (encrypt URL to protect tokens)
+    encryption = URLEncryption()
+    room = Room(
+        name="test-room",
+        ics_url=encryption.encrypt_url("https://example.com/calendar.ics?token=secret"),
+        ics_enabled=True
+    )
+
+    # Mock ICS fetch
+    with patch('requests.get') as mock_get:
+        mock_get.return_value.text = sample_ics_content
+
+        # Run sync
+        sync_room_calendar(room.id)
+
+        # Verify events created
+        events = CalendarEvent.query.filter_by(room_id=room.id).all()
+        assert len(events) > 0
+```
+
+## Common ICS Provider Configurations
+
+### Google Calendar
+- URL Format: `https://calendar.google.com/calendar/ical/{calendar_id}/private-{token}/basic.ics`
+- Authentication via token embedded in URL
+- Updates every 3-8 hours by default
+
+### Outlook/Office 365
+- URL Format: `https://outlook.office365.com/owa/calendar/{id}/calendar.ics`
+- May include token in URL path or query parameters
+- Real-time updates
+
+### Apple iCloud
+- URL Format: `webcal://p{XX}-caldav.icloud.com/published/2/{token}`
+- Convert webcal:// to https://
+- Token embedded in URL path
+- Public calendars only
+
+### Nextcloud/ownCloud
+- URL Format: `https://cloud.example.com/remote.php/dav/public-calendars/{token}`
+- Token embedded in URL path
+- Configurable update frequency
+
+## Migration from CalDAV
+
+If migrating from an existing CalDAV implementation:
+
+1. **Database Migration**: Rename fields from `caldav_*` to `ics_*`
+2. **URL Conversion**: Most CalDAV servers provide ICS export endpoints
+3. **Authentication**: Convert from username/password to URL-embedded tokens
+4. **Remove Dependencies**: Uninstall caldav library, add icalendar
+5. **Update Background Tasks**: Replace CalDAV sync with ICS fetch
+
+## Performance Optimizations
+
+1. **Caching**: Use ETag/Last-Modified headers to avoid refetching unchanged calendars
+2. **Incremental Sync**: Store last sync timestamp, only process new/modified events
+3. **Batch Processing**: Process multiple room calendars in parallel
+4. **Connection Pooling**: Reuse HTTP connections for multiple requests
+5. **Compression**: Support gzip encoding for large ICS files
+
+## Monitoring and Debugging
+
+### Metrics to Track
+- Sync success/failure rate per room
+- Average sync duration
+- ICS file sizes
+- Number of events processed
+- Failed event matches
+
+### Debug Logging
+```python
+logger.debug(f"Fetching ICS from {room.ics_url}")
+logger.debug(f"ICS content size: {len(ics_content)} bytes")
+logger.debug(f"Found {len(events)} matching events")
+logger.debug(f"Event UIDs: {[e['external_id'] for e in events]}")
+```
+
+### Common Issues
+1. **SSL Certificate Errors**: Add certificate validation options
+2. **Timeout Issues**: Increase timeout for large calendars
+3. **Encoding Problems**: Handle various character encodings
+4. **Timezone Mismatches**: Always convert to UTC
+5. **Memory Issues**: Stream large ICS files instead of loading entirely
--- a/PLAN.md
+++ b/PLAN.md
@@ -0,0 +1,337 @@
+# ICS Calendar Integration Plan
+
+## Core Concept
+ICS calendar URLs are attached to rooms (not users) to enable automatic meeting tracking and management through periodic fetching of calendar data.
+
+## Database Schema Updates
+
+### 1. Add ICS configuration to rooms
+- Add `ics_url` field to room table (URL to .ics file, may include auth token)
+- Add `ics_fetch_interval` field to room table (default: 5 minutes, configurable)
+- Add `ics_enabled` boolean field to room table
+- Add `ics_last_sync` timestamp field to room table
+
+### 2. Create calendar_events table
+- `id` - UUID primary key
+- `room_id` - Foreign key to room
+- `external_id` - ICS event UID
+- `title` - Event title
+- `description` - Event description
+- `start_time` - Event start timestamp
+- `end_time` - Event end timestamp
+- `attendees` - JSON field with attendee list and status
+- `location` - Meeting location (should contain room name)
+- `last_synced` - Last sync timestamp
+- `is_deleted` - Boolean flag for soft delete (preserve past events)
+- `ics_raw_data` - TEXT field to store raw VEVENT data for reference
+
+### 3. Update meeting table
+- Add `calendar_event_id` - Foreign key to calendar_events
+- Add `calendar_metadata` - JSON field for additional calendar data
+- Remove unique constraint on room_id + active status (allow multiple active meetings per room)
+
+## Backend Implementation
+
+### 1. ICS Sync Service
+- Create background task that runs based on room's `ics_fetch_interval` (default: 5 minutes)
+- For each room with ICS enabled, fetch the .ics file via HTTP/HTTPS
+- Parse ICS file using icalendar library
+- Extract VEVENT components and filter events looking for room URL (e.g., "https://reflector.monadical.com/max")
+- Store matching events in calendar_events table
+- Mark events as "upcoming" if start_time is within next 30 minutes
+- Pre-create Whereby meetings 1 minute before start (ensures no delay when users join)
+- Soft-delete future events that were removed from calendar (set is_deleted=true)
+- Never delete past events (preserve for historical record)
+- Support authenticated ICS feeds via tokens embedded in URL
+
+### 2. Meeting Management Updates
+- Allow multiple active meetings per room
+- Pre-create meeting record 1 minute before calendar event starts (ensures meeting is ready)
+- Link meeting to calendar_event for metadata
+- Keep meeting active for 15 minutes after last participant leaves (grace period)
+- Don't auto-close if new participant joins within grace period
+
+### 3. API Endpoints
+- `GET /v1/rooms/{room_name}/meetings` - List all active and upcoming meetings for a room
+  - Returns filtered data based on user role (owner vs participant)
+- `GET /v1/rooms/{room_name}/meetings/upcoming` - List upcoming meetings (next 30 min)
+  - Returns filtered data based on user role
+- `POST /v1/rooms/{room_name}/meetings/{meeting_id}/join` - Join specific meeting
+- `PATCH /v1/rooms/{room_id}` - Update room settings (including ICS configuration)
+  - ICS fields only visible/editable by room owner
+- `POST /v1/rooms/{room_name}/ics/sync` - Trigger manual ICS sync
+  - Only accessible by room owner
+- `GET /v1/rooms/{room_name}/ics/status` - Get ICS sync status and last fetch time
+  - Only accessible by room owner
+
+## Frontend Implementation
+
+### 1. Room Settings Page
+- Add ICS configuration section
+- Field for ICS URL (e.g., Google Calendar private URL, Outlook ICS export)
+- Field for fetch interval (dropdown: 1 min, 5 min, 10 min, 30 min, 1 hour)
+- Test connection button (validates ICS file can be fetched and parsed)
+- Manual sync button
+- Show last sync time and next scheduled sync
+
+### 2. Meeting Selection Page (New)
+- Show when accessing `/room/{room_name}`
+- **Host view** (room owner):
+  - Full calendar event details
+  - Meeting title and description
+  - Complete attendee list with RSVP status
+  - Number of current participants
+  - Duration (how long it's been running)
+- **Participant view** (non-owners):
+  - Meeting title only
+  - Date and time
+  - Number of current participants
+  - Duration (how long it's been running)
+  - No attendee list or description (privacy)
+- Display upcoming meetings (visible 30min before):
+  - Show countdown to start
+  - Can click to join early → redirected to waiting page
+  - Waiting page shows countdown until meeting starts
+  - Meeting pre-created by background task (ready when users arrive)
+- Option to create unscheduled meeting (uses existing flow)
+
+### 3. Meeting Room Updates
+- Show calendar metadata in meeting info
+- Display invited attendees vs actual participants
+- Show meeting title from calendar event
+
+## Meeting Lifecycle
+
+### 1. Meeting Creation
+- Automatic: Pre-created 1 minute before calendar event starts (ensures Whereby room is ready)
+- Manual: User creates unscheduled meeting (existing `/rooms/{room_name}/meeting` endpoint)
+- Background task handles pre-creation to avoid delays when users join
+
+### 2. Meeting Join Rules
+- Can join active meetings immediately
+- Can see upcoming meetings 30 minutes before start
+- Can click to join upcoming meetings early → sent to waiting page
+- Waiting page automatically transitions to meeting at scheduled time
+- Unscheduled meetings always joinable (current behavior)
+
+### 3. Meeting Closure Rules
+- All meetings: 15-minute grace period after last participant leaves
+- If participant rejoins within grace period, keep meeting active
+- Calendar meetings: Force close 30 minutes after scheduled end time
+- Unscheduled meetings: Keep active for 8 hours (current behavior)
+
+## ICS Parsing Logic
+
+### 1. Event Matching
+- Parse ICS file using Python icalendar library
+- Iterate through VEVENT components
+- Check LOCATION field for full FQDN URL (e.g., "https://reflector.monadical.com/max")
+- Check DESCRIPTION for room URL or mention
+- Support multiple formats:
+  - Full URL: "https://reflector.monadical.com/max"
+  - With /room path: "https://reflector.monadical.com/room/max"
+  - Partial paths: "room/max", "/max room"
+
+### 2. Attendee Extraction
+- Parse ATTENDEE properties from VEVENT
+- Extract email (MAILTO), name (CN parameter), and RSVP status (PARTSTAT)
+- Store as JSON in calendar_events.attendees
+
+### 3. Sync Strategy
+- Fetch complete ICS file (contains all events)
+- Filter events from (now - 1 hour) to (now + 24 hours) for processing
+- Update existing events if LAST-MODIFIED or SEQUENCE changed
+- Delete future events that no longer exist in ICS (start_time > now)
+- Keep past events for historical record (never delete if start_time < now)
+- Handle recurring events (RRULE) - expand to individual instances
+- Track deleted calendar events to clean up future meetings
+- Cache ICS file hash to detect changes and skip unnecessary processing
+
+## Security Considerations
+
+### 1. ICS URL Security
+- ICS URLs may contain authentication tokens (e.g., Google Calendar private URLs)
+- Store full ICS URLs encrypted using Fernet to protect embedded tokens
+- Validate ICS URLs (must be HTTPS for production)
+- Never expose full ICS URLs in API responses (return masked version)
+- Rate limit ICS fetching to prevent abuse
+
+### 2. Room Access
+- Only room owner can configure ICS URL
+- ICS URL shown as masked version to room owner (hides embedded tokens)
+- ICS settings not visible to other users
+- Meeting list visible to all room participants
+- ICS fetch logs only visible to room owner
+
+### 3. Meeting Privacy
+- Full calendar details visible only to room owner
+- Participants see limited info: title, date/time only
+- Attendee list and description hidden from non-owners
+- Meeting titles visible in room listing to all
+
+## Implementation Phases
+
+### Phase 1: Database and ICS Setup (Week 1) ✅ COMPLETED (2025-08-18)
+1. ✅ Created database migrations for ICS fields and calendar_events table
+   - Added ics_url, ics_fetch_interval, ics_enabled, ics_last_sync, ics_last_etag to room table
+   - Created calendar_event table with ics_uid (instead of external_id) and proper typing
+   - Added calendar_event_id and calendar_metadata (JSONB) to meeting table
+   - Removed server_default from datetime fields for consistency
+2. ✅ Installed icalendar Python library for ICS parsing
+   - Added icalendar>=6.0.0 to dependencies
+   - No encryption needed - ICS URLs are read-only
+3. ✅ Built ICS fetch and sync service
+   - Simple HTTP fetching without unnecessary validation
+   - Proper TypedDict typing for event data structures
+   - Supports any standard ICS format
+   - Event matching on full room URL only
+4. ✅ API endpoints for ICS configuration
+   - Room model updated to support ICS fields via existing PATCH endpoint
+   - POST /v1/rooms/{room_name}/ics/sync - Trigger manual sync (owner only)
+   - GET /v1/rooms/{room_name}/ics/status - Get sync status (owner only)
+   - GET /v1/rooms/{room_name}/meetings - List meetings with privacy controls
+   - GET /v1/rooms/{room_name}/meetings/upcoming - List upcoming meetings
+5. ✅ Celery background tasks for periodic sync
+   - sync_room_ics - Sync individual room calendar
+   - sync_all_ics_calendars - Check all rooms and queue sync based on fetch intervals
+   - pre_create_upcoming_meetings - Pre-create Whereby meetings 1 minute before start
+   - Tasks scheduled in beat schedule (every minute for checking, respects individual intervals)
+6. ✅ Tests written and passing
+   - 6 tests for Room ICS fields
+   - 7 tests for CalendarEvent model
+   - 7 tests for ICS sync service
+   - 11 tests for API endpoints
+   - 6 tests for background tasks
+   - All 31 ICS-related tests passing
+
+### Phase 2: Meeting Management (Week 2) ✅ COMPLETED (2025-08-19)
+1. ✅ Updated meeting lifecycle logic with grace period support
+   - 15-minute grace period after last participant leaves
+   - Automatic reactivation when participants rejoin
+   - Force close calendar meetings 30 minutes after scheduled end
+2. ✅ Support multiple active meetings per room
+   - Removed unique constraint on active meetings
+   - Added get_all_active_for_room() method
+   - Added get_active_by_calendar_event() method
+3. ✅ Implemented grace period logic
+   - Added last_participant_left_at and grace_period_minutes fields
+   - Process meetings task handles grace period checking
+   - Whereby webhooks clear grace period on participant join
+4. ✅ Link meetings to calendar events
+   - Pre-created meetings properly linked via calendar_event_id
+   - Calendar metadata stored with meeting
+   - API endpoints for listing and joining specific meetings
+
+### Phase 3: Frontend Meeting Selection (Week 3)
+1. Build meeting selection page
+2. Show active and upcoming meetings
+3. Implement waiting page for early joiners
+4. Add automatic transition from waiting to meeting
+5. Support unscheduled meeting creation
+
+### Phase 4: Calendar Integration UI (Week 4)
+1. Add ICS settings to room configuration
+2. Display calendar metadata in meetings
+3. Show attendee information
+4. Add sync status indicators
+5. Show fetch interval and next sync time
+
+## Success Metrics
+- Zero merged meetings from consecutive calendar events
+- Successful ICS sync from major providers (Google Calendar, Outlook, Apple Calendar, Nextcloud)
+- Meeting join accuracy: correct meeting 100% of the time
+- Grace period prevents 90% of accidental meeting closures
+- Configurable fetch intervals reduce unnecessary API calls
+
+## Design Decisions
+1. **ICS attached to room, not user** - Prevents duplicate meetings from multiple calendars
+2. **Multiple active meetings per room** - Supported with meeting selection page
+3. **Grace period for rejoining** - 15 minutes after last participant leaves
+4. **Upcoming meeting visibility** - Show 30 minutes before, join only on time
+5. **Calendar data storage** - Attached to meeting record for full context
+6. **No "ad-hoc" meetings** - Use existing meeting creation flow (unscheduled meetings)
+7. **ICS configuration via room PATCH** - Reuse existing room configuration endpoint
+8. **Event deletion handling** - Soft-delete future events, preserve past meetings
+9. **Configurable fetch interval** - Balance between freshness and server load
+10. **ICS over CalDAV** - Simpler implementation, wider compatibility, no complex auth
+
+## Phase 2 Implementation Files
+
+### Database Migrations
+- `/server/migrations/versions/6025e9b2bef2_remove_one_active_meeting_per_room_.py` - Remove unique constraint
+- `/server/migrations/versions/d4a1c446458c_add_grace_period_fields_to_meeting.py` - Add grace period fields
+
+### Updated Models
+- `/server/reflector/db/meetings.py` - Added grace period fields and new query methods
+
+### Updated Services
+- `/server/reflector/worker/process.py` - Enhanced with grace period logic and multiple meeting support
+
+### Updated API
+- `/server/reflector/views/rooms.py` - Added endpoints for listing active meetings and joining specific meetings
+- `/server/reflector/views/whereby.py` - Clear grace period on participant join
+
+### Tests
+- `/server/tests/test_multiple_active_meetings.py` - Comprehensive tests for Phase 2 features (5 tests)
+
+## Phase 1 Implementation Files Created
+
+### Database Models
+- `/server/reflector/db/rooms.py` - Updated with ICS fields (url, fetch_interval, enabled, last_sync, etag)
+- `/server/reflector/db/calendar_events.py` - New CalendarEvent model with ics_uid and proper typing
+- `/server/reflector/db/meetings.py` - Updated with calendar_event_id and calendar_metadata (JSONB)
+
+### Services
+- `/server/reflector/services/ics_sync.py` - ICS fetching and parsing with TypedDict for proper typing
+
+### API Endpoints
+- `/server/reflector/views/rooms.py` - Added ICS management endpoints with privacy controls
+
+### Background Tasks
+- `/server/reflector/worker/ics_sync.py` - Celery tasks for automatic periodic sync
+- `/server/reflector/worker/app.py` - Updated beat schedule for ICS tasks
+
+### Tests
+- `/server/tests/test_room_ics.py` - Room model ICS fields tests (6 tests)
+- `/server/tests/test_calendar_event.py` - CalendarEvent model tests (7 tests)
+- `/server/tests/test_ics_sync.py` - ICS sync service tests (7 tests)
+- `/server/tests/test_room_ics_api.py` - API endpoint tests (11 tests)
+- `/server/tests/test_ics_background_tasks.py` - Background task tests (6 tests)
+
+### Key Design Decisions
+- No encryption needed - ICS URLs are read-only access
+- Using ics_uid instead of external_id for clarity
+- Proper TypedDict typing for event data structures
+- Removed unnecessary URL validation and webcal handling
+- calendar_metadata in meetings stores flexible calendar data (organizer, recurrence, etc)
+- Background tasks query all rooms directly to avoid filtering issues
+- Sync intervals respected per-room configuration
+
+## Implementation Approach
+
+### ICS Fetching vs CalDAV
+- **ICS Benefits**:
+  - Simpler implementation (HTTP GET vs CalDAV protocol)
+  - Wider compatibility (all calendar apps can export ICS)
+  - No authentication complexity (simple URL with optional token)
+  - Easier debugging (ICS is plain text)
+  - Lower server requirements (no CalDAV library dependencies)
+
+### Supported Calendar Providers
+1. **Google Calendar**: Private ICS URL from calendar settings
+2. **Outlook/Office 365**: ICS export URL from calendar sharing
+3. **Apple Calendar**: Published calendar ICS URL
+4. **Nextcloud**: Public/private calendar ICS export
+5. **Any CalDAV server**: Via ICS export endpoint
+
+### ICS URL Examples
+- Google: `https://calendar.google.com/calendar/ical/{calendar_id}/private-{token}/basic.ics`
+- Outlook: `https://outlook.live.com/owa/calendar/{id}/calendar.ics`
+- Custom: `https://example.com/calendars/room-schedule.ics`
+
+### Fetch Interval Configuration
+- 1 minute: For critical/high-activity rooms
+- 5 minutes (default): Balance of freshness and efficiency
+- 10 minutes: Standard meeting rooms
+- 30 minutes: Low-activity rooms
+- 1 hour: Rarely-used rooms or stable schedules
--- a/README.md
+++ b/README.md
@@ -1,60 +1,43 @@
 <div align="center">
-<img width="100" alt="image" src="https://github.com/user-attachments/assets/66fb367b-2c89-4516-9912-f47ac59c6a7f"/>

 # Reflector

-Reflector is an AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation and summarization for audio content and live meetings. It works 100% with local models (whisper/parakeet, pyannote, seamless-m4t, and your local llm like phi-4).
+Reflector Audio Management and Analysis is a cutting-edge web application under development by Monadical. It utilizes AI to record meetings, providing a permanent record with transcripts, translations, and automated summaries.

-[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml)
+[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml)
 [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
 </div>
-</div>
+
+## Screenshots
 <table>
  <tr>
    <td>
-      <a href="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97" />
+      <a href="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3" />
      </a>
    </td>
    <td>
-      <a href="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c" />
+      <a href="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33" />
      </a>
    </td>
    <td>
-      <a href="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897" />
-      </a>
-    </td>
-    <td>
-      <a href="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4" />
+      <a href="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc" />
      </a>
    </td>
  </tr>
 </table>

-## What is Reflector?
-
-Reflector is a web application that utilizes local models to process audio content, providing:
-
- **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) (English) models
- **Speaker Diarization**: Identify and label different speakers using [Pyannote](https://github.com/pyannote/pyannote-audio) 3.1
- **Live Translation**: Translate audio content in real-time to many languages with [Facebook Seamless-M4T](https://github.com/facebookresearch/seamless_communication)
- **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
- **Meeting Recording**: Create permanent records of meetings with searchable transcripts
-
-Currently we provide [modal.com](https://modal.com/) gpu template to deploy.
-
 ## Background

 The project architecture consists of three primary components:

- **Back-End**: Python server that offers an API and data persistence, found in `server/`.
 - **Front-End**: NextJS React project hosted on Vercel, located in `www/`.
- **GPU implementation**: Providing services such as speech-to-text transcription, topic generation, automated summaries, and translations.
+- **Back-End**: Python server that offers an API and data persistence, found in `server/`.
+- **GPU implementation**: Providing services such as speech-to-text transcription, topic generation, automated summaries, and translations. Most reliable option is Modal deployment

-It also uses authentik for authentication if activated.
+It also uses authentik for authentication if activated, and Vercel for deployment and configuration of the front-end.

 ## Contribution Guidelines

@@ -89,8 +72,6 @@ Note: We currently do not have instructions for Windows users.

 ## Installation

-*Note: we're working toward better installation, theses instructions are not accurate for now*
-
 ### Frontend

 Start with `cd www`.
@@ -99,10 +80,11 @@ Start with `cd www`.

 ```bash
 pnpm install
-cp .env.example .env
+cp .env_template .env
+cp config-template.ts config.ts
 ```

-Then, fill in the environment variables in `.env` as needed. If you are unsure on how to proceed, ask in Zulip.
+Then, fill in the environment variables in `.env` and the configuration in `config.ts` as needed. If you are unsure on how to proceed, ask in Zulip.

 **Run in development mode**

@@ -167,47 +149,3 @@ You can manually process an audio file by calling the process tool:
 ```bash
 uv run python -m reflector.tools.process path/to/audio.wav
 ```
-
-## Reprocessing any transcription
-
-```bash
-uv run -m reflector.tools.process_transcript 81ec38d1-9dd7-43d2-b3f8-51f4d34a07cd --sync
-```
-
-## Build-time env variables
-
-Next.js projects are more used to NEXT_PUBLIC_ prefixed buildtime vars. We don't have those for the reason we need to serve a ccustomizable prebuild docker container.
-
-Instead, all the variables are runtime. Variables needed to the frontend are served to the frontend app at initial render.
-
-It also means there's no static prebuild and no static files to serve for js/html.
-
-## Feature Flags
-
-Reflector uses environment variable-based feature flags to control application functionality. These flags allow you to enable or disable features without code changes.
-
-### Available Feature Flags
-
-| Feature Flag | Environment Variable |
-|-------------|---------------------|
-| `requireLogin` | `FEATURE_REQUIRE_LOGIN` |
-| `privacy` | `FEATURE_PRIVACY` |
-| `browse` | `FEATURE_BROWSE` |
-| `sendToZulip` | `FEATURE_SEND_TO_ZULIP` |
-| `rooms` | `FEATURE_ROOMS` |
-
-### Setting Feature Flags
-
-Feature flags are controlled via environment variables using the pattern `FEATURE_{FEATURE_NAME}` where `{FEATURE_NAME}` is the SCREAMING_SNAKE_CASE version of the feature name.
-
-**Examples:**
-```bash
-# Enable user authentication requirement
-FEATURE_REQUIRE_LOGIN=true
-
-# Disable browse functionality
-FEATURE_BROWSE=false
-
-# Enable Zulip integration
-FEATURE_SEND_TO_ZULIP=true
-```
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -6,7 +6,6 @@ services:
      - 1250:1250
    volumes:
      - ./server/:/app/
-      - /app/.venv
    env_file:
      - ./server/.env
    environment:
@@ -17,7 +16,6 @@ services:
      context: server
    volumes:
      - ./server/:/app/
-      - /app/.venv
    env_file:
      - ./server/.env
    environment:
@@ -28,7 +26,6 @@ services:
      context: server
    volumes:
      - ./server/:/app/
-      - /app/.venv
    env_file:
      - ./server/.env
    environment:
@@ -39,7 +36,7 @@ services:
    ports:
      - 6379:6379
  web:
-    image: node:22-alpine
+    image: node:18
    ports:
      - "3000:3000"
    command: sh -c "corepack enable && pnpm install && pnpm dev"
@@ -50,8 +47,6 @@ services:
      - /app/node_modules
    env_file:
      - ./www/.env.local
-    environment:
-      - NODE_ENV=development

  postgres:
    image: postgres:17
--- a/docker-compose.prod.yml
+++ b/docker-compose.prod.yml
@@ -1,113 +0,0 @@
-# Production Docker Compose configuration
-# Usage: docker compose -f docker-compose.prod.yml up -d
-#
-# Prerequisites:
-# 1. Copy .env.example to .env and configure for both server/ and www/
-# 2. Copy Caddyfile.example to Caddyfile and edit with your domains
-# 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
-
-services:
-  web:
-    image: monadicalsas/reflector-frontend:latest
-    restart: unless-stopped
-    env_file:
-      - ./www/.env
-    pull_policy: always
-    environment:
-      - KV_URL=redis://redis:6379
-    depends_on:
-      - redis
-
-  server:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: server
-    depends_on:
-      - postgres
-      - redis
-    volumes:
-      - server_data:/app/data
-      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-  worker:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: worker
-    depends_on:
-      - postgres
-      - redis
-    volumes:
-      - server_data:/app/data
-      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-  beat:
-    image: monadicalsas/reflector-backend:latest
-    restart: unless-stopped
-    env_file:
-      - ./server/.env
-    environment:
-      ENTRYPOINT: beat
-    depends_on:
-      - postgres
-      - redis
-
-  redis:
-    image: redis:7.2-alpine
-    restart: unless-stopped
-    healthcheck:
-      test: ["CMD", "redis-cli", "ping"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-    volumes:
-      - redis_data:/data
-
-  postgres:
-    image: postgres:17-alpine
-    restart: unless-stopped
-    environment:
-      POSTGRES_USER: reflector
-      POSTGRES_PASSWORD: reflector
-      POSTGRES_DB: reflector
-    volumes:
-      - postgres_data:/var/lib/postgresql/data
-    healthcheck:
-      test: ["CMD-SHELL", "pg_isready -U reflector"]
-      interval: 30s
-      timeout: 3s
-      retries: 3
-
-  caddy:
-    image: caddy:2-alpine
-    restart: unless-stopped
-    ports:
-      - "80:80"
-      - "443:443"
-    volumes:
-      - ./Caddyfile:/etc/caddy/Caddyfile:ro
-      - caddy_data:/data
-      - caddy_config:/config
-    depends_on:
-      - web
-      - server
-
-  docs:
-    build: ./docs
-    restart: unless-stopped
-
-volumes:
-  redis_data:
-  postgres_data:
-  server_data:
-  caddy_data:
-  caddy_config:
-
-networks:
-  default:
-    attachable: true
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,20 +0,0 @@
-# Dependencies
-/node_modules
-
-# Production
-/build
-
-# Generated files
-.docusaurus
-.cache-loader
-
-# Misc
-.DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
--- a/docs/Dockerfile
+++ b/docs/Dockerfile
@@ -1,39 +0,0 @@
-FROM node:18-alpine AS builder
-WORKDIR /app
-
-# Install curl for fetching OpenAPI spec
-RUN apk add --no-cache curl
-
-# Copy package files
-COPY package*.json ./
-
-# Install dependencies
-RUN npm ci
-
-# Copy source
-COPY . .
-
-# Fetch OpenAPI spec from production API
-ARG OPENAPI_URL=https://api-reflector.monadical.com/openapi.json
-RUN mkdir -p ./static && curl -sf "${OPENAPI_URL}" -o ./static/openapi.json || echo '{}' > ./static/openapi.json
-
-# Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
-RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
-
-# Build static site (skip prebuild hook by calling docusaurus directly)
-RUN npx docusaurus build
-
-# Production image
-FROM nginx:alpine
-
-# Copy built static files
-COPY --from=builder /app/build /usr/share/nginx/html
-
-# Healthcheck for container orchestration
-HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
-  CMD wget --no-verbose --tries=1 --spider http://localhost/ || exit 1
-
-# Expose port
-EXPOSE 80
-
-CMD ["nginx", "-g", "daemon off;"]
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,41 +0,0 @@
-# Website
-
-This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
-
-### Installation
-
-```
-$ yarn
-```
-
-### Local Development
-
-```
-$ yarn start
-```
-
-This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
-
-### Build
-
-```
-$ yarn build
-```
-
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
-
-### Deployment
-
-Using SSH:
-
-```
-$ USE_SSH=true yarn deploy
-```
-
-Not using SSH:
-
-```
-$ GIT_USER=<Your GitHub username> yarn deploy
-```
-
-If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
--- a/docs/TODO.md
+++ b/docs/TODO.md
@@ -1,170 +0,0 @@
-# Documentation TODO List
-
-This file tracks information needed from the user to complete the documentation.
-
-## Required Information
-
-### Processing Times & Costs
-
-Please provide actual numbers for:
-
- [ ] **Modal.com GPU Costs**
-  - Cost per hour of audio for Whisper transcription
-  - Cost per hour of audio for Pyannote diarization
-  - Cost per hour of audio for Seamless-M4T translation
-  - Typical GPU instance used (T4, A10, etc.)
-
- [ ] **RunPod LLM Costs**
-  - Cost per 1000 tokens for summarization
-  - Model used (phi-4-unsloth-bnb-4bit)
-  - RTX 4000 Ada instance cost per hour
-
- [ ] **AWS S3 Storage**
-  - Cost per GB per month
-  - Data transfer costs
-  - Typical storage requirements per hour of audio
-
- [ ] **Whereby API**
-  - Monthly cost structure
-  - API call limits
-  - Room participant limits
-
- [ ] **Actual Processing Times**
-  - Whisper tiny model: X minutes per hour of audio
-  - Whisper base model: X minutes per hour of audio
-  - Whisper large-v3 model: X minutes per hour of audio
-  - Diarization: X minutes per hour of audio
-  - Translation: X minutes per hour of audio
-
-### Screenshots Needed
-
-Location: `/docs/static/screenshots/`
-
-Please provide screenshots of:
-
- [ ] **Dashboard Overview** - Main dashboard showing recent transcripts
- [ ] **Live Transcription** - Active transcription in progress
- [ ] **Meeting Room Interface** - Whereby room with participants
- [ ] **Transcript with Diarization** - Showing speaker labels
- [ ] **Settings Page** - Configuration options
- [ ] **API Documentation** - OpenAPI/Swagger interface
- [ ] **File Upload Interface** - Drag and drop upload
- [ ] **Translation View** - Showing original and translated text
- [ ] **Summary View** - Generated summary and topics
-
-### Setup Screenshots
-
-Please provide step-by-step screenshots for:
-
- [ ] **Modal.com Setup**
-  - Creating account
-  - Getting API keys
-  - Deploying functions
-
- [ ] **Whereby Configuration**
-  - Creating developer account
-  - Getting API credentials
-  - Setting up rooms
-
- [ ] **AWS S3 Setup**
-  - Creating bucket
-  - Setting permissions
-  - Getting access keys
-
- [ ] **Authentik Integration**
-  - Adding application
-  - Configuring OAuth
-  - Setting up users
-
-### Technical Details
-
-Please provide specific values for:
-
- [ ] **WebRTC Configuration**
-  - Exact UDP port range used (e.g., 10000-20000)
-  - STUN server configuration (if any)
-  - ICE candidate gathering timeout
-  - https://docs.daily.co/guides/privacy-and-security/corporate-firewalls-nats-allowed-ip-list
-
- [ ] **Worker Configuration**
-  - Default Celery worker count
-  - Worker memory limits
-  - Queue priorities
-
- [ ] **Redis Requirements**
-  - Typical memory usage
-  - Persistence configuration
-  - Eviction policies
-
- [ ] **PostgreSQL**
-  - Expected database growth (MB per hour of audio)
-  - Recommended connection pool size
-  - Backup strategy
-
- [ ] **Performance Metrics**
-  - Average transcription accuracy (WER)
-  - Average diarization accuracy (DER)
-  - Translation quality scores
-  - Typical latency for live streaming
-
-### Configuration Examples
-
-Please provide real-world examples for:
-
- [ ] **Production .env file** (sanitized)
- [ ] **Caddy configuration** for production
- [ ] **Docker compose** for production deployment
- [ ] **Nginx configuration** (if alternative to Caddy)
-
-### API Examples
-
-Please provide:
-
- [ ] **Sample API requests** for common operations
- [ ] **WebSocket message examples**
- [ ] **Webhook payload examples**
- [ ] **Error response examples**
-
-## How to Add Information
-
-1. **For text information**: Edit the relevant markdown files in `/docs/docs/`
-2. **For screenshots**: Add to `/docs/static/screenshots/` and reference in docs
-3. **For code examples**: Add to documentation with proper syntax highlighting
-
-## Priority Items
-
-High priority (blocks documentation completeness):
-1. Modal.com costs and setup steps
-2. Basic screenshots (dashboard, transcription)
-3. Docker deployment configuration
-
-Medium priority (enhances documentation):
-1. Performance metrics
-2. Advanced configuration examples
-3. Troubleshooting scenarios
-
-Low priority (nice to have):
-1. Video tutorials
-2. Architecture diagrams
-3. Benchmark comparisons
-
-## Documentation Structure
-
-Once information is provided, update these files:
- `/docs/docs/installation/modal-setup.md` - Add Modal.com setup screenshots
- `/docs/docs/installation/whereby-setup.md` - Add Whereby configuration steps
- `/docs/docs/reference/configuration.md` - Add environment variable details
- `/docs/docs/pipelines/file-pipeline.md` - Add actual processing times
- `/docs/docs/pipelines/live-pipeline.md` - Add latency measurements
-
-## Notes
-
- Replace placeholder values with actual data
- Ensure all sensitive information is sanitized
- Test all configuration examples before documenting
- Verify all costs are up-to-date
-
---
-
-Last updated: 2025-08-20
-Contact: [Your Email]
--- a/docs/create-docs.sh
+++ b/docs/create-docs.sh
@@ -1,777 +0,0 @@
-#!/bin/bash
-
-# Create directory structure
-mkdir -p docs/concepts
-mkdir -p docs/installation
-mkdir -p docs/pipelines
-mkdir -p docs/reference/architecture
-mkdir -p docs/reference/processors
-mkdir -p docs/reference/api
-
-# Create all documentation files with content
-echo "Creating documentation files..."
-
-# Concepts - Modes
-cat > docs/concepts/modes.md << 'EOF'
---
-sidebar_position: 2
-title: Operating Modes
---
-
-# Operating Modes
-
-Reflector operates in two distinct modes to accommodate different use cases and security requirements.
-
-## Public Mode
-
-Public mode provides immediate access to core transcription features without requiring authentication.
-
-### Features Available
- **File Upload**: Process audio files up to 2GB
- **Live Transcription**: Stream audio from microphone
- **Basic Processing**: Transcription and diarization
- **Temporary Storage**: Results available for 24 hours
-
-### Limitations
- No persistent storage
- No meeting rooms
- Limited to single-user sessions
- No team collaboration features
-
-### Use Cases
- Quick transcription needs
- Testing and evaluation
- Individual users
- Public demonstrations
-
-## Private Mode
-
-Private mode unlocks the full potential of Reflector with authentication and persistent storage.
-
-### Additional Features
- **Virtual Meeting Rooms**: Whereby integration
- **Team Collaboration**: Share transcripts with team
- **Persistent Storage**: Long-term transcript archive
- **Advanced Analytics**: Meeting insights and trends
- **Custom Integration**: Webhooks and API access
- **User Management**: Role-based access control
-
-### Authentication Options
-
-#### Authentik Integration
-Enterprise-grade SSO with support for:
- SAML 2.0
- OAuth 2.0 / OIDC
- LDAP / Active Directory
- Multi-factor authentication
-
-#### JWT Authentication
-Stateless token-based auth for:
- API access
- Service-to-service communication
- Mobile applications
-
-### Room Management
-
-Virtual rooms provide dedicated spaces for meetings:
- **Persistent URLs**: Same link for recurring meetings
- **Access Control**: Invite-only or open rooms
- **Recording Consent**: Automatic consent management
- **Custom Settings**: Per-room configuration
-
-## Mode Selection
-
-The mode is determined by your deployment configuration:
-
-```yaml
-# Public Mode (no authentication)
-REFLECTOR_AUTH_BACKEND=none
-
-# Private Mode (with authentication)
-REFLECTOR_AUTH_BACKEND=jwt
-# or
-REFLECTOR_AUTH_BACKEND=authentik
-```
-
-## Feature Comparison
-
-| Feature | Public Mode | Private Mode |
-|---------|------------|--------------|
-| File Upload | ✅ | ✅ |
-| Live Transcription | ✅ | ✅ |
-| Speaker Diarization | ✅ | ✅ |
-| Translation | ✅ | ✅ |
-| Summarization | ✅ | ✅ |
-| Meeting Rooms | ❌ | ✅ |
-| Persistent Storage | ❌ | ✅ |
-| Team Collaboration | ❌ | ✅ |
-| API Access | Limited | Full |
-| User Management | ❌ | ✅ |
-| Custom Branding | ❌ | ✅ |
-| Analytics | ❌ | ✅ |
-| Webhooks | ❌ | ✅ |
-
-## Security Considerations
-
-### Public Mode Security
- Rate limiting to prevent abuse
- File size restrictions
- Automatic cleanup of old data
- No PII storage
-
-### Private Mode Security
- Encrypted data storage
- Audit logging
- Session management
- Access control lists
- Data retention policies
-
-## Choosing the Right Mode
-
-### Choose Public Mode if:
- You need quick, one-time transcriptions
- You're evaluating Reflector
- You don't need persistent storage
- You're processing non-sensitive content
-
-### Choose Private Mode if:
- You need team collaboration
- You require persistent storage
- You're processing sensitive content
- You need meeting room functionality
- You want advanced analytics
-EOF
-
-# Concepts - Independence
-cat > docs/concepts/independence.md << 'EOF'
---
-sidebar_position: 3
-title: Data Independence
---
-
-# Data Independence & Privacy
-
-Reflector is designed with privacy and data independence as core principles, giving you complete control over your data and processing.
-
-## Privacy by Design
-
-### No Third-Party Data Sharing
-
-Your audio and transcripts are never shared with third parties:
- **Local Processing**: All ML models can run on your infrastructure
- **No Training on User Data**: Your content is never used to improve models
- **Isolated Processing**: Each transcript is processed in isolation
- **No Analytics Tracking**: No usage analytics sent to external services
-
-### Data Ownership
-
-You maintain complete ownership of all data:
- **Export Anytime**: Download all your transcripts and audio
- **Delete on Demand**: Permanent deletion with no recovery
- **API Access**: Full programmatic access to your data
- **No Vendor Lock-in**: Standard formats for easy migration
-
-## Processing Transparency
-
-### What Happens to Your Audio
-
-1. **Upload/Stream**: Audio received by your server
-2. **Temporary Storage**: Stored only for processing duration
-3. **Processing**: ML models process audio locally or on Modal
-4. **Results Storage**: Transcripts stored in your database
-5. **Cleanup**: Original audio deleted (unless configured otherwise)
-
-### Local vs Cloud Processing
-
-#### Local Processing
-When configured for local processing:
- All models run on your hardware
- No data leaves your infrastructure
- Complete air-gap capability
- Higher hardware requirements
-
-#### Modal.com Processing
-When using Modal for GPU acceleration:
- Audio chunks sent to Modal for processing
- Processed immediately and deleted
- No long-term storage on Modal
- Modal's security: SOC 2 Type II compliant
-
-### Data Retention
-
-Default retention policies:
- **Public Mode**: 24 hours then automatic deletion
- **Private Mode**: Configurable (default: indefinite)
- **Audio Files**: Deleted after processing (configurable)
- **Transcripts**: Retained based on policy
-
-## Compliance Features
-
-### GDPR Compliance
-
- **Right to Access**: Export all user data
- **Right to Deletion**: Permanent data removal
- **Data Portability**: Standard export formats
- **Privacy by Default**: Minimal data collection
-
-### HIPAA Considerations
-
-For healthcare deployments:
- **Self-hosted Option**: Complete infrastructure control
- **Encryption**: At rest and in transit
- **Audit Logging**: Complete access trail
- **Access Controls**: Role-based permissions
-
-### Industry Standards
-
- **TLS 1.3**: Modern encryption for data in transit
- **AES-256**: Encryption for data at rest
- **JWT Tokens**: Secure, stateless authentication
- **OWASP Guidelines**: Security best practices
-
-## Self-Hosted Deployment
-
-### Complete Independence
-
-Self-hosting provides maximum control:
- **Your Infrastructure**: Run on your servers
- **Your Network**: No external connections required
- **Your Policies**: Implement custom retention
- **Your Compliance**: Meet specific requirements
-
-### Air-Gap Capability
-
-Reflector can run completely offline:
-1. Download all models during setup
-2. Configure for local processing only
-3. Disable all external integrations
-4. Run in isolated network environment
-
-## Data Flow Control
-
-### Configurable Processing
-
-Control where each step happens:
-
-```yaml
-# All local processing
-TRANSCRIPT_BACKEND=local
-DIARIZATION_BACKEND=local
-TRANSLATION_BACKEND=local
-
-# Hybrid approach
-TRANSCRIPT_BACKEND=modal  # Fast GPU processing
-DIARIZATION_BACKEND=local # Sensitive speaker data
-TRANSLATION_BACKEND=modal  # Non-sensitive translation
-```
-
-### Storage Options
-
-Choose where data is stored:
- **Local Filesystem**: Complete control
- **PostgreSQL**: Self-hosted database
- **S3-Compatible**: MinIO or AWS with encryption
- **Hybrid**: Different storage for different data types
-
-## Security Architecture
-
-### Defense in Depth
-
-Multiple layers of security:
-1. **Network Security**: Firewalls and VPNs
-2. **Application Security**: Input validation and sanitization
-3. **Data Security**: Encryption and access controls
-4. **Operational Security**: Logging and monitoring
-
-### Zero Trust Principles
-
- **Verify Everything**: All requests authenticated
- **Least Privilege**: Minimal permissions granted
- **Assume Breach**: Design for compromise containment
- **Encrypt Everything**: No plaintext transmission
-
-## Audit and Compliance
-
-### Audit Logging
-
-Comprehensive logging of:
- **Access Events**: Who accessed what and when
- **Processing Events**: What was processed and how
- **Configuration Changes**: System modifications
- **Security Events**: Failed authentication attempts
-
-### Compliance Reporting
-
-Generate reports for:
- **Data Processing**: What data was processed
- **Data Access**: Who accessed the data
- **Data Retention**: What was retained or deleted
- **Security Events**: Security-related incidents
-
-## Best Practices
-
-### For Maximum Privacy
-
-1. **Self-host** all components
-2. **Use local processing** for all models
-3. **Implement short retention** periods
-4. **Encrypt all storage** at rest
-5. **Use VPN** for all connections
-6. **Regular audits** of access logs
-
-### For Balanced Approach
-
-1. **Self-host core services** (database, API)
-2. **Use Modal for processing** (faster, cost-effective)
-3. **Implement encryption** everywhere
-4. **Regular backups** with encryption
-5. **Monitor access** patterns
-EOF
-
-# Concepts - Pipeline
-cat > docs/concepts/pipeline.md << 'EOF'
---
-sidebar_position: 4
-title: Processing Pipeline
---
-
-# Processing Pipeline
-
-Reflector uses a sophisticated pipeline architecture to process audio efficiently and accurately.
-
-## Pipeline Overview
-
-The processing pipeline consists of modular components that can be combined and configured based on your needs:
-
-```mermaid
-graph LR
-    A[Audio Input] --> B[Pre-processing]
-    B --> C[Chunking]
-    C --> D[Transcription]
-    D --> E[Diarization]
-    E --> F[Alignment]
-    F --> G[Post-processing]
-    G --> H[Output]
-```
-
-## Pipeline Components
-
-### Audio Input
-
-Accepts various input sources:
- **File Upload**: MP3, WAV, M4A, WebM, MP4
- **WebRTC Stream**: Live browser audio
- **Recording Integration**: Whereby recordings
- **API Upload**: Direct API submission
-
-### Pre-processing
-
-Prepares audio for optimal processing:
- **Format Conversion**: Convert to 16kHz mono WAV
- **Normalization**: Adjust volume to -23 LUFS
- **Noise Reduction**: Optional background noise removal
- **Validation**: Check duration and quality
-
-### Chunking
-
-Splits audio for parallel processing:
- **Fixed Size**: 30-second chunks by default
- **Overlap**: 1-second overlap for continuity
- **Smart Boundaries**: Attempt to split at silence
- **Metadata**: Track chunk positions
-
-### Transcription
-
-Converts speech to text:
- **Model Selection**: Whisper or Parakeet
- **Language Detection**: Automatic or specified
- **Timestamp Generation**: Word-level timing
- **Confidence Scores**: Quality indicators
-
-### Diarization
-
-Identifies different speakers:
- **Voice Activity Detection**: Find speech segments
- **Speaker Embedding**: Extract voice characteristics
- **Clustering**: Group similar voices
- **Label Assignment**: Assign speaker IDs
-
-### Alignment
-
-Merges all processing results:
- **Chunk Assembly**: Combine transcription chunks
- **Speaker Mapping**: Align speakers with text
- **Overlap Resolution**: Handle chunk boundaries
- **Timeline Creation**: Build unified timeline
-
-### Post-processing
-
-Enhances the final output:
- **Formatting**: Apply punctuation and capitalization
- **Translation**: Convert to target languages
- **Summarization**: Generate concise summaries
- **Topic Extraction**: Identify key themes
- **Action Items**: Extract tasks and decisions
-
-## Processing Modes
-
-### Batch Processing
-
-For uploaded files:
- Optimized for throughput
- Parallel chunk processing
- Higher accuracy models
- Complete file analysis
-
-### Stream Processing
-
-For live audio:
- Optimized for latency
- Sequential processing
- Real-time feedback
- Progressive results
-
-### Hybrid Processing
-
-For meetings:
- Stream during meeting
- Batch after completion
- Best of both modes
- Maximum accuracy
-
-## Pipeline Configuration
-
-### Model Selection
-
-Choose models based on requirements:
-
-```python
-# High accuracy (slower)
-config = {
-    "transcription_model": "whisper-large-v3",
-    "diarization_model": "pyannote-3.1",
-    "translation_model": "seamless-m4t-large"
-}
-
-# Balanced (default)
-config = {
-    "transcription_model": "whisper-base",
-    "diarization_model": "pyannote-3.1",
-    "translation_model": "seamless-m4t-medium"
-}
-
-# Fast processing
-config = {
-    "transcription_model": "whisper-tiny",
-    "diarization_model": "pyannote-3.1-fast",
-    "translation_model": "seamless-m4t-small"
-}
-```
-
-### Processing Options
-
-Customize pipeline behavior:
-
-```yaml
-# Parallel processing
-max_parallel_chunks: 10
-chunk_size_seconds: 30
-chunk_overlap_seconds: 1
-
-# Quality settings
-enable_noise_reduction: true
-enable_normalization: true
-min_speech_confidence: 0.5
-
-# Post-processing
-enable_translation: true
-target_languages: ["es", "fr", "de"]
-enable_summarization: true
-summary_length: "medium"
-```
-
-## Performance Characteristics
-
-### Processing Times
-
-For 1 hour of audio:
-
-| Pipeline Config | Processing Time | Accuracy |
-|----------------|-----------------|----------|
-| Fast | 2-3 minutes | 85-90% |
-| Balanced | 5-8 minutes | 92-95% |
-| High Accuracy | 15-20 minutes | 95-98% |
-
-### Resource Usage
-
-| Component | CPU Usage | Memory | GPU |
-|-----------|-----------|---------|-----|
-| Transcription | Medium | 2-4 GB | Required |
-| Diarization | High | 4-8 GB | Required |
-| Translation | Low | 2-3 GB | Optional |
-| Post-processing | Low | 1-2 GB | Not needed |
-
-## Pipeline Orchestration
-
-### Celery Task Chain
-
-The pipeline is orchestrated using Celery:
-
-```python
-chain = (
-    chunk_audio.s(audio_id) |
-    group(transcribe_chunk.s(chunk) for chunk in chunks) |
-    merge_transcriptions.s() |
-    diarize_audio.s() |
-    align_speakers.s() |
-    post_process.s()
-)
-```
-
-### Error Handling
-
-Robust error recovery:
- **Automatic Retry**: Failed tasks retry up to 3 times
- **Partial Recovery**: Continue with successful chunks
- **Fallback Models**: Use alternative models on failure
- **Error Reporting**: Detailed error messages
-
-### Progress Tracking
-
-Real-time progress updates:
- **Chunk Progress**: Track individual chunk processing
- **Overall Progress**: Percentage completion
- **ETA Calculation**: Estimated completion time
- **WebSocket Updates**: Live progress to clients
-
-## Optimization Strategies
-
-### GPU Utilization
-
-Maximize GPU efficiency:
- **Batch Processing**: Process multiple chunks together
- **Model Caching**: Keep models loaded in memory
- **Dynamic Batching**: Adjust batch size based on GPU memory
- **Multi-GPU Support**: Distribute across available GPUs
-
-### Memory Management
-
-Efficient memory usage:
- **Streaming Processing**: Process large files in chunks
- **Garbage Collection**: Clean up after each chunk
- **Memory Limits**: Prevent out-of-memory errors
- **Disk Caching**: Use disk for large intermediate results
-
-### Network Optimization
-
-Minimize network overhead:
- **Compression**: Compress audio before transfer
- **CDN Integration**: Use CDN for static assets
- **Connection Pooling**: Reuse network connections
- **Parallel Uploads**: Multiple concurrent uploads
-
-## Quality Assurance
-
-### Accuracy Metrics
-
-Monitor processing quality:
- **Word Error Rate (WER)**: Transcription accuracy
- **Diarization Error Rate (DER)**: Speaker identification accuracy
- **Translation BLEU Score**: Translation quality
- **Summary Coherence**: Summary quality metrics
-
-### Validation Steps
-
-Ensure output quality:
- **Confidence Thresholds**: Filter low-confidence segments
- **Consistency Checks**: Verify timeline consistency
- **Language Validation**: Ensure correct language detection
- **Format Validation**: Check output format compliance
-
-## Advanced Features
-
-### Custom Models
-
-Use your own models:
- **Fine-tuned Whisper**: Domain-specific models
- **Custom Diarization**: Trained on your speakers
- **Specialized Post-processing**: Industry-specific formatting
-
-### Pipeline Extensions
-
-Add custom processing steps:
- **Sentiment Analysis**: Analyze emotional tone
- **Entity Extraction**: Identify people, places, organizations
- **Custom Metrics**: Calculate domain-specific metrics
- **Integration Hooks**: Call external services
-EOF
-
-# Create installation documentation
-cat > docs/installation/overview.md << 'EOF'
---
-sidebar_position: 1
-title: Installation Overview
---
-
-# Installation Overview
-
-Reflector is designed for self-hosted deployment, giving you complete control over your infrastructure and data.
-
-## Deployment Options
-
-### Docker Deployment (Recommended)
-
-The easiest way to deploy Reflector:
- Pre-configured containers
- Automated dependency management
- Consistent environment
- Easy updates
-
-### Manual Installation
-
-For custom deployments:
- Greater control over configuration
- Integration with existing infrastructure
- Custom optimization options
- Development environments
-
-## Requirements
-
-### System Requirements
-
-**Minimum Requirements:**
- CPU: 4 cores
- RAM: 8 GB
- Storage: 50 GB
- OS: Ubuntu 20.04+ or similar Linux
-
-**Recommended Requirements:**
- CPU: 8+ cores
- RAM: 16 GB
- Storage: 100 GB SSD
- GPU: NVIDIA GPU with 8GB+ VRAM (for local processing)
-
-### Network Requirements
-
- Public IP address (for WebRTC)
- Ports: 80, 443, 8000, 3000
- Domain name (for SSL)
- SSL certificate (Let's Encrypt supported)
-
-## Required Services
-
-### Core Services
-
-These services are required for basic operation:
-
-1. **PostgreSQL** - Primary database
-2. **Redis** - Message broker and cache
-3. **Docker** - Container runtime
-
-### GPU Processing
-
-Choose one:
- **Modal.com** - Serverless GPU (recommended)
- **Local GPU** - Self-hosted GPU processing
-
-### Optional Services
-
-Enhance functionality with:
- **AWS S3** - Long-term storage
- **Whereby** - Video conferencing rooms
- **Authentik** - Enterprise authentication
- **Zulip** - Chat integration
-
-## Quick Start
-
-### Using Docker Compose
-
-1. Clone the repository:
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector
-```
-
-2. Navigate to docker directory:
-```bash
-cd docker
-```
-
-3. Copy and configure environment:
-```bash
-cp .env.example .env
-# Edit .env with your settings
-```
-
-4. Start services:
-```bash
-docker compose up -d
-```
-
-5. Access Reflector:
- Frontend: https://your-domain.com
- API: https://your-domain.com/api
-
-## Configuration Overview
-
-### Essential Configuration
-
-```env
-# Database
-DATABASE_URL=postgresql://user:pass@localhost/reflector
-
-# Redis
-REDIS_URL=redis://localhost:6379
-
-# Modal.com (for GPU processing)
-TRANSCRIPT_MODAL_API_KEY=your-key
-DIARIZATION_MODAL_API_KEY=your-key
-
-# Domain
-DOMAIN=your-domain.com
-```
-
-### Security Configuration
-
-```env
-# Authentication
-REFLECTOR_AUTH_BACKEND=jwt
-NEXTAUTH_SECRET=generate-strong-secret
-
-# SSL (handled by Caddy)
-# Automatic with Let's Encrypt
-```
-
-## Service Architecture
-
-```mermaid
-graph TD
-    A[Caddy Reverse Proxy] --> B[Frontend - Next.js]
-    A --> C[Backend - FastAPI]
-    C --> D[PostgreSQL]
-    C --> E[Redis]
-    C --> F[Celery Workers]
-    F --> G[Modal.com GPU]
-```
-
-## Next Steps
-
-1. **Review Requirements**: [System Requirements](./requirements)
-2. **Docker Setup**: [Docker Deployment Guide](./docker-setup)
-3. **Configure Services**:
-   - [Modal.com Setup](./modal-setup)
-   - [Whereby Setup](./whereby-setup)
-   - [AWS S3 Setup](./aws-setup)
-4. **Optional Services**:
-   - [Authentik Setup](./authentik-setup)
-   - [Zulip Setup](./zulip-setup)
-
-## Getting Help
-
- [Troubleshooting Guide](../reference/troubleshooting)
- [GitHub Issues](https://github.com/monadical-sas/reflector/issues)
- [Community Discord](#)
-EOF
-
-chmod +x create-docs.sh
-echo "Documentation creation script ready. Run ./create-docs.sh to generate all docs."
--- a/docs/docs/concepts/modes.md
+++ b/docs/docs/concepts/modes.md
@@ -1,125 +0,0 @@
---
-sidebar_position: 2
-title: Operating Modes
---
-
-# Operating Modes
-
-Reflector operates in two distinct modes to accommodate different use cases and security requirements.
-
-## Public Mode
-
-Public mode provides immediate access to core transcription features without requiring authentication.
-
-### Features Available
- **File Upload**: Process audio files up to 2GB
- **Live Transcription**: Stream audio from microphone
- **Basic Processing**: Transcription and diarization
- **Temporary Storage**: Results available for 24 hours
-
-### Limitations
- No persistent storage
- No meeting rooms
- Limited to single-user sessions
- No team collaboration features
-
-### Use Cases
- Quick transcription needs
- Testing and evaluation
- Individual users
- Public demonstrations
-
-## Private Mode
-
-Private mode unlocks the full potential of Reflector with authentication and persistent storage.
-
-### Additional Features
- **Virtual Meeting Rooms**: Whereby and Daily.co integration
- **Team Collaboration**: Share transcripts with team
- **Persistent Storage**: Long-term transcript archive
- **Advanced Analytics**: Meeting insights and trends
- **Custom Integration**: Webhooks and API access
- **User Management**: Role-based access control
-
-### Authentication Options
-
-#### Authentik Integration
-Enterprise-grade SSO with support for:
- SAML 2.0
- OAuth 2.0 / OIDC
- LDAP / Active Directory
- Multi-factor authentication
-
-#### JWT Authentication
-Stateless token-based auth for:
- API access
- Service-to-service communication
- Mobile applications
-
-### Room Management
-
-Virtual rooms provide dedicated spaces for meetings:
- **Persistent URLs**: Same link for recurring meetings
- **Access Control**: Invite-only or open rooms
- **Recording Consent**: Automatic consent management
- **Custom Settings**: Per-room configuration
-
-## Mode Selection
-
-The mode is determined by your deployment configuration:
-
-```yaml
-# Public Mode (no authentication)
-REFLECTOR_AUTH_BACKEND=none
-
-# Private Mode (with authentication)
-REFLECTOR_AUTH_BACKEND=jwt
-# or
-REFLECTOR_AUTH_BACKEND=authentik
-```
-
-## Feature Comparison
-
-| Feature | Public Mode | Private Mode |
-|---------|------------|--------------|
-| File Upload | ✅ | ✅ |
-| Live Transcription | ✅ | ✅ |
-| Speaker Diarization | ✅ | ✅ |
-| Translation | ✅ | ✅ |
-| Summarization | ✅ | ✅ |
-| Meeting Rooms | ❌ | ✅ |
-| Persistent Storage | ❌ | ✅ |
-| Team Collaboration | ❌ | ✅ |
-| API Access | Limited | Full |
-| User Management | ❌ | ✅ |
-| Custom Branding | ❌ | ✅ |
-| Analytics | ❌ | ✅ |
-| Webhooks | ❌ | ✅ |
-
-## Security Considerations
-
-### Public Mode Security
- File size restrictions
- Automatic cleanup of old data
-
-### Private Mode Security
- Encrypted data storage
- Audit logging
- Session management
- Access control lists
- Data retention policies
-
-## Choosing the Right Mode
-
-### Choose Public Mode if:
- You need quick, one-time transcriptions
- You're evaluating Reflector
- You don't need persistent storage
- You're processing non-sensitive content
-
-### Choose Private Mode if:
- You need team collaboration
- You require persistent storage
- You're processing sensitive content
- You need meeting room functionality
- You want advanced analytics
--- a/docs/docs/concepts/overview.md
+++ b/docs/docs/concepts/overview.md
@@ -1,194 +0,0 @@
---
-sidebar_position: 1
-title: Architecture Overview
---
-
-# Architecture Overview
-
-Reflector is built as a modern, scalable, microservices-based application designed to handle audio processing workloads efficiently while maintaining data privacy and control.
-
-## System Components
-
-### Frontend Application
-
-The user interface is built with **Next.js 14** using the App Router pattern, providing:
-
- Server-side rendering for optimal performance
- Real-time WebSocket connections for live transcription
- WebRTC support for audio streaming and live meetings
- Responsive design with Chakra UI components
-
-### Backend API Server
-
-The core API is powered by **FastAPI**, a modern Python framework that provides:
-
- High-performance async request handling
- Automatic OpenAPI documentation generation
- Type safety with Pydantic models
- WebSocket support for real-time updates
-
-### Processing Pipeline
-
-Audio processing is handled through a modular pipeline architecture:
-
-```
-Audio Input → Chunking → Transcription → Diarization → Post-Processing → Storage
-```
-
-Each step can run independently and in parallel, allowing for:
- Scalable processing of large files
- Real-time streaming capabilities
- Fault tolerance and retry mechanisms
-
-### Worker Architecture
-
-Background tasks are managed by **Celery** workers with **Redis** as the message broker:
-
- Distributed task processing
- Priority queues for time-sensitive operations
- Automatic retry on failure
- Progress tracking and notifications
-
-### GPU Acceleration
-
-ML models run on GPU-accelerated infrastructure:
-
- **Modal.com** for serverless GPU processing
- Support for local GPU deployment (coming soon)
- Automatic scaling based on demand
- Cost-effective pay-per-use model
-
-## Data Flow
-
-### File Processing Flow
-
-1. **Upload**: User uploads audio file through web interface
-2. **Storage**: File stored temporarily or in S3
-3. **Queue**: Processing job added to Celery queue
-4. **Chunking**: Audio split into 30-second segments
-5. **Parallel Processing**: Chunks processed simultaneously
-6. **Assembly**: Results merged and aligned
-7. **Post-Processing**: Summary, topics, translation
-8. **Delivery**: Results stored and user notified
-
-### Live Streaming Flow
-
-1. **WebRTC Connection**: Browser establishes peer connection
-2. **Audio Capture**: Microphone audio streamed to server
-3. **Buffering**: Audio buffered for processing
-4. **VAD**: Voice activity detection segments speech
-5. **Real-time Processing**: Segments transcribed immediately
-6. **WebSocket Updates**: Results streamed back to client
-7. **Continuous Assembly**: Full transcript built progressively
-
-## Deployment Architecture
-
-### Container-Based Deployment
-
-All components are containerized for consistent deployment:
-
-```yaml
-services:
-  frontend:    # Next.js application
-  backend:     # FastAPI server
-  worker:      # Celery workers
-  redis:       # Message broker
-  postgres:    # Database
-  caddy:       # Reverse proxy
-```
-
-### Networking
-
- **Host Network Mode**: Required for WebRTC/ICE compatibility
- **Caddy Reverse Proxy**: Handles SSL termination and routing
- **WebSocket Upgrade**: Supports real-time connections
-
-## Scalability Considerations
-
-### Horizontal Scaling
-
- **Stateless Backend**: Multiple API server instances
- **Worker Pools**: Add workers based on queue depth
- **Database Pooling**: Connection management for concurrent access
-
-### Vertical Scaling
-
- **GPU Workers**: Scale up for faster model inference
- **Memory Optimization**: Efficient audio buffering
- **CPU Optimization**: Multi-threaded processing where applicable
-
-## Security Architecture
-
-### Authentication & Authorization
-
- **JWT Tokens**: Stateless authentication
- **Authentik Integration**: Enterprise SSO support
- **Role-Based Access**: Granular permissions
-
-### Data Protection
-
- **Encryption at Rest**: Database and S3 encryption
- **Encryption in Transit**: TLS for all connections
- **Temporary Storage**: Automatic cleanup of processed files
-
-### Privacy by Design
-
- **Local Processing**: Option to process entirely on-premises
- **No Training on User Data**: Models are pre-trained
- **Data Isolation**: Multi-tenant data separation
-
-## Integration Points
-
-### External Services
-
- **Modal.com**: GPU processing
- **AWS S3**: Long-term storage
- **Whereby**: Video conferencing rooms
- **Zulip**: Chat integration (optional)
-
-### APIs and Webhooks
-
- **RESTful API**: Standard CRUD operations
- **WebSocket API**: Real-time updates
- **Webhook Notifications**: Processing completion events
- **OpenAPI Specification**: Machine-readable API definition
-
-## Performance Optimization
-
-### Caching Strategy
-
- **Redis Cache**: Frequently accessed data
- **CDN**: Static asset delivery
- **Browser Cache**: Client-side optimization
-
-### Database Optimization
-
- **Indexed Queries**: Fast search and retrieval
- **Connection Pooling**: Efficient resource usage
- **Query Optimization**: N+1 query prevention
-
-### Processing Optimization
-
- **Batch Processing**: Efficient GPU utilization
- **Parallel Execution**: Multi-core CPU usage
- **Stream Processing**: Reduced memory footprint
-
-## Monitoring and Observability
-
-### Metrics Collection
-
- **Application Metrics**: Request rates, response times
- **System Metrics**: CPU, memory, disk usage
- **Business Metrics**: Transcription accuracy, processing times
-
-### Logging
-
- **Structured Logging**: JSON format for analysis
- **Log Aggregation**: Centralized log management
- **Error Tracking**: Sentry integration
-
-### Health Checks
-
- **Liveness Probes**: Component availability
- **Readiness Probes**: Service readiness
- **Dependency Checks**: External service status
--- a/docs/docs/concepts/pipeline.md
+++ b/docs/docs/concepts/pipeline.md
@@ -1,272 +0,0 @@
---
-sidebar_position: 4
-title: Processing Pipeline
---
-
-# Processing Pipeline
-
-Reflector uses a modular pipeline architecture to process audio efficiently and accurately.
-
-## Pipeline Overview
-
-The processing pipeline consists of modular components that can be combined and configured based on your needs:
-
-```mermaid
-graph LR
-    A[Audio Input] --> B[Pre-processing]
-    B --> C[Chunking]
-    C --> D[Transcription]
-    D --> E[Diarization]
-    E --> F[Alignment]
-    F --> G[Post-processing]
-    G --> H[Output]
-```
-
-## Pipeline Components
-
-### Audio Input
-
-Accepts various input sources:
- **File Upload**: MP3, WAV, M4A, WebM, MP4
- **WebRTC Stream**: Live browser audio
- **Recording Integration**: Whereby recordings
- **API Upload**: Direct API submission
-
-### Pre-processing
-
-Prepares audio for optimal processing:
- **Format Conversion**: Convert to 16kHz mono WAV
- **Noise Reduction**: Optional background noise removal
- **Validation**: Check duration and quality
-
-### Chunking
-
-Splits audio for parallel processing:
- **Fixed Size**: 30-second chunks by default
- **Overlap**: 1-second overlap for continuity
- **Silence Detection**: Attempt to split at silence
- **Metadata**: Track chunk positions
-
-### Transcription
-
-Converts speech to text:
- **Model Selection**: Whisper or Parakeet
- **Language Detection**: Automatic or specified
- **Timestamp Generation**: Word-level timing
- **Confidence Scores**: Quality indicators
-
-### Diarization
-
-Identifies different speakers:
- **Voice Activity Detection**: Find speech segments
- **Speaker Embedding**: Extract voice characteristics
- **Clustering**: Group similar voices
- **Label Assignment**: Assign speaker IDs
-
-### Alignment
-
-Merges all processing results:
- **Chunk Assembly**: Combine transcription chunks
- **Speaker Mapping**: Align speakers with text
- **Overlap Resolution**: Handle chunk boundaries
- **Timeline Creation**: Build unified timeline
-
-### Post-processing
-
-Enhances the final output:
- **Formatting**: Apply punctuation and capitalization
- **Translation**: Convert to target languages
- **Summarization**: Generate concise summaries
- **Topic Extraction**: Identify key themes
- **Action Items**: Extract tasks and decisions
-
-## Processing Modes
-
-### Batch Processing
-
-For uploaded files:
- Optimized for throughput
- Parallel chunk processing
- Higher accuracy models
- Complete file analysis
-
-### Stream Processing
-
-For live audio:
- Optimized for latency
- Sequential processing
- Real-time feedback
- Progressive results
-
-### Hybrid Processing
-
-For meetings:
- Stream during meeting
- Batch after completion
- Best of both modes
- Maximum accuracy
-
-## Pipeline Configuration
-
-### Model Selection
-
-Choose models based on requirements:
-
-```python
-# High accuracy (slower)
-config = {
-    "transcription_model": "whisper-large-v3",
-    "diarization_model": "pyannote-3.1",
-    "translation_model": "seamless-m4t-large"
-}
-
-# Balanced (default)
-config = {
-    "transcription_model": "whisper-base",
-    "diarization_model": "pyannote-3.1",
-    "translation_model": "seamless-m4t-medium"
-}
-
-# Fast processing
-config = {
-    "transcription_model": "whisper-tiny",
-    "diarization_model": "pyannote-3.1-fast",
-    "translation_model": "seamless-m4t-small"
-}
-```
-
-### Processing Options
-
-Customize pipeline behavior:
-
-```yaml
-# Parallel processing
-max_parallel_chunks: 10
-chunk_size_seconds: 30
-chunk_overlap_seconds: 1
-
-# Quality settings
-enable_noise_reduction: true
-min_speech_confidence: 0.5
-
-# Post-processing
-enable_translation: true
-target_languages: ["es", "fr", "de"]
-enable_summarization: true
-summary_length: "medium"
-```
-
-## Performance Characteristics
-
-### Processing Times
-
-For 1 hour of audio:
-
-| Pipeline Config | Processing Time | Accuracy |
-|----------------|-----------------|----------|
-| Fast | 2-3 minutes | 85-90% |
-| Balanced | 5-8 minutes | 92-95% |
-| High Accuracy | 15-20 minutes | 95-98% |
-
-### Resource Usage
-
-| Component | CPU Usage | Memory | GPU |
-|-----------|-----------|---------|-----|
-| Transcription | Medium | 2-4 GB | Required |
-| Diarization | High | 4-8 GB | Required |
-| Translation | Low | 2-3 GB | Optional |
-| Post-processing | Low | 1-2 GB | Not needed |
-
-## Pipeline Orchestration
-
-### Celery Task Chain
-
-The pipeline is orchestrated using Celery:
-
-```python
-chain = (
-    chunk_audio.s(audio_id) |
-    group(transcribe_chunk.s(chunk) for chunk in chunks) |
-    merge_transcriptions.s() |
-    diarize_audio.s() |
-    align_speakers.s() |
-    post_process.s()
-)
-```
-
-### Error Handling
-
-Error recovery:
- **Automatic Retry**: Failed tasks retry up to 3 times
- **Partial Recovery**: Continue with successful chunks
- **Fallback Models**: Use alternative models on failure
- **Error Reporting**: Detailed error messages
-
-### Progress Tracking
-
-Real-time progress updates:
- **Chunk Progress**: Track individual chunk processing
- **Overall Progress**: Percentage completion
- **ETA Calculation**: Estimated completion time
- **WebSocket Updates**: Live progress to clients
-
-## Optimization Strategies
-
-### GPU Utilization
-
-Maximize GPU efficiency:
- **Batch Processing**: Process multiple chunks together
- **Model Caching**: Keep models loaded in memory
- **Dynamic Batching**: Adjust batch size based on GPU memory
- **Multi-GPU Support**: Distribute across available GPUs
-
-### Memory Management
-
-Efficient memory usage:
- **Streaming Processing**: Process large files in chunks
- **Garbage Collection**: Clean up after each chunk
- **Memory Limits**: Prevent out-of-memory errors
- **Disk Caching**: Use disk for large intermediate results
-
-### Network Optimization
-
-Minimize network overhead:
- **Compression**: Compress audio before transfer
- **CDN Integration**: Use CDN for static assets
- **Connection Pooling**: Reuse network connections
- **Parallel Uploads**: Multiple concurrent uploads
-
-## Quality Assurance
-
-### Accuracy Metrics
-
-Monitor processing quality:
- **Word Error Rate (WER)**: Transcription accuracy
- **Diarization Error Rate (DER)**: Speaker identification accuracy
- **Translation BLEU Score**: Translation quality
- **Summary Coherence**: Summary quality metrics
-
-### Validation Steps
-
-Ensure output quality:
- **Confidence Thresholds**: Filter low-confidence segments
- **Consistency Checks**: Verify timeline consistency
- **Language Validation**: Ensure correct language detection
- **Format Validation**: Check output format compliance
-
-## Advanced Features
-
-### Custom Models
-
-Use your own models:
- **Fine-tuned Whisper**: Domain-specific models
- **Custom Diarization**: Trained on your speakers
- **Specialized Post-processing**: Industry-specific formatting
-
-### Pipeline Extensions
-
-Add custom processing steps:
- **Sentiment Analysis**: Analyze emotional tone
- **Entity Extraction**: Identify people, places, organizations
- **Custom Metrics**: Calculate domain-specific metrics
- **Integration Hooks**: Call external services
--- a/docs/docs/installation/auth-setup.md
+++ b/docs/docs/installation/auth-setup.md
@@ -1,285 +0,0 @@
---
-sidebar_position: 5
-title: Authentication Setup
---
-
-# Authentication Setup
-
-This page covers authentication setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
-
-Reflector uses [Authentik](https://goauthentik.io/) for OAuth/OIDC authentication. This guide walks you through setting up Authentik and connecting it to Reflector.
-
-The guide simplistically sets Authentic on the same server as Reflector. You can use your own Authentic instance instead.
-
-## Overview
-
-Reflector's authentication flow:
-1. User clicks "Sign In" on frontend
-2. Frontend redirects to Authentik login page
-3. User authenticates with Authentik
-4. Authentik redirects back with OAuth tokens
-5. Frontend stores tokens, backends verify JWT signature
-
-## Option 1: Self-Hosted Authentik (Same Server)
-
-This setup runs Authentik on the same server as Reflector, with Caddy proxying to both.
-
-### Deploy Authentik
-
-```bash
-# Create directory for Authentik
-mkdir -p ~/authentik && cd ~/authentik
-
-# Download docker-compose file
-curl -O https://goauthentik.io/docker-compose.yml
-
-# Generate secrets and bootstrap credentials
-cat > .env << 'EOF'
-PG_PASS=$(openssl rand -base64 36 | tr -d '\n')
-AUTHENTIK_SECRET_KEY=$(openssl rand -base64 60 | tr -d '\n')
-# Privacy-focused choice for self-hosted deployments
-AUTHENTIK_ERROR_REPORTING__ENABLED=false
-AUTHENTIK_BOOTSTRAP_PASSWORD=YourSecurePassword123
-AUTHENTIK_BOOTSTRAP_EMAIL=admin@example.com
-EOF
-
-# Start Authentik
-sudo docker compose up -d
-```
-
-Authentik takes ~2 minutes to run migrations and apply blueprints on first start.
-
-### Connect Authentik to Reflector's Network
-
-If Authentik runs in a separate Docker Compose project, connect it to Reflector's network so Caddy can proxy to it:
-
-```bash
-# Wait for Authentik to be healthy
-# Connect Authentik server to Reflector's network
-sudo docker network connect reflector_default authentik-server-1
-```
-
-**Important:** This step must be repeated if you restart Authentik with `docker compose down`. Add it to your deployment scripts or use `docker compose up -d` (which preserves containers) instead of down/up.
-
-### Add Authentik to Caddy
-
-Uncomment the Authentik section in your `Caddyfile` and set your domain:
-
-```bash
-nano Caddyfile
-```
-
-Uncomment and edit:
-```
-{$AUTHENTIK_DOMAIN:authentik.example.com} {
-    reverse_proxy authentik-server-1:9000
-}
-```
-
-Reload Caddy:
-```bash
-docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
-```
-
-### Create OAuth2 Provider in Authentik
-
-**Option A: Automated Setup (Recommended)**
-
-**Location: Reflector server**
-
-Run the setup script from the Reflector repository:
-
-```bash
-ssh user@your-server-ip
-cd ~/reflector
-./scripts/setup-authentik-oauth.sh https://authentik.example.com YourSecurePassword123 https://app.example.com
-```
-
-**Important:** The script must be run from the `~/reflector` directory on your server, as it creates files using relative paths.
-
-The script will output the configuration values to add to your `.env` files. Skip to "Update docker-compose.prod.yml".
-
-**Option B: Manual Setup**
-
-1. **Login to Authentik Admin** at `https://authentik.example.com/`
-   - Username: `akadmin`
-   - Password: The `AUTHENTIK_BOOTSTRAP_PASSWORD` you set in .env
-
-2. **Create OAuth2 Provider:**
-   - Go to **Applications > Providers > Create**
-   - Select **OAuth2/OpenID Provider**
-   - Configure:
-     - **Name**: `Reflector`
-     - **Authorization flow**: `default-provider-authorization-implicit-consent`
-     - **Client type**: `Confidential`
-     - **Client ID**: Note this value (auto-generated)
-     - **Client Secret**: Note this value (auto-generated)
-     - **Redirect URIs**: Add entry with:
-       ```
-       https://app.example.com/api/auth/callback/authentik
-       ```
-   - Scroll down to **Advanced protocol settings**
-   - In **Scopes**, add these three mappings:
-     - `authentik default OAuth Mapping: OpenID 'email'`
-     - `authentik default OAuth Mapping: OpenID 'openid'`
-     - `authentik default OAuth Mapping: OpenID 'profile'`
-   - Click **Finish**
-
-3. **Create Application:**
-   - Go to **Applications > Applications > Create**
-   - Configure:
-     - **Name**: `Reflector`
-     - **Slug**: `reflector` (auto-filled)
-     - **Provider**: Select the `Reflector` provider you just created
-   - Click **Create**
-
-### Get Public Key for JWT Verification
-
-**Location: Reflector server**
-
-Extract the public key from Authentik's JWKS endpoint:
-
-```bash
-mkdir -p ~/reflector/server/reflector/auth/jwt/keys
-curl -s https://authentik.example.com/application/o/reflector/jwks/ | \
-  jq -r '.keys[0].x5c[0]' | base64 -d | openssl x509 -pubkey -noout \
-  > ~/reflector/server/reflector/auth/jwt/keys/authentik_public.pem
-```
-
-### Update docker-compose.prod.yml
-
-**Location: Reflector server**
-
-**Note:** This step is already done in the current `docker-compose.prod.yml`. Verify the volume mounts exist:
-
-```yaml
-server:
-  image: monadicalsas/reflector-backend:latest
-  # ... other config ...
-  volumes:
-    - server_data:/app/data
-    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-
-worker:
-  image: monadicalsas/reflector-backend:latest
-  # ... other config ...
-  volumes:
-    - server_data:/app/data
-    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
-```
-
-### Configure Reflector Backend
-
-**Location: Reflector server**
-
-Update `server/.env`:
-```env
-# Authentication
-AUTH_BACKEND=jwt
-AUTH_JWT_PUBLIC_KEY=authentik_public.pem
-AUTH_JWT_AUDIENCE=<your-client-id>
-CORS_ALLOW_CREDENTIALS=true
-```
-
-Replace `<your-client-id>` with the Client ID from previous steps.
-
-### Configure Reflector Frontend
-
-**Location: Reflector server**
-
-Update `www/.env`:
-```env
-# Authentication
-FEATURE_REQUIRE_LOGIN=true
-
-# Authentik OAuth
-AUTHENTIK_ISSUER=https://authentik.example.com/application/o/reflector
-AUTHENTIK_REFRESH_TOKEN_URL=https://authentik.example.com/application/o/token/
-AUTHENTIK_CLIENT_ID=<your-client-id>
-AUTHENTIK_CLIENT_SECRET=<your-client-secret>
-
-# NextAuth
-NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
-```
-
-### Restart Services
-
-**Location: Reflector server**
-
-```bash
-cd ~/reflector
-sudo docker compose -f docker-compose.prod.yml up -d --force-recreate server worker web
-```
-
-### Verify Authentication
-
-1. Visit `https://app.example.com`
-2. Click "Log in" or navigate to `/api/auth/signin`
-3. Click "Sign in with Authentik"
-4. Login with your Authentik credentials
-5. You should be redirected back and see "Log out" in the header
-
-## Option 2: Disable Authentication
-
-For testing or internal deployments where authentication isn't needed:
-
-**Backend `server/.env`:**
-```env
-AUTH_BACKEND=none
-```
-
-**Frontend `www/.env`:**
-```env
-FEATURE_REQUIRE_LOGIN=false
-```
-
-**Note:** The pre-built Docker images have `FEATURE_REQUIRE_LOGIN=true` baked in. To disable auth, you'll need to rebuild the frontend image with the env var set at build time, or set up Authentik.
-
-## Troubleshooting
-
-### "Invalid redirect URI" error
- Verify the redirect URI in Authentik matches exactly:
-  ```
-  https://app.example.com/api/auth/callback/authentik
-  ```
- Check for trailing slashes - they must match exactly
-
-### "Invalid audience" JWT error
- Ensure `AUTH_JWT_AUDIENCE` in `server/.env` matches the Client ID from Authentik
- The audience value is the OAuth Client ID, not the issuer URL
-
-### "JWT verification failed" error
- Verify the public key file is mounted in the container
- Check `AUTH_JWT_PUBLIC_KEY` points to the correct filename
- Ensure the key was extracted from the correct provider's JWKS endpoint
-
-### Caddy returns 503 for Authentik
- Verify Authentik container is connected to Reflector's network:
-  ```bash
-  sudo docker network connect reflector_default authentik-server-1
-  ```
- Check Authentik is healthy: `cd ~/authentik && sudo docker compose ps`
-
-### Users can't access protected pages
- Verify `FEATURE_REQUIRE_LOGIN=true` in frontend
- Check `AUTH_BACKEND=jwt` in backend
- Verify CORS settings allow credentials
-
-### Token refresh errors
- Ensure Redis is running (frontend uses Redis for token caching)
- Verify `KV_URL` is set correctly in frontend env
- Check `AUTHENTIK_REFRESH_TOKEN_URL` is correct
-
-## API Key Authentication
-
-For programmatic access (scripts, integrations), users can generate API keys:
-
-1. Login to Reflector
-2. Go to Settings > API Keys
-3. Click "Generate New Key"
-4. Use the key in requests:
-   ```bash
-   curl -H "X-API-Key: your-api-key" https://api.example.com/v1/transcripts
-   ```
-
-API keys are stored hashed and can be revoked at any time.
--- a/docs/docs/installation/daily-setup.md
+++ b/docs/docs/installation/daily-setup.md
@@ -1,165 +0,0 @@
---
-sidebar_position: 6
-title: Daily.co Setup
---
-
-# Daily.co Setup
-
-This page covers Daily.co video platform setup for live meeting rooms. For the complete deployment guide, see [Deployment Guide](./overview).
-
-Daily.co enables live video meetings with automatic recording and transcription.
-
-## What You'll Set Up
-
-```
-User joins meeting → Daily.co video room → Recording to S3 → [Webhook] → Reflector transcribes
-```
-
-## Prerequisites
-
- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
- [ ] **AWS account** - For S3 storage
- [ ] **Reflector deployed** - Complete steps from [Deployment Guide](./overview)
-
---
-
-## Create Daily.co Account
-
-1. Visit https://dashboard.daily.co and sign up
-2. Verify your email
-3. Note your subdomain (e.g., `yourname.daily.co` → subdomain is `yourname`)
-
---
-
-## Get Daily.co API Key
-
-1. In Daily.co dashboard, go to **Developers**
-2. Click **API Keys**
-3. Click **Create API Key**
-4. Copy the key (starts with a long string)
-
-Save this for later.
-
---
-
-## Create AWS S3 Bucket
-
-Daily.co needs somewhere to store recordings before Reflector processes them.
-
-```bash
-# Choose a unique bucket name
-BUCKET_NAME="reflector-dailyco-yourname" # -yourname is not a requirement, you can name the bucket as you wish
-AWS_REGION="us-east-1"
-
-# Create bucket
-aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
-
-# Enable versioning (required)
-aws s3api put-bucket-versioning \
-  --bucket $BUCKET_NAME \
-  --versioning-configuration Status=Enabled
-```
-
---
-
-## Create IAM Role for Daily.co
-
-Daily.co needs permission to write recordings to your S3 bucket.
-
-Follow the guide https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket
-
-Save the role ARN - you'll need it soon.
-
-It looks like: `arn:aws:iam::123456789012:role/DailyCo`
-
-Shortly, you'll need to set up a role and give this role your s3 bucket access
-
-No additional setup is required from Daily.co settings website side: the app code takes care of letting Daily know where to save the recordings.
-
---
-
-## Configure Reflector
-
-**Location: Reflector server**
-
-Add to `server/.env`:
-
-```env
-# Daily.co Configuration
-DEFAULT_VIDEO_PLATFORM=daily
-DAILY_API_KEY=<your-api-key-from-daily-setup>
-DAILY_SUBDOMAIN=<your-subdomain-from-daily-setup>
-
-# S3 Storage for Daily.co recordings
-DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket-from-daily-setup>
-DAILYCO_STORAGE_AWS_REGION=us-east-1
-DAILYCO_STORAGE_AWS_ROLE_ARN=<your-role-arn-from-daily-setup>
-
-# Transcript storage (should already be configured from main setup)
-# TRANSCRIPT_STORAGE_BACKEND=aws
-# TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=<your-key>
-# TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=<your-secret>
-# TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=<your-bucket-name>
-# TRANSCRIPT_STORAGE_AWS_REGION=<your-bucket-region>
-```
-
---
-
-## Restart Services
-
-After changing `.env` files, reload with `up -d`:
-
-```bash
-sudo docker compose -f docker-compose.prod.yml up -d server worker
-```
-
-**Note**: `docker compose up -d` detects env changes and recreates containers automatically.
-
---
-
-## Test Live Room
-
-1. Visit your Reflector frontend: `https://app.example.com`
-2. Go to **Rooms**
-3. Click **Create Room**
-4. Select **Daily** as the platform
-5. Allow camera/microphone access
-6. You should see Daily.co video interface
-7. Speak for 10-20 seconds
-8. Leave the meeting
-9. Recording should appear in **Transcripts** within 5 minutes (if webhooks aren't set up yet, see [Webhook Configuration](#webhook-configuration-optional) below)
-
---
-
-## Troubleshooting
-
-### Recording doesn't appear in S3
-
-1. Check Daily.co dashboard → **Logs** for errors
-2. Verify IAM role trust policy has correct Daily.co account ID and your Daily.co subdomain
-3. Verify that the bucket has
-
-### Recording in S3 but not transcribed
-
-1. Check webhook is configured (Reflector should auto-create it)
-2. Check worker logs:
-   ```bash
-   docker compose -f docker-compose.prod.yml logs worker --tail 50
-   ```
-3. Verify `DAILYCO_STORAGE_AWS_*` vars in `server/.env`
-
-### "Access Denied" when Daily.co tries to write to S3
-
-1. Double-check IAM role ARN in Daily.co settings
-2. Verify bucket name matches exactly
-3. Check IAM policy has `s3:PutObject` permission
-
---
-
-## Webhook Configuration [optional]
-
-`manage_daily_webhook.py` script guides you through creating a webhook for Daily recordings.
-
-The webhook isn't required - polling mechanism is the default and performed automatically.
-
-This guide won't go deep into webhook setup.
--- a/docs/docs/installation/docker-setup.md
+++ b/docs/docs/installation/docker-setup.md
@@ -1,192 +0,0 @@
---
-sidebar_position: 3
-title: Docker Reference
---
-
-# Docker Reference
-
-This page documents the Docker Compose configuration for Reflector. For the complete deployment guide, see [Deployment Guide](./overview).
-
-## Services
-
-The `docker-compose.prod.yml` includes these services:
-
-| Service | Image | Purpose |
-|---------|-------|---------|
-| `web` | `monadicalsas/reflector-frontend` | Next.js frontend |
-| `server` | `monadicalsas/reflector-backend` | FastAPI backend |
-| `worker` | `monadicalsas/reflector-backend` | Celery worker for background tasks |
-| `beat` | `monadicalsas/reflector-backend` | Celery beat scheduler |
-| `redis` | `redis:7.2-alpine` | Message broker and cache |
-| `postgres` | `postgres:17-alpine` | Primary database |
-| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
-
-## Environment Files
-
-Reflector uses two separate environment files:
-
-### Backend (`server/.env`)
-
-Used by: `server`, `worker`, `beat`
-
-Key variables:
-```env
-# Database connection
-DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-
-# Redis
-REDIS_HOST=redis
-CELERY_BROKER_URL=redis://redis:6379/1
-CELERY_RESULT_BACKEND=redis://redis:6379/1
-
-# API domain and CORS
-BASE_URL=https://api.example.com
-CORS_ORIGIN=https://app.example.com
-
-# Modal GPU processing
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://...
-TRANSCRIPT_MODAL_API_KEY=...
-```
-
-### Frontend (`www/.env`)
-
-Used by: `web`
-
-Key variables:
-```env
-# Domain configuration
-SITE_URL=https://app.example.com
-API_URL=https://api.example.com
-WEBSOCKET_URL=wss://api.example.com
-SERVER_API_URL=http://server:1250
-
-# Authentication
-NEXTAUTH_URL=https://app.example.com
-NEXTAUTH_SECRET=...
-```
-
-Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-side (SSR).
-
-## Volumes
-
-| Volume | Purpose |
-|--------|---------|
-| `redis_data` | Redis persistence |
-| `postgres_data` | PostgreSQL data |
-| `server_data` | Uploaded files, local storage |
-| `caddy_data` | SSL certificates |
-| `caddy_config` | Caddy configuration |
-
-## Network
-
-All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
-
-## Common Commands
-
-### Start all services
-```bash
-docker compose -f docker-compose.prod.yml up -d
-```
-
-### View logs
-```bash
-# All services
-docker compose -f docker-compose.prod.yml logs -f
-
-# Specific service
-docker compose -f docker-compose.prod.yml logs server --tail 50
-```
-
-### Restart a service
-```bash
-# Quick restart (doesn't reload .env changes)
-docker compose -f docker-compose.prod.yml restart server
-
-# Reload .env and restart
-docker compose -f docker-compose.prod.yml up -d server
-```
-
-### Run database migrations
-```bash
-docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
-```
-
-### Access database
-```bash
-docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
-```
-
-### Pull latest images
-```bash
-docker compose -f docker-compose.prod.yml pull
-docker compose -f docker-compose.prod.yml up -d
-```
-
-### Stop all services
-```bash
-docker compose -f docker-compose.prod.yml down
-```
-
-### Full reset (WARNING: deletes data)
-```bash
-docker compose -f docker-compose.prod.yml down -v
-```
-
-## Customization
-
-### Using a different database
-
-To use an external PostgreSQL:
-
-1. Remove `postgres` service from compose file
-2. Update `DATABASE_URL` in `server/.env`:
-   ```env
-   DATABASE_URL=postgresql+asyncpg://user:pass@external-host:5432/reflector
-   ```
-
-### Using external Redis
-
-1. Remove `redis` service from compose file
-2. Update Redis settings in `server/.env`:
-   ```env
-   REDIS_HOST=external-redis-host
-   CELERY_BROKER_URL=redis://external-redis-host:6379/1
-   ```
-
-### Adding Authentik
-
-To add Authentik for authentication, see [Authentication Setup](./auth-setup). Quick steps:
-
-1. Deploy Authentik separately
-2. Connect to Reflector's network:
-   ```bash
-   docker network connect reflector_default authentik-server-1
-   ```
-3. Add to Caddyfile:
-   ```
-   authentik.example.com {
-       reverse_proxy authentik-server-1:9000
-   }
-   ```
-
-## Caddyfile Reference
-
-The Caddyfile supports environment variable substitution:
-
-```
-{$FRONTEND_DOMAIN:app.example.com} {
-    reverse_proxy web:3000
-}
-
-{$API_DOMAIN:api.example.com} {
-    reverse_proxy server:1250
-}
-```
-
-Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
-
-### Reload Caddy after changes
-```bash
-docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
-```
--- a/docs/docs/installation/docs-deployment.md
+++ b/docs/docs/installation/docs-deployment.md
@@ -1,139 +0,0 @@
---
-sidebar_position: 10
-title: Docs Website Deployment
---
-
-# Docs Website Deployment
-
-This guide covers deploying the Reflector documentation website. **This is optional and intended for internal/experimental use only.**
-
-## Overview
-
-The documentation is built using Docusaurus and deployed as a static nginx-served site.
-
-## Prerequisites
-
- Reflector already deployed (Steps 1-7 from [Deployment Guide](./overview))
- DNS A record for docs subdomain (e.g., `docs.example.com`)
-
-## Deployment Steps
-
-### Step 1: Pre-fetch OpenAPI Spec
-
-The docs site includes API reference from your running backend. Fetch it before building:
-
-```bash
-cd ~/reflector
-docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
-```
-
-This creates `docs/static/openapi.json` (should be ~70KB) which will be copied during Docker build.
-
-**Why not fetch during build?** Docker build containers are network-isolated and can't access the running backend services.
-
-### Step 2: Verify Dockerfile
-
-The Dockerfile is already in `docs/Dockerfile`:
-
-```dockerfile
-FROM node:18-alpine AS builder
-WORKDIR /app
-
-# Copy package files
-COPY package*.json ./
-
-# Inshall dependencies
-RUN npm ci
-
-# Copy source (includes static/openapi.json if pre-fetched)
-COPY . .
-
-# Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
-RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
-
-# Build static site
-RUN npx docusaurus build
-
-FROM nginx:alpine
-COPY --from=builder /app/build /usr/share/nginx/html
-EXPOSE 80
-CMD ["nginx", "-g", "daemon off;"]
-```
-
-### Step 3: Add Docs Service to docker-compose.prod.yml
-
-Add this service to `docker-compose.prod.yml`:
-
-```yaml
-docs:
-  build: ./docs
-  restart: unless-stopped
-  networks:
-    - default
-```
-
-### Step 4: Add Caddy Route
-
-Add to `Caddyfile`:
-
-```
-{$DOCS_DOMAIN:docs.example.com} {
-    reverse_proxy docs:80
-}
-```
-
-### Step 5: Build and Deploy
-
-```bash
-cd ~/reflector
-docker compose -f docker-compose.prod.yml up -d --build docs
-docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
-```
-
-### Step 6: Verify
-
-```bash
-# Check container status
-docker compose -f docker-compose.prod.yml ps docs
-# Should show "Up"
-
-# Test URL
-curl -I https://docs.example.com
-# Should return HTTP/2 200
-```
-
-Visit `https://docs.example.com` in your browser
-
-## Updating Documentation
-
-When docs are updated:
-
-```bash
-cd ~/reflector
-git pull
-
-# Refresh OpenAPI spec from backend
-docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
-
-# Rebuild docs
-docker compose -f docker-compose.prod.yml up -d --build docs
-```
-
-## Troubleshooting
-
-### Missing openapi.json during build
- Make sure you ran the pre-fetch step first (Step 1)
- Verify `docs/static/openapi.json` exists and is ~70KB
- Re-run: `docker compose exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json`
-
-### Build fails with "Docusaurus found broken links"
- This happens if `onBrokenLinks: 'throw'` is set in docusaurus.config.ts
- Solution is already in Dockerfile: uses `sed` to change to `'warn'` during build
-
-### 404 on all pages
- Docusaurus baseUrl might be wrong - should be `/` for custom domain
- Check `docs/docusaurus.config.ts`: `baseUrl: '/'`
-
-### Docs not updating after rebuild
- Force rebuild: `docker compose -f docker-compose.prod.yml build --no-cache docs`
- Then: `docker compose -f docker-compose.prod.yml up -d docs`
--- a/docs/docs/installation/modal-setup.md
+++ b/docs/docs/installation/modal-setup.md
@@ -1,171 +0,0 @@
---
-sidebar_position: 4
-title: Modal.com Setup
---
-
-# Modal.com Setup
-
-This page covers Modal.com GPU setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
-
-Reflector uses [Modal.com](https://modal.com) for GPU-accelerated audio processing. This guide walks you through deploying the required GPU functions.
-
-## What is Modal.com?
-
-Modal is a serverless GPU platform. You deploy Python code that runs on their GPUs, and pay only for actual compute time. Reflector uses Modal for:
-
- **Transcription**: Whisper model for speech-to-text
- **Diarization**: Pyannote model for speaker identification
-
-## Prerequisites
-
-1. **Modal.com account** - Sign up at https://modal.com (free tier available)
-2. **HuggingFace account** - Required for Pyannote diarization models:
-   - Create account at https://huggingface.co
-   - Accept **both** Pyannote licenses:
-     - https://huggingface.co/pyannote/speaker-diarization-3.1
-     - https://huggingface.co/pyannote/segmentation-3.0
-   - Generate access token at https://huggingface.co/settings/tokens
-
-## Deployment
-
-**Location: YOUR LOCAL COMPUTER (laptop/desktop)**
-
-Modal CLI requires browser authentication, so this must run on a machine with a browser - not on a headless server.
-
-### Install Modal CLI
-
-```bash
-pip install modal
-```
-
-### Authenticate with Modal
-
-```bash
-modal setup
-```
-
-This opens your browser for authentication. Complete the login flow.
-
-### Clone Repository and Deploy
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/modal_deployments
-./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
-```
-
-Or run interactively (script will prompt for token):
-```bash
-./deploy-all.sh
-```
-
-### What the Script Does
-
-1. **Prompts for HuggingFace token** - Needed to download the Pyannote diarization model
-2. **Generates API key** - Creates a secure random key for authenticating requests to GPU functions
-3. **Creates Modal secrets**:
-   - `hf_token` - Your HuggingFace token
-   - `reflector-gpu` - The generated API key
-4. **Deploys GPU functions** - Transcriber (Whisper) and Diarizer (Pyannote)
-5. **Outputs configuration** - Prints URLs and API key to console
-
-### Example Output
-
-```
-==========================================
-Reflector GPU Functions Deployment
-==========================================
-
-Generating API key for GPU services...
-Creating Modal secrets...
-  -> Creating secret: hf_token
-  -> Creating secret: reflector-gpu
-
-Deploying transcriber (Whisper)...
-  -> https://yourname--reflector-transcriber-web.modal.run
-
-Deploying diarizer (Pyannote)...
-  -> https://yourname--reflector-diarizer-web.modal.run
-
-==========================================
-Deployment complete!
-==========================================
-
-Copy these values to your server's server/.env file:
-
-# --- Modal GPU Configuration ---
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=abc123...
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=abc123...
-# --- End Modal Configuration ---
-```
-
-Copy the output and paste it into your `server/.env` file on your server.
-
-## Costs
-
-Modal charges based on GPU compute time:
- Functions scale to zero when not in use (no cost when idle)
- You only pay for actual processing time
- Free tier includes $30/month of credits
-
-Typical costs for audio processing:
- Transcription: ~$0.01-0.05 per minute of audio
- Diarization: ~$0.02-0.10 per minute of audio
-
-## Troubleshooting
-
-### "Modal CLI not installed"
-```bash
-pip install modal
-```
-
-### "Not authenticated with Modal"
-```bash
-modal setup
-# Complete browser authentication
-```
-
-### "Failed to create secret hf_token"
- Verify your HuggingFace token is valid
- Ensure you've accepted the Pyannote license
- Token needs `read` permission
-
-### Deployment fails
-Check the Modal dashboard for detailed error logs:
- Visit https://modal.com/apps
- Click on the failed function
- View build and runtime logs
-
-### Re-running deployment
-The script is safe to re-run. It will:
- Update existing secrets if they exist
- Redeploy functions with latest code
- Output new configuration (API key stays the same if secret exists)
-
-## Manual Deployment (Advanced)
-
-If you prefer to deploy functions individually:
-
-```bash
-cd gpu/modal_deployments
-
-# Create secrets manually
-modal secret create hf_token HF_TOKEN=your-hf-token
-modal secret create reflector-gpu REFLECTOR_GPU_APIKEY=$(openssl rand -hex 32)
-
-# Deploy each function
-modal deploy reflector_transcriber.py
-modal deploy reflector_diarizer.py
-```
-
-## Monitoring
-
-View your deployed functions and their usage:
- **Modal Dashboard**: https://modal.com/apps
- **Function logs**: Click on any function to view logs
- **Usage**: View compute time and costs in the dashboard
--- a/docs/docs/installation/overview.md
+++ b/docs/docs/installation/overview.md
@@ -1,401 +0,0 @@
---
-sidebar_position: 1
-title: Deployment Guide
---
-
-# Deployment Guide
-
-This guide walks you through deploying Reflector from scratch. Follow these steps in order.
-
-## What You'll Set Up
-
-```mermaid
-flowchart LR
-    User --> Caddy["Caddy (auto-SSL)"]
-    Caddy --> Frontend["Frontend (Next.js)"]
-    Caddy --> Backend["Backend (FastAPI)"]
-    Backend --> PostgreSQL
-    Backend --> Redis
-    Backend --> Workers["Celery Workers"]
-    Workers --> PostgreSQL
-    Workers --> Redis
-    Workers --> GPU["GPU Processing<br/>(Modal.com OR Self-hosted)"]
-```
-
-## Prerequisites
-
-Before starting, you need:
-
- **Production server** -  4+ cores, 8GB+ RAM, public IP
- **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
- **GPU processing** - Choose one:
-  - Modal.com account, OR
-  - GPU server with NVIDIA GPU (8GB+ VRAM)
- **HuggingFace account** - Free at https://huggingface.co
-  - Accept both Pyannote licenses (required for speaker diarization):
-    - https://huggingface.co/pyannote/speaker-diarization-3.1
-    - https://huggingface.co/pyannote/segmentation-3.0
- **LLM API** - For summaries and topic detection. Choose one:
-  - OpenAI API key at https://platform.openai.com/account/api-keys, OR
-  - Any OpenAI-compatible endpoint (vLLM, LiteLLM, Ollama, etc.)
- **AWS S3 bucket** - For storing audio files and transcripts (see [S3 Setup](#create-s3-bucket-for-transcript-storage) below)
-
-### Optional (for live meeting rooms)
-
- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
- [ ] **AWS S3 bucket + IAM Role** - For Daily.co recording storage (separate from transcript storage)
-
---
-
-## Configure DNS
-
-```
-Type: A    Name: app    Value: <your-server-ip>
-Type: A    Name: api    Value: <your-server-ip>
-```
-
---
-
-## Deploy GPU Processing
-
-Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
-
-| | **Modal.com (Cloud)** | **Self-Hosted GPU** |
-|---|---|---|
-| **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
-| **Pricing** | Pay-per-use | Fixed infrastructure cost |
-
-### Option A: Modal.com (Serverless Cloud GPU)
-
-#### Accept HuggingFace Licenses
-
-Visit both pages and click "Accept":
- https://huggingface.co/pyannote/speaker-diarization-3.1
- https://huggingface.co/pyannote/segmentation-3.0
-
-Generate a token at https://huggingface.co/settings/tokens
-
-#### Deploy to Modal
-
-There's an install script to help with this setup. It's using modal API to set all necessary moving parts.
-
-As an alternative, all those operations that script does could be performed in modal settings in modal UI.
-
-```bash
-pip install modal
-modal setup  # opens browser for authentication
-
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/modal_deployments
-./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
-```
-
-**Save the output** - copy the configuration block, you'll need it soon.
-
-See [Modal Setup](./modal-setup) for troubleshooting and details.
-
-### Option B: Self-Hosted GPU
-
-**Location: YOUR GPU SERVER**
-
-Requires: NVIDIA GPU with 8GB+ VRAM, Ubuntu 22.04+, 40-50GB disk (Docker) or 25-30GB (Systemd).
-
-See [Self-Hosted GPU Setup](./self-hosted-gpu-setup) for complete instructions. Quick summary:
-
-1. Install NVIDIA drivers and Docker (or uv for systemd)
-2. Clone repository: `git clone https://github.com/monadical-sas/reflector.git`
-3. Configure `.env` with HuggingFace token
-4. Start service (Docker compose or systemd)
-5. Set up Caddy reverse proxy for HTTPS
-
-**Save your API key and HTTPS URL** - you'll need them soon.
-
---
-
-## Prepare Server
-
-**Location: dedicated reflector server**
-
-### Install Docker
-
-```bash
-ssh user@your-server-ip
-
-curl -fsSL https://get.docker.com | sh
-sudo usermod -aG docker $USER
-
-# Log out and back in for group changes
-exit
-ssh user@your-server-ip
-
-docker --version  # verify
-```
-
-### Firewall
-
-Ensure ports 80 (HTTP) and 443 (HTTPS) are open for inbound traffic. The method varies by cloud provider and OS configuration.
-
-### Clone Repository
-
-The Docker images contain all application code. You clone the repository for configuration files and the compose definition:
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector
-```
-
---
-
-## Create S3 Bucket for Transcript Storage
-
-Reflector requires AWS S3 to store audio files during processing.
-
-### Create Bucket
-
-```bash
-# Choose a unique bucket name
-BUCKET_NAME="reflector-transcripts-yourname"
-AWS_REGION="us-east-1"
-
-# Create bucket
-aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
-```
-
-### Create IAM User
-
-Create an IAM user with S3 access for Reflector:
-
-1. Go to AWS IAM Console → Users → Create User
-2. Name: `reflector-transcripts`
-3. Attach policy: `AmazonS3FullAccess` (or create a custom policy for just your bucket)
-4. Create access key (Access key ID + Secret access key)
-
-Save these credentials - you'll need them in the next step.
-
---
-
-## Configure Environment
-
-Reflector has two env files:
- `server/.env` - Backend configuration
- `www/.env` - Frontend configuration
-
-### Backend Configuration
-
-```bash
-cp server/.env.example server/.env
-nano server/.env
-```
-
-**Required settings:**
-```env
-# Database (defaults work with docker-compose.prod.yml)
-DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
-
-# Redis
-REDIS_HOST=redis
-CELERY_BROKER_URL=redis://redis:6379/1
-CELERY_RESULT_BACKEND=redis://redis:6379/1
-
-# Your domains
-BASE_URL=https://api.example.com
-CORS_ORIGIN=https://app.example.com
-CORS_ALLOW_CREDENTIALS=true
-
-# Secret key - generate with: openssl rand -hex 32
-SECRET_KEY=<your-generated-secret>
-
-# GPU Processing - choose ONE option:
-
-# Option A: Modal.com (paste from deploy-all.sh output)
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=<from-deploy-all.sh-output>
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=<from-deploy-all.sh-output>
-
-# Option B: Self-hosted GPU (use your GPU server URL and API key)
-# TRANSCRIPT_BACKEND=modal
-# TRANSCRIPT_URL=https://gpu.example.com
-# TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
-# DIARIZATION_BACKEND=modal
-# DIARIZATION_URL=https://gpu.example.com
-# DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
-
-# Storage - where to store audio files and transcripts (requires AWS S3)
-TRANSCRIPT_STORAGE_BACKEND=aws
-TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
-TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
-TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
-TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
-
-# LLM - for generating titles, summaries, and topics
-LLM_API_KEY=sk-your-openai-api-key
-LLM_MODEL=gpt-4o-mini
-# LLM_URL=https://api.openai.com/v1  # Optional: custom endpoint (vLLM, LiteLLM, Ollama, etc.)
-
-# Auth - disable for initial setup (see a dedicated step for authentication)
-AUTH_BACKEND=none
-```
-
-### Frontend Configuration
-
-```bash
-cp www/.env.example www/.env
-nano www/.env
-```
-
-**Required settings:**
-```env
-# Your domains
-SITE_URL=https://app.example.com
-API_URL=https://api.example.com
-WEBSOCKET_URL=wss://api.example.com
-SERVER_API_URL=http://server:1250
-
-# NextAuth
-NEXTAUTH_URL=https://app.example.com
-NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
-
-# Disable login requirement for initial setup
-FEATURE_REQUIRE_LOGIN=false
-```
-
---
-
-## Configure Caddy
-
-```bash
-cp Caddyfile.example Caddyfile
-nano Caddyfile
-```
-
-Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy's env var substitution - you can either edit the file directly or set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables.
-
-```
-{$FRONTEND_DOMAIN:app.example.com} {
-    reverse_proxy web:3000
-}
-
-{$API_DOMAIN:api.example.com} {
-    reverse_proxy server:1250
-}
-```
-
---
-
-## Start Services
-
-```bash
-docker compose -f docker-compose.prod.yml up -d
-```
-
-Wait for PostgreSQL to be ready, then run migrations:
-
-```bash
-# Wait for postgres to be healthy (may take 30-60 seconds on first run)
-docker compose -f docker-compose.prod.yml exec postgres pg_isready -U reflector
-
-# Run database migrations
-docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
-```
-
---
-
-## Verify Deployment
-
-### Check services
-```bash
-docker compose -f docker-compose.prod.yml ps
-# All should show "Up"
-```
-
-### Test API
-```bash
-curl https://api.example.com/health
-# Should return: {"status":"healthy"}
-```
-
-### Test Frontend
- Visit https://app.example.com
- You should see the Reflector interface
- Try uploading an audio file to test transcription
-
---
-
-## Enable Authentication (Required for Live Rooms)
-
-By default, Reflector is open (no login required). **Authentication is required if you want to use Live Meeting Rooms.**
-
-See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
-
-Quick summary:
-1. Deploy Authentik on your server
-2. Create OAuth provider in Authentik
-3. Extract public key for JWT verification
-4. Update `server/.env`: `AUTH_BACKEND=jwt` + `AUTH_JWT_AUDIENCE`
-5. Update `www/.env`: `FEATURE_REQUIRE_LOGIN=true` + Authentik credentials
-6. Mount JWT keys volume and restart services
-
---
-
-## Enable Live Meeting Rooms
-
-**Requires: Authentication Step**
-
-Live rooms require Daily.co and AWS S3. See [Daily.co Setup](./daily-setup) for complete S3/IAM configuration instructions.
-
-Note that Reflector also supports Whereby as a call provider - this doc doesn't cover its setup yet.
-
-Quick config - Add to `server/.env`:
-
-```env
-DEFAULT_VIDEO_PLATFORM=daily
-DAILY_API_KEY=<from-daily.co-dashboard>
-DAILY_SUBDOMAIN=<your-daily-subdomain>
-
-# S3 for recording storage
-DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket>
-DAILYCO_STORAGE_AWS_REGION=us-east-1
-DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
-```
-
-Reload env and restart:
-```bash
-docker compose -f docker-compose.prod.yml up -d server worker
-```
-
---
-
-## Troubleshooting
-
-### Check logs for errors
-```bash
-docker compose -f docker-compose.prod.yml logs server --tail 20
-docker compose -f docker-compose.prod.yml logs worker --tail 20
-```
-
-### Services won't start
-```bash
-docker compose -f docker-compose.prod.yml logs
-```
-
-### CORS errors in browser
- Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
- Reload env: `docker compose -f docker-compose.prod.yml up -d server`
-
-### SSL certificate errors
- Caddy auto-provisions Let's Encrypt certificates
- Ensure ports 80 and 443 are open
- Check: `docker compose -f docker-compose.prod.yml logs caddy`
-
-### Transcription not working
- Check Modal dashboard: https://modal.com/apps
- Verify URLs in `server/.env` match deployed functions
- Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
-
-### "Login required" but auth not configured
- Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
- Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
-
--- a/docs/docs/installation/requirements.md
+++ b/docs/docs/installation/requirements.md
@@ -1,60 +0,0 @@
---
-sidebar_position: 2
-title: System Requirements
---
-
-# System Requirements
-
-This page lists hardware and software requirements. For the complete deployment guide, see [Deployment Guide](./overview).
-
-## Server Requirements
-
-### Minimum Requirements
-
- **CPU**: 4 cores
- **RAM**: 8 GB
- **Storage**: 50 GB SSD
- **OS**: Ubuntu 22.04+ or compatible Linux
- **Network**: Public IP address
-
-### Recommended Requirements
-
- **CPU**: 8+ cores
- **RAM**: 16 GB
- **Storage**: 100 GB SSD
- **Network**: 1 Gbps connection
-
-## Software Requirements
-
- Docker Engine 20.10+
- Docker Compose 2.0+
-
-## External Services
-
-### Required
-
- **Two domain names** - One for frontend (e.g., `app.example.com`), one for API (e.g., `api.example.com`)
- **Modal.com account** - For GPU-accelerated transcription and diarization (free tier available)
- **HuggingFace account** - For Pyannote diarization model access
- **OpenAI API key** - For generating summaries and topic detection (https://platform.openai.com/account/api-keys)
-
-### Required for Live Meeting Rooms
-
- **Daily.co account** - For video conferencing (free tier available at https://dashboard.daily.co)
- **AWS S3 bucket + IAM Role** - For Daily.co to store recordings
- **Another AWS S3 bucket (optional, can reuse the one above)** - For Reflector to store "compiled" mp3 files and transient diarization process temporary files
-
-### Optional
-
- **AWS S3** - For cloud storage of recordings and transcripts
- **Authentik** - For SSO/OIDC authentication
- **Sentry** - For error tracking
-
-## Development Requirements
-
-For local development only (not required for production deployment):
-
- Node.js 22+ (for frontend development)
- Python 3.12+ (for backend development)
- pnpm (for frontend package management)
- uv (for Python package management)
--- a/docs/docs/installation/self-hosted-gpu-setup.md
+++ b/docs/docs/installation/self-hosted-gpu-setup.md
@@ -1,465 +0,0 @@
---
-sidebar_position: 5
-title: Self-Hosted GPU Setup
---
-
-# Self-Hosted GPU Setup
-
-This guide covers deploying Reflector's GPU processing on your own server instead of Modal.com. For the complete deployment guide, see [Deployment Guide](./overview).
-
-## When to Use Self-Hosted GPU
-
-**Choose self-hosted GPU if you:**
- Have GPU hardware available (NVIDIA required)
- Want full control over processing
- Prefer fixed infrastructure costs over pay-per-use
- Have privacy or data locality requirements
- Need to process audio without external API calls
-
-**Choose Modal.com instead if you:**
- Don't have GPU hardware
- Want zero infrastructure management
- Prefer pay-per-use pricing
- Need instant scaling for variable workloads
-
-See [Modal.com Setup](./modal-setup) for cloud GPU deployment.
-
-## What Gets Deployed
-
-The self-hosted GPU service provides the same API endpoints as Modal:
- `POST /v1/audio/transcriptions` - Whisper transcription
- `POST /diarize` - Pyannote speaker diarization
-
-Your main Reflector server connects to this service exactly like it connects to Modal - only the URL changes.
-
-## Prerequisites
-
-### Hardware
- **GPU**: NVIDIA GPU with 8GB+ VRAM (tested on Tesla T4 with 15GB)
- **CPU**: 4+ cores recommended
- **RAM**: 8GB minimum, 16GB recommended
- **Disk**:
-  - Docker method: 40-50GB minimum
-  - Systemd method: 25-30GB minimum
-
-### Software
- Public IP address
- Domain name with DNS A record pointing to server
-
-### Accounts
- **HuggingFace account** with accepted Pyannote licenses:
-  - https://huggingface.co/pyannote/speaker-diarization-3.1
-  - https://huggingface.co/pyannote/segmentation-3.0
- **HuggingFace access token** from https://huggingface.co/settings/tokens
-
-## Choose Deployment Method
-
---
-
-## Docker Deployment
-
-### Step 1: Install NVIDIA Driver
-
-```bash
-sudo apt update
-sudo apt install -y nvidia-driver-535
-
-# Load kernel modules
-sudo modprobe nvidia
-
-# Verify installation
-nvidia-smi
-```
-
-Expected output: GPU details with driver version and CUDA version.
-
-### Step 2: Install Docker
-
-```bash
-curl -fsSL https://get.docker.com | sudo sh
-sudo usermod -aG docker $USER
-
-# Log out and back in for group changes
-exit
-# SSH back in
-```
-
-### Step 3: Install NVIDIA Container Toolkit
-
-```bash
-# Add NVIDIA repository
-curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
-  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
-
-curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
-  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
-  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
-
-# Install toolkit
-sudo apt-get update
-sudo apt-get install -y nvidia-container-toolkit
-
-# Configure Docker runtime
-sudo nvidia-ctk runtime configure --runtime=docker
-sudo systemctl restart docker
-```
-
-### Step 4: Clone Repository and Configure
-
-```bash
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/self_hosted
-
-# Create environment file
-cat > .env << EOF
-REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
-HF_TOKEN=your_huggingface_token_here
-EOF
-
-# Note the generated API key - you'll need it for main server config
-cat .env
-```
-
-### Step 5: Create Docker Compose File
-
-```bash
-cat > compose.yml << 'EOF'
-services:
-  reflector_gpu:
-    build:
-      context: .
-    ports:
-      - "8000:8000"
-    env_file:
-      - .env
-    volumes:
-      - ./cache:/root/.cache
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: all
-              capabilities: [gpu]
-    restart: unless-stopped
-EOF
-```
-
-### Step 6: Build and Start
-
-```bash
-# Build image (takes ~5 minutes, downloads ~10GB)
-sudo docker compose build
-
-# Start service
-sudo docker compose up -d
-
-# Wait for startup and verify
-sleep 30
-sudo docker compose logs
-```
-
-Look for: `INFO: Application startup complete. Uvicorn running on http://0.0.0.0:8000`
-
-### Step 7: Verify GPU Access
-
-```bash
-# Check GPU is accessible from container
-sudo docker exec $(sudo docker ps -q) nvidia-smi
-```
-
-Should show GPU with ~3GB VRAM used (models loaded).
-
---
-
-## Systemd Deployment
-
-### Step 1: Install NVIDIA Driver
-
-```bash
-sudo apt update
-sudo apt install -y nvidia-driver-535
-
-# Load kernel modules
-sudo modprobe nvidia
-
-# Verify installation
-nvidia-smi
-```
-
-### Step 2: Install Dependencies
-
-```bash
-# Install ffmpeg
-sudo apt install -y ffmpeg
-
-# Install uv package manager
-curl -LsSf https://astral.sh/uv/install.sh | sh
-source ~/.local/bin/env
-
-# Clone repository
-git clone https://github.com/monadical-sas/reflector.git
-cd reflector/gpu/self_hosted
-```
-
-### Step 3: Configure Environment
-
-```bash
-# Create environment file
-cat > .env << EOF
-REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
-HF_TOKEN=your_huggingface_token_here
-EOF
-
-# Note the generated API key
-cat .env
-```
-
-### Step 4: Install Python Packages
-
-```bash
-# Install dependencies (~3GB download)
-uv sync
-```
-
-### Step 5: Create Systemd Service
-
-```bash
-# Generate library paths for NVIDIA packages
-export NVIDIA_LIBS=$(find ~/reflector/gpu/self_hosted/.venv/lib/python3.12/site-packages/nvidia -name lib -type d | tr '\n' ':')
-
-# Load environment variables
-source ~/reflector/gpu/self_hosted/.env
-
-# Create service file
-sudo tee /etc/systemd/system/reflector-gpu.service << EOFSVC
-[Unit]
-Description=Reflector GPU Service (Transcription & Diarization)
-After=network.target
-
-[Service]
-Type=simple
-User=$USER
-WorkingDirectory=$HOME/reflector/gpu/self_hosted
-Environment="PATH=$HOME/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
-Environment="HF_TOKEN=${HF_TOKEN}"
-Environment="REFLECTOR_GPU_APIKEY=${REFLECTOR_GPU_APIKEY}"
-Environment="LD_LIBRARY_PATH=${NVIDIA_LIBS}"
-ExecStart=$HOME/reflector/gpu/self_hosted/.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-Restart=always
-RestartSec=10
-
-[Install]
-WantedBy=multi-user.target
-EOFSVC
-
-# Enable and start
-sudo systemctl daemon-reload
-sudo systemctl enable reflector-gpu
-sudo systemctl start reflector-gpu
-```
-
-### Step 6: Verify Service
-
-```bash
-# Check status
-sudo systemctl status reflector-gpu
-
-# View logs
-sudo journalctl -u reflector-gpu -f
-```
-
-Look for: `INFO: Application startup complete.`
-
---
-
-## Configure HTTPS with Caddy
-
-Both deployment methods need HTTPS for production. Caddy handles SSL automatically.
-
-### Install Caddy
-
-```bash
-sudo apt install -y debian-keyring debian-archive-keyring apt-transport-https curl
-
-curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | \
-  sudo gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg
-
-curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | \
-  sudo tee /etc/apt/sources.list.d/caddy-stable.list
-
-sudo apt update
-sudo apt install -y caddy
-```
-
-### Configure Reverse Proxy
-
-```bash
-sudo tee /etc/caddy/Caddyfile << 'EOF'
-gpu.example.com {
-    reverse_proxy localhost:8000
-}
-EOF
-
-# Reload Caddy (auto-provisions SSL certificate)
-sudo systemctl reload caddy
-```
-
-Replace `gpu.example.com` with your domain.
-
-### Verify HTTPS
-
-```bash
-curl -I https://gpu.example.com/docs
-# Should return HTTP/2 200
-```
-
---
-
-## Configure Main Reflector Server
-
-On your main Reflector server, update `server/.env`:
-
-```env
-# GPU Processing - Self-hosted
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://gpu.example.com
-TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://gpu.example.com
-DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
-```
-
-**Note:** The backend type is `modal` because the self-hosted GPU service implements the same API contract as Modal.com. This allows you to switch between cloud and self-hosted GPU processing by only changing the URL and API key.
-
-Restart services to apply:
-
-```bash
-docker compose -f docker-compose.prod.yml restart server worker
-```
-
---
-
-## Service Management
-
-All commands in this section assume you're in `~/reflector/gpu/self_hosted/`.
-
-### Docker
-
-```bash
-# View logs
-sudo docker compose logs -f
-
-# Restart service
-sudo docker compose restart
-
-# Stop service
-sudo docker compose down
-
-# Check status
-sudo docker compose ps
-```
-
-### Systemd
-
-```bash
-# View logs
-sudo journalctl -u reflector-gpu -f
-
-# Restart service
-sudo systemctl restart reflector-gpu
-
-# Stop service
-sudo systemctl stop reflector-gpu
-
-# Check status
-sudo systemctl status reflector-gpu
-```
-
-### Monitor GPU
-
-```bash
-# Check GPU usage
-nvidia-smi
-
-# Watch in real-time
-watch -n 1 nvidia-smi
-```
-
-**Typical GPU memory usage:**
- Idle (models loaded): ~3GB VRAM
- During transcription: ~4-5GB VRAM
-
---
-
-## Troubleshooting
-
-### nvidia-smi fails after driver install
-
-```bash
-# Manually load kernel modules
-sudo modprobe nvidia
-nvidia-smi
-```
-
-### Service fails with "Could not download pyannote pipeline"
-
-1. Verify HF_TOKEN is valid: `echo $HF_TOKEN`
-2. Check model access at https://huggingface.co/pyannote/speaker-diarization-3.1
-3. Regenerate service/compose with correct token
-4. Restart service
-
-### cuDNN library loading errors (Systemd only)
-
-Symptom: `Unable to load libcudnn_cnn.so`
-
-Regenerate the systemd service file - the `LD_LIBRARY_PATH` must include all NVIDIA package directories.
-
-### Cannot connect to HTTPS endpoint
-
-1. Verify DNS resolves: `dig +short gpu.example.com`
-2. Check firewall: `sudo ufw status` (ports 80, 443 must be open)
-3. Check Caddy: `sudo systemctl status caddy`
-4. View Caddy logs: `sudo journalctl -u caddy -n 50`
-
-### SSL certificate not provisioning
-
-Requirements for Let's Encrypt:
- Ports 80 and 443 publicly accessible
- DNS resolves to server's public IP
- Valid domain (not localhost or private IP)
-
-### Docker container won't start
-
-```bash
-# Check logs
-sudo docker compose logs
-
-# Common issues:
-# - Port 8000 already in use
-# - GPU not accessible (nvidia-ctk not configured)
-# - Missing .env file
-```
-
---
-
-## Updating
-
-### Docker
-
-```bash
-cd ~/reflector/gpu/self_hosted
-git pull
-sudo docker compose build
-sudo docker compose up -d
-```
-
-### Systemd
-
-```bash
-cd ~/reflector/gpu/self_hosted
-git pull
-uv sync
-sudo systemctl restart reflector-gpu
-```
--- a/docs/docs/installation/whereby-setup.md
+++ b/docs/docs/installation/whereby-setup.md
@@ -1,7 +0,0 @@
---
-title: whereby setup
---
-
-# whereby setup
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/installation/zulip-setup.md
+++ b/docs/docs/installation/zulip-setup.md
@@ -1,7 +0,0 @@
---
-title: zulip setup
---
-
-# zulip setup
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/intro.md
+++ b/docs/docs/intro.md
@@ -1,61 +0,0 @@
---
-sidebar_position: 1
-title: Introduction
---
-
-# Welcome to Reflector
-
-Reflector is a privacy-focused, self-hosted AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation, and summarization for audio content and live meetings. With complete control over your data and infrastructure, you can run models on your own hardware (roadmap - currently supports Modal.com for GPU processing).
-
-## What is Reflector?
-
-Reflector is a web application that utilizes AI to process audio content, providing:
-
- **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://github.com/NVIDIA/NeMo) (English) models
- **Speaker Diarization**: Identify and label different speakers using [Pyannote](https://github.com/pyannote/pyannote-audio) 3.1
- **Live Translation**: Translate audio content in real-time to 100+ languages with [Facebook Seamless-M4T](https://github.com/facebookresearch/seamless_communication)
- **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
- **Meeting Recording**: Create permanent records of meetings with searchable transcripts
-
-## Features
-
-| Feature | Public Mode | Private Mode |
-|---------|------------|--------------|
-| **Authentication** | None required | Required |
-| **Audio Upload** | ✅ | ✅ |
-| **Live Microphone Streaming** | ✅ | ✅ |
-| **Transcription** | ✅ | ✅ |
-| **Speaker Diarization** | ✅ | ✅ |
-| **Translation** | ✅ | ✅ |
-| **Topic Detection** | ✅ | ✅ |
-| **Summarization** | ✅ | ✅ |
-| **Virtual Meeting Rooms (Whereby)** | ❌ | ✅ |
-| **Browse Transcripts Page** | ❌ | ✅ |
-| **Search Functionality** | ❌ | ✅ |
-| **Persistent Storage** | ❌ | ✅ |
-
-## Architecture Overview
-
-Reflector consists of three main components:
-
- **Frontend**: React application built with Next.js 14
- **Backend**: Python server using FastAPI
- **Processing**: Scalable GPU workers for ML inference (Modal.com or local)
-
-## Getting Started
-
-Ready to deploy Reflector? Head over to our [Installation Guide](./installation/overview) to set up your own instance.
-
-For a quick overview of how Reflector processes audio, check out our [Pipeline Documentation](./pipelines/overview).
-
-## Open Source
-
-Reflector is open source software developed by [Monadical](https://monadical.com) and licensed under the **MIT License**. We welcome contributions from the community!
-
- [GitHub Repository](https://github.com/monadical-sas/reflector)
- [Issue Tracker](https://github.com/monadical-sas/reflector/issues)
- [Pull Requests](https://github.com/monadical-sas/reflector/pulls)
-
-## Support
-
-Need help? Reach out to the community through GitHub Discussions.
--- a/docs/docs/pipelines/file-pipeline.md
+++ b/docs/docs/pipelines/file-pipeline.md
@@ -1,343 +0,0 @@
---
-sidebar_position: 2
-title: File Processing Pipeline
---
-
-# File Processing Pipeline
-
-The file processing pipeline handles uploaded audio files, optimizing for accuracy and throughput.
-
-## Pipeline Stages
-
-### 1. Input Stage
-
-**Accepted Formats:**
- MP3 (most common)
- WAV (uncompressed)
- M4A (Apple format)
- WebM (browser recordings)
- MP4 (video with audio track)
-
-**File Validation:**
- Maximum size: 2GB (configurable)
- Minimum duration: 5 seconds
- Maximum duration: 6 hours
- Sample rate: Any (will be resampled)
-
-### 2. Pre-processing
-
-**Audio Normalization:**
-```yaml
-# Convert to standard format
- Sample rate: 16kHz (Whisper requirement)
- Channels: Mono
- Bit depth: 16-bit
- Format: WAV
-```
-
-**Noise Reduction (Optional):**
- Background noise removal
- Echo cancellation
- High-pass filter for rumble
-
-### 3. Chunking Strategy
-
-**Default Configuration:**
-```yaml
-chunk_size: 30  # seconds
-overlap: 1      # seconds
-max_parallel: 10
-silence_detection: true
-```
-
-**Chunking with Silence Detection:**
- Detects silence periods
- Attempts to break at natural pauses
- Maintains context with overlap
- Preserves sentence boundaries
-
-**Chunk Metadata:**
-```json
-{
-  "chunk_id": "chunk_001",
-  "start_time": 0.0,
-  "end_time": 30.0,
-  "duration": 30.0,
-  "has_speech": true,
-  "audio_hash": "sha256:..."
-}
-```
-
-### 4. Transcription Processing
-
-**Whisper Models:**
-
-| Model | Size | Speed | Accuracy | Use Case |
-|-------|------|-------|----------|----------|
-| tiny | 39M | Very Fast | 85% | Quick drafts |
-| base | 74M | Fast | 89% | Good balance |
-| small | 244M | Medium | 91% | Better accuracy |
-| medium | 769M | Slow | 93% | High quality |
-| large-v3 | 1550M | Very Slow | 96% | Best quality |
-
-**Processing Configuration:**
-```python
-transcription_config = {
-    "model": "whisper-base",
-    "language": "auto",  # or specify: "en", "es", etc.
-    "task": "transcribe",  # or "translate"
-    "temperature": 0,  # deterministic
-    "compression_ratio_threshold": 2.4,
-    "no_speech_threshold": 0.6,
-    "condition_on_previous_text": True,
-    "initial_prompt": None,  # optional context
-}
-```
-
-**Parallel Processing:**
- Each chunk processed independently
- GPU batching for efficiency
- Automatic load balancing
- Failure isolation
-
-### 5. Diarization (Speaker Identification)
-
-**Pyannote 3.1 Pipeline:**
-
-1. **Voice Activity Detection (VAD)**
-   - Identifies speech segments
-   - Filters out silence and noise
-   - Precision: 95%+
-
-2. **Speaker Embedding**
-   - Extracts voice characteristics
-   - 256-dimensional vectors
-   - Speaker-invariant features
-
-3. **Clustering**
-   - Groups similar voice embeddings
-   - Agglomerative clustering
-   - Automatic speaker count detection
-
-4. **Segmentation**
-   - Assigns speaker labels to time segments
-   - Handles overlapping speech
-   - Minimum segment duration: 0.5s
-
-**Configuration:**
-```python
-diarization_config = {
-    "min_speakers": 1,
-    "max_speakers": 10,
-    "min_duration": 0.5,
-    "clustering": "AgglomerativeClustering",
-    "embedding_model": "speechbrain/spkrec-ecapa-voxceleb",
-}
-```
-
-### 6. Alignment & Merging
-
-**Chunk Assembly:**
-```python
-# Merge overlapping segments
-for chunk in chunks:
-    # Remove overlap duplicates
-    if chunk.start < previous.end:
-        chunk.text = resolve_overlap(previous, chunk)
-
-    # Maintain timeline
-    merged_transcript.append(chunk)
-```
-
-**Speaker Alignment:**
- Map diarization timeline to transcript
- Resolve speaker changes mid-sentence
- Handle multiple speakers per segment
-
-**Quality Checks:**
- Timeline consistency
- No gaps in transcript
- Speaker label continuity
- Confidence score validation
-
-### 7. Post-processing Chain
-
-**Text Formatting:**
- Sentence capitalization
- Punctuation restoration
- Number formatting
- Acronym detection
-
-**Translation (Optional):**
-```python
-translation_config = {
-    "model": "facebook/seamless-m4t-medium",
-    "source_lang": "auto",
-    "target_langs": ["es", "fr", "de"],
-    "preserve_formatting": True
-}
-```
-
-**Topic Detection:**
- LLM-based analysis
- Extract 3-5 key topics
- Keyword extraction
- Entity recognition
-
-**Summarization:**
-```python
-summary_config = {
-    "model": "openai-compatible",
-    "max_length": 500,
-    "style": "bullets",  # or "paragraph"
-    "include_action_items": True,
-    "include_decisions": True
-}
-```
-
-### 8. Storage & Delivery
-
-**Database Storage:**
-```sql
-- Main transcript record
-INSERT INTO transcripts (
-    id, title, duration, language,
-    transcript_text, transcript_json,
-    speakers, topics, summary,
-    created_at, processing_time
-) VALUES (...);
-
-- Processing metadata
-INSERT INTO processing_metadata (
-    transcript_id, model_versions,
-    chunk_count, total_chunks,
-    error_count, warnings
-) VALUES (...);
-```
-
-**File Storage:**
- Original audio: S3 (optional)
- Processed chunks: Temporary (24h)
- Transcript exports: JSON, SRT, VTT, TXT
-
-**Notification:**
-```json
-{
-  "type": "webhook",
-  "url": "https://your-app.com/webhook",
-  "payload": {
-    "transcript_id": "...",
-    "status": "completed",
-    "duration": 3600,
-    "processing_time": 180
-  }
-}
-```
-
-## Processing Times
-
-**Estimated times for 1 hour of audio:**
-
-| Component | Fast Mode | Balanced | High Quality |
-|-----------|-----------|----------|--------------|
-| Pre-processing | 10s | 10s | 10s |
-| Transcription | 60s | 180s | 600s |
-| Diarization | 30s | 60s | 120s |
-| Post-processing | 20s | 30s | 60s |
-| **Total** | **2 min** | **5 min** | **13 min** |
-
-## Error Handling
-
-### Retry Strategy
-
-```python
-@celery.task(
-    bind=True,
-    max_retries=3,
-    default_retry_delay=60,
-    retry_backoff=True
-)
-def process_chunk(self, chunk_id):
-    try:
-        # Process chunk
-        result = transcribe(chunk_id)
-    except Exception as exc:
-        # Exponential backoff
-        raise self.retry(exc=exc)
-```
-
-### Partial Recovery
-
- Continue with successful chunks
- Mark failed chunks in output
- Provide partial transcript
- Report processing issues
-
-### Fallback Options
-
-1. **Model Fallback:**
-   - If large model fails, try medium
-   - If GPU fails, try CPU
-   - If Modal fails, try local
-
-2. **Quality Degradation:**
-   - Reduce chunk size
-   - Disable post-processing
-   - Skip diarization if needed
-
-## Optimization Tips
-
-### For Speed
-
-1. Use smaller models (tiny/base)
-2. Increase parallel chunks
-3. Disable diarization
-4. Skip post-processing
-5. Use GPU acceleration
-
-### For Accuracy
-
-1. Use larger models (medium/large)
-2. Enable all pre-processing
-3. Reduce chunk size
-4. Enable silence detection
-5. Multiple pass processing
-
-### For Cost
-
-1. Use Modal spot instances
-2. Batch multiple files
-3. Cache common phrases
-4. Optimize chunk size
-5. Selective post-processing
-
-## Monitoring
-
-### Metrics to Track
-
-```python
-metrics = {
-    "processing_time": histogram,
-    "chunk_success_rate": gauge,
-    "model_accuracy": histogram,
-    "queue_depth": gauge,
-    "gpu_utilization": gauge,
-    "cost_per_hour": counter
-}
-```
-
-### Quality Metrics
-
- Word Error Rate (WER)
- Diarization Error Rate (DER)
- Confidence scores
- Processing speed
- User feedback
-
-### Alerts
-
- Processing time > 30 minutes
- Error rate > 5%
- Queue depth > 100
- GPU memory > 90%
- Cost spike detected
--- a/docs/docs/pipelines/live-pipeline.md
+++ b/docs/docs/pipelines/live-pipeline.md
@@ -1,7 +0,0 @@
---
-title: Live pipeline
---
-
-# Live pipeline
-
-Documentation coming soon.
--- a/docs/docs/pipelines/overview.md
+++ b/docs/docs/pipelines/overview.md
@@ -1,7 +0,0 @@
---
-title: overview
---
-
-# overview
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/api.md
+++ b/docs/docs/reference/api.md
@@ -1,441 +0,0 @@
---
-title: API Reference
---
-
-# API Reference
-
-The Reflector API provides a comprehensive RESTful interface for audio transcription, meeting management, and real-time streaming capabilities.
-
-## Base URL
-
-```
-http://localhost:8000/v1
-```
-
-All API endpoints are prefixed with `/v1/` for versioning.
-
-## Authentication
-
-Reflector supports multiple authentication modes:
-
- **No Authentication** (Public Mode): Basic transcription and upload functionality
- **JWT Authentication** (Private Mode): Full feature access including meeting rooms and persistent storage
- **OAuth/OIDC via Authentik**: Enterprise single sign-on integration
-
-## Core Endpoints
-
-### Transcripts
-
-Manage audio transcriptions and their associated metadata.
-
-#### List Transcripts
-```http
-GET /v1/transcripts/
-```
-
-Returns a paginated list of transcripts with filtering options.
-
-#### Create Transcript
-```http
-POST /v1/transcripts/
-```
-
-Create a new transcript from uploaded audio or initialize for streaming.
-
-#### Get Transcript
-```http
-GET /v1/transcripts/{transcript_id}
-```
-
-Retrieve detailed information about a specific transcript.
-
-#### Update Transcript
-```http
-PATCH /v1/transcripts/{transcript_id}
-```
-
-Update transcript metadata, summary, or processing status.
-
-#### Delete Transcript
-```http
-DELETE /v1/transcripts/{transcript_id}
-```
-
-Remove a transcript and its associated data.
-
-### Audio Processing
-
-#### Upload Audio
-```http
-POST /v1/transcripts_audio/{transcript_id}/upload
-```
-
-Upload an audio file for transcription processing.
-
-**Supported formats:**
- WAV, MP3, M4A, FLAC, OGG
- Maximum file size: 500MB
- Sample rates: 8kHz - 48kHz
-
-#### Download Audio
-```http
-GET /v1/transcripts_audio/{transcript_id}/download
-```
-
-Download the original or processed audio file.
-
-#### Stream Audio
-```http
-GET /v1/transcripts_audio/{transcript_id}/stream
-```
-
-Stream audio content with range support for progressive playback.
-
-### WebRTC Streaming
-
-Real-time audio streaming via WebRTC for live transcription.
-
-#### Initialize WebRTC Session
-```http
-POST /v1/transcripts_webrtc/{transcript_id}/offer
-```
-
-Create a WebRTC offer for establishing a peer connection.
-
-#### Complete WebRTC Handshake
-```http
-POST /v1/transcripts_webrtc/{transcript_id}/answer
-```
-
-Submit the WebRTC answer to complete connection setup.
-
-### WebSocket Streaming
-
-Real-time updates and live transcription via WebSocket.
-
-#### WebSocket Endpoint
-```ws
-ws://localhost:8000/v1/transcripts_websocket/{transcript_id}
-```
-
-Receive real-time transcription updates, speaker changes, and processing status.
-
-**Message Types:**
- `transcription`: New transcribed text segments
- `diarization`: Speaker identification updates
- `status`: Processing status changes
- `error`: Error notifications
-
-### Meetings
-
-Manage virtual meeting rooms and recordings.
-
-#### List Meetings
-```http
-GET /v1/meetings/
-```
-
-Get all meetings for the authenticated user.
-
-#### Create Meeting
-```http
-POST /v1/meetings/
-```
-
-Initialize a new meeting room with Whereby integration.
-
-#### Join Meeting
-```http
-POST /v1/meetings/{meeting_id}/join
-```
-
-Join an existing meeting and start recording.
-
-#### End Meeting
-```http
-POST /v1/meetings/{meeting_id}/end
-```
-
-End the meeting and finalize the recording.
-
-### Rooms
-
-Virtual meeting room configuration and management.
-
-#### List Rooms
-```http
-GET /v1/rooms/
-```
-
-Get available meeting rooms.
-
-#### Create Room
-```http
-POST /v1/rooms/
-```
-
-Create a new persistent meeting room.
-
-#### Update Room Settings
-```http
-PATCH /v1/rooms/{room_id}
-```
-
-Modify room configuration and permissions.
-
-## Response Formats
-
-### Success Response
-```json
-{
-  "id": "uuid",
-  "created_at": "2025-01-20T10:00:00Z",
-  "updated_at": "2025-01-20T10:30:00Z",
-  "data": {...}
-}
-```
-
-### Error Response
-```json
-{
-  "error": {
-    "code": "ERROR_CODE",
-    "message": "Human-readable error message",
-    "details": {...}
-  }
-}
-```
-
-### Status Codes
-
- `200 OK`: Successful request
- `201 Created`: Resource created successfully
- `204 No Content`: Successful deletion
- `400 Bad Request`: Invalid request parameters
- `401 Unauthorized`: Authentication required
- `403 Forbidden`: Insufficient permissions
- `404 Not Found`: Resource not found
- `409 Conflict`: Resource conflict
- `422 Unprocessable Entity`: Validation error
- `429 Too Many Requests`: Rate limit exceeded
- `500 Internal Server Error`: Server error
-
-## WebSocket Protocol
-
-The WebSocket connection provides real-time updates during transcription processing. The server sends structured messages to communicate different events and data updates.
-
-### Connection
-```javascript
-const ws = new WebSocket('ws://localhost:8000/v1/transcripts_websocket/{transcript_id}');
-```
-
-### Message Types and Formats
-
-#### Transcription Update
-Sent when new text is transcribed from the audio stream.
-```json
-{
-  "type": "transcription",
-  "data": {
-    "text": "The transcribed text segment",
-    "speaker": "Speaker 1",
-    "timestamp": 1705745623.456,
-    "confidence": 0.95,
-    "segment_id": "seg_001",
-    "is_final": true
-  }
-}
-```
-
-#### Diarization Update
-Sent when speaker changes are detected or speaker labels are updated.
-```json
-{
-  "type": "diarization",
-  "data": {
-    "speaker": "Speaker 2",
-    "speaker_id": "spk_002",
-    "start_time": 1705745620.123,
-    "end_time": 1705745625.456,
-    "confidence": 0.87
-  }
-}
-```
-
-#### Processing Status
-Sent to indicate changes in the processing pipeline status.
-```json
-{
-  "type": "status",
-  "data": {
-    "status": "processing",
-    "stage": "transcription",
-    "progress": 45.5,
-    "message": "Processing audio chunk 12 of 26"
-  }
-}
-```
-
-Status values:
- `initializing`: Setting up processing pipeline
- `processing`: Active transcription/diarization
- `completed`: Processing finished successfully
- `failed`: Processing encountered an error
- `paused`: Processing temporarily suspended
-
-#### Summary Update
-Sent when AI-generated summaries or topics are available.
-```json
-{
-  "type": "summary",
-  "data": {
-    "summary": "Brief summary of the conversation",
-    "topics": ["topic1", "topic2", "topic3"],
-    "action_items": ["action 1", "action 2"],
-    "key_points": ["point 1", "point 2"]
-  }
-}
-```
-
-#### Error Messages
-Sent when errors occur during processing.
-```json
-{
-  "type": "error",
-  "data": {
-    "code": "AUDIO_FORMAT_ERROR",
-    "message": "Unsupported audio format",
-    "details": {
-      "format": "unknown",
-      "sample_rate": 0
-    },
-    "recoverable": false
-  }
-}
-```
-
-#### Heartbeat/Keepalive
-Sent periodically to maintain the connection.
-```json
-{
-  "type": "ping",
-  "data": {
-    "timestamp": 1705745630.000
-  }
-}
-```
-
-### Client-to-Server Messages
-
-Clients can send control messages to the server:
-
-#### Start/Resume Processing
-```json
-{
-  "action": "start",
-  "params": {}
-}
-```
-
-#### Pause Processing
-```json
-{
-  "action": "pause",
-  "params": {}
-}
-```
-
-#### Request Status
-```json
-{
-  "action": "get_status",
-  "params": {}
-}
-```
-
-## OpenAPI Specification
-
-The complete OpenAPI 3.0 specification is available at:
-
-```
-http://localhost:8000/v1/openapi.json
-```
-
-You can import this specification into tools like:
- Postman
- Insomnia
- Swagger UI
- OpenAPI Generator (for client SDK generation)
-
-## SDK Support
-
-While Reflector doesn't provide official SDKs, you can generate client libraries using the OpenAPI specification with tools like:
-
- **Python**: `openapi-python-client`
- **TypeScript**: `openapi-typescript-codegen`
- **Go**: `oapi-codegen`
- **Java**: `openapi-generator`
-
-## Example Usage
-
-### Python Example
-```python
-import requests
-
-# Upload and transcribe audio
-with open('meeting.mp3', 'rb') as f:
-    response = requests.post(
-        'http://localhost:8000/v1/transcripts/',
-        files={'file': f}
-    )
-    transcript_id = response.json()['id']
-
-# Check transcription status
-status = requests.get(
-    f'http://localhost:8000/v1/transcripts/{transcript_id}'
-).json()
-
-print(f"Transcription status: {status['status']}")
-```
-
-### JavaScript WebSocket Example
-```javascript
-// Connect to WebSocket for real-time transcription updates
-const ws = new WebSocket(`ws://localhost:8000/v1/transcripts_websocket/${transcriptId}`);
-
-ws.onopen = () => {
-    console.log('Connected to transcription WebSocket');
-};
-
-ws.onmessage = (event) => {
-    const message = JSON.parse(event.data);
-
-    switch(message.type) {
-        case 'transcription':
-            console.log(`[${message.data.speaker}]: ${message.data.text}`);
-            break;
-        case 'diarization':
-            console.log(`Speaker change: ${message.data.speaker}`);
-            break;
-        case 'status':
-            console.log(`Status: ${message.data.status}`);
-            break;
-        case 'error':
-            console.error(`Error: ${message.data.message}`);
-            break;
-    }
-};
-
-ws.onerror = (error) => {
-    console.error('WebSocket error:', error);
-};
-
-ws.onclose = () => {
-    console.log('WebSocket connection closed');
-};
-```
-
-## Need Help?
-
- Review [example implementations](https://github.com/monadical-sas/reflector/tree/main/examples)
- Open an issue on [GitHub](https://github.com/monadical-sas/reflector/issues)
--- a/docs/docs/reference/api/overview.md
+++ b/docs/docs/reference/api/overview.md
@@ -1,7 +0,0 @@
---
-title: overview
---
-
-# overview
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/backend.md
+++ b/docs/docs/reference/architecture/backend.md
@@ -1,7 +0,0 @@
---
-title: backend
---
-
-# backend
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/database.md
+++ b/docs/docs/reference/architecture/database.md
@@ -1,7 +0,0 @@
---
-title: database
---
-
-# database
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/frontend.md
+++ b/docs/docs/reference/architecture/frontend.md
@@ -1,7 +0,0 @@
---
-title: frontend
---
-
-# frontend
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/overview.md
+++ b/docs/docs/reference/architecture/overview.md
@@ -1,7 +0,0 @@
---
-title: overview
---
-
-# overview
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/workers.md
+++ b/docs/docs/reference/architecture/workers.md
@@ -1,7 +0,0 @@
---
-title: workers
---
-
-# workers
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/configuration.md
+++ b/docs/docs/reference/configuration.md
@@ -1,7 +0,0 @@
---
-title: configuration
---
-
-# configuration
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/analysis.md
+++ b/docs/docs/reference/processors/analysis.md
@@ -1,7 +0,0 @@
---
-title: analysis
---
-
-# analysis
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/diarization.md
+++ b/docs/docs/reference/processors/diarization.md
@@ -1,7 +0,0 @@
---
-title: diarization
---
-
-# diarization
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/transcription.md
+++ b/docs/docs/reference/processors/transcription.md
@@ -1,7 +0,0 @@
---
-title: transcription
---
-
-# transcription
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/translation.md
+++ b/docs/docs/reference/processors/translation.md
@@ -1,7 +0,0 @@
---
-title: translation
---
-
-# translation
-
-Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/roadmap.md
+++ b/docs/docs/roadmap.md
@@ -1,139 +0,0 @@
---
-sidebar_position: 100
-title: Roadmap
---
-
-# Product Roadmap
-
-Our development roadmap for Reflector, focusing on expanding capabilities while maintaining privacy and performance.
-
-## Planned Features
-
-### 🌍 Multi-Language Support Enhancement
-
-**Current State:**
- Whisper supports 99+ languages for transcription
- Parakeet supports English only with high accuracy
- Translation available to 100+ languages
-
-**Planned Improvements:**
- Default language selection per room/user
- Automatic language detection improvements
- Multi-language diarization support
- RTL (Right-to-Left) language UI support
- Language-specific post-processing rules
-
-### 🏠 Self-Hosted Room Providers
-
-**Jitsi Integration**
-
-Moving beyond Whereby to support self-hosted video conferencing:
-
- No API keys required
- Complete control over video infrastructure
- Custom branding and configuration
- Lower operational costs
- Enhanced privacy with self-hosted video
-
-**Implementation Plan:**
- WebRTC bridge for Jitsi Meet
- Room management API integration
- Recording synchronization
- Participant tracking
-
-### 📅 Calendar Integration
-
-**Planned Capabilities:**
- Google Calendar synchronization
- Microsoft Outlook integration
- Automatic meeting room creation
- Pre-meeting document preparation
- Post-meeting transcript delivery
- Recurring meeting support
-
-**Features:**
- Auto-join scheduled meetings
- Calendar-based access control
- Meeting agenda import
- Action item export to calendar
-
-### 🖥️ Self-Hosted GPU Service
-
-**For organizations with dedicated GPU hardware (H100, A100, RTX 4090):**
-
-**Docker GPU Worker Image:**
- Self-contained processing service
- CUDA 11/12 support
- Pre-loaded models:
-  - Whisper (all sizes)
-  - Pyannote diarization
-  - Seamless-M4T translation
- Automatic model management
-
-**Deployment Options:**
- Kubernetes GPU operators
- Docker Compose with nvidia-docker
- Bare metal installation
- Hybrid cloud/on-premise
-
-**Benefits:**
- No Modal.com dependency
- Complete data isolation
- Predictable costs
- Maximum performance
- Custom model support
-
-## Future Considerations
-
-### Enhanced Analytics
- Meeting insights dashboard
- Speaker participation metrics
- Topic trends over time
- Team collaboration patterns
-
-### Advanced AI Features
- Real-time sentiment analysis
- Emotion detection
- Meeting quality scores
- Automated coaching suggestions
-
-### Integration Ecosystem
- Slack/Teams notifications
- CRM integration (Salesforce, HubSpot)
- Project management tools (Jira, Asana)
- Knowledge bases (Notion, Confluence)
-
-### Performance Improvements
- WebAssembly for client-side processing
- Edge computing support
- 5G network optimization
- Blockchain for transcript verification
-
-## Contributing
-
-We welcome community contributions! Areas where you can help:
-
-1. **Language Support**: Add support for your language
-2. **Integrations**: Connect with your favorite tools
-3. **Models**: Fine-tune models for specific domains
-4. **Documentation**: Improve guides and examples
-
-See our [Contributing Guide](https://github.com/monadical-sas/reflector/blob/main/CONTRIBUTING.md) for details.
-
-## Timeline
-
-We don't provide specific dates as development depends on community contributions and priorities. Features are generally released when they're ready and properly tested.
-
-## Feature Requests
-
-Have an idea for Reflector? We'd love to hear it!
-
- [Open a GitHub Issue](https://github.com/monadical-sas/reflector/issues/new)
- [Join our Discord](#)
- [Email us](mailto:reflector@monadical.com)
-
-## Stay Updated
-
- Watch our [GitHub repository](https://github.com/monadical-sas/reflector)
- Follow our [blog](#)
- Subscribe to our [newsletter](#)
--- a/docs/docusaurus.config.ts
+++ b/docs/docusaurus.config.ts
@@ -1,163 +0,0 @@
-import {themes as prismThemes} from 'prism-react-renderer';
-import type {Config} from '@docusaurus/types';
-import type * as Preset from '@docusaurus/preset-classic';
-import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
-
-const config: Config = {
-  title: 'Reflector',
-  tagline: 'AI-powered audio transcription and meeting analysis platform',
-  favicon: 'img/favicon.ico',
-
-  url: 'https://monadical-sas.github.io',
-  baseUrl: '/',
-
-  organizationName: 'monadical-sas',
-  projectName: 'reflector',
-
-  onBrokenLinks: 'throw',
-  onBrokenMarkdownLinks: 'warn',
-
-  markdown: {
-    mermaid: true,
-  },
-
-  i18n: {
-    defaultLocale: 'en',
-    locales: ['en'],
-  },
-
-  presets: [
-    [
-      'classic',
-      {
-        docs: {
-          sidebarPath: './sidebars.ts',
-          editUrl: 'https://github.com/monadical-sas/reflector/tree/main/docs/',
-        },
-        blog: false,
-        theme: {
-          customCss: './src/css/custom.css',
-        },
-      } satisfies Preset.Options,
-    ],
-  ],
-
-  plugins: [
-    [
-      'docusaurus-plugin-openapi-docs',
-      {
-        id: 'openapi',
-        docsPluginId: 'classic',
-        config: {
-          reflectorapi: {
-            specPath: 'static/openapi.json', // Use local file fetched by script
-            outputDir: 'docs/reference/api-generated',
-            sidebarOptions: {
-              groupPathsBy: 'tag',
-              categoryLinkSource: 'tag',
-            },
-            downloadUrl: '/openapi.json',
-            hideSendButton: false,
-            showExtensions: true,
-          },
-        } satisfies OpenApiPlugin.Options,
-      },
-    ],
-  ],
-
-  themes: ['docusaurus-theme-openapi-docs', '@docusaurus/theme-mermaid'],
-
-  themeConfig: {
-    image: 'img/reflector-social-card.jpg',
-    colorMode: {
-      defaultMode: 'light',
-      disableSwitch: false,
-      respectPrefersColorScheme: true,
-    },
-    navbar: {
-      title: 'Reflector',
-      logo: {
-        alt: 'Reflector Logo',
-        src: 'img/reflector-logo.svg',
-      },
-      items: [
-        {
-          type: 'docSidebar',
-          sidebarId: 'tutorialSidebar',
-          position: 'left',
-          label: 'Documentation',
-        },
-        {
-          to: '/docs/reference/api',
-          label: 'API',
-          position: 'left',
-        },
-        {
-          href: 'https://github.com/monadical-sas/reflector',
-          label: 'GitHub',
-          position: 'right',
-        },
-      ],
-    },
-    footer: {
-      style: 'dark',
-      links: [
-        {
-          title: 'Documentation',
-          items: [
-            {
-              label: 'Introduction',
-              to: '/docs/intro',
-            },
-            {
-              label: 'Installation',
-              to: '/docs/installation/overview',
-            },
-            {
-              label: 'API Reference',
-              to: '/docs/reference/api',
-            },
-          ],
-        },
-        {
-          title: 'Resources',
-          items: [
-            {
-              label: 'Architecture',
-              to: '/docs/reference/architecture/overview',
-            },
-            {
-              label: 'Pipelines',
-              to: '/docs/pipelines/overview',
-            },
-            {
-              label: 'Roadmap',
-              to: '/docs/roadmap',
-            },
-          ],
-        },
-        {
-          title: 'More',
-          items: [
-            {
-              label: 'GitHub',
-              href: 'https://github.com/monadical-sas/reflector',
-            },
-            {
-              label: 'Docker Hub',
-              href: 'https://hub.docker.com/r/reflector/backend',
-            },
-          ],
-        },
-      ],
-      copyright: `Copyright © ${new Date().getFullYear()} <a href="https://monadical.com" target="_blank" rel="noopener noreferrer">Monadical</a>. Licensed under MIT. Built with Docusaurus.`,
-    },
-    prism: {
-      theme: prismThemes.github,
-      darkTheme: prismThemes.dracula,
-      additionalLanguages: ['python', 'bash', 'docker', 'yaml'],
-    },
-  } satisfies Preset.ThemeConfig,
-};
-
-export default config;
--- a/docs/package-lock.json
+++ b/docs/package-lock.json
--- a/docs/package.json
+++ b/docs/package.json
@@ -1,53 +0,0 @@
-{
-  "name": "docs",
-  "version": "0.0.0",
-  "private": true,
-  "scripts": {
-    "docusaurus": "docusaurus",
-    "start": "docusaurus start",
-    "build": "docusaurus build",
-    "swizzle": "docusaurus swizzle",
-    "deploy": "docusaurus deploy",
-    "clear": "docusaurus clear",
-    "serve": "docusaurus serve",
-    "write-translations": "docusaurus write-translations",
-    "write-heading-ids": "docusaurus write-heading-ids",
-    "typecheck": "tsc",
-    "fetch-openapi": "./scripts/fetch-openapi.sh",
-    "gen-api-docs": "npm run fetch-openapi && docusaurus gen-api-docs reflector",
-    "prebuild": "npm run fetch-openapi"
-  },
-  "dependencies": {
-    "@docusaurus/core": "3.6.3",
-    "@docusaurus/preset-classic": "3.6.3",
-    "@mdx-js/react": "^3.0.0",
-    "clsx": "^2.0.0",
-    "docusaurus-plugin-openapi-docs": "^4.5.1",
-    "docusaurus-theme-openapi-docs": "^4.5.1",
-    "@docusaurus/theme-mermaid": "3.6.3",
-    "prism-react-renderer": "^2.3.0",
-    "react": "^18.0.0",
-    "react-dom": "^18.0.0"
-  },
-  "devDependencies": {
-    "@docusaurus/module-type-aliases": "3.6.3",
-    "@docusaurus/tsconfig": "3.6.3",
-    "@docusaurus/types": "3.6.3",
-    "typescript": "~5.6.2"
-  },
-  "browserslist": {
-    "production": [
-      ">0.5%",
-      "not dead",
-      "not op_mini all"
-    ],
-    "development": [
-      "last 3 chrome version",
-      "last 3 firefox version",
-      "last 5 safari version"
-    ]
-  },
-  "engines": {
-    "node": ">=18.0"
-  }
-}
--- a/docs/scripts/fetch-openapi.sh
+++ b/docs/scripts/fetch-openapi.sh
@@ -1,115 +0,0 @@
-#!/bin/bash
-
-# Script to fetch OpenAPI specification from FastAPI backend
-# Used during documentation build process
-
-set -e
-
-echo "📡 Fetching OpenAPI specification from FastAPI backend..."
-
-# Colors for output
-RED='\033[0;31m'
-GREEN='\033[0;32m'
-YELLOW='\033[1;33m'
-NC='\033[0m' # No Color
-
-# Configuration
-BACKEND_DIR="../server"
-OPENAPI_OUTPUT="./static/openapi.json"
-SERVER_PORT=1250  # Reflector uses port 1250 by default
-MAX_WAIT=30
-
-# Check if backend directory exists
-if [ ! -d "$BACKEND_DIR" ]; then
-    echo -e "${RED}Error: Backend directory not found at $BACKEND_DIR${NC}"
-    exit 1
-fi
-
-# Function to check if server is running
-check_server() {
-    curl -s -o /dev/null -w "%{http_code}" "http://localhost:${SERVER_PORT}/openapi.json" 2>/dev/null
-}
-
-# Function to cleanup on exit
-cleanup() {
-    if [ ! -z "$SERVER_PID" ]; then
-        echo -e "\n${YELLOW}Stopping FastAPI server (PID: $SERVER_PID)...${NC}"
-        kill $SERVER_PID 2>/dev/null || true
-        wait $SERVER_PID 2>/dev/null || true
-    fi
-}
-
-# Set trap to cleanup on exit
-trap cleanup EXIT INT TERM
-
-# Change to backend directory
-cd "$BACKEND_DIR"
-
-# Check if uv is installed
-if ! command -v uv &> /dev/null; then
-    echo -e "${YELLOW}uv not found, checking for python...${NC}"
-    if command -v python3 &> /dev/null; then
-        PYTHON_CMD="python3"
-    elif command -v python &> /dev/null; then
-        PYTHON_CMD="python"
-    else
-        echo -e "${RED}Error: Neither uv nor python found${NC}"
-        exit 1
-    fi
-    RUN_CMD="$PYTHON_CMD -m"
-else
-    RUN_CMD="uv run -m"
-fi
-
-# Start the FastAPI server in the background (let it use default port 1250)
-echo -e "${YELLOW}Starting FastAPI server...${NC}"
-$RUN_CMD reflector.app > /dev/null 2>&1 &
-SERVER_PID=$!
-
-# Wait for server to be ready
-echo -n "Waiting for server to be ready"
-WAITED=0
-while [ $WAITED -lt $MAX_WAIT ]; do
-    if [ "$(check_server)" = "200" ]; then
-        echo -e " ${GREEN}✓${NC}"
-        break
-    fi
-    echo -n "."
-    sleep 1
-    WAITED=$((WAITED + 1))
-done
-
-if [ $WAITED -ge $MAX_WAIT ]; then
-    echo -e " ${RED}✗${NC}"
-    echo -e "${RED}Error: Server failed to start within ${MAX_WAIT} seconds${NC}"
-    exit 1
-fi
-
-# Change back to docs directory
-cd - > /dev/null
-
-# Create static directory if it doesn't exist
-mkdir -p "$(dirname "$OPENAPI_OUTPUT")"
-
-# Fetch the OpenAPI specification
-echo -e "${YELLOW}Fetching OpenAPI specification...${NC}"
-if curl -s "http://localhost:${SERVER_PORT}/openapi.json" -o "$OPENAPI_OUTPUT"; then
-    echo -e "${GREEN}✓ OpenAPI specification saved to $OPENAPI_OUTPUT${NC}"
-
-    # Validate JSON
-    if command -v jq &> /dev/null; then
-        if jq empty "$OPENAPI_OUTPUT" 2>/dev/null; then
-            echo -e "${GREEN}✓ OpenAPI specification is valid JSON${NC}"
-            # Pretty print the JSON
-            jq . "$OPENAPI_OUTPUT" > "${OPENAPI_OUTPUT}.tmp" && mv "${OPENAPI_OUTPUT}.tmp" "$OPENAPI_OUTPUT"
-        else
-            echo -e "${RED}Error: Invalid JSON in OpenAPI specification${NC}"
-            exit 1
-        fi
-    fi
-else
-    echo -e "${RED}Error: Failed to fetch OpenAPI specification${NC}"
-    exit 1
-fi
-
-echo -e "${GREEN}✅ OpenAPI specification successfully fetched!${NC}"
--- a/docs/sidebars.ts
+++ b/docs/sidebars.ts
@@ -1,95 +0,0 @@
-import type {SidebarsConfig} from '@docusaurus/plugin-content-docs';
-
-const sidebars: SidebarsConfig = {
-  tutorialSidebar: [
-    'intro',
-    {
-      type: 'category',
-      label: 'Concepts',
-      collapsed: false,
-      items: [
-        'concepts/overview',
-        'concepts/modes',
-        'concepts/pipeline',
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Installation',
-      collapsed: false,
-      items: [
-        'installation/overview',
-        'installation/requirements',
-        'installation/docker-setup',
-        'installation/modal-setup',
-        'installation/self-hosted-gpu-setup',
-        'installation/auth-setup',
-        'installation/daily-setup',
-        {
-          type: 'category',
-          label: 'Other Integrations',
-          collapsed: true,
-          items: [
-            'installation/whereby-setup',
-            'installation/zulip-setup',
-          ],
-        },
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Pipelines',
-      items: [
-        'pipelines/overview',
-        'pipelines/file-pipeline',
-        'pipelines/live-pipeline',
-      ],
-    },
-    {
-      type: 'category',
-      label: 'Reference',
-      items: [
-        {
-          type: 'category',
-          label: 'Architecture',
-          items: [
-            'reference/architecture/overview',
-            'reference/architecture/backend',
-            'reference/architecture/frontend',
-            'reference/architecture/workers',
-            'reference/architecture/database',
-          ],
-        },
-        {
-          type: 'category',
-          label: 'Processors',
-          items: [
-            'reference/processors/transcription',
-            'reference/processors/diarization',
-            'reference/processors/translation',
-            'reference/processors/analysis',
-          ],
-        },
-        {
-          type: 'category',
-          label: 'API',
-          items: [
-            {
-              type: 'doc',
-              id: 'reference/api/overview',
-            },
-            {
-              type: 'link',
-              label: 'OpenAPI Reference',
-              href: '/docs/reference/api',
-            },
-          ],
-        },
-        'reference/configuration',
-      ],
-    },
-    'roadmap',
-  ],
-};
-
-export default sidebars;
--- a/docs/src/components/HomepageFeatures/index.tsx
+++ b/docs/src/components/HomepageFeatures/index.tsx
@@ -1,70 +0,0 @@
-import clsx from 'clsx';
-import Heading from '@theme/Heading';
-import styles from './styles.module.css';
-
-type FeatureItem = {
-  title: string;
-  Svg: React.ComponentType<React.ComponentProps<'svg'>>;
-  description: JSX.Element;
-};
-
-const FeatureList: FeatureItem[] = [
-  {
-    title: 'Easy to Use',
-    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
-    description: (
-      <>
-        Docusaurus was designed from the ground up to be easily installed and
-        used to get your website up and running quickly.
-      </>
-    ),
-  },
-  {
-    title: 'Focus on What Matters',
-    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
-    description: (
-      <>
-        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go
-        ahead and move your docs into the <code>docs</code> directory.
-      </>
-    ),
-  },
-  {
-    title: 'Powered by React',
-    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
-    description: (
-      <>
-        Extend or customize your website layout by reusing React. Docusaurus can
-        be extended while reusing the same header and footer.
-      </>
-    ),
-  },
-];
-
-function Feature({title, Svg, description}: FeatureItem) {
-  return (
-    <div className={clsx('col col--4')}>
-      <div className="text--center">
-        <Svg className={styles.featureSvg} role="img" />
-      </div>
-      <div className="text--center padding-horiz--md">
-        <Heading as="h3">{title}</Heading>
-        <p>{description}</p>
-      </div>
-    </div>
-  );
-}
-
-export default function HomepageFeatures(): JSX.Element {
-  return (
-    <section className={styles.features}>
-      <div className="container">
-        <div className="row">
-          {FeatureList.map((props, idx) => (
-            <Feature key={idx} {...props} />
-          ))}
-        </div>
-      </div>
-    </section>
-  );
-}
--- a/docs/src/components/HomepageFeatures/styles.module.css
+++ b/docs/src/components/HomepageFeatures/styles.module.css
@@ -1,11 +0,0 @@
-.features {
-  display: flex;
-  align-items: center;
-  padding: 2rem 0;
-  width: 100%;
-}
-
-.featureSvg {
-  height: 200px;
-  width: 200px;
-}
--- a/docs/src/css/custom.css
+++ b/docs/src/css/custom.css
@@ -1,46 +0,0 @@
-/**
- * Reflector Documentation Theme
- * Based on frontend colors from www/app/styles/theme.ts
- */
-
-@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap');
-
-:root {
-  --ifm-color-primary: #3158E2;
-  --ifm-color-primary-dark: #2847C9;
-  --ifm-color-primary-darker: #2442BF;
-  --ifm-color-primary-darkest: #1D369C;
-  --ifm-color-primary-light: #4A6FE5;
-  --ifm-color-primary-lighter: #5F81E8;
-  --ifm-color-primary-lightest: #8DA6F0;
-
-  --ifm-background-color: #FFFFFF;
-  --ifm-background-surface-color: #F4F4F4;
-  --ifm-font-color-base: #1A202C;
-  --ifm-font-color-secondary: #838383;
-
-  --ifm-code-font-size: 95%;
-  --docusaurus-highlighted-code-line-bg: rgba(49, 88, 226, 0.1);
-
-  --ifm-font-family-base: 'Poppins', system-ui, -apple-system, sans-serif;
-  --ifm-font-family-monospace: 'Fira Code', 'Monaco', 'Consolas', monospace;
-  --ifm-navbar-background-color: #FFFFFF;
-  --ifm-heading-font-weight: 600;
-}
-
-[data-theme='dark'] {
-  --ifm-color-primary: #B1CBFF;
-  --ifm-color-primary-dark: #91B3FF;
-  --ifm-color-primary-darker: #81A7FF;
-  --ifm-color-primary-darkest: #5189FF;
-  --ifm-color-primary-light: #D1DFFF;
-  --ifm-color-primary-lighter: #E1EBFF;
-  --ifm-color-primary-lightest: #F0F5FF;
-
-  --ifm-background-color: #0C0D0E;
-  --ifm-background-surface-color: #1A202C;
-  --ifm-font-color-base: #E2E8F0;
-  --ifm-font-color-secondary: #A0AEC0;
-  --docusaurus-highlighted-code-line-bg: rgba(177, 203, 255, 0.1);
-  --ifm-navbar-background-color: #1A202C;
-}
--- a/docs/src/pages/index.module.css
+++ b/docs/src/pages/index.module.css
@@ -1,23 +0,0 @@
-/**
- * CSS files with the .module.css suffix will be treated as CSS modules
- * and scoped locally.
- */
-
-.heroBanner {
-  padding: 4rem 0;
-  text-align: center;
-  position: relative;
-  overflow: hidden;
-}
-
-@media screen and (max-width: 996px) {
-  .heroBanner {
-    padding: 2rem;
-  }
-}
-
-.buttons {
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}
--- a/docs/src/pages/index.tsx
+++ b/docs/src/pages/index.tsx
@@ -1,7 +0,0 @@
-import React from 'react';
-import { Redirect } from '@docusaurus/router';
-import useBaseUrl from '@docusaurus/useBaseUrl';
-
-export default function Home(): JSX.Element {
-  return <Redirect to={useBaseUrl('/docs/intro')} />;
-}
--- a/docs/src/pages/markdown-page.md
+++ b/docs/src/pages/markdown-page.md
@@ -1,7 +0,0 @@
---
-title: Markdown page example
---
-
-# Markdown page example
-
-You don't need React to write simple standalone pages.
--- a/docs/static/.nojekyll
+++ b/docs/static/.nojekyll
--- a/docs/static/img/docusaurus-social-card.jpg
+++ b/docs/static/img/docusaurus-social-card.jpg
--- a/docs/static/img/docusaurus.png
+++ b/docs/static/img/docusaurus.png
--- a/docs/static/img/favicon.ico
+++ b/docs/static/img/favicon.ico
--- a/docs/static/img/logo.svg
+++ b/docs/static/img/logo.svg
@@ -1,17 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
-<style type="text/css">
-	.st0{fill:#B6B6B6;}
-	.st1{fill:#4A4A4A;}
-</style>
-<g>
-	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
-	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
-</g>
-<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
-</image>
-<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
-</image>
-</svg>
--- a/docs/static/img/reflector-logo.svg
+++ b/docs/static/img/reflector-logo.svg
@@ -1,17 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
-<style type="text/css">
-	.st0{fill:#B6B6B6;}
-	.st1{fill:#4A4A4A;}
-</style>
-<g>
-	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
-	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
-</g>
-<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
-</image>
-<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
-</image>
-</svg>
--- a/docs/static/img/undraw_docusaurus_mountain.svg
+++ b/docs/static/img/undraw_docusaurus_mountain.svg
@@ -1,171 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
-  <title>Easy to Use</title>
-  <g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
-    <g id="Group_11" data-name="Group 11" transform="translate(57 56)">
-      <path id="Path_83" data-name="Path 83" d="M1017.81,560.461c-5.27,45.15-16.22,81.4-31.25,110.31-20,38.52-54.21,54.04-84.77,70.28a193.275,193.275,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.282,657.282,0,0,0-104.09-13.16q-14.97-.675-29.97-.67c-15.42.02-293.07,5.29-360.67-131.57-16.69-33.76-28.13-75-32.24-125.27-11.63-142.12,52.29-235.46,134.74-296.47,155.97-115.41,369.76-110.57,523.43,7.88C941.15,276.621,1036.99,396.031,1017.81,560.461Z" transform="translate(-56 -106.019)" fill="#3f3d56"/>
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-      <path id="Path_85" data-name="Path 85" d="M302,282.962l26-57,36,83-31-60Z" opacity="0.1"/>
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-      <circle id="Ellipse_11" data-name="Ellipse 11" cx="3" cy="3" r="3" transform="translate(479 98.962)" fill="#f2f2f2"/>
-      <circle id="Ellipse_12" data-name="Ellipse 12" cx="3" cy="3" r="3" transform="translate(396 201.962)" fill="#f2f2f2"/>
-      <circle id="Ellipse_13" data-name="Ellipse 13" cx="2" cy="2" r="2" transform="translate(600 220.962)" fill="#f2f2f2"/>
-      <circle id="Ellipse_14" data-name="Ellipse 14" cx="2" cy="2" r="2" transform="translate(180 265.962)" fill="#f2f2f2"/>
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-      <circle id="Ellipse_16" data-name="Ellipse 16" cx="2" cy="2" r="2" transform="translate(736 192.962)" fill="#f2f2f2"/>
-      <circle id="Ellipse_17" data-name="Ellipse 17" cx="2" cy="2" r="2" transform="translate(858 344.962)" fill="#f2f2f2"/>
-      <path id="Path_89" data-name="Path 89" d="M306,121.222h-2.76v-2.76h-1.48v2.76H299V122.7h2.76v2.759h1.48V122.7H306Z" fill="#f2f2f2"/>
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-      <ellipse id="Ellipse_18" data-name="Ellipse 18" cx="544" cy="30" rx="544" ry="30" transform="translate(0 583.962)" fill="#3f3d56"/>
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-  <path d="M410.74039,545.70532a3.31768,3.31768,0,1,0,0-6.63536,3.41133,3.41133,0,0,0-.42333.04247c-.02655-.09953-.04911-.19907-.077-.29859a3.319,3.319,0,0,0-1.278-6.37923,3.28174,3.28174,0,0,0-2.00122.68742q-.10947-.11346-.22294-.22295a3.282,3.282,0,0,0,.67149-1.98265,3.31768,3.31768,0,0,0-6.37-1.2992,13.27078,13.27078,0,1,0,0,25.54082,3.31768,3.31768,0,0,0,6.37-1.2992,3.282,3.282,0,0,0-.67149-1.98265q.11347-.10947.22294-.22294a3.28174,3.28174,0,0,0,2.00122.68742,3.31768,3.31768,0,0,0,1.278-6.37923c.02786-.0982.05042-.19907.077-.29859a3.41325,3.41325,0,0,0,.42333.04246" transform="translate(-35.5 -118.5)" fill="#44d860" fill-rule="evenodd" />
-  <path d="M424.01111,447.8338a3.60349,3.60349,0,0,1-.65028-.06636,3.34415,3.34415,0,0,1-.62372-.18579,3.44679,3.44679,0,0,1-.572-.30522,5.02708,5.02708,0,0,1-.50429-.4114,3.88726,3.88726,0,0,1-.41007-.50428,3.27532,3.27532,0,0,1-.55737-1.84463,3.60248,3.60248,0,0,1,.06636-.65027,3.82638,3.82638,0,0,1,.18447-.62373,3.48858,3.48858,0,0,1,.30656-.57064,3.197,3.197,0,0,1,.91436-.91568,3.44685,3.44685,0,0,1,.572-.30523,3.344,3.344,0,0,1,.62372-.18578,3.06907,3.06907,0,0,1,1.30053,0,3.22332,3.22332,0,0,1,1.19436.491,5.02835,5.02835,0,0,1,.50429.41139,4.8801,4.8801,0,0,1,.41139.50429,3.38246,3.38246,0,0,1,.30522.57064,3.47806,3.47806,0,0,1,.25215,1.274A3.36394,3.36394,0,0,1,426.36,446.865a5.02708,5.02708,0,0,1-.50429.4114,3.3057,3.3057,0,0,1-1.84463.55737m26.54143-1.65884a3.38754,3.38754,0,0,1-2.35024-.96877,5.04185,5.04185,0,0,1-.41007-.50428,3.27532,3.27532,0,0,1-.55737-1.84463,3.38659,3.38659,0,0,1,.96744-2.34892,5.02559,5.02559,0,0,1,.50429-.41139,3.44685,3.44685,0,0,1,.572-.30523,3.3432,3.3432,0,0,1,.62373-.18579,3.06952,3.06952,0,0,1,1.30052,0,3.22356,3.22356,0,0,1,1.19436.491,5.02559,5.02559,0,0,1,.50429.41139,3.38792,3.38792,0,0,1,.96876,2.34892,3.72635,3.72635,0,0,1-.06636.65026,3.37387,3.37387,0,0,1-.18579.62373,4.71469,4.71469,0,0,1-.30522.57064,4.8801,4.8801,0,0,1-.41139.50429,5.02559,5.02559,0,0,1-.50429.41139,3.30547,3.30547,0,0,1-1.84463.55737" transform="translate(-35.5 -118.5)" fill-rule="evenodd" />
-</svg>
--- a/docs/static/openapi.json
+++ b/docs/static/openapi.json
--- a/docs/transcript.md
+++ b/docs/transcript.md
@@ -1,241 +0,0 @@
-# Transcript Formats
-
-The Reflector API provides multiple output formats for transcript data through the `transcript_format` query parameter on the GET `/v1/transcripts/{id}` endpoint.
-
-## Overview
-
-When retrieving a transcript, you can specify the desired format using the `transcript_format` query parameter. The API supports four formats optimized for different use cases:
-
- **text** - Plain text with speaker names (default)
- **text-timestamped** - Timestamped text with speaker names
- **webvtt-named** - WebVTT subtitle format with participant names
- **json** - Structured JSON segments with full metadata
-
-All formats include participant information when available, resolving speaker IDs to actual names.
-
-## Query Parameter Usage
-
-```
-GET /v1/transcripts/{id}?transcript_format={format}
-```
-
-### Parameters
-
- `transcript_format` (optional): The desired output format
-  - Type: `"text" | "text-timestamped" | "webvtt-named" | "json"`
-  - Default: `"text"`
-
-## Format Descriptions
-
-### Text Format (`text`)
-
-**Use case:** Simple, human-readable transcript for display or export.
-
-**Format:** Speaker names followed by their dialogue, one line per segment.
-
-**Example:**
-```
-John Smith: Hello everyone
-Jane Doe: Hi there
-John Smith: How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=text
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "text",
-  "transcript": "John Smith: Hello everyone\nJane Doe: Hi there\nJohn Smith: How are you today?",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### Text Timestamped Format (`text-timestamped`)
-
-**Use case:** Transcript with timing information for navigation or reference.
-
-**Format:** `[MM:SS]` timestamp prefix before each speaker and dialogue.
-
-**Example:**
-```
-[00:00] John Smith: Hello everyone
-[00:05] Jane Doe: Hi there
-[00:12] John Smith: How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=text-timestamped
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "text-timestamped",
-  "transcript": "[00:00] John Smith: Hello everyone\n[00:05] Jane Doe: Hi there\n[00:12] John Smith: How are you today?",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### WebVTT Named Format (`webvtt-named`)
-
-**Use case:** Subtitle files for video players, accessibility tools, or video editing.
-
-**Format:** Standard WebVTT subtitle format with voice tags using participant names.
-
-**Example:**
-```
-WEBVTT
-
-00:00:00.000 --> 00:00:05.000
-<v John Smith>Hello everyone
-
-00:00:05.000 --> 00:00:12.000
-<v Jane Doe>Hi there
-
-00:00:12.000 --> 00:00:18.000
-<v John Smith>How are you today?
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=webvtt-named
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "webvtt-named",
-  "transcript": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v John Smith>Hello everyone\n\n...",
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-### JSON Format (`json`)
-
-**Use case:** Programmatic access with full timing and speaker metadata.
-
-**Format:** Array of segment objects with speaker information, text content, and precise timing.
-
-**Example:**
-```json
-[
-  {
-    "speaker": 0,
-    "speaker_name": "John Smith",
-    "text": "Hello everyone",
-    "start": 0.0,
-    "end": 5.0
-  },
-  {
-    "speaker": 1,
-    "speaker_name": "Jane Doe",
-    "text": "Hi there",
-    "start": 5.0,
-    "end": 12.0
-  },
-  {
-    "speaker": 0,
-    "speaker_name": "John Smith",
-    "text": "How are you today?",
-    "start": 12.0,
-    "end": 18.0
-  }
-]
-```
-
-**Request:**
-```bash
-GET /v1/transcripts/{id}?transcript_format=json
-```
-
-**Response:**
-```json
-{
-  "id": "transcript_123",
-  "name": "Meeting Recording",
-  "transcript_format": "json",
-  "transcript": [
-    {
-      "speaker": 0,
-      "speaker_name": "John Smith",
-      "text": "Hello everyone",
-      "start": 0.0,
-      "end": 5.0
-    },
-    {
-      "speaker": 1,
-      "speaker_name": "Jane Doe",
-      "text": "Hi there",
-      "start": 5.0,
-      "end": 12.0
-    }
-  ],
-  "participants": [
-    {"id": "p1", "speaker": 0, "name": "John Smith"},
-    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
-  ],
-  ...
-}
-```
-
-## Response Structure
-
-All formats return the same base transcript metadata with an additional `transcript_format` field and format-specific `transcript` field:
-
-### Common Fields
-
- `id`: Transcript identifier
- `user_id`: Owner user ID (if authenticated)
- `name`: Transcript name
- `status`: Processing status
- `locked`: Whether transcript is locked for editing
- `duration`: Total duration in seconds
- `title`: Auto-generated or custom title
- `short_summary`: Brief summary
- `long_summary`: Detailed summary
- `created_at`: Creation timestamp
- `share_mode`: Access control setting
- `source_language`: Original audio language
- `target_language`: Translation target language
- `reviewed`: Whether transcript has been reviewed
- `meeting_id`: Associated meeting ID (if applicable)
- `source_kind`: Source type (live, file, room)
- `room_id`: Associated room ID (if applicable)
- `audio_deleted`: Whether audio has been deleted
- `participants`: Array of participant objects with speaker mappings
-
-### Format-Specific Fields
-
- `transcript_format`: The format identifier (discriminator field)
- `transcript`: The formatted transcript content (string for text/webvtt formats, array for json format)
-
-## Speaker Name Resolution
-
-All formats resolve speaker IDs to participant names when available:
-
- If a participant exists for the speaker ID, their name is used
- If no participant exists, a default name like "Speaker 0" is generated
- Speaker IDs are integers (0, 1, 2, etc.) assigned during diarization
--- a/docs/tsconfig.json
+++ b/docs/tsconfig.json
@@ -1,8 +0,0 @@
-{
-  // This file is not used in compilation. It is here just for a nice editor experience.
-  "extends": "@docusaurus/tsconfig",
-  "compilerOptions": {
-    "baseUrl": "."
-  },
-  "exclude": [".docusaurus", "build"]
-}
--- a/gpu/modal_deployments/.gitignore
+++ b/gpu/modal_deployments/.gitignore
@@ -1,33 +0,0 @@
-# OS / Editor
-.DS_Store
-.vscode/
-.idea/
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Logs
-*.log
-
-# Env and secrets
-.env
-.env.*
-*.env
-*.secret
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Modal local state (if any)
-modal_mounts/
-.modal_cache/
--- a/gpu/modal_deployments/README.md
+++ b/gpu/modal_deployments/README.md
@@ -1,171 +0,0 @@
-# Reflector GPU implementation - Transcription and LLM
-
-This repository hold an API for the GPU implementation of the Reflector API service,
-and use [Modal.com](https://modal.com)
-
- `reflector_diarizer.py` - Diarization API
- `reflector_transcriber.py` - Transcription API (Whisper)
- `reflector_transcriber_parakeet.py` - Transcription API (NVIDIA Parakeet)
- `reflector_translator.py` - Translation API
-
-## Modal.com deployment
-
-Create a modal secret, and name it `reflector-gpu`.
-It should contain an `REFLECTOR_APIKEY` environment variable with a value.
-
-The deployment is done using [Modal.com](https://modal.com) service.
-
-```
-$ modal deploy reflector_transcriber.py
-...
-└── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run
-
-$ modal deploy reflector_transcriber_parakeet.py
-...
-└── 🔨 Created web => https://xxxx--reflector-transcriber-parakeet-web.modal.run
-
-$ modal deploy reflector_llm.py
-...
-└── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
-```
-
-Then in your reflector api configuration `.env`, you can set these keys:
-
-```
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://xxxx--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=REFLECTOR_APIKEY
-
-TRANSLATION_BACKEND=modal
-TRANSLATION_URL=https://xxxx--reflector-translator-web.modal.run
-TRANSLATION_MODAL_API_KEY=REFLECTOR_APIKEY
-```
-
-## API
-
-Authentication must be passed with the `Authorization` header, using the `bearer` scheme.
-
-```
-Authorization: bearer <REFLECTOR_APIKEY>
-```
-
-### LLM
-
-`POST /llm`
-
-**request**
-```
-{
-    "prompt": "xxx"
-}
-```
-
-**response**
-```
-{
-    "text": "xxx completed"
-}
-```
-
-### Transcription
-
-#### Parakeet Transcriber (`reflector_transcriber_parakeet.py`)
-
-NVIDIA Parakeet is a state-of-the-art ASR model optimized for real-time transcription with superior word-level timestamps.
-
-**GPU Configuration:**
- **A10G GPU** - Used for `/v1/audio/transcriptions` endpoint (small files, live transcription)
-  - Higher concurrency (max_inputs=10)
-  - Optimized for multiple small audio files
-  - Supports batch processing for efficiency
-
- **L40S GPU** - Used for `/v1/audio/transcriptions-from-url` endpoint (large files)
-  - Lower concurrency but more powerful processing
-  - Optimized for single large audio files
-  - VAD-based chunking for long-form audio
-
-##### `/v1/audio/transcriptions` - Small file transcription
-
-**request** (multipart/form-data)
- `file` or `files[]` - audio file(s) to transcribe
- `model` - model name (default: `nvidia/parakeet-tdt-0.6b-v2`)
- `language` - language code (default: `en`)
- `batch` - whether to use batch processing for multiple files (default: `true`)
-
-**response**
-```json
-{
-    "text": "transcribed text",
-    "words": [
-        {"word": "hello", "start": 0.0, "end": 0.5},
-        {"word": "world", "start": 0.5, "end": 1.0}
-    ],
-    "filename": "audio.mp3"
-}
-```
-
-For multiple files with batch=true:
-```json
-{
-    "results": [
-        {
-            "filename": "audio1.mp3",
-            "text": "transcribed text",
-            "words": [...]
-        },
-        {
-            "filename": "audio2.mp3",
-            "text": "transcribed text",
-            "words": [...]
-        }
-    ]
-}
-```
-
-##### `/v1/audio/transcriptions-from-url` - Large file transcription
-
-**request** (application/json)
-```json
-{
-    "audio_file_url": "https://example.com/audio.mp3",
-    "model": "nvidia/parakeet-tdt-0.6b-v2",
-    "language": "en",
-    "timestamp_offset": 0.0
-}
-```
-
-**response**
-```json
-{
-    "text": "transcribed text from large file",
-    "words": [
-        {"word": "hello", "start": 0.0, "end": 0.5},
-        {"word": "world", "start": 0.5, "end": 1.0}
-    ]
-}
-```
-
-**Supported file types:** mp3, mp4, mpeg, mpga, m4a, wav, webm
-
-#### Whisper Transcriber (`reflector_transcriber.py`)
-
-`POST /transcribe`
-
-**request** (multipart/form-data)
-
- `file` - audio file
- `language` - language code (e.g. `en`)
-
-**response**
-```
-{
-    "text": "xxx",
-    "words": [
-        {"text": "xxx", "start": 0.0, "end": 1.0}
-    ]
-}
-```
--- a/gpu/modal_deployments/deploy-all.sh
+++ b/gpu/modal_deployments/deploy-all.sh
@@ -1,150 +0,0 @@
-#!/bin/bash
-set -e
-
-# --- Usage ---
-usage() {
-    echo "Usage: $0 [OPTIONS]"
-    echo ""
-    echo "Options:"
-    echo "  --hf-token TOKEN    HuggingFace token"
-    echo "  --help              Show this help message"
-    echo ""
-    echo "Examples:"
-    echo "  $0                              # Interactive mode"
-    echo "  $0 --hf-token hf_xxxxx          # Non-interactive mode"
-    echo ""
-    exit 0
-}
-
-# --- Parse Arguments ---
-HF_TOKEN=""
-while [[ $# -gt 0 ]]; do
-    case $1 in
-        --hf-token)
-            HF_TOKEN="$2"
-            shift 2
-            ;;
-        --help)
-            usage
-            ;;
-        *)
-            echo "Unknown option: $1"
-            usage
-            ;;
-    esac
-done
-
-echo "=========================================="
-echo "Reflector GPU Functions Deployment"
-echo "=========================================="
-echo ""
-
-# --- Check Dependencies ---
-if ! command -v modal &> /dev/null; then
-    echo "Error: Modal CLI not installed."
-    echo "  Install with: pip install modal"
-    exit 1
-fi
-
-if ! command -v openssl &> /dev/null; then
-    echo "Error: openssl not found."
-    echo "  Mac: brew install openssl"
-    echo "  Ubuntu: sudo apt-get install openssl"
-    exit 1
-fi
-
-# Check Modal authentication
-if ! modal profile current &> /dev/null; then
-    echo "Error: Not authenticated with Modal."
-    echo "  Run: modal setup"
-    exit 1
-fi
-
-# --- HuggingFace Token Setup ---
-if [ -z "$HF_TOKEN" ]; then
-    echo "HuggingFace token required for Pyannote diarization model."
-    echo "1. Create account at https://huggingface.co"
-    echo "2. Accept license at https://huggingface.co/pyannote/speaker-diarization-3.1"
-    echo "3. Generate token at https://huggingface.co/settings/tokens"
-    echo ""
-    read -p "Enter your HuggingFace token: " HF_TOKEN
-fi
-
-if [ -z "$HF_TOKEN" ]; then
-    echo "Error: HuggingFace token is required for diarization"
-    exit 1
-fi
-
-# Basic token format validation
-if [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
-    echo "Warning: HuggingFace tokens usually start with 'hf_'"
-    if [ -t 0 ]; then
-        read -p "Continue anyway? (y/n): " confirm
-        if [ "$confirm" != "y" ]; then
-            exit 1
-        fi
-    else
-        echo "Non-interactive mode: proceeding anyway"
-    fi
-fi
-
-# --- Auto-generate reflector<->GPU API Key ---
-echo ""
-echo "Generating API key for GPU services..."
-API_KEY=$(openssl rand -hex 32)
-
-# --- Create Modal Secrets ---
-echo "Creating Modal secrets..."
-
-# Create or update hf_token secret (delete first if exists)
-if modal secret list 2>/dev/null | grep -q "hf_token"; then
-    echo "  -> Recreating secret: hf_token"
-    modal secret delete hf_token --yes 2>/dev/null || true
-fi
-echo "  -> Creating secret: hf_token"
-modal secret create hf_token HF_TOKEN="$HF_TOKEN"
-
-# Create or update reflector-gpu secret (delete first if exists)
-if modal secret list 2>/dev/null | grep -q "reflector-gpu"; then
-    echo "  -> Recreating secret: reflector-gpu"
-    modal secret delete reflector-gpu --yes 2>/dev/null || true
-fi
-echo "  -> Creating secret: reflector-gpu"
-modal secret create reflector-gpu REFLECTOR_GPU_APIKEY="$API_KEY"
-
-# --- Deploy Functions ---
-echo ""
-echo "Deploying transcriber (Whisper)..."
-TRANSCRIBER_URL=$(modal deploy reflector_transcriber.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
-if [ -z "$TRANSCRIBER_URL" ]; then
-    echo "Error: Failed to deploy transcriber. Check Modal dashboard for details."
-    exit 1
-fi
-echo "  -> $TRANSCRIBER_URL"
-
-echo ""
-echo "Deploying diarizer (Pyannote)..."
-DIARIZER_URL=$(modal deploy reflector_diarizer.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
-if [ -z "$DIARIZER_URL" ]; then
-    echo "Error: Failed to deploy diarizer. Check Modal dashboard for details."
-    exit 1
-fi
-echo "  -> $DIARIZER_URL"
-
-# --- Output Configuration ---
-echo ""
-echo "=========================================="
-echo "Deployment complete!"
-echo "=========================================="
-echo ""
-echo "Copy these values to your server's server/.env file:"
-echo ""
-echo "# --- Modal GPU Configuration ---"
-echo "TRANSCRIPT_BACKEND=modal"
-echo "TRANSCRIPT_URL=$TRANSCRIBER_URL"
-echo "TRANSCRIPT_MODAL_API_KEY=$API_KEY"
-echo ""
-echo "DIARIZATION_BACKEND=modal"
-echo "DIARIZATION_URL=$DIARIZER_URL"
-echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
-echo "# --- End Modal Configuration ---"
--- a/gpu/modal_deployments/reflector_diarizer.py
+++ b/gpu/modal_deployments/reflector_diarizer.py
@@ -1,261 +0,0 @@
-"""
-Reflector GPU backend - diarizer
-===================================
-"""
-
-import os
-import uuid
-from typing import Mapping, NewType
-from urllib.parse import urlparse
-
-import modal
-
-PYANNOTE_MODEL_NAME: str = "pyannote/speaker-diarization-3.1"
-MODEL_DIR = "/root/diarization_models"
-UPLOADS_PATH = "/uploads"
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-
-DiarizerUniqFilename = NewType("DiarizerUniqFilename", str)
-AudioFileExtension = NewType("AudioFileExtension", str)
-
-app = modal.App(name="reflector-diarizer")
-
-# Volume for temporary file uploads
-upload_volume = modal.Volume.from_name("diarizer-uploads", create_if_missing=True)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py
-#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py (this file)
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
-    parsed_url = urlparse(url)
-    url_path = parsed_url.path
-
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return AudioFileExtension(ext)
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return AudioFileExtension("mp3")
-    if "audio/wav" in content_type:
-        return AudioFileExtension("wav")
-    if "audio/mp4" in content_type:
-        return AudioFileExtension("mp4")
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return AudioFileExtension("webm")
-
-    raise ValueError(
-        f"Unsupported audio format for URL: {url}. "
-        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-    )
-
-
-def download_audio_to_volume(
-    audio_file_url: str,
-) -> tuple[DiarizerUniqFilename, AudioFileExtension]:
-    import requests
-    from fastapi import HTTPException
-
-    print(f"Checking audio file at: {audio_file_url}")
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    print(f"Downloading audio file from: {audio_file_url}")
-    response = requests.get(audio_file_url, allow_redirects=True)
-
-    if response.status_code != 200:
-        print(f"Download failed with status {response.status_code}: {response.text}")
-        raise HTTPException(
-            status_code=response.status_code,
-            detail=f"Failed to download audio file: {response.status_code}",
-        )
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = DiarizerUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
-    file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-    print(f"Writing file to: {file_path} (size: {len(response.content)} bytes)")
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    upload_volume.commit()
-    print(f"File saved as: {unique_filename}")
-    return unique_filename, audio_suffix
-
-
-def migrate_cache_llm():
-    """
-    XXX The cache for model files in Transformers v4.22.0 has been updated.
-    Migrating your old cache. This is a one-time only operation. You can
-    interrupt this and resume the migration later on by calling
-    `transformers.utils.move_cache()`.
-    """
-    from transformers.utils.hub import move_cache
-
-    print("Moving LLM cache")
-    move_cache(cache_dir=MODEL_DIR, new_cache_dir=MODEL_DIR)
-    print("LLM cache moved")
-
-
-def download_pyannote_audio():
-    from pyannote.audio import Pipeline
-
-    Pipeline.from_pretrained(
-        PYANNOTE_MODEL_NAME,
-        cache_dir=MODEL_DIR,
-        use_auth_token=os.environ["HF_TOKEN"],
-    )
-
-
-diarizer_image = (
-    modal.Image.debian_slim(python_version="3.10")
-    .pip_install(
-        "pyannote.audio==3.1.0",
-        "requests",
-        "onnx",
-        "torchaudio",
-        "onnxruntime-gpu",
-        "torch==2.0.0",
-        "transformers==4.34.0",
-        "sentencepiece",
-        "protobuf",
-        "numpy<2",
-        "huggingface_hub",
-        "hf-transfer",
-    )
-    .run_function(
-        download_pyannote_audio,
-        secrets=[modal.Secret.from_name("hf_token")],
-    )
-    .run_function(migrate_cache_llm)
-    .env(
-        {
-            "LD_LIBRARY_PATH": (
-                "/usr/local/lib/python3.10/site-packages/nvidia/cudnn/lib/:"
-                "/opt/conda/lib/python3.10/site-packages/nvidia/cublas/lib/"
-            )
-        }
-    )
-)
-
-
-@app.cls(
-    gpu="A100",
-    timeout=60 * 30,
-    image=diarizer_image,
-    volumes={UPLOADS_PATH: upload_volume},
-    enable_memory_snapshot=True,
-    experimental_options={"enable_gpu_snapshot": True},
-    secrets=[
-        modal.Secret.from_name("hf_token"),
-    ],
-)
-@modal.concurrent(max_inputs=1)
-class Diarizer:
-    @modal.enter(snap=True)
-    def enter(self):
-        import torch
-        from pyannote.audio import Pipeline
-
-        self.use_gpu = torch.cuda.is_available()
-        self.device = "cuda" if self.use_gpu else "cpu"
-        print(f"Using device: {self.device}")
-        self.diarization_pipeline = Pipeline.from_pretrained(
-            PYANNOTE_MODEL_NAME,
-            cache_dir=MODEL_DIR,
-            use_auth_token=os.environ["HF_TOKEN"],
-        )
-        self.diarization_pipeline.to(torch.device(self.device))
-
-    @modal.method()
-    def diarize(self, filename: str, timestamp: float = 0.0):
-        import torchaudio
-
-        upload_volume.reload()
-
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        print(f"Diarizing audio from: {file_path}")
-        waveform, sample_rate = torchaudio.load(file_path)
-        diarization = self.diarization_pipeline(
-            {"waveform": waveform, "sample_rate": sample_rate}
-        )
-
-        words = []
-        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
-            words.append(
-                {
-                    "start": round(timestamp + diarization_segment.start, 3),
-                    "end": round(timestamp + diarization_segment.end, 3),
-                    "speaker": int(speaker[-2:]),
-                }
-            )
-        print("Diarization complete")
-        return {"diarization": words}
-
-
-# -------------------------------------------------------------------
-# Web API
-# -------------------------------------------------------------------
-
-
-@app.function(
-    timeout=60 * 10,
-    scaledown_window=60 * 3,
-    secrets=[
-        modal.Secret.from_name("reflector-gpu"),
-    ],
-    volumes={UPLOADS_PATH: upload_volume},
-    image=diarizer_image,
-)
-@modal.concurrent(max_inputs=40)
-@modal.asgi_app()
-def web():
-    from fastapi import Depends, FastAPI, HTTPException, status
-    from fastapi.security import OAuth2PasswordBearer
-    from pydantic import BaseModel
-
-    diarizerstub = Diarizer()
-
-    app = FastAPI()
-
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
-            raise HTTPException(
-                status_code=status.HTTP_401_UNAUTHORIZED,
-                detail="Invalid API key",
-                headers={"WWW-Authenticate": "Bearer"},
-            )
-
-    class DiarizationResponse(BaseModel):
-        result: dict
-
-    @app.post("/diarize", dependencies=[Depends(apikey_auth)])
-    def diarize(audio_file_url: str, timestamp: float = 0.0) -> DiarizationResponse:
-        unique_filename, audio_suffix = download_audio_to_volume(audio_file_url)
-
-        try:
-            func = diarizerstub.diarize.spawn(
-                filename=unique_filename, timestamp=timestamp
-            )
-            result = func.get()
-            return result
-        finally:
-            try:
-                file_path = f"{UPLOADS_PATH}/{unique_filename}"
-                print(f"Deleting file: {file_path}")
-                os.remove(file_path)
-                upload_volume.commit()
-            except Exception as e:
-                print(f"Error cleaning up {unique_filename}: {e}")
-
-    return app
--- a/gpu/modal_deployments/reflector_transcriber.py
+++ b/gpu/modal_deployments/reflector_transcriber.py
@@ -1,634 +0,0 @@
-import os
-import sys
-import threading
-import uuid
-from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
-from urllib.parse import urlparse
-
-import modal
-
-MODEL_NAME = "large-v2"
-MODEL_COMPUTE_TYPE: str = "float16"
-MODEL_NUM_WORKERS: int = 1
-MINUTES = 60  # seconds
-SAMPLERATE = 16000
-UPLOADS_PATH = "/uploads"
-CACHE_PATH = "/models"
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-VAD_CONFIG = {
-    "batch_max_duration": 30.0,
-    "silence_padding": 0.5,
-    "window_size": 512,
-}
-
-
-WhisperUniqFilename = NewType("WhisperUniqFilename", str)
-AudioFileExtension = NewType("AudioFileExtension", str)
-
-app = modal.App("reflector-transcriber")
-
-model_cache = modal.Volume.from_name("models", create_if_missing=True)
-upload_volume = modal.Volume.from_name("whisper-uploads", create_if_missing=True)
-
-
-class TimeSegment(NamedTuple):
-    """Represents a time segment with start and end times."""
-
-    start: float
-    end: float
-
-
-class AudioSegment(NamedTuple):
-    """Represents an audio segment with timing and audio data."""
-
-    start: float
-    end: float
-    audio: any
-
-
-class TranscriptResult(NamedTuple):
-    """Represents a transcription result with text and word timings."""
-
-    text: str
-    words: list["WordTiming"]
-
-
-class WordTiming(TypedDict):
-    """Represents a word with its timing information."""
-
-    word: str
-    start: float
-    end: float
-
-
-def download_model():
-    from faster_whisper import download_model
-
-    model_cache.reload()
-
-    download_model(MODEL_NAME, cache_dir=CACHE_PATH)
-
-    model_cache.commit()
-
-
-image = (
-    modal.Image.debian_slim(python_version="3.12")
-    .env(
-        {
-            "HF_HUB_ENABLE_HF_TRANSFER": "1",
-            "LD_LIBRARY_PATH": (
-                "/usr/local/lib/python3.12/site-packages/nvidia/cudnn/lib/:"
-                "/opt/conda/lib/python3.12/site-packages/nvidia/cublas/lib/"
-            ),
-        }
-    )
-    .apt_install("ffmpeg")
-    .pip_install(
-        "huggingface_hub==0.27.1",
-        "hf-transfer==0.1.9",
-        "torch==2.5.1",
-        "faster-whisper==1.1.1",
-        "fastapi==0.115.12",
-        "python-multipart",
-        "requests",
-        "librosa==0.10.1",
-        "numpy<2",
-        "silero-vad==5.1.0",
-    )
-    .run_function(download_model, volumes={CACHE_PATH: model_cache})
-)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py
-#   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
-    parsed_url = urlparse(url)
-    url_path = parsed_url.path
-
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return AudioFileExtension(ext)
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return AudioFileExtension("mp3")
-    if "audio/wav" in content_type:
-        return AudioFileExtension("wav")
-    if "audio/mp4" in content_type:
-        return AudioFileExtension("mp4")
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return AudioFileExtension("webm")
-
-    raise ValueError(
-        f"Unsupported audio format for URL: {url}. "
-        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-    )
-
-
-def download_audio_to_volume(
-    audio_file_url: str,
-) -> tuple[WhisperUniqFilename, AudioFileExtension]:
-    import requests
-    from fastapi import HTTPException
-
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = WhisperUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
-    file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    upload_volume.commit()
-    return unique_filename, audio_suffix
-
-
-def pad_audio(audio_array, sample_rate: int = SAMPLERATE):
-    """Add 0.5s of silence if audio is shorter than the silence_padding window.
-
-    Whisper does not require this strictly, but aligning behavior with Parakeet
-    avoids edge-case crashes on extremely short inputs and makes comparisons easier.
-    """
-    import numpy as np
-
-    audio_duration = len(audio_array) / sample_rate
-    if audio_duration < VAD_CONFIG["silence_padding"]:
-        silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
-        silence = np.zeros(silence_samples, dtype=np.float32)
-        return np.concatenate([audio_array, silence])
-    return audio_array
-
-
-@app.cls(
-    gpu="A10G",
-    timeout=5 * MINUTES,
-    scaledown_window=5 * MINUTES,
-    image=image,
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-)
-@modal.concurrent(max_inputs=10)
-class TranscriberWhisperLive:
-    """Live transcriber class for small audio segments (A10G).
-
-    Mirrors the Parakeet live class API but uses Faster-Whisper under the hood.
-    """
-
-    @modal.enter()
-    def enter(self):
-        import faster_whisper
-        import torch
-
-        self.lock = threading.Lock()
-        self.use_gpu = torch.cuda.is_available()
-        self.device = "cuda" if self.use_gpu else "cpu"
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=MODEL_COMPUTE_TYPE,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-            local_files_only=True,
-        )
-        print(f"Model is on device: {self.device}")
-
-    @modal.method()
-    def transcribe_segment(
-        self,
-        filename: str,
-        language: str = "en",
-    ):
-        """Transcribe a single uploaded audio file by filename."""
-        upload_volume.reload()
-
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        with self.lock:
-            with NoStdStreams():
-                segments, _ = self.model.transcribe(
-                    file_path,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-        segments = list(segments)
-        text = "".join(segment.text for segment in segments).strip()
-        words = [
-            {
-                "word": word.word,
-                "start": round(float(word.start), 2),
-                "end": round(float(word.end), 2),
-            }
-            for segment in segments
-            for word in segment.words
-        ]
-
-        return {"text": text, "words": words}
-
-    @modal.method()
-    def transcribe_batch(
-        self,
-        filenames: list[str],
-        language: str = "en",
-    ):
-        """Transcribe multiple uploaded audio files and return per-file results."""
-        upload_volume.reload()
-
-        results = []
-        for filename in filenames:
-            file_path = f"{UPLOADS_PATH}/{filename}"
-            if not os.path.exists(file_path):
-                raise FileNotFoundError(f"Batch file not found: {file_path}")
-
-            with self.lock:
-                with NoStdStreams():
-                    segments, _ = self.model.transcribe(
-                        file_path,
-                        language=language,
-                        beam_size=5,
-                        word_timestamps=True,
-                        vad_filter=True,
-                        vad_parameters={"min_silence_duration_ms": 500},
-                    )
-
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start), 2),
-                    "end": round(float(w.end), 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-
-            results.append(
-                {
-                    "filename": filename,
-                    "text": text,
-                    "words": words,
-                }
-            )
-
-        return results
-
-
-@app.cls(
-    gpu="L40S",
-    timeout=15 * MINUTES,
-    image=image,
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-)
-class TranscriberWhisperFile:
-    """File transcriber for larger/longer audio, using VAD-driven batching (L40S)."""
-
-    @modal.enter()
-    def enter(self):
-        import faster_whisper
-        import torch
-        from silero_vad import load_silero_vad
-
-        self.lock = threading.Lock()
-        self.use_gpu = torch.cuda.is_available()
-        self.device = "cuda" if self.use_gpu else "cpu"
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=MODEL_COMPUTE_TYPE,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-            local_files_only=True,
-        )
-        self.vad_model = load_silero_vad(onnx=False)
-
-    @modal.method()
-    def transcribe_segment(
-        self, filename: str, timestamp_offset: float = 0.0, language: str = "en"
-    ):
-        import librosa
-        import numpy as np
-        from silero_vad import VADIterator
-
-        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
-        # If you modify this function, you MUST update all copies:
-        #   - gpu/modal_deployments/reflector_transcriber.py (this file)
-        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-        #   - gpu/self_hosted/app/services/transcriber.py
-        def vad_segments(
-            audio_array,
-            sample_rate: int = SAMPLERATE,
-            window_size: int = VAD_CONFIG["window_size"],
-        ) -> Generator[TimeSegment, None, None]:
-            """Generate speech segments as TimeSegment using Silero VAD."""
-            iterator = VADIterator(self.vad_model, sampling_rate=sample_rate)
-            audio_duration = len(audio_array) / float(SAMPLERATE)
-            start = None
-            for i in range(0, len(audio_array), window_size):
-                chunk = audio_array[i : i + window_size]
-                if len(chunk) < window_size:
-                    chunk = np.pad(
-                        chunk, (0, window_size - len(chunk)), mode="constant"
-                    )
-                speech = iterator(chunk)
-                if not speech:
-                    continue
-                if "start" in speech:
-                    start = speech["start"]
-                    continue
-                if "end" in speech and start is not None:
-                    end = speech["end"]
-                    yield TimeSegment(
-                        start / float(SAMPLERATE), end / float(SAMPLERATE)
-                    )
-                    start = None
-            # Handle case where audio ends while speech is still active
-            if start is not None:
-                yield TimeSegment(start / float(SAMPLERATE), audio_duration)
-            iterator.reset_states()
-
-        upload_volume.reload()
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        audio_array, _sr = librosa.load(file_path, sr=SAMPLERATE, mono=True)
-
-        # Batch segments up to ~30s windows by merging contiguous VAD segments
-        merged_batches: list[TimeSegment] = []
-        batch_start = None
-        batch_end = None
-        max_duration = VAD_CONFIG["batch_max_duration"]
-        for segment in vad_segments(audio_array):
-            seg_start, seg_end = segment.start, segment.end
-            if batch_start is None:
-                batch_start, batch_end = seg_start, seg_end
-                continue
-            if seg_end - batch_start <= max_duration:
-                batch_end = seg_end
-            else:
-                merged_batches.append(TimeSegment(batch_start, batch_end))
-                batch_start, batch_end = seg_start, seg_end
-        if batch_start is not None and batch_end is not None:
-            merged_batches.append(TimeSegment(batch_start, batch_end))
-
-        all_text = []
-        all_words = []
-
-        for segment in merged_batches:
-            start_time, end_time = segment.start, segment.end
-            s_idx = int(start_time * SAMPLERATE)
-            e_idx = int(end_time * SAMPLERATE)
-            segment = audio_array[s_idx:e_idx]
-            segment = pad_audio(segment, SAMPLERATE)
-
-            with self.lock:
-                segments, _ = self.model.transcribe(
-                    segment,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
-                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-            if text:
-                all_text.append(text)
-            all_words.extend(words)
-
-        return {"text": " ".join(all_text), "words": all_words}
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py
-#   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: dict) -> str:
-    from urllib.parse import urlparse
-
-    from fastapi import HTTPException
-
-    url_path = urlparse(url).path
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return ext
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return "mp3"
-    if "audio/wav" in content_type:
-        return "wav"
-    if "audio/mp4" in content_type:
-        return "mp4"
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return "webm"
-
-    raise HTTPException(
-        status_code=400,
-        detail=(
-            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-        ),
-    )
-
-
-def download_audio_to_volume(audio_file_url: str) -> tuple[str, str]:
-    import requests
-    from fastapi import HTTPException
-
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-    file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    upload_volume.commit()
-    return unique_filename, audio_suffix
-
-
-@app.function(
-    scaledown_window=60,
-    timeout=600,
-    secrets=[
-        modal.Secret.from_name("reflector-gpu"),
-    ],
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-    image=image,
-)
-@modal.concurrent(max_inputs=40)
-@modal.asgi_app()
-def web():
-    from fastapi import (
-        Body,
-        Depends,
-        FastAPI,
-        Form,
-        HTTPException,
-        UploadFile,
-        status,
-    )
-    from fastapi.security import OAuth2PasswordBearer
-
-    transcriber_live = TranscriberWhisperLive()
-    transcriber_file = TranscriberWhisperFile()
-
-    app = FastAPI()
-
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey == os.environ["REFLECTOR_GPU_APIKEY"]:
-            return
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail="Invalid API key",
-            headers={"WWW-Authenticate": "Bearer"},
-        )
-
-    class TranscriptResponse(dict):
-        pass
-
-    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
-    def transcribe(
-        file: UploadFile = None,
-        files: list[UploadFile] | None = None,
-        model: str = Form(MODEL_NAME),
-        language: str = Form("en"),
-        batch: bool = Form(False),
-    ):
-        if not file and not files:
-            raise HTTPException(
-                status_code=400, detail="Either 'file' or 'files' parameter is required"
-            )
-        if batch and not files:
-            raise HTTPException(
-                status_code=400, detail="Batch transcription requires 'files'"
-            )
-
-        upload_files = [file] if file else files
-
-        uploaded_filenames: list[str] = []
-        for upload_file in upload_files:
-            audio_suffix = upload_file.filename.split(".")[-1]
-            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-                    ),
-                )
-
-            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-            file_path = f"{UPLOADS_PATH}/{unique_filename}"
-            with open(file_path, "wb") as f:
-                content = upload_file.file.read()
-                f.write(content)
-            uploaded_filenames.append(unique_filename)
-
-        upload_volume.commit()
-
-        try:
-            if batch and len(upload_files) > 1:
-                func = transcriber_live.transcribe_batch.spawn(
-                    filenames=uploaded_filenames,
-                    language=language,
-                )
-                results = func.get()
-                return {"results": results}
-
-            results = []
-            for filename in uploaded_filenames:
-                func = transcriber_live.transcribe_segment.spawn(
-                    filename=filename,
-                    language=language,
-                )
-                result = func.get()
-                result["filename"] = filename
-                results.append(result)
-
-            return {"results": results} if len(results) > 1 else results[0]
-        finally:
-            for filename in uploaded_filenames:
-                try:
-                    file_path = f"{UPLOADS_PATH}/{filename}"
-                    os.remove(file_path)
-                except Exception:
-                    pass
-            upload_volume.commit()
-
-    @app.post("/v1/audio/transcriptions-from-url", dependencies=[Depends(apikey_auth)])
-    def transcribe_from_url(
-        audio_file_url: str = Body(
-            ..., description="URL of the audio file to transcribe"
-        ),
-        model: str = Body(MODEL_NAME),
-        language: str = Body("en"),
-        timestamp_offset: float = Body(0.0),
-    ):
-        unique_filename, _audio_suffix = download_audio_to_volume(audio_file_url)
-        try:
-            func = transcriber_file.transcribe_segment.spawn(
-                filename=unique_filename,
-                timestamp_offset=timestamp_offset,
-                language=language,
-            )
-            result = func.get()
-            return result
-        finally:
-            try:
-                file_path = f"{UPLOADS_PATH}/{unique_filename}"
-                os.remove(file_path)
-                upload_volume.commit()
-            except Exception:
-                pass
-
-    return app
-
-
-class NoStdStreams:
-    def __init__(self):
-        self.devnull = open(os.devnull, "w")
-
-    def __enter__(self):
-        self._stdout, self._stderr = sys.stdout, sys.stderr
-        self._stdout.flush()
-        self._stderr.flush()
-        sys.stdout, sys.stderr = self.devnull, self.devnull
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout, sys.stderr = self._stdout, self._stderr
-        self.devnull.close()
--- a/gpu/modal_deployments/reflector_transcriber_parakeet.py
+++ b/gpu/modal_deployments/reflector_transcriber_parakeet.py
@@ -1,676 +0,0 @@
-import logging
-import os
-import sys
-import threading
-import uuid
-from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
-from urllib.parse import urlparse
-
-import modal
-
-MODEL_NAME = "nvidia/parakeet-tdt-0.6b-v2"
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-SAMPLERATE = 16000
-UPLOADS_PATH = "/uploads"
-CACHE_PATH = "/cache"
-VAD_CONFIG = {
-    "batch_max_duration": 30.0,
-    "silence_padding": 0.5,
-    "window_size": 512,
-}
-
-ParakeetUniqFilename = NewType("ParakeetUniqFilename", str)
-AudioFileExtension = NewType("AudioFileExtension", str)
-
-
-class TimeSegment(NamedTuple):
-    """Represents a time segment with start and end times."""
-
-    start: float
-    end: float
-
-
-class AudioSegment(NamedTuple):
-    """Represents an audio segment with timing and audio data."""
-
-    start: float
-    end: float
-    audio: any
-
-
-class TranscriptResult(NamedTuple):
-    """Represents a transcription result with text and word timings."""
-
-    text: str
-    words: list["WordTiming"]
-
-
-class WordTiming(TypedDict):
-    """Represents a word with its timing information."""
-
-    word: str
-    start: float
-    end: float
-
-
-app = modal.App("reflector-transcriber-parakeet")
-
-# Volume for caching model weights
-model_cache = modal.Volume.from_name("parakeet-model-cache", create_if_missing=True)
-# Volume for temporary file uploads
-upload_volume = modal.Volume.from_name("parakeet-uploads", create_if_missing=True)
-
-image = (
-    modal.Image.from_registry(
-        "nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04", add_python="3.12"
-    )
-    .env(
-        {
-            "HF_HUB_ENABLE_HF_TRANSFER": "1",
-            "HF_HOME": "/cache",
-            "DEBIAN_FRONTEND": "noninteractive",
-            "CXX": "g++",
-            "CC": "g++",
-        }
-    )
-    .apt_install("ffmpeg")
-    .pip_install(
-        "hf_transfer==0.1.9",
-        "huggingface_hub[hf-xet]==0.31.2",
-        "nemo_toolkit[asr]==2.5.0",
-        "cuda-python==12.8.0",
-        "fastapi==0.115.12",
-        "numpy<2",
-        "librosa==0.11.0",
-        "requests",
-        "silero-vad==6.2.0",
-        "torch",
-    )
-    .entrypoint([])  # silence chatty logs by container on start
-)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py
-#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py (this file)
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
-    parsed_url = urlparse(url)
-    url_path = parsed_url.path
-
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return AudioFileExtension(ext)
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return AudioFileExtension("mp3")
-    if "audio/wav" in content_type:
-        return AudioFileExtension("wav")
-    if "audio/mp4" in content_type:
-        return AudioFileExtension("mp4")
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return AudioFileExtension("webm")
-
-    raise ValueError(
-        f"Unsupported audio format for URL: {url}. "
-        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-    )
-
-
-def download_audio_to_volume(
-    audio_file_url: str,
-) -> tuple[ParakeetUniqFilename, AudioFileExtension]:
-    import requests
-    from fastapi import HTTPException
-
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = ParakeetUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
-    file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    upload_volume.commit()
-    return unique_filename, audio_suffix
-
-
-def pad_audio(audio_array, sample_rate: int = SAMPLERATE):
-    """Add 0.5 seconds of silence if audio is less than 500ms.
-
-    This is a workaround for a Parakeet bug where very short audio (<500ms) causes:
-    ValueError: `char_offsets`: [] and `processed_tokens`: [157, 834, 834, 841]
-    have to be of the same length
-
-    See: https://github.com/NVIDIA/NeMo/issues/8451
-    """
-    import numpy as np
-
-    audio_duration = len(audio_array) / sample_rate
-    if audio_duration < 0.5:
-        silence_samples = int(sample_rate * 0.5)
-        silence = np.zeros(silence_samples, dtype=np.float32)
-        return np.concatenate([audio_array, silence])
-    return audio_array
-
-
-@app.cls(
-    gpu="A10G",
-    timeout=600,
-    scaledown_window=300,
-    image=image,
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-    enable_memory_snapshot=True,
-    experimental_options={"enable_gpu_snapshot": True},
-)
-@modal.concurrent(max_inputs=10)
-class TranscriberParakeetLive:
-    @modal.enter(snap=True)
-    def enter(self):
-        import nemo.collections.asr as nemo_asr
-
-        logging.getLogger("nemo_logger").setLevel(logging.CRITICAL)
-
-        self.lock = threading.Lock()
-        self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=MODEL_NAME)
-        device = next(self.model.parameters()).device
-        print(f"Model is on device: {device}")
-
-    @modal.method()
-    def transcribe_segment(
-        self,
-        filename: str,
-    ):
-        import librosa
-
-        upload_volume.reload()
-
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
-        padded_audio = pad_audio(audio_array, sample_rate)
-
-        with self.lock:
-            with NoStdStreams():
-                (output,) = self.model.transcribe([padded_audio], timestamps=True)
-
-        text = output.text.strip()
-        words: list[WordTiming] = [
-            WordTiming(
-                # XXX the space added here is to match the output of whisper
-                # whisper add space to each words, while parakeet don't
-                word=word_info["word"] + " ",
-                start=round(word_info["start"], 2),
-                end=round(word_info["end"], 2),
-            )
-            for word_info in output.timestamp["word"]
-        ]
-
-        return {"text": text, "words": words}
-
-    @modal.method()
-    def transcribe_batch(
-        self,
-        filenames: list[str],
-    ):
-        import librosa
-
-        upload_volume.reload()
-
-        results = []
-        audio_arrays = []
-
-        # Load all audio files with padding
-        for filename in filenames:
-            file_path = f"{UPLOADS_PATH}/{filename}"
-            if not os.path.exists(file_path):
-                raise FileNotFoundError(f"Batch file not found: {file_path}")
-
-            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
-            padded_audio = pad_audio(audio_array, sample_rate)
-            audio_arrays.append(padded_audio)
-
-        with self.lock:
-            with NoStdStreams():
-                outputs = self.model.transcribe(audio_arrays, timestamps=True)
-
-        # Process results for each file
-        for i, (filename, output) in enumerate(zip(filenames, outputs)):
-            text = output.text.strip()
-
-            words: list[WordTiming] = [
-                WordTiming(
-                    word=word_info["word"] + " ",
-                    start=round(word_info["start"], 2),
-                    end=round(word_info["end"], 2),
-                )
-                for word_info in output.timestamp["word"]
-            ]
-
-            results.append(
-                {
-                    "filename": filename,
-                    "text": text,
-                    "words": words,
-                }
-            )
-
-        return results
-
-
-# L40S class for file transcription (bigger files)
-@app.cls(
-    gpu="L40S",
-    timeout=900,
-    image=image,
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-    enable_memory_snapshot=True,
-    experimental_options={"enable_gpu_snapshot": True},
-)
-class TranscriberParakeetFile:
-    @modal.enter(snap=True)
-    def enter(self):
-        import nemo.collections.asr as nemo_asr
-        import torch
-        from silero_vad import load_silero_vad
-
-        logging.getLogger("nemo_logger").setLevel(logging.CRITICAL)
-
-        self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=MODEL_NAME)
-        device = next(self.model.parameters()).device
-        print(f"Model is on device: {device}")
-
-        torch.set_num_threads(1)
-        self.vad_model = load_silero_vad(onnx=False)
-        print("Silero VAD initialized")
-
-    @modal.method()
-    def transcribe_segment(
-        self,
-        filename: str,
-        timestamp_offset: float = 0.0,
-    ):
-        import librosa
-        import numpy as np
-        from silero_vad import VADIterator
-
-        def load_and_convert_audio(file_path):
-            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
-            return audio_array
-
-        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
-        # If you modify this function, you MUST update all copies:
-        #   - gpu/modal_deployments/reflector_transcriber.py
-        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py (this file)
-        #   - gpu/self_hosted/app/services/transcriber.py
-        def vad_segment_generator(
-            audio_array,
-        ) -> Generator[TimeSegment, None, None]:
-            """Generate speech segments using VAD with start/end sample indices"""
-            vad_iterator = VADIterator(self.vad_model, sampling_rate=SAMPLERATE)
-            audio_duration = len(audio_array) / float(SAMPLERATE)
-            window_size = VAD_CONFIG["window_size"]
-            start = None
-
-            for i in range(0, len(audio_array), window_size):
-                chunk = audio_array[i : i + window_size]
-                if len(chunk) < window_size:
-                    chunk = np.pad(
-                        chunk, (0, window_size - len(chunk)), mode="constant"
-                    )
-
-                speech_dict = vad_iterator(chunk)
-                if not speech_dict:
-                    continue
-
-                if "start" in speech_dict:
-                    start = speech_dict["start"]
-                    continue
-
-                if "end" in speech_dict and start is not None:
-                    end = speech_dict["end"]
-                    start_time = start / float(SAMPLERATE)
-                    end_time = end / float(SAMPLERATE)
-
-                    yield TimeSegment(start_time, end_time)
-                    start = None
-
-            if start is not None:
-                start_time = start / float(SAMPLERATE)
-                yield TimeSegment(start_time, audio_duration)
-
-            vad_iterator.reset_states()
-
-        def batch_speech_segments(
-            segments: Generator[TimeSegment, None, None], max_duration: int
-        ) -> Generator[TimeSegment, None, None]:
-            """
-            Input segments:
-              [0-2] [3-5] [6-8] [10-11] [12-15] [17-19] [20-22]
-
-                                  ↓ (max_duration=10)
-
-              Output batches:
-              [0-8]           [10-19]          [20-22]
-
-            Note: silences are kept for better transcription, previous implementation was
-            passing segments separatly, but the output was less accurate.
-            """
-            batch_start_time = None
-            batch_end_time = None
-
-            for segment in segments:
-                start_time, end_time = segment.start, segment.end
-                if batch_start_time is None or batch_end_time is None:
-                    batch_start_time = start_time
-                    batch_end_time = end_time
-                    continue
-
-                total_duration = end_time - batch_start_time
-
-                if total_duration <= max_duration:
-                    batch_end_time = end_time
-                    continue
-
-                yield TimeSegment(batch_start_time, batch_end_time)
-                batch_start_time = start_time
-                batch_end_time = end_time
-
-            if batch_start_time is None or batch_end_time is None:
-                return
-
-            yield TimeSegment(batch_start_time, batch_end_time)
-
-        def batch_segment_to_audio_segment(
-            segments: Generator[TimeSegment, None, None],
-            audio_array,
-        ) -> Generator[AudioSegment, None, None]:
-            """Extract audio segments and apply padding for Parakeet compatibility.
-
-            Uses pad_audio to ensure segments are at least 0.5s long, preventing
-            Parakeet crashes. This padding may cause slight timing overlaps between
-            segments, which are corrected by enforce_word_timing_constraints.
-            """
-            for segment in segments:
-                start_time, end_time = segment.start, segment.end
-                start_sample = int(start_time * SAMPLERATE)
-                end_sample = int(end_time * SAMPLERATE)
-                audio_segment = audio_array[start_sample:end_sample]
-
-                padded_segment = pad_audio(audio_segment, SAMPLERATE)
-
-                yield AudioSegment(start_time, end_time, padded_segment)
-
-        def transcribe_batch(model, audio_segments: list) -> list:
-            with NoStdStreams():
-                outputs = model.transcribe(audio_segments, timestamps=True)
-            return outputs
-
-        def enforce_word_timing_constraints(
-            words: list[WordTiming],
-        ) -> list[WordTiming]:
-            """Enforce that word end times don't exceed the start time of the next word.
-
-            Due to silence padding added in batch_segment_to_audio_segment for better
-            transcription accuracy, word timings from different segments may overlap.
-            This function ensures there are no overlaps by adjusting end times.
-            """
-            if len(words) <= 1:
-                return words
-
-            enforced_words = []
-            for i, word in enumerate(words):
-                enforced_word = word.copy()
-
-                if i < len(words) - 1:
-                    next_start = words[i + 1]["start"]
-                    if enforced_word["end"] > next_start:
-                        enforced_word["end"] = next_start
-
-                enforced_words.append(enforced_word)
-
-            return enforced_words
-
-        def emit_results(
-            results: list,
-            segments_info: list[AudioSegment],
-        ) -> Generator[TranscriptResult, None, None]:
-            """Yield transcribed text and word timings from model output, adjusting timestamps to absolute positions."""
-            for i, (output, segment) in enumerate(zip(results, segments_info)):
-                start_time, end_time = segment.start, segment.end
-                text = output.text.strip()
-                words: list[WordTiming] = [
-                    WordTiming(
-                        word=word_info["word"] + " ",
-                        start=round(
-                            word_info["start"] + start_time + timestamp_offset, 2
-                        ),
-                        end=round(word_info["end"] + start_time + timestamp_offset, 2),
-                    )
-                    for word_info in output.timestamp["word"]
-                ]
-
-                yield TranscriptResult(text, words)
-
-        upload_volume.reload()
-
-        file_path = f"{UPLOADS_PATH}/{filename}"
-        if not os.path.exists(file_path):
-            raise FileNotFoundError(f"File not found: {file_path}")
-
-        audio_array = load_and_convert_audio(file_path)
-        total_duration = len(audio_array) / float(SAMPLERATE)
-
-        all_text_parts: list[str] = []
-        all_words: list[WordTiming] = []
-
-        raw_segments = vad_segment_generator(audio_array)
-        speech_segments = batch_speech_segments(
-            raw_segments,
-            VAD_CONFIG["batch_max_duration"],
-        )
-        audio_segments = batch_segment_to_audio_segment(speech_segments, audio_array)
-
-        for batch in audio_segments:
-            audio_segment = batch.audio
-            results = transcribe_batch(self.model, [audio_segment])
-
-            for result in emit_results(
-                results,
-                [batch],
-            ):
-                if not result.text:
-                    continue
-                all_text_parts.append(result.text)
-                all_words.extend(result.words)
-
-        all_words = enforce_word_timing_constraints(all_words)
-
-        combined_text = " ".join(all_text_parts)
-        return {"text": combined_text, "words": all_words}
-
-
-@app.function(
-    scaledown_window=60,
-    timeout=600,
-    secrets=[
-        modal.Secret.from_name("reflector-gpu"),
-    ],
-    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
-    image=image,
-)
-@modal.concurrent(max_inputs=40)
-@modal.asgi_app()
-def web():
-    import os
-    import uuid
-
-    from fastapi import (
-        Body,
-        Depends,
-        FastAPI,
-        Form,
-        HTTPException,
-        UploadFile,
-        status,
-    )
-    from fastapi.security import OAuth2PasswordBearer
-    from pydantic import BaseModel
-
-    transcriber_live = TranscriberParakeetLive()
-    transcriber_file = TranscriberParakeetFile()
-
-    app = FastAPI()
-
-    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey == os.environ["REFLECTOR_GPU_APIKEY"]:
-            return
-        raise HTTPException(
-            status_code=status.HTTP_401_UNAUTHORIZED,
-            detail="Invalid API key",
-            headers={"WWW-Authenticate": "Bearer"},
-        )
-
-    class TranscriptResponse(BaseModel):
-        result: dict
-
-    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
-    def transcribe(
-        file: UploadFile = None,
-        files: list[UploadFile] | None = None,
-        model: str = Form(MODEL_NAME),
-        language: str = Form("en"),
-        batch: bool = Form(False),
-    ):
-        # Parakeet only supports English
-        if language != "en":
-            raise HTTPException(
-                status_code=400,
-                detail=f"Parakeet model only supports English. Got language='{language}'",
-            )
-        # Handle both single file and multiple files
-        if not file and not files:
-            raise HTTPException(
-                status_code=400, detail="Either 'file' or 'files' parameter is required"
-            )
-        if batch and not files:
-            raise HTTPException(
-                status_code=400, detail="Batch transcription requires 'files'"
-            )
-
-        upload_files = [file] if file else files
-
-        # Upload files to volume
-        uploaded_filenames = []
-        for upload_file in upload_files:
-            audio_suffix = upload_file.filename.split(".")[-1]
-            assert audio_suffix in SUPPORTED_FILE_EXTENSIONS
-
-            # Generate unique filename
-            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-            file_path = f"{UPLOADS_PATH}/{unique_filename}"
-
-            print(f"Writing file to: {file_path}")
-            with open(file_path, "wb") as f:
-                content = upload_file.file.read()
-                f.write(content)
-
-            uploaded_filenames.append(unique_filename)
-
-        upload_volume.commit()
-
-        try:
-            # Use A10G live transcriber for per-file transcription
-            if batch and len(upload_files) > 1:
-                # Use batch transcription
-                func = transcriber_live.transcribe_batch.spawn(
-                    filenames=uploaded_filenames,
-                )
-                results = func.get()
-                return {"results": results}
-
-            # Per-file transcription
-            results = []
-            for filename in uploaded_filenames:
-                func = transcriber_live.transcribe_segment.spawn(
-                    filename=filename,
-                )
-                result = func.get()
-                result["filename"] = filename
-                results.append(result)
-
-            return {"results": results} if len(results) > 1 else results[0]
-
-        finally:
-            for filename in uploaded_filenames:
-                try:
-                    file_path = f"{UPLOADS_PATH}/{filename}"
-                    print(f"Deleting file: {file_path}")
-                    os.remove(file_path)
-                except Exception as e:
-                    print(f"Error deleting {filename}: {e}")
-
-            upload_volume.commit()
-
-    @app.post("/v1/audio/transcriptions-from-url", dependencies=[Depends(apikey_auth)])
-    def transcribe_from_url(
-        audio_file_url: str = Body(
-            ..., description="URL of the audio file to transcribe"
-        ),
-        model: str = Body(MODEL_NAME),
-        language: str = Body("en", description="Language code (only 'en' supported)"),
-        timestamp_offset: float = Body(0.0),
-    ):
-        # Parakeet only supports English
-        if language != "en":
-            raise HTTPException(
-                status_code=400,
-                detail=f"Parakeet model only supports English. Got language='{language}'",
-            )
-        unique_filename, audio_suffix = download_audio_to_volume(audio_file_url)
-
-        try:
-            func = transcriber_file.transcribe_segment.spawn(
-                filename=unique_filename,
-                timestamp_offset=timestamp_offset,
-            )
-            result = func.get()
-            return result
-        finally:
-            try:
-                file_path = f"{UPLOADS_PATH}/{unique_filename}"
-                print(f"Deleting file: {file_path}")
-                os.remove(file_path)
-                upload_volume.commit()
-            except Exception as e:
-                print(f"Error cleaning up {unique_filename}: {e}")
-
-    return app
-
-
-class NoStdStreams:
-    def __init__(self):
-        self.devnull = open(os.devnull, "w")
-
-    def __enter__(self):
-        self._stdout, self._stderr = sys.stdout, sys.stderr
-        self._stdout.flush()
-        self._stderr.flush()
-        sys.stdout, sys.stderr = self.devnull, self.devnull
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout, sys.stderr = self._stdout, self._stderr
-        self.devnull.close()
--- a/gpu/self_hosted/.env.example
+++ b/gpu/self_hosted/.env.example
@@ -1,2 +0,0 @@
-REFLECTOR_GPU_APIKEY=
-HF_TOKEN=
--- a/gpu/self_hosted/.gitignore
+++ b/gpu/self_hosted/.gitignore
@@ -1,38 +0,0 @@
-cache/
-
-# OS / Editor
-.DS_Store
-.vscode/
-.idea/
-
-# Python
-__pycache__/
-*.py[cod]
-*$py.class
-
-# Env and secrets
-.env
-*.env
-*.secret
-HF_TOKEN
-REFLECTOR_GPU_APIKEY
-
-# Virtual env / uv
-.venv/
-venv/
-ENV/
-uv/
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Logs
-*.log
--- a/gpu/self_hosted/DEV_SETUP.md
+++ b/gpu/self_hosted/DEV_SETUP.md
@@ -1,137 +0,0 @@
-# Local Development GPU Setup
-
-Run transcription and diarization locally for development/testing.
-
-> **For production deployment**, see the [Self-Hosted GPU Setup Guide](../../docs/docs/installation/self-hosted-gpu-setup.md).
-
-## Prerequisites
-
-1. **Python 3.12+** and **uv** package manager
-2. **FFmpeg** installed and on PATH
-3. **HuggingFace account** with access to pyannote models
-
-### Accept Pyannote Licenses (Required)
-
-Before first run, accept licenses for these gated models (logged into HuggingFace):
- https://hf.co/pyannote/speaker-diarization-3.1
- https://hf.co/pyannote/segmentation-3.0
-
-## Quick Start
-
-### 1. Install dependencies
-
-```bash
-cd gpu/self_hosted
-uv sync
-```
-
-### 2. Start the GPU service
-
-```bash
-cd gpu/self_hosted
-HF_TOKEN=<your-huggingface-token> \
-REFLECTOR_GPU_APIKEY=dev-key-12345 \
-.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-Note: The `.env` file is NOT auto-loaded. Pass env vars explicitly or use:
-```bash
-export HF_TOKEN=<your-token>
-export REFLECTOR_GPU_APIKEY=dev-key-12345
-.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-### 3. Configure Reflector to use local GPU
-
-Edit `server/.env`:
-
-```bash
-# Transcription - local GPU service
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=http://host.docker.internal:8000
-TRANSCRIPT_MODAL_API_KEY=dev-key-12345
-
-# Diarization - local GPU service
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=http://host.docker.internal:8000
-DIARIZATION_MODAL_API_KEY=dev-key-12345
-```
-
-Note: Use `host.docker.internal` because Reflector server runs in Docker.
-
-### 4. Restart Reflector server
-
-```bash
-cd server
-docker compose restart server worker
-```
-
-## Testing
-
-### Test transcription
-
-```bash
-curl -s -X POST http://localhost:8000/v1/audio/transcriptions \
-  -H "Authorization: Bearer dev-key-12345" \
-  -F "file=@/path/to/audio.wav" \
-  -F "language=en"
-```
-
-### Test diarization
-
-```bash
-curl -s -X POST "http://localhost:8000/diarize?audio_file_url=<audio-url>" \
-  -H "Authorization: Bearer dev-key-12345"
-```
-
-## Platform Notes
-
-### macOS (ARM)
-
-Docker build fails - CUDA packages are x86_64 only. Use local Python instead:
-```bash
-uv sync
-HF_TOKEN=xxx REFLECTOR_GPU_APIKEY=xxx .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
-```
-
-### Linux with NVIDIA GPU
-
-Docker works with CUDA acceleration:
-```bash
-docker compose up -d
-```
-
-### CPU-only
-
-Works on any platform, just slower. PyTorch auto-detects and falls back to CPU.
-
-## Switching Back to Modal.com
-
-Edit `server/.env`:
-
-```bash
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=<modal-api-key>
-
-DIARIZATION_BACKEND=modal
-DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
-DIARIZATION_MODAL_API_KEY=<modal-api-key>
-```
-
-## Troubleshooting
-
-### "Could not download pyannote pipeline"
- Accept model licenses at HuggingFace (see Prerequisites)
- Verify HF_TOKEN is set and valid
-
-### Service won't start
- Check port 8000 is free: `lsof -i :8000`
- Kill orphan processes if needed
-
-### Transcription returns empty text
- Ensure audio contains speech (not just tones/silence)
- Check audio format is supported (wav, mp3, etc.)
-
-### Deprecation warnings from torchaudio/pyannote
- Safe to ignore - doesn't affect functionality
--- a/gpu/self_hosted/Dockerfile
+++ b/gpu/self_hosted/Dockerfile
@@ -1,46 +0,0 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_NO_CACHE=1
-
-WORKDIR /tmp
-RUN apt-get update \
- && apt-get install -y \
-    ffmpeg \
-    curl \
-    ca-certificates \
-    gnupg \
-    wget \
- && apt-get clean
-# Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
-ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
-RUN dpkg -i /cuda-keyring.deb \
- && rm /cuda-keyring.deb \
- && apt-get update \
- && apt-get install -y --no-install-recommends \
-    cuda-cudart-12-6 \
-    libcublas-12-6 \
-    libcudnn9-cuda-12 \
-    libcudnn9-dev-cuda-12 \
- && apt-get clean \
- && rm -rf /var/lib/apt/lists/*
-ADD https://astral.sh/uv/install.sh /uv-installer.sh
-RUN sh /uv-installer.sh && rm /uv-installer.sh
-ENV PATH="/root/.local/bin/:$PATH"
-ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"
-
-RUN mkdir -p /app
-WORKDIR /app
-COPY pyproject.toml uv.lock /app/
-
-
-COPY ./app /app/app
-COPY ./main.py /app/
-COPY ./runserver.sh /app/
-
-EXPOSE 8000
-
-CMD ["sh", "/app/runserver.sh"]
-
-
--- a/gpu/self_hosted/README.md
+++ b/gpu/self_hosted/README.md
@@ -1,77 +0,0 @@
-# Self-hosted Model API
-
-Run transcription, translation, and diarization services compatible with Reflector's GPU Model API. Works on CPU or GPU.
-
-Environment variables
-
- REFLECTOR_GPU_APIKEY: Optional Bearer token. If unset, auth is disabled.
- HF_TOKEN: Optional. Required for diarization to download pyannote pipelines
-
-Requirements
-
- FFmpeg must be installed and on PATH (used for URL-based and segmented transcription)
- Python 3.12+
- NVIDIA GPU optional. If available, it will be used automatically
-
-Local run
-Set env vars in self_hosted/.env file
-uv sync
-
-uv run uvicorn main:app --host 0.0.0.0 --port 8000
-
-Authentication
-
- If REFLECTOR_GPU_APIKEY is set, include header: Authorization: Bearer <key>
-
-Endpoints
-
- POST /v1/audio/transcriptions
-
-  - multipart/form-data
-  - fields: file (single file) OR files[] (multiple files), language, batch (true/false)
-  - response: single { text, words, filename } or { results: [ ... ] }
-
- POST /v1/audio/transcriptions-from-url
-
-  - application/json
-  - body: { audio_file_url, language, timestamp_offset }
-  - response: { text, words }
-
- POST /translate
-
-  - text: query parameter
-  - body (application/json): { source_language, target_language }
-  - response: { text: { <src>: original, <tgt>: translated } }
-
- POST /diarize
-  - query parameters: audio_file_url, timestamp (optional)
-  - requires HF_TOKEN to be set (for pyannote)
-  - response: { diarization: [ { start, end, speaker } ] }
-
-OpenAPI docs
-
- Visit /docs when the server is running
-
-Docker
-
- Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above
-
-# Setup
-
-[SETUP.md](SETUP.md)
-
-# Conformance tests
-
-## From this directory
-
-TRANSCRIPT_URL=http://localhost:8000 \
-TRANSCRIPT_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_transcript.py
-
-TRANSLATION_URL=http://localhost:8000 \
-TRANSLATION_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_translation.py
-
-DIARIZATION_URL=http://localhost:8000 \
-DIARIZATION_API_KEY=dev-key \
-uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_diarization.py
--- a/gpu/self_hosted/app/auth.py
+++ b/gpu/self_hosted/app/auth.py
@@ -1,19 +0,0 @@
-import os
-
-from fastapi import Depends, HTTPException, status
-from fastapi.security import OAuth2PasswordBearer
-
-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
-
-
-def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-    required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
-    if not required_key:
-        return
-    if apikey == required_key:
-        return
-    raise HTTPException(
-        status_code=status.HTTP_401_UNAUTHORIZED,
-        detail="Invalid API key",
-        headers={"WWW-Authenticate": "Bearer"},
-    )
--- a/gpu/self_hosted/app/config.py
+++ b/gpu/self_hosted/app/config.py
@@ -1,12 +0,0 @@
-from pathlib import Path
-
-SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-SAMPLE_RATE = 16000
-VAD_CONFIG = {
-    "batch_max_duration": 30.0,
-    "silence_padding": 0.5,
-    "window_size": 512,
-}
-
-# App-level paths
-UPLOADS_PATH = Path("/tmp/whisper-uploads")
--- a/gpu/self_hosted/app/factory.py
+++ b/gpu/self_hosted/app/factory.py
@@ -1,30 +0,0 @@
-from contextlib import asynccontextmanager
-
-from fastapi import FastAPI
-
-from .routers.diarization import router as diarization_router
-from .routers.transcription import router as transcription_router
-from .routers.translation import router as translation_router
-from .services.transcriber import WhisperService
-from .services.diarizer import PyannoteDiarizationService
-from .utils import ensure_dirs
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    ensure_dirs()
-    whisper_service = WhisperService()
-    whisper_service.load()
-    app.state.whisper = whisper_service
-    diarization_service = PyannoteDiarizationService()
-    diarization_service.load()
-    app.state.diarizer = diarization_service
-    yield
-
-
-def create_app() -> FastAPI:
-    app = FastAPI(lifespan=lifespan)
-    app.include_router(transcription_router)
-    app.include_router(translation_router)
-    app.include_router(diarization_router)
-    return app
--- a/gpu/self_hosted/app/routers/diarization.py
+++ b/gpu/self_hosted/app/routers/diarization.py
@@ -1,30 +0,0 @@
-from typing import List
-
-from fastapi import APIRouter, Depends, Request
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-from ..services.diarizer import PyannoteDiarizationService
-from ..utils import download_audio_file
-
-router = APIRouter(tags=["diarization"])
-
-
-class DiarizationSegment(BaseModel):
-    start: float
-    end: float
-    speaker: int
-
-
-class DiarizationResponse(BaseModel):
-    diarization: List[DiarizationSegment]
-
-
-@router.post(
-    "/diarize", dependencies=[Depends(apikey_auth)], response_model=DiarizationResponse
-)
-def diarize(request: Request, audio_file_url: str, timestamp: float = 0.0):
-    with download_audio_file(audio_file_url) as (file_path, _ext):
-        file_path = str(file_path)
-        diarizer: PyannoteDiarizationService = request.app.state.diarizer
-        return diarizer.diarize_file(file_path, timestamp=timestamp)
--- a/gpu/self_hosted/app/routers/transcription.py
+++ b/gpu/self_hosted/app/routers/transcription.py
@@ -1,109 +0,0 @@
-import uuid
-from typing import Optional, Union
-
-from fastapi import APIRouter, Body, Depends, Form, HTTPException, Request, UploadFile
-from pydantic import BaseModel
-from pathlib import Path
-from ..auth import apikey_auth
-from ..config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
-from ..services.transcriber import MODEL_NAME
-from ..utils import cleanup_uploaded_files, download_audio_file
-
-router = APIRouter(prefix="/v1/audio", tags=["transcription"])
-
-
-class WordTiming(BaseModel):
-    word: str
-    start: float
-    end: float
-
-
-class TranscriptResult(BaseModel):
-    text: str
-    words: list[WordTiming]
-    filename: Optional[str] = None
-
-
-class TranscriptBatchResponse(BaseModel):
-    results: list[TranscriptResult]
-
-
-@router.post(
-    "/transcriptions",
-    dependencies=[Depends(apikey_auth)],
-    response_model=Union[TranscriptResult, TranscriptBatchResponse],
-)
-def transcribe(
-    request: Request,
-    file: UploadFile = None,
-    files: list[UploadFile] | None = None,
-    model: str = Form(MODEL_NAME),
-    language: str = Form("en"),
-    batch: bool = Form(False),
-):
-    service = request.app.state.whisper
-    if not file and not files:
-        raise HTTPException(
-            status_code=400, detail="Either 'file' or 'files' parameter is required"
-        )
-    if batch and not files:
-        raise HTTPException(
-            status_code=400, detail="Batch transcription requires 'files'"
-        )
-
-    upload_files = [file] if file else files
-
-    uploaded_paths: list[Path] = []
-    with cleanup_uploaded_files(uploaded_paths):
-        for upload_file in upload_files:
-            audio_suffix = upload_file.filename.split(".")[-1].lower()
-            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
-                raise HTTPException(
-                    status_code=400,
-                    detail=(
-                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-                    ),
-                )
-            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-            file_path = UPLOADS_PATH / unique_filename
-            with open(file_path, "wb") as f:
-                content = upload_file.file.read()
-                f.write(content)
-            uploaded_paths.append(file_path)
-
-        if batch and len(upload_files) > 1:
-            results = []
-            for path in uploaded_paths:
-                result = service.transcribe_file(str(path), language=language)
-                result["filename"] = path.name
-                results.append(result)
-            return {"results": results}
-
-        results = []
-        for path in uploaded_paths:
-            result = service.transcribe_file(str(path), language=language)
-            result["filename"] = path.name
-            results.append(result)
-
-        return {"results": results} if len(results) > 1 else results[0]
-
-
-@router.post(
-    "/transcriptions-from-url",
-    dependencies=[Depends(apikey_auth)],
-    response_model=TranscriptResult,
-)
-def transcribe_from_url(
-    request: Request,
-    audio_file_url: str = Body(..., description="URL of the audio file to transcribe"),
-    model: str = Body(MODEL_NAME),
-    language: str = Body("en"),
-    timestamp_offset: float = Body(0.0),
-):
-    service = request.app.state.whisper
-    with download_audio_file(audio_file_url) as (file_path, _ext):
-        file_path = str(file_path)
-        result = service.transcribe_vad_url_segment(
-            file_path=file_path, timestamp_offset=timestamp_offset, language=language
-        )
-        return result
--- a/gpu/self_hosted/app/routers/translation.py
+++ b/gpu/self_hosted/app/routers/translation.py
@@ -1,28 +0,0 @@
-from typing import Dict
-
-from fastapi import APIRouter, Body, Depends
-from pydantic import BaseModel
-
-from ..auth import apikey_auth
-from ..services.translator import TextTranslatorService
-
-router = APIRouter(tags=["translation"])
-
-translator = TextTranslatorService()
-
-
-class TranslationResponse(BaseModel):
-    text: Dict[str, str]
-
-
-@router.post(
-    "/translate",
-    dependencies=[Depends(apikey_auth)],
-    response_model=TranslationResponse,
-)
-def translate(
-    text: str,
-    source_language: str = Body("en"),
-    target_language: str = Body("fr"),
-):
-    return translator.translate(text, source_language, target_language)
--- a/gpu/self_hosted/app/services/diarizer.py
+++ b/gpu/self_hosted/app/services/diarizer.py
@@ -1,42 +0,0 @@
-import os
-import threading
-
-import torch
-import torchaudio
-from pyannote.audio import Pipeline
-
-
-class PyannoteDiarizationService:
-    def __init__(self):
-        self._pipeline = None
-        self._device = "cpu"
-        self._lock = threading.Lock()
-
-    def load(self):
-        self._device = "cuda" if torch.cuda.is_available() else "cpu"
-        self._pipeline = Pipeline.from_pretrained(
-            "pyannote/speaker-diarization-3.1",
-            use_auth_token=os.environ.get("HF_TOKEN"),
-        )
-        self._pipeline.to(torch.device(self._device))
-
-    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
-        if self._pipeline is None:
-            self.load()
-        waveform, sample_rate = torchaudio.load(file_path)
-        with self._lock:
-            diarization = self._pipeline(
-                {"waveform": waveform, "sample_rate": sample_rate}
-            )
-        words = []
-        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
-            words.append(
-                {
-                    "start": round(timestamp + diarization_segment.start, 3),
-                    "end": round(timestamp + diarization_segment.end, 3),
-                    "speaker": int(speaker[-2:])
-                    if speaker and speaker[-2:].isdigit()
-                    else 0,
-                }
-            )
-        return {"diarization": words}
--- a/gpu/self_hosted/app/services/transcriber.py
+++ b/gpu/self_hosted/app/services/transcriber.py
@@ -1,217 +0,0 @@
-import os
-import shutil
-import subprocess
-import threading
-from typing import Generator
-
-import faster_whisper
-import librosa
-import numpy as np
-import torch
-from fastapi import HTTPException
-from silero_vad import VADIterator, load_silero_vad
-
-from ..config import SAMPLE_RATE, VAD_CONFIG
-
-# Whisper configuration (service-local defaults)
-MODEL_NAME = "large-v2"
-# None delegates compute type to runtime: float16 on CUDA, int8 on CPU
-MODEL_COMPUTE_TYPE = None
-MODEL_NUM_WORKERS = 1
-CACHE_PATH = os.path.join(os.path.expanduser("~"), ".cache", "reflector-whisper")
-from ..utils import NoStdStreams
-
-
-class WhisperService:
-    def __init__(self):
-        self.model = None
-        self.device = "cpu"
-        self.lock = threading.Lock()
-
-    def load(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        compute_type = MODEL_COMPUTE_TYPE or (
-            "float16" if self.device == "cuda" else "int8"
-        )
-        self.model = faster_whisper.WhisperModel(
-            MODEL_NAME,
-            device=self.device,
-            compute_type=compute_type,
-            num_workers=MODEL_NUM_WORKERS,
-            download_root=CACHE_PATH,
-        )
-
-    def pad_audio(self, audio_array, sample_rate: int = SAMPLE_RATE):
-        audio_duration = len(audio_array) / sample_rate
-        if audio_duration < VAD_CONFIG["silence_padding"]:
-            silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
-            silence = np.zeros(silence_samples, dtype=np.float32)
-            return np.concatenate([audio_array, silence])
-        return audio_array
-
-    def enforce_word_timing_constraints(self, words: list[dict]) -> list[dict]:
-        if len(words) <= 1:
-            return words
-        enforced: list[dict] = []
-        for i, word in enumerate(words):
-            current = dict(word)
-            if i < len(words) - 1:
-                next_start = words[i + 1]["start"]
-                if current["end"] > next_start:
-                    current["end"] = next_start
-            enforced.append(current)
-        return enforced
-
-    def transcribe_file(self, file_path: str, language: str = "en") -> dict:
-        input_for_model: str | "object" = file_path
-        try:
-            audio_array, _sample_rate = librosa.load(
-                file_path, sr=SAMPLE_RATE, mono=True
-            )
-            if len(audio_array) / float(SAMPLE_RATE) < VAD_CONFIG["silence_padding"]:
-                input_for_model = self.pad_audio(audio_array, SAMPLE_RATE)
-        except Exception:
-            pass
-
-        with self.lock:
-            with NoStdStreams():
-                segments, _ = self.model.transcribe(
-                    input_for_model,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-
-        segments = list(segments)
-        text = "".join(segment.text for segment in segments).strip()
-        words = [
-            {
-                "word": word.word,
-                "start": round(float(word.start), 2),
-                "end": round(float(word.end), 2),
-            }
-            for segment in segments
-            for word in segment.words
-        ]
-        words = self.enforce_word_timing_constraints(words)
-        return {"text": text, "words": words}
-
-    def transcribe_vad_url_segment(
-        self, file_path: str, timestamp_offset: float = 0.0, language: str = "en"
-    ) -> dict:
-        def load_audio_via_ffmpeg(input_path: str, sample_rate: int) -> np.ndarray:
-            ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg"
-            cmd = [
-                ffmpeg_bin,
-                "-nostdin",
-                "-threads",
-                "1",
-                "-i",
-                input_path,
-                "-f",
-                "f32le",
-                "-acodec",
-                "pcm_f32le",
-                "-ac",
-                "1",
-                "-ar",
-                str(sample_rate),
-                "pipe:1",
-            ]
-            try:
-                proc = subprocess.run(
-                    cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
-                )
-            except Exception as e:
-                raise HTTPException(status_code=400, detail=f"ffmpeg failed: {e}")
-            audio = np.frombuffer(proc.stdout, dtype=np.float32)
-            return audio
-
-        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
-        # If you modify this function, you MUST update all copies:
-        #   - gpu/modal_deployments/reflector_transcriber.py
-        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-        #   - gpu/self_hosted/app/services/transcriber.py (this file)
-        def vad_segments(
-            audio_array,
-            sample_rate: int = SAMPLE_RATE,
-            window_size: int = VAD_CONFIG["window_size"],
-        ) -> Generator[tuple[float, float], None, None]:
-            vad_model = load_silero_vad(onnx=False)
-            iterator = VADIterator(vad_model, sampling_rate=sample_rate)
-            start = None
-            for i in range(0, len(audio_array), window_size):
-                chunk = audio_array[i : i + window_size]
-                if len(chunk) < window_size:
-                    chunk = np.pad(
-                        chunk, (0, window_size - len(chunk)), mode="constant"
-                    )
-                speech = iterator(chunk)
-                if not speech:
-                    continue
-                if "start" in speech:
-                    start = speech["start"]
-                    continue
-                if "end" in speech and start is not None:
-                    end = speech["end"]
-                    yield (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE))
-                    start = None
-            # Handle case where audio ends while speech is still active
-            if start is not None:
-                audio_duration = len(audio_array) / float(sample_rate)
-                yield (start / float(SAMPLE_RATE), audio_duration)
-            iterator.reset_states()
-
-        audio_array = load_audio_via_ffmpeg(file_path, SAMPLE_RATE)
-
-        merged_batches: list[tuple[float, float]] = []
-        batch_start = None
-        batch_end = None
-        max_duration = VAD_CONFIG["batch_max_duration"]
-        for seg_start, seg_end in vad_segments(audio_array):
-            if batch_start is None:
-                batch_start, batch_end = seg_start, seg_end
-                continue
-            if seg_end - batch_start <= max_duration:
-                batch_end = seg_end
-            else:
-                merged_batches.append((batch_start, batch_end))
-                batch_start, batch_end = seg_start, seg_end
-        if batch_start is not None and batch_end is not None:
-            merged_batches.append((batch_start, batch_end))
-
-        all_text = []
-        all_words = []
-        for start_time, end_time in merged_batches:
-            s_idx = int(start_time * SAMPLE_RATE)
-            e_idx = int(end_time * SAMPLE_RATE)
-            segment = audio_array[s_idx:e_idx]
-            segment = self.pad_audio(segment, SAMPLE_RATE)
-            with self.lock:
-                segments, _ = self.model.transcribe(
-                    segment,
-                    language=language,
-                    beam_size=5,
-                    word_timestamps=True,
-                    vad_filter=True,
-                    vad_parameters={"min_silence_duration_ms": 500},
-                )
-            segments = list(segments)
-            text = "".join(seg.text for seg in segments).strip()
-            words = [
-                {
-                    "word": w.word,
-                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
-                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
-                }
-                for seg in segments
-                for w in seg.words
-            ]
-            if text:
-                all_text.append(text)
-            all_words.extend(words)
-
-        all_words = self.enforce_word_timing_constraints(all_words)
-        return {"text": " ".join(all_text), "words": all_words}
--- a/gpu/self_hosted/app/services/translator.py
+++ b/gpu/self_hosted/app/services/translator.py
@@ -1,44 +0,0 @@
-import threading
-
-from transformers import MarianMTModel, MarianTokenizer, pipeline
-
-
-class TextTranslatorService:
-    """Simple text-to-text translator using HuggingFace MarianMT models.
-
-    This mirrors the modal translator API shape but uses text translation only.
-    """
-
-    def __init__(self):
-        self._pipeline = None
-        self._lock = threading.Lock()
-
-    def load(self, source_language: str = "en", target_language: str = "fr"):
-        # Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr
-        model_name = self._resolve_model_name(source_language, target_language)
-        tokenizer = MarianTokenizer.from_pretrained(model_name)
-        model = MarianMTModel.from_pretrained(model_name)
-        self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
-
-    def _resolve_model_name(self, src: str, tgt: str) -> str:
-        # Minimal mapping; extend as needed
-        pair = (src.lower(), tgt.lower())
-        mapping = {
-            ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
-            ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
-            ("en", "es"): "Helsinki-NLP/opus-mt-en-es",
-            ("es", "en"): "Helsinki-NLP/opus-mt-es-en",
-            ("en", "de"): "Helsinki-NLP/opus-mt-en-de",
-            ("de", "en"): "Helsinki-NLP/opus-mt-de-en",
-        }
-        return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
-
-    def translate(self, text: str, source_language: str, target_language: str) -> dict:
-        if self._pipeline is None:
-            self.load(source_language, target_language)
-        with self._lock:
-            results = self._pipeline(
-                text, src_lang=source_language, tgt_lang=target_language
-            )
-        translated = results[0]["translation_text"] if results else ""
-        return {"text": {source_language: text, target_language: translated}}
--- a/gpu/self_hosted/app/utils.py
+++ b/gpu/self_hosted/app/utils.py
@@ -1,115 +0,0 @@
-import logging
-import os
-import sys
-import uuid
-from contextlib import contextmanager
-from typing import Mapping
-from urllib.parse import urlparse
-from pathlib import Path
-
-import requests
-from fastapi import HTTPException
-
-from .config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
-
-logger = logging.getLogger(__name__)
-
-
-class NoStdStreams:
-    def __init__(self):
-        self.devnull = open(os.devnull, "w")
-
-    def __enter__(self):
-        self._stdout, self._stderr = sys.stdout, sys.stderr
-        self._stdout.flush()
-        self._stderr.flush()
-        sys.stdout, sys.stderr = self.devnull, self.devnull
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        sys.stdout, sys.stderr = self._stdout, self._stderr
-        self.devnull.close()
-
-
-def ensure_dirs():
-    UPLOADS_PATH.mkdir(parents=True, exist_ok=True)
-
-
-# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
-# If you modify the audio format detection logic, you MUST update all copies:
-#   - gpu/self_hosted/app/utils.py (this file)
-#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
-#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
-#   - gpu/modal_deployments/reflector_diarizer.py
-def detect_audio_format(url: str, headers: Mapping[str, str]) -> str:
-    url_path = urlparse(url).path
-    for ext in SUPPORTED_FILE_EXTENSIONS:
-        if url_path.lower().endswith(f".{ext}"):
-            return ext
-
-    content_type = headers.get("content-type", "").lower()
-    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
-        return "mp3"
-    if "audio/wav" in content_type:
-        return "wav"
-    if "audio/mp4" in content_type:
-        return "mp4"
-    if "audio/webm" in content_type or "video/webm" in content_type:
-        return "webm"
-
-    raise HTTPException(
-        status_code=400,
-        detail=(
-            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
-        ),
-    )
-
-
-def download_audio_to_uploads(audio_file_url: str) -> tuple[Path, str]:
-    response = requests.head(audio_file_url, allow_redirects=True)
-    if response.status_code == 404:
-        raise HTTPException(status_code=404, detail="Audio file not found")
-
-    response = requests.get(audio_file_url, allow_redirects=True)
-    response.raise_for_status()
-
-    audio_suffix = detect_audio_format(audio_file_url, response.headers)
-    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
-    file_path: Path = UPLOADS_PATH / unique_filename
-
-    with open(file_path, "wb") as f:
-        f.write(response.content)
-
-    return file_path, audio_suffix
-
-
-@contextmanager
-def download_audio_file(audio_file_url: str):
-    """Download an audio file to UPLOADS_PATH and remove it after use.
-
-    Yields (file_path: Path, audio_suffix: str).
-    """
-    file_path, audio_suffix = download_audio_to_uploads(audio_file_url)
-    try:
-        yield file_path, audio_suffix
-    finally:
-        try:
-            file_path.unlink(missing_ok=True)
-        except Exception as e:
-            logger.error("Error deleting temporary file %s: %s", file_path, e)
-
-
-@contextmanager
-def cleanup_uploaded_files(file_paths: list[Path]):
-    """Ensure provided file paths are removed after use.
-
-    The provided list can be populated inside the context; all present entries
-    at exit will be deleted.
-    """
-    try:
-        yield file_paths
-    finally:
-        for path in list(file_paths):
-            try:
-                path.unlink(missing_ok=True)
-            except Exception as e:
-                logger.error("Error deleting temporary file %s: %s", path, e)
--- a/gpu/self_hosted/compose.yml
+++ b/gpu/self_hosted/compose.yml
@@ -1,10 +0,0 @@
-services:
-  reflector_gpu:
-    build:
-      context: .
-    ports:
-      - "8000:8000"
-    env_file:
-      - .env
-    volumes:
-      - ./cache:/root/.cache
--- a/gpu/self_hosted/main.py
+++ b/gpu/self_hosted/main.py
@@ -1,3 +0,0 @@
-from app.factory import create_app
-
-app = create_app()
--- a/gpu/self_hosted/pyproject.toml
+++ b/gpu/self_hosted/pyproject.toml
@@ -1,19 +0,0 @@
-[project]
-name = "reflector-gpu"
-version = "0.1.0"
-description = "Self-hosted GPU service for speech transcription, diarization, and translation via FastAPI."
-readme = "README.md"
-requires-python = ">=3.12"
-dependencies = [
-    "fastapi[standard]>=0.116.1",
-    "uvicorn[standard]>=0.30.0",
-    "torch>=2.3.0",
-    "faster-whisper>=1.1.0",
-    "librosa==0.10.1",
-    "numpy<2",
-    "silero-vad==5.1.0",
-    "transformers>=4.35.0",
-    "sentencepiece",
-    "pyannote.audio==3.1.0",
-    "torchaudio>=2.3.0",
-]
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Mathieu Virbel	311d453e41	feat: implement frontend for calendar integration (Phase 3 & 4) ## Frontend Implementation ### Meeting Selection & Management - Created MeetingSelection component for choosing between multiple active meetings - Shows both active meetings and upcoming calendar events (30 min ahead) - Displays meeting metadata with privacy controls (owner-only details) - Supports creation of unscheduled meetings alongside calendar meetings ### Waiting Room - Added waiting page for users joining before scheduled start time - Shows countdown timer until meeting begins - Auto-transitions to meeting when calendar event becomes active - Handles early joining with proper routing ### Meeting Info Panel - Created collapsible info panel showing meeting details - Displays calendar metadata (title, description, attendees) - Shows participant count and duration - Privacy-aware: sensitive info only visible to room owners ### ICS Configuration UI - Integrated ICS settings into room configuration dialog - Test connection functionality with immediate feedback - Manual sync trigger with detailed results - Shows last sync time and ETag for monitoring - Configurable sync intervals (1 min to 1 hour) ### Routing & Navigation - New /room/{roomName} route for meeting selection - Waiting room at /room/{roomName}/wait?eventId={id} - Classic room page at /{roomName} with meeting info - Uses sessionStorage to pass selected meeting between pages ### API Integration - Added new endpoints for active/upcoming meetings - Regenerated TypeScript client with latest OpenAPI spec - Proper error handling and loading states - Auto-refresh every 30 seconds for live updates ### UI/UX Improvements - Color-coded badges for meeting status - Attendee status indicators (accepted/declined/tentative) - Responsive design with Chakra UI components - Clear visual hierarchy between active and upcoming meetings - Smart truncation for long attendee lists This completes the frontend implementation for calendar integration, enabling users to seamlessly join scheduled meetings from their calendar applications.	2025-08-18 19:29:56 -06:00
Mathieu Virbel	f286f0882c	feat: implement Phase 2 - Multiple active meetings per room with grace period This commit adds support for multiple concurrent meetings per room, implementing grace period logic and improved meeting lifecycle management for calendar integration. ## Database Changes - Remove unique constraint preventing multiple active meetings per room - Add last_participant_left_at field to track when meeting becomes empty - Add grace_period_minutes field (default: 15) for configurable grace period ## Meeting Controller Enhancements - Add get_all_active_for_room() to retrieve all active meetings for a room - Add get_active_by_calendar_event() to find meetings by calendar event ID - Maintain backward compatibility with existing get_active() method ## New API Endpoints - GET /rooms/{room_name}/meetings/active - List all active meetings - POST /rooms/{room_name}/meetings/{meeting_id}/join - Join specific meeting ## Meeting Lifecycle Improvements - 15-minute grace period after last participant leaves - Automatic reactivation when participant rejoins during grace period - Force close calendar meetings 30 minutes after scheduled end time - Update process_meetings task to handle multiple active meetings ## Whereby Integration - Clear grace period when participants join via webhook events - Track participant count for grace period management ## Testing - Add comprehensive tests for multiple active meetings - Test grace period behavior and participant rejoin scenarios - Test calendar meeting force closure logic - All 5 new tests passing This enables proper calendar integration with overlapping meetings while preventing accidental meeting closures through the grace period mechanism.	2025-08-18 19:03:41 -06:00
Mathieu Virbel	ffcafb3bf2	feat: add Celery background tasks for ICS sync	2025-08-18 17:22:41 -06:00
Mathieu Virbel	27075d840c	feat: add ICS calendar API endpoints for room configuration and sync	2025-08-18 17:03:23 -06:00
Mathieu Virbel	30b5cd45e3	feat: calendar integration	2025-08-18 16:51:30 -06:00