feat: add background information field to room model

- Add background_information field to Room database table and model - Create database migration for the new field - Update API schemas (CreateRoom, UpdateRoom) to handle background_information - Integrate room context into AI summarization prompts - Add background_information field to frontend room form - Update TypeScript types from regenerated OpenAPI spec The background information will be used to provide context for AI-generated summaries, helping create more appropriate and relevant meeting summaries. 🤖 Generated with [opencode](https://opencode.ai) Co-Authored-By: opencode <noreply@opencode.ai>
2025-12-20 20:29:06 +00:00 · 2025-07-29 01:53:13 +00:00
419 changed files with 20537 additions and 56203 deletions
--- a/.github/pull_request_template.md
+++ b/.github/pull_request_template.md
@@ -1,21 +1,19 @@
-<!--- Provide a general summary of your changes in the Title above -->
+## ⚠️ Insert the PR TITLE replacing this text ⚠️

-## Description
-<!--- Describe your changes in detail -->
+⚠️ Describe your PR replacing this text. Post screenshots or videos whenever possible. ⚠️

-## Related Issue
-<!--- This project only accepts pull requests related to open issues -->
-<!--- If suggesting a new feature or change, please discuss it in an issue first -->
-<!--- If fixing a bug, there should be an issue describing it with steps to reproduce -->
-<!--- Please link to the issue here: -->
+### Checklist

-## Motivation and Context
-<!--- Why is this change required? What problem does it solve? -->
-<!--- If it fixes an open issue, please link to the issue here. -->
+ - [ ] My branch is updated with main (mandatory)
+ - [ ] I wrote unit tests for this (if applies)
+ - [ ] I have included migrations and tested them locally (if applies)
+ - [ ] I have manually tested this feature locally

-## How Has This Been Tested?
-<!--- Please describe in detail how you tested your changes. -->
-<!--- Include details of your testing environment, and the tests you ran to -->
-<!--- see how your change affects other areas of the code, etc. -->
+> IMPORTANT: Remember that you are responsible for merging this PR after it's been reviewed, and once deployed
+> you should perform manual testing to make sure everything went smoothly.
+
+### Urgency
+
+ - [ ] Urgent (deploy ASAP)
+ - [ ] Non-urgent (deploying in next release is ok)

-## Screenshots (if appropriate):
--- a/.github/workflows/conventional_commit_pr.yml
+++ b/.github/workflows/conventional_commit_pr.yml
@@ -0,0 +1,19 @@
+name: Conventional commit PR
+
+on: [pull_request]
+
+jobs:
+  cog_check_job:
+    runs-on: ubuntu-latest
+    name: check conventional commit compliance
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          # pick the pr HEAD instead of the merge commit
+          ref: ${{ github.event.pull_request.head.sha }}
+
+      - name: Conventional commit check
+        uses: cocogitto/cocogitto-action@v3
+        with:
+          check-latest-tag-only: true
--- 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/**"
@@ -19,43 +17,10 @@ on:
 jobs:
  test-migrations:
    runs-on: ubuntu-latest
-    concurrency:
-      group: db-ubuntu-latest-${{ github.ref }}
-      cancel-in-progress: true
-    services:
-      postgres:
-        image: postgres:17
-        env:
-          POSTGRES_USER: reflector
-          POSTGRES_PASSWORD: reflector
-          POSTGRES_DB: reflector
-        ports:
-          - 5432:5432
-        options: >-
-          --health-cmd pg_isready -h 127.0.0.1 -p 5432
-          --health-interval 10s
-          --health-timeout 5s
-          --health-retries 5
-
-    env:
-      DATABASE_URL: postgresql://reflector:reflector@localhost:5432/reflector

    steps:
      - uses: actions/checkout@v4

-      - name: Install PostgreSQL client
-        run: sudo apt-get update && sudo apt-get install -y postgresql-client | cat
-
-      - name: Wait for Postgres
-        run: |
-          for i in {1..30}; do
-            if pg_isready -h localhost -p 5432; then
-              echo "Postgres is ready"
-              break
-            fi
-            echo "Waiting for Postgres... ($i)" && sleep 1
-          done
-
      - name: Install uv
        uses: astral-sh/setup-uv@v3
        with:
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -8,30 +8,18 @@ env:
  ECR_REPOSITORY: reflector

 jobs:
-  build:
-    strategy:
-      matrix:
-        include:
-          - platform: linux/amd64
-            runner: linux-amd64
-            arch: amd64
-          - platform: linux/arm64
-            runner: linux-arm64
-            arch: arm64
-
-    runs-on: ${{ matrix.runner }}
+  deploy:
+    runs-on: ubuntu-latest

    permissions:
+      deployments: write
      contents: read

-    outputs:
-      registry: ${{ steps.login-ecr.outputs.registry }}
-
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3

      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v4
+        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 }}
@@ -39,52 +27,21 @@ jobs:

      - name: Login to Amazon ECR
        id: login-ecr
-        uses: aws-actions/amazon-ecr-login@v2
+        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@v3
+        uses: docker/setup-buildx-action@v2

-      - name: Build and push ${{ matrix.arch }}
-        uses: docker/build-push-action@v5
+      - name: Build and push
+        id: docker_build
+        uses: docker/build-push-action@v4
        with:
          context: server
-          platforms: ${{ matrix.platform }}
+          platforms: linux/amd64,linux/arm64
          push: true
-          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest-${{ matrix.arch }}
-          cache-from: type=gha,scope=${{ matrix.arch }}
-          cache-to: type=gha,mode=max,scope=${{ matrix.arch }}
-          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
-          provenance: false
-
-  create-manifest:
-    runs-on: ubuntu-latest
-    needs: [build]
-
-    permissions:
-      deployments: write
-      contents: read
-
-    steps:
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v4
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ env.AWS_REGION }}
-
-      - name: Login to Amazon ECR
-        uses: aws-actions/amazon-ecr-login@v2
-
-      - name: Create and push multi-arch manifest
-        run: |
-          # Get the registry URL (since we can't easily access job outputs in matrix)
-          ECR_REGISTRY=$(aws ecr describe-registry --query 'registryId' --output text).dkr.ecr.${{ env.AWS_REGION }}.amazonaws.com
-
-          docker manifest create \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-amd64 \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-arm64
-
-          docker manifest push $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest
-
-          echo "✅ Multi-arch manifest pushed: $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest"
+          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
--- a/.github/workflows/docker-frontend.yml
+++ b/.github/workflows/docker-frontend.yml
@@ -1,57 +0,0 @@
-name: Build and Push Frontend Docker Image
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - 'www/**'
-      - '.github/workflows/docker-frontend.yml'
-  workflow_dispatch:
-
-env:
-  REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}-frontend
-
-jobs:
-  build-and-push:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: read
-      packages: write
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Log in to GitHub Container Registry
-        uses: docker/login-action@v3
-        with:
-          registry: ${{ env.REGISTRY }}
-          username: ${{ github.actor }}
-          password: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Extract metadata
-        id: meta
-        uses: docker/metadata-action@v5
-        with:
-          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
-          tags: |
-            type=ref,event=branch
-            type=sha,prefix={{branch}}-
-            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: ./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
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -1,24 +0,0 @@
-name: pre-commit
-
-on:
-  pull_request:
-  push:
-    branches: [main]
-
-jobs:
-  pre-commit:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v5
-      - uses: actions/setup-python@v5
-      - uses: pnpm/action-setup@v4
-        with:
-          version: 10
-      - uses: actions/setup-node@v4
-        with:
-          node-version: 22
-          cache: "pnpm"
-          cache-dependency-path: "www/pnpm-lock.yaml"
-      - name: Install dependencies
-        run: cd www && pnpm install --frozen-lockfile
-      - uses: pre-commit/action@v3.0.1
--- 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
@@ -13,8 +13,3 @@ restart-dev.sh
 data/
 www/REFACTOR.md
 www/reload-frontend
-server/test.sqlite
-CLAUDE.local.md
-www/.env.development
-www/.env.production
-.playwright-mcp
--- a/.gitleaksignore
+++ b/.gitleaksignore
@@ -1 +0,0 @@
-b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,10 +3,10 @@
 repos:
  - repo: local
    hooks:
-      - id: format
-        name: run format
+      - id: yarn-format
+        name: run yarn format
        language: system
-        entry: bash -c 'cd www && pnpm format'
+        entry: bash -c 'cd www && yarn format'
        pass_filenames: false
        files: ^www/

@@ -15,20 +15,25 @@ repos:
    hooks:
      - id: debug-statements
      - id: trailing-whitespace
+        exclude: ^server/trials
      - id: detect-private-key

+  - repo: https://github.com/psf/black
+    rev: 24.1.1
+    hooks:
+      - id: black
+        files: ^server/(reflector|tests)/
+
+  - repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        name: isort (python)
+        files: ^server/(gpu|evaluate|reflector)/
+        args: [ "--profile", "black", "--filter-files" ]
+
  - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.2
+    rev: v0.6.5
    hooks:
      - id: ruff
-        args:
-          - --fix
-          # Uses select rules from server/pyproject.toml
-        files: ^server/
-      - id: ruff-format
-        files: ^server/
-
-  - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.28.0
-    hooks:
-      - id: gitleaks
+        files: ^server/(reflector|tests)/
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,214 +1,5 @@
 # Changelog

-## [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)
-
-
-### Bug Fixes
-
-* delayed waveform loading ([#538](https://github.com/Monadical-SAS/reflector/issues/538)) ([ef64146](https://github.com/Monadical-SAS/reflector/commit/ef64146325d03f64dd9a1fe40234fb3e7e957ae2))
-
-## [0.6.0](https://github.com/Monadical-SAS/reflector/compare/v0.5.0...v0.6.0) (2025-08-05)
-
-
-### ⚠ BREAKING CHANGES
-
-* Configuration keys have changed. Update your .env file:
-    - TRANSCRIPT_MODAL_API_KEY → TRANSCRIPT_API_KEY
-    - LLM_MODAL_API_KEY → (removed, use TRANSCRIPT_API_KEY)
-    - Add DIARIZATION_API_KEY and TRANSLATE_API_KEY if using those services
-
-### Features
-
-* implement service-specific Modal API keys with auto processor pattern ([#528](https://github.com/Monadical-SAS/reflector/issues/528)) ([650befb](https://github.com/Monadical-SAS/reflector/commit/650befb291c47a1f49e94a01ab37d8fdfcd2b65d))
-* use llamaindex everywhere ([#525](https://github.com/Monadical-SAS/reflector/issues/525)) ([3141d17](https://github.com/Monadical-SAS/reflector/commit/3141d172bc4d3b3d533370c8e6e351ea762169bf))
-
-
-### Miscellaneous Chores
-
-* **main:** release 0.6.0 ([ecdbf00](https://github.com/Monadical-SAS/reflector/commit/ecdbf003ea2476c3e95fd231adaeb852f2943df0))
-
-## [0.5.0](https://github.com/Monadical-SAS/reflector/compare/v0.4.0...v0.5.0) (2025-07-31)
-
-
-### Features
-
-* new summary using phi-4 and llama-index ([#519](https://github.com/Monadical-SAS/reflector/issues/519)) ([1bf9ce0](https://github.com/Monadical-SAS/reflector/commit/1bf9ce07c12f87f89e68a1dbb3b2c96c5ee62466))
-
-
-### Bug Fixes
-
-* remove unused settings and utils files ([#522](https://github.com/Monadical-SAS/reflector/issues/522)) ([2af4790](https://github.com/Monadical-SAS/reflector/commit/2af4790e4be9e588f282fbc1bb171c88a03d6479))
-
 ## [0.4.0](https://github.com/Monadical-SAS/reflector/compare/v0.3.2...v0.4.0) (2025-07-25)


--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -62,28 +62,29 @@ uv run python -m reflector.tools.process path/to/audio.wav
 **Setup:**
 ```bash
 # Install dependencies
-pnpm install
+yarn install

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

 **Development:**
 ```bash
 # Start development server
-pnpm dev
+yarn dev

 # Generate TypeScript API client from OpenAPI spec
-pnpm openapi
+yarn openapi

 # Lint code
-pnpm lint
+yarn lint

 # Format code
-pnpm format
+yarn format

 # Build for production
-pnpm build
+yarn build
 ```

 ### Docker Compose (Full Stack)
@@ -143,15 +144,13 @@ All endpoints prefixed `/v1/`:
 **Backend** (`server/.env`):
 - `DATABASE_URL` - Database connection string
 - `REDIS_URL` - Redis broker for Celery
- `TRANSCRIPT_BACKEND=modal` + `TRANSCRIPT_MODAL_API_KEY` - Modal.com transcription
- `DIARIZATION_BACKEND=modal` + `DIARIZATION_MODAL_API_KEY` - Modal.com diarization
- `TRANSLATION_BACKEND=modal` + `TRANSLATION_MODAL_API_KEY` - Modal.com translation
+- `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET` - Modal.com GPU processing
 - `WHEREBY_API_KEY` - Video platform integration
 - `REFLECTOR_AUTH_BACKEND` - Authentication method (none, jwt)

 **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
@@ -173,7 +172,3 @@ Modal.com integration for scalable ML processing:
 - **Audio Routing**: Use BlackHole (Mac) for merging multiple audio sources
 - **WebRTC**: Ensure proper CORS configuration for cross-origin streaming
 - **Database**: Run `uv run alembic upgrade head` after pulling schema changes
-
-## Pipeline/worker related info
-
-If you need to do any worker/pipeline related work, search for "Pipeline" classes and their "create" or "build" methods to find the main processor sequence. Look for task orchestration patterns (like "chord", "group", or "chain") to identify the post-processing flow with parallel execution chains. This will give you abstract vision on how processing pipeling is organized.
--- a/CODER_BRIEFING.md
+++ b/CODER_BRIEFING.md
@@ -1,345 +0,0 @@
-# Multi-Provider Video Platform Implementation - Coder Briefing
-
-## Your Mission
-
-Implement multi-provider video platform support in Reflector, allowing the system to work with both Whereby and Daily.co video conferencing providers. The goal is to abstract the current Whereby-only implementation and add Daily.co as a second provider, with the ability to switch between them via environment variables.
-
-**Branch:** `igor/dailico-2` (you're already on it)
-
-**Estimated Time:** 12-16 hours (senior engineer)
-
-**Complexity:** Medium-High (requires careful integration with existing codebase)
-
---
-
-## What You Have
-
-### 1. **PLAN.md** - Your Technical Specification (2,452 lines)
-   - Complete step-by-step implementation guide
-   - All code examples you need
-   - Architecture diagrams and design rationale
-   - Testing strategy and success metrics
-   - **Read this first** to understand the overall approach
-
-### 2. **IMPLEMENTATION_GUIDE.md** - Your Practical Guide
-   - What to copy vs. adapt vs. rewrite
-   - Common pitfalls and how to avoid them
-   - Verification checklists for each phase
-   - Decision trees for implementation choices
-   - **Use this as your day-to-day reference**
-
-### 3. **Reference Implementation** - `./reflector-dailyco-reference/`
-   - Working implementation from 2.5 months ago
-   - Good architecture and patterns
-   - **BUT:** 91 commits behind current main, DO NOT merge directly
-   - Use for inspiration and code patterns only
-
---
-
-## Critical Context: Why Not Just Merge?
-
-The reference branch (`origin/igor/feat-dailyco`) was started on August 1, 2025 and is now severely diverged from main:
-
- **91 commits behind main**
- Main has 12x more changes (45,840 insertions vs 3,689)
- Main added: calendar integration, webhooks, full-text search, React Query migration, security fixes
- Reference removed: features that main still has and needs
-
-**Merging would be a disaster.** We're implementing fresh on current main, using the reference for validated patterns.
-
---
-
-## High-Level Approach
-
-### Phase 1: Analysis (2 hours)
- Study current Whereby integration
- Define abstraction requirements
- Create standard data models
-
-### Phase 2: Abstraction Layer (4-5 hours)
- Build platform abstraction (base class, registry, factory)
- Extract Whereby into the abstraction
- Update database schema (add `platform` field)
- Integrate into rooms.py **without breaking calendar/webhooks**
-
-### Phase 3: Daily.co Implementation (4-5 hours)
- Implement Daily.co client
- Add webhook handler
- Create frontend components (rewrite API calls for React Query)
- Add recording processing
-
-### Phase 4: Testing (2-3 hours)
- Unit tests for platform abstraction
- Integration tests for webhooks
- Manual testing with both providers
-
---
-
-## Key Files You'll Touch
-
-### Backend (New)
-```
-server/reflector/video_platforms/
-├── __init__.py
-├── base.py              ← Abstract base class
-├── models.py            ← Platform, MeetingData, VideoPlatformConfig
-├── registry.py          ← Platform registration system
-├── factory.py           ← Client creation and config
-├── whereby.py           ← Whereby client wrapper
-├── daily.py             ← Daily.co client
-└── mock.py              ← Mock client for testing
-
-server/reflector/views/daily.py       ← Daily.co webhooks
-server/tests/test_video_platforms.py  ← Platform tests
-server/tests/test_daily_webhook.py    ← Webhook tests
-```
-
-### Backend (Modified - Careful!)
-```
-server/reflector/settings.py          ← Add Daily.co settings
-server/reflector/db/rooms.py          ← Add platform field, PRESERVE calendar fields
-server/reflector/db/meetings.py       ← Add platform field
-server/reflector/views/rooms.py       ← Integrate abstraction, PRESERVE calendar/webhooks
-server/reflector/worker/process.py    ← Add process_recording_from_url task
-server/reflector/app.py               ← Register daily router
-server/env.example                    ← Document new env vars
-```
-
-### Frontend (New)
-```
-www/app/[roomName]/components/
-├── RoomContainer.tsx    ← Platform router
-├── DailyRoom.tsx        ← Daily.co component (rewrite API calls!)
-└── WherebyRoom.tsx      ← Extract existing logic
-```
-
-### Frontend (Modified)
-```
-www/app/[roomName]/page.tsx           ← Use RoomContainer
-www/package.json                      ← Add @daily-co/daily-js
-```
-
-### Database
-```
-server/migrations/versions/XXXXXX_add_platform_support.py  ← Generate fresh migration
-```
-
---
-
-## Critical Warnings ⚠️
-
-### 1. **DO NOT Copy Database Migrations**
-The reference migration has the wrong `down_revision` and is based on old schema.
-```bash
-# Instead:
-cd server
-uv run alembic revision -m "add_platform_support"
-# Then edit the generated file
-```
-
-### 2. **DO NOT Remove Main's Features**
-Main has calendar integration, webhooks, ICS sync that reference doesn't have.
-When modifying `rooms.py`, only change meeting creation logic, preserve everything else.
-
-### 3. **DO NOT Copy Frontend API Calls**
-Reference uses old OpenAPI client. Main uses React Query.
-Check how main currently makes API calls and replicate that pattern.
-
-### 4. **DO NOT Copy package.json/migrations**
-These files are severely outdated in reference.
-
-### 5. **Preserve Type Safety**
-Use `TYPE_CHECKING` imports to avoid circular dependencies:
-```python
-from typing import TYPE_CHECKING
-if TYPE_CHECKING:
-    from reflector.db.rooms import Room
-```
-
---
-
-## How to Start
-
-### Day 1 Morning: Setup & Understanding (2-3 hours)
-```bash
-# 1. Verify you're on the right branch
-git branch
-# Should show: igor/dailico-2
-
-# 2. Read the docs (in order)
-# - PLAN.md (skim to understand scope, read Phase 1 carefully)
-# - IMPLEMENTATION_GUIDE.md (read fully, bookmark it)
-
-# 3. Study current Whereby integration
-cat server/reflector/views/rooms.py | grep -A 20 "whereby"
-cat www/app/[roomName]/page.tsx
-
-# 4. Check reference implementation structure
-ls -la reflector-dailyco-reference/server/reflector/video_platforms/
-```
-
-### Day 1 Afternoon: Phase 1 Execution (2-3 hours)
-```bash
-# 5. Copy video_platforms directory from reference
-cp -r reflector-dailyco-reference/server/reflector/video_platforms/ \
-      server/reflector/
-
-# 6. Review and fix imports
-cd server
-uv run ruff check reflector/video_platforms/
-
-# 7. Add settings to settings.py (see PLAN.md Phase 2.7)
-
-# 8. Test imports work
-uv run python -c "from reflector.video_platforms import create_platform_client; print('OK')"
-```
-
-### Day 2: Phase 2 - Database & Integration (4-5 hours)
-```bash
-# 9. Generate migration
-uv run alembic revision -m "add_platform_support"
-# Edit the file following PLAN.md Phase 2.8
-
-# 10. Update Room/Meeting models
-# Add platform field, PRESERVE all existing fields
-
-# 11. Integrate into rooms.py
-# Carefully modify meeting creation, preserve calendar/webhooks
-
-# 12. Add Daily.co webhook handler
-cp reflector-dailyco-reference/server/reflector/views/daily.py \
-   server/reflector/views/
-# Register in app.py
-```
-
-### Day 3: Phase 3 - Frontend & Testing (4-5 hours)
-```bash
-# 13. Create frontend components
-mkdir -p www/app/[roomName]/components
-
-# 14. Add Daily.co dependency
-cd www
-pnpm add @daily-co/daily-js@^0.81.0
-
-# 15. Create RoomContainer, DailyRoom, WherebyRoom
-# IMPORTANT: Rewrite API calls using React Query patterns
-
-# 16. Regenerate types
-pnpm openapi
-
-# 17. Copy and adapt tests
-cp reflector-dailyco-reference/server/tests/test_*.py server/tests/
-
-# 18. Run tests
-cd server
-REDIS_HOST=localhost \
-CELERY_BROKER_URL=redis://localhost:6379/1 \
-uv run pytest tests/test_video_platforms.py -v
-```
-
---
-
-## Verification Checklist
-
-After implementation, all of these must pass:
-
-**Backend:**
- [ ] `cd server && uv run ruff check .` passes
- [ ] `uv run alembic upgrade head` works cleanly
- [ ] `uv run pytest tests/test_video_platforms.py` passes
- [ ] Can import: `from reflector.video_platforms import create_platform_client`
- [ ] Settings has all Daily.co variables
-
-**Frontend:**
- [ ] `cd www && pnpm lint` passes
- [ ] No TypeScript errors
- [ ] `pnpm openapi` generates platform field
- [ ] No `@ts-ignore` for platform field
-
-**Integration:**
- [ ] Whereby meetings still work (existing flow unchanged)
- [ ] Calendar/webhook features still work in rooms.py
- [ ] env.example documents all new variables
-
---
-
-## When You're Stuck
-
-### Check These Resources:
-1. **PLAN.md** - Detailed code examples for your exact scenario
-2. **IMPLEMENTATION_GUIDE.md** - Common pitfalls section
-3. **Reference code** - See how it was solved before
-4. **Git diff** - Compare reference to your implementation
-
-### Compare Files:
-```bash
-# See what reference did
-diff reflector-dailyco-reference/server/reflector/views/rooms.py \
-     server/reflector/views/rooms.py
-
-# See what changed in main since reference branch
-git log --oneline --since="2025-08-01" -- server/reflector/views/rooms.py
-```
-
-### Common Issues:
- **Circular imports:** Use `TYPE_CHECKING` pattern
- **Tests fail with postgres error:** Use `REDIS_HOST=localhost` env vars
- **Frontend API calls broken:** Check current React Query patterns in main
- **Migrations fail:** Ensure you generated fresh, not copied
-
---
-
-## Success Looks Like
-
-When you're done:
- ✅ All tests pass
- ✅ Linting passes
- ✅ Can create Whereby meetings (unchanged behavior)
- ✅ Can create Daily.co meetings (with env vars)
- ✅ Calendar/webhooks still work
- ✅ Frontend has no TypeScript errors
- ✅ Platform selection via environment variables works
-
---
-
-## Communication
-
-If you need clarification on requirements, have questions about architecture decisions, or find issues with the spec, document them clearly with:
- What you expected
- What you found
- Your proposed solution
-
-The PLAN.md document is comprehensive but you may find edge cases. Use your engineering judgment and document decisions.
-
---
-
-## Final Notes
-
-**This is not a simple copy-paste job.** You're doing careful integration work where you need to:
- Understand the abstraction pattern (PLAN.md)
- Preserve all of main's features
- Adapt reference code to current patterns
- Think about edge cases and testing
-
-Take your time with Phase 2 (rooms.py integration) - that's where most bugs will come from if you accidentally break calendar/webhook features.
-
-**Good luck! You've got comprehensive specs, working reference code, and a clean starting point. You can do this.**
-
---
-
-## Quick Reference
-
-```bash
-# Your workspace
-├── PLAN.md                        ← Complete technical spec (read first)
-├── IMPLEMENTATION_GUIDE.md        ← Practical guide (bookmark this)
-├── CODER_BRIEFING.md             ← This file
-└── reflector-dailyco-reference/   ← Reference implementation (inspiration only)
-
-# Key commands
-cd server && uv run ruff check .                    # Lint backend
-cd www && pnpm lint                                  # Lint frontend
-cd server && uv run alembic revision -m "..."       # Create migration
-cd www && pnpm openapi                              # Regenerate types
-cd server && uv run pytest -v                       # Run tests
-```
--- a/IMPLEMENTATION_GUIDE.md
+++ b/IMPLEMENTATION_GUIDE.md
@@ -1,489 +0,0 @@
-# Daily.co Implementation Guide
-
-## Overview
-Implement multi-provider video platform support (Whereby + Daily.co) following PLAN.md.
-
-## Reference Code Location
- **Reference branch:** `origin/igor/feat-dailyco` (on remote)
- **Worktree location:** `./reflector-dailyco-reference/`
- **Status:** Reference only - DO NOT merge or copy directly
-
-## What Exists in Reference Branch (For Inspiration)
-
-### ✅ Can Use As Reference (Well-Implemented)
-```
-server/reflector/video_platforms/
-├── base.py              ← Platform abstraction (good design, copy-safe)
-├── models.py            ← Data models (copy-safe)
-├── registry.py          ← Registry pattern (copy-safe)
-├── factory.py           ← Factory pattern (needs settings updates)
-├── whereby.py           ← Whereby client (needs adaptation)
-├── daily.py             ← Daily.co client (needs adaptation)
-└── mock.py              ← Mock client (copy-safe for tests)
-
-server/reflector/views/daily.py       ← Webhook handler (needs adaptation)
-server/tests/test_video_platforms.py  ← Tests (good reference)
-server/tests/test_daily_webhook.py    ← Tests (good reference)
-
-www/app/[roomName]/components/
-├── RoomContainer.tsx    ← Platform router (needs React Query)
-├── DailyRoom.tsx        ← Daily component (needs React Query)
-└── WherebyRoom.tsx      ← Whereby extraction (needs React Query)
-```
-
-### ⚠️ Needs Significant Changes (Use Logic Only)
- `server/reflector/db/rooms.py` - Reference removed calendar/webhook fields that main has
- `server/reflector/db/meetings.py` - Same issue (missing user_id handling differences)
- `server/reflector/views/rooms.py` - Main has calendar integration, webhooks, ICS sync
- `server/reflector/worker/process.py` - Main has different recording flow
- Migration files - Must regenerate against current main schema
-
-### ❌ Do NOT Use (Outdated/Incompatible)
- `package.json`/`pnpm-lock.yaml` - Main uses different dependency versions
- Frontend API client calls - Main uses React Query (reference uses old OpenAPI client)
- Database migrations - Must create new ones from scratch
- Any files that delete features present in main (search, calendar, webhooks)
-
-## Key Differences: Reference vs Current Main
-
-| Aspect | Reference Branch | Current Main | Action Required |
-|--------|------------------|--------------|-----------------|
-| **API client** | Old OpenAPI generated | React Query hooks | Rewrite all API calls |
-| **Database schema** | Simplified (removed features) | Has calendar, webhooks, full-text search | Merge carefully, preserve main features |
-| **Settings** | Aug 2025 structure | Current structure | Adapt carefully |
-| **Migrations** | Branched from Aug 1 | Current main (91+ commits ahead) | Regenerate from scratch |
-| **Frontend deps** | `@daily-co/daily-js@0.81.0` | Check current versions | Update to compatible versions |
-| **Package manager** | yarn | pnpm (maybe both?) | Use what main uses |
-
-## Branch Divergence Analysis
-
-**The reference branch is 91 commits behind main and severely diverged:**
- Reference: 8 commits, 3,689 insertions, 425 deletions
- Main since divergence: 320 files changed, 45,840 insertions, 16,827 deletions
- **Main has 12x more changes**
-
-**Major features in main that reference lacks:**
-1. Calendar integration (ICS sync with rooms)
-2. Self-hosted GPU API infrastructure
-3. Frontend OpenAPI React Query migration
-4. Full-text search (backend + frontend)
-5. Webhook system for room events
-6. Environment variable migration
-7. Security fixes and auth improvements
-8. Docker production frontend
-9. Meeting user ID removal (schema change)
-10. NextJS version upgrades
-
-**High conflict risk files:**
- `server/reflector/views/rooms.py` - 12x more changes in main
- `server/reflector/db/rooms.py` - Main added 7+ fields
- `www/package.json` - NextJS major version bump
- Database migrations - 20+ new migrations in main
-
-## Implementation Approach
-
-### Phase 1: Copy Clean Abstractions (1-2 hours)
-
-**Files to copy directly from reference:**
-```bash
-# Core abstraction (review but mostly safe to copy)
-cp -r reflector-dailyco-reference/server/reflector/video_platforms/ \
-      server/reflector/
-
-# BUT review each file for:
-# - Import paths (make sure they match current main)
-# - Settings references (adapt to current settings.py)
-# - Type imports (ensure no circular dependencies)
-```
-
-**After copying, immediately:**
-```bash
-cd server
-# Check for issues
-uv run ruff check reflector/video_platforms/
-# Fix any import errors or type issues
-```
-
-### Phase 2: Adapt to Current Main (2-3 hours)
-
-**2.1 Settings Integration**
-
-File: `server/reflector/settings.py`
-
-Add at the appropriate location (near existing Whereby settings):
-
-```python
-# Daily.co API Integration (NEW)
-DAILY_API_KEY: str | None = None
-DAILY_WEBHOOK_SECRET: str | None = None
-DAILY_SUBDOMAIN: str | None = None
-AWS_DAILY_S3_BUCKET: str | None = None
-AWS_DAILY_S3_REGION: str = "us-west-2"
-AWS_DAILY_ROLE_ARN: str | None = None
-
-# Platform Migration Feature Flags (NEW)
-DAILY_MIGRATION_ENABLED: bool = False  # Conservative default
-DAILY_MIGRATION_ROOM_IDS: list[str] = []
-DEFAULT_VIDEO_PLATFORM: Literal["whereby", "daily"] = "whereby"
-```
-
-**2.2 Database Migration**
-
-⚠️ **CRITICAL: Do NOT copy migration from reference**
-
-Generate new migration:
-```bash
-cd server
-uv run alembic revision -m "add_platform_support"
-```
-
-Edit the generated migration file to add `platform` column:
-```python
-def upgrade():
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column("platform", sa.String(), nullable=False, server_default="whereby")
-        )
-
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column("platform", sa.String(), nullable=False, server_default="whereby")
-        )
-```
-
-**2.3 Update Database Models**
-
-File: `server/reflector/db/rooms.py`
-
-Add platform field (preserve all existing fields from main):
-```python
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from reflector.video_platforms.models import Platform
-
-class Room:
-    # ... ALL existing fields from main (calendar, webhooks, etc.) ...
-
-    # NEW: Platform field
-    platform: "Platform" = sqlalchemy.Column(
-        sqlalchemy.String,
-        nullable=False,
-        server_default="whereby",
-    )
-```
-
-File: `server/reflector/db/meetings.py`
-
-Same approach - add platform field, preserve everything from main.
-
-**2.4 Integrate Platform Abstraction into rooms.py**
-
-⚠️ **This is the most delicate part - main has calendar/webhook features**
-
-File: `server/reflector/views/rooms.py`
-
-Strategy:
-1. Add imports at top
-2. Modify meeting creation logic only
-3. Preserve all calendar/webhook/ICS logic from main
-
-```python
-# Add imports
-from reflector.video_platforms import (
-    create_platform_client,
-    get_platform_for_room,
-)
-
-# In create_meeting endpoint:
-# OLD: Direct Whereby API calls
-# NEW: Platform abstraction
-
-# Find the meeting creation section and replace:
-platform = get_platform_for_room(room.id)
-client = create_platform_client(platform)
-
-meeting_data = await client.create_meeting(
-    room_name_prefix=room.name,
-    end_date=meeting_data.end_date,
-    room=room,
-)
-
-# Then create Meeting record with meeting_data.platform, meeting_data.meeting_id, etc.
-```
-
-**2.5 Add Daily.co Webhook Handler**
-
-Copy from reference, minimal changes needed:
-```bash
-cp reflector-dailyco-reference/server/reflector/views/daily.py \
-   server/reflector/views/
-```
-
-Register in `server/reflector/app.py`:
-```python
-from reflector.views import daily
-
-app.include_router(daily.router, prefix="/v1/daily", tags=["daily"])
-```
-
-**2.6 Add Recording Processing Task**
-
-File: `server/reflector/worker/process.py`
-
-Add the `process_recording_from_url` task from reference (copy the function).
-
-### Phase 3: Frontend Adaptation (3-4 hours)
-
-**3.1 Determine Current API Client Pattern**
-
-First, check how main currently makes API calls:
-```bash
-cd www
-grep -r "api\." app/ | head -20
-# Look for patterns like: api.v1Something()
-```
-
-**3.2 Create Components**
-
-Copy component structure from reference but **rewrite all API calls**:
-
-```bash
-mkdir -p www/app/[roomName]/components
-```
-
-Files to create:
- `RoomContainer.tsx` - Platform router (mostly copy-safe, just fix imports)
- `DailyRoom.tsx` - Needs React Query API calls
- `WherebyRoom.tsx` - Extract current room page logic
-
-**Example React Query pattern** (adapt to your actual API):
-```typescript
-import { api } from '@/app/api/client'
-
-// In DailyRoom.tsx
-const handleConsent = async () => {
-  try {
-    await api.v1MeetingAudioConsent({
-      path: { meeting_id: meeting.id },
-      body: { consent: true },
-    })
-    // ...
-  } catch (error) {
-    // ...
-  }
-}
-```
-
-**3.3 Add Daily.co Dependency**
-
-Check current package manager:
-```bash
-cd www
-ls package-lock.json yarn.lock pnpm-lock.yaml
-```
-
-Then install:
-```bash
-# If using pnpm
-pnpm add @daily-co/daily-js@^0.81.0
-
-# If using yarn
-yarn add @daily-co/daily-js@^0.81.0
-```
-
-**3.4 Update TypeScript Types**
-
-After backend changes, regenerate types:
-```bash
-cd www
-pnpm openapi  # or yarn openapi
-```
-
-This should pick up the new `platform` field on Meeting type.
-
-### Phase 4: Testing (2-3 hours)
-
-**4.1 Copy Test Structure**
-
-```bash
-cp reflector-dailyco-reference/server/tests/test_video_platforms.py \
-   server/tests/
-
-cp reflector-dailyco-reference/server/tests/test_daily_webhook.py \
-   server/tests/
-```
-
-**4.2 Fix Test Imports and Fixtures**
-
-Update imports to match current test infrastructure:
- Check `server/tests/conftest.py` for fixture patterns
- Update database access patterns if changed
- Fix any import errors
-
-**4.3 Run Tests**
-
-```bash
-cd server
-# Run with environment variables for Mac
-REDIS_HOST=localhost \
-CELERY_BROKER_URL=redis://localhost:6379/1 \
-CELERY_RESULT_BACKEND=redis://localhost:6379/1 \
-uv run pytest tests/test_video_platforms.py -v
-```
-
-### Phase 5: Environment Configuration
-
-**Update `server/env.example`:**
-
-Add at the end:
-```bash
-# Daily.co API Integration
-DAILY_API_KEY=your-daily-api-key
-DAILY_WEBHOOK_SECRET=your-daily-webhook-secret
-DAILY_SUBDOMAIN=your-subdomain
-AWS_DAILY_S3_BUCKET=your-daily-bucket
-AWS_DAILY_S3_REGION=us-west-2
-AWS_DAILY_ROLE_ARN=arn:aws:iam::ACCOUNT:role/DailyRecording
-
-# Platform Selection
-DAILY_MIGRATION_ENABLED=false           # Master switch
-DAILY_MIGRATION_ROOM_IDS=[]            # Specific room IDs
-DEFAULT_VIDEO_PLATFORM=whereby          # Default platform
-```
-
-## Decision Tree: Copy vs Adapt vs Rewrite
-
-```
-┌─ Is it pure abstraction logic? (base.py, registry.py, models.py)
-│  YES → Copy directly, review imports
-│  NO  → Continue ↓
-│
-├─ Does it touch database models?
-│  YES → Adapt carefully, preserve main's fields
-│  NO  → Continue ↓
-│
-├─ Does it make API calls on frontend?
-│  YES → Rewrite using React Query
-│  NO  → Continue ↓
-│
-├─ Is it a database migration?
-│  YES → Generate fresh from current schema
-│  NO  → Continue ↓
-│
-└─ Does it touch rooms.py or core business logic?
-   YES → Merge carefully, preserve calendar/webhooks
-   NO  → Safe to adapt from reference
-```
-
-## Verification Checklist
-
-After each phase, verify:
-
-**Phase 1 (Abstraction Layer):**
- [ ] `uv run ruff check server/reflector/video_platforms/` passes
- [ ] No circular import errors
- [ ] Can import `from reflector.video_platforms import create_platform_client`
-
-**Phase 2 (Backend Integration):**
- [ ] `uv run ruff check server/` passes
- [ ] Migration file generated (not copied)
- [ ] Room and Meeting models have platform field
- [ ] rooms.py still has calendar/webhook features
-
-**Phase 3 (Frontend):**
- [ ] `pnpm lint` passes
- [ ] No TypeScript errors
- [ ] No `@ts-ignore` for platform field
- [ ] API calls use React Query patterns
-
-**Phase 4 (Testing):**
- [ ] Tests can be collected: `pytest tests/test_video_platforms.py --collect-only`
- [ ] Database fixtures work
- [ ] Mock platform works
-
-**Phase 5 (Config):**
- [ ] env.example has Daily.co variables
- [ ] settings.py has all new variables
- [ ] No duplicate variable definitions
-
-## Common Pitfalls
-
-### 1. Database Schema Conflicts
-**Problem:** Reference removed fields that main has (calendar, webhooks)
-**Solution:** Always preserve main's fields, only add platform field
-
-### 2. Migration Conflicts
-**Problem:** Reference migration has wrong `down_revision`
-**Solution:** Always generate fresh migration from current main
-
-### 3. Frontend API Calls
-**Problem:** Reference uses old API client patterns
-**Solution:** Check current main's API usage, replicate that pattern
-
-### 4. Import Errors
-**Problem:** Circular imports with TYPE_CHECKING
-**Solution:** Use `if TYPE_CHECKING:` for Room/Meeting imports in video_platforms
-
-### 5. Test Database Issues
-**Problem:** Tests fail with "could not translate host name 'postgres'"
-**Solution:** Use environment variables: `REDIS_HOST=localhost DATABASE_URL=...`
-
-### 6. Preserved Features Broken
-**Problem:** Calendar/webhook features stop working
-**Solution:** Carefully review rooms.py diff, only change meeting creation, not calendar logic
-
-## File Modification Summary
-
-**New files (can copy):**
- `server/reflector/video_platforms/*.py` (entire directory)
- `server/reflector/views/daily.py`
- `server/tests/test_video_platforms.py`
- `server/tests/test_daily_webhook.py`
- `www/app/[roomName]/components/RoomContainer.tsx`
- `www/app/[roomName]/components/DailyRoom.tsx`
- `www/app/[roomName]/components/WherebyRoom.tsx`
-
-**Modified files (careful merging):**
- `server/reflector/settings.py` - Add Daily.co settings
- `server/reflector/db/rooms.py` - Add platform field
- `server/reflector/db/meetings.py` - Add platform field
- `server/reflector/views/rooms.py` - Integrate platform abstraction
- `server/reflector/worker/process.py` - Add process_recording_from_url
- `server/reflector/app.py` - Register daily router
- `server/env.example` - Add Daily.co variables
- `www/app/[roomName]/page.tsx` - Use RoomContainer
- `www/package.json` - Add @daily-co/daily-js
-
-**Generated files (do not copy):**
- `server/migrations/versions/XXXXXX_add_platform_support.py` - Generate fresh
-
-## Success Metrics
-
-Implementation is complete when:
- [ ] All tests pass (including new platform tests)
- [ ] Linting passes (ruff, pnpm lint)
- [ ] Migration applies cleanly: `uv run alembic upgrade head`
- [ ] Can create Whereby meeting (existing flow unchanged)
- [ ] Can create Daily.co meeting (with env vars set)
- [ ] Frontend loads without TypeScript errors
- [ ] No features from main were accidentally removed
-
-## Getting Help
-
-**Reference documentation locations:**
- Implementation plan: `PLAN.md`
- Reference implementation: `./reflector-dailyco-reference/`
- Current main codebase: `./ ` (current directory)
-
-**Compare implementations:**
-```bash
-# Compare specific files
-diff reflector-dailyco-reference/server/reflector/video_platforms/base.py \
-     server/reflector/video_platforms/base.py
-
-# See what changed in rooms.py between reference branch point and now
-git log --oneline --since="2025-08-01" -- server/reflector/views/rooms.py
-```
-
-**Key insight:** The reference branch validates the approach and provides working code patterns, but you're implementing fresh against current main to avoid merge conflicts and preserve all new features.
--- a/PLAN.md
+++ b/PLAN.md
--- 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)
-[![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
-</div>
+[![Tests](https://github.com/monadical-sas/cubbi/actions/workflows/pytests.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/cubbi/actions/workflows/pytests.yml)
+[![License: MIT](https://img.shields.io/badge/license-AGPL--v3-green.svg)](https://opensource.org/licenses/AGPL-v3)
 </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

@@ -89,25 +72,24 @@ 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`.
+Start with `cd backend`.

 **Installation**

 ```bash
-pnpm install
-cp .env.example .env
+yarn install
+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**

 ```bash
-pnpm dev
+yarn dev
 ```

 Then (after completing server setup and starting it) open [http://localhost:3000](http://localhost:3000) to view it in the browser.
@@ -117,7 +99,7 @@ Then (after completing server setup and starting it) open [http://localhost:3000
 To generate the TypeScript files from the openapi.json file, make sure the python server is running, then run:

 ```bash
-pnpm openapi
+yarn openapi
 ```

 ### Backend
@@ -167,41 +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
 ```
-
-## 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:
@@ -39,19 +36,16 @@ services:
    ports:
      - 6379:6379
  web:
-    image: node:22-alpine
+    image: node:18
    ports:
      - "3000:3000"
-    command: sh -c "corepack enable && pnpm install && pnpm dev"
+    command: sh -c "yarn install && yarn dev"
    restart: unless-stopped
    working_dir: /app
    volumes:
      - ./www:/app/
-      - /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
@@ -1,39 +0,0 @@
-# Production Docker Compose configuration for Frontend
-# Usage: docker compose -f docker-compose.prod.yml up -d
-
-services:
-  web:
-    build:
-      context: ./www
-      dockerfile: Dockerfile
-    image: reflector-frontend:latest
-    environment:
-      - KV_URL=${KV_URL:-redis://redis:6379}
-      - SITE_URL=${SITE_URL}
-      - API_URL=${API_URL}
-      - WEBSOCKET_URL=${WEBSOCKET_URL}
-      - NEXTAUTH_URL=${NEXTAUTH_URL:-http://localhost:3000}
-      - NEXTAUTH_SECRET=${NEXTAUTH_SECRET:-changeme-in-production}
-      - AUTHENTIK_ISSUER=${AUTHENTIK_ISSUER}
-      - AUTHENTIK_CLIENT_ID=${AUTHENTIK_CLIENT_ID}
-      - AUTHENTIK_CLIENT_SECRET=${AUTHENTIK_CLIENT_SECRET}
-      - AUTHENTIK_REFRESH_TOKEN_URL=${AUTHENTIK_REFRESH_TOKEN_URL}
-      - SENTRY_DSN=${SENTRY_DSN}
-      - SENTRY_IGNORE_API_RESOLUTION_ERROR=${SENTRY_IGNORE_API_RESOLUTION_ERROR:-1}
-    depends_on:
-      - redis
-    restart: unless-stopped
-
-  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
-
-volumes:
-  redis_data:
--- 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/reflector_diarizer.py
+++ b/gpu/modal_deployments/reflector_diarizer.py
@@ -1,253 +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)
-
-
-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")
-
-    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="3.10.8")
-    .pip_install(
-        "pyannote.audio==3.1.0",
-        "requests",
-        "onnx",
-        "torchaudio",
-        "onnxruntime-gpu",
-        "torch==2.0.0",
-        "transformers==4.34.0",
-        "sentencepiece",
-        "protobuf",
-        "numpy",
-        "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
@@ -1,608 +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",
-        "requests",
-        "librosa==0.10.1",
-        "numpy<2",
-        "silero-vad==5.1.0",
-    )
-    .run_function(download_model, volumes={CACHE_PATH: model_cache})
-)
-
-
-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")
-
-    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
-
-        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)
-            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
-            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}
-
-
-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"
-
-    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
@@ -1,658 +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==0.10.1",
-        "requests",
-        "silero-vad==5.1.0",
-        "torch",
-    )
-    .entrypoint([])  # silence chatty logs by container on start
-)
-
-
-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")
-
-    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
-
-        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)
-            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
-
-            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]
-*$py.class
-
-# Env and secrets
-.env
-*.env
-*.secret
-HF_TOKEN
-REFLECTOR_GPU_APIKEY
-
-# Virtual env / uv
-.venv/
-venv/
-ENV/
-uv/
-
-# Build / dist
-build/
-dist/
-.eggs/
-*.egg-info/
-
-# Coverage / test
-.pytest_cache/
-.coverage*
-htmlcov/
-
-# Logs
-*.log
--- a/gpu/self_hosted/Dockerfile
+++ b/gpu/self_hosted/Dockerfile
@@ -1,46 +0,0 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_NO_CACHE=1
-
-WORKDIR /tmp
-RUN apt-get update \
- && apt-get install -y \
-    ffmpeg \
-    curl \
-    ca-certificates \
-    gnupg \
-    wget \
- && apt-get clean
-# Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
-ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
-RUN dpkg -i /cuda-keyring.deb \
- && rm /cuda-keyring.deb \
- && apt-get update \
- && apt-get install -y --no-install-recommends \
-    cuda-cudart-12-6 \
-    libcublas-12-6 \
-    libcudnn9-cuda-12 \
-    libcudnn9-dev-cuda-12 \
- && apt-get clean \
- && rm -rf /var/lib/apt/lists/*
-ADD https://astral.sh/uv/install.sh /uv-installer.sh
-RUN sh /uv-installer.sh && rm /uv-installer.sh
-ENV PATH="/root/.local/bin/:$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
@@ -1,73 +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
-
-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
@@ -1,208 +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
-
-        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
-            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
@@ -1,107 +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)
-
-
-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"
-
-    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/gpu/self_hosted/runserver.sh
+++ b/gpu/self_hosted/runserver.sh
@@ -1,17 +0,0 @@
-#!/bin/sh
-set -e
-
-export PATH="/root/.local/bin:$PATH"
-cd /app
-
-# Install Python dependencies at runtime (first run or when FORCE_SYNC=1)
-if [ ! -d "/app/.venv" ] || [ "$FORCE_SYNC" = "1" ]; then
-  echo "[startup] Installing Python dependencies with uv..."
-  uv sync --compile-bytecode --locked
-else
-  echo "[startup] Using existing virtual environment at /app/.venv"
-fi
-
-exec uv run uvicorn main:app --host 0.0.0.0 --port 8000
-
-
--- a/gpu/self_hosted/uv.lock
+++ b/gpu/self_hosted/uv.lock
--- a/server/.env_template
+++ b/server/.env_template
@@ -0,0 +1,16 @@
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=***REMOVED***
+
+LLM_BACKEND=modal
+LLM_URL=https://monadical-sas--reflector-llm-web.modal.run
+LLM_MODAL_API_KEY=***REMOVED***
+
+TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
+ZEPHYR_LLM_URL=https://monadical-sas--reflector-llm-zephyr-web.modal.run
+DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
+
+BASE_URL=https://xxxxx.ngrok.app
+DIARIZATION_ENABLED=false
+
+SQS_POLLING_TIMEOUT_SECONDS=60
--- a/server/.gitignore
+++ b/server/.gitignore
@@ -176,8 +176,7 @@ artefacts/
 audio_*.wav

 # ignore local database
-*.sqlite3
-*.db
+reflector.sqlite3
 data/

 dump.rdb
--- a/server/DAILYCO_TEST.md
+++ b/server/DAILYCO_TEST.md
@@ -1,613 +0,0 @@
-# Daily.co Integration Test Plan
-
-## ✅ IMPLEMENTATION STATUS: Real Transcription Active
-
-**This test validates Daily.co multitrack recording integration with REAL transcription/diarization.**
-
-The implementation includes complete audio processing pipeline:
- **Multitrack recordings** from Daily.co S3 (separate audio stream per participant)
- **PyAV-based audio mixdown** with PTS-based track alignment
- **Real transcription** via Modal GPU backend (Whisper)
- **Real diarization** via Modal GPU backend (speaker identification)
- **Per-track transcription** with timestamp synchronization
- **Complete database entities** (recording, transcript, topics, participants, words)
-
-**Processing pipeline** (`PipelineMainMultitrack`):
-1. Download all audio tracks from Daily.co S3
-2. Align tracks by PTS (presentation timestamp) to handle late joiners
-3. Mix tracks into single audio file for unified playback
-4. Transcribe each track individually with proper offset handling
-5. Perform diarization on mixed audio
-6. Generate topics, summaries, and word-level timestamps
-7. Convert audio to MP3 and generate waveform visualization
-
-**Note:** A stub processor (`process_daily_recording`) exists for testing webhook flow without GPU costs, but the production code path uses `process_multitrack_recording` with full ML pipeline.
-
---
-
-## Prerequisites
-
-**1. Environment Variables** (check in `.env.development.local`):
-```bash
-# Daily.co API Configuration
-DAILY_API_KEY=<key>
-DAILY_SUBDOMAIN=monadical
-DAILY_WEBHOOK_SECRET=<base64-encoded-secret>
-AWS_DAILY_S3_BUCKET=reflector-dailyco-local
-AWS_DAILY_S3_REGION=us-east-1
-AWS_DAILY_ROLE_ARN=arn:aws:iam::950402358378:role/DailyCo
-DAILY_MIGRATION_ENABLED=true
-DAILY_MIGRATION_ROOM_IDS=["552640fd-16f2-4162-9526-8cf40cd2357e"]
-
-# Transcription/Diarization Backend (Required for real processing)
-DIARIZATION_BACKEND=modal
-DIARIZATION_MODAL_API_KEY=<modal-api-key>
-# TRANSCRIPTION_BACKEND is not explicitly set (uses default/modal)
-```
-
-**2. Services Running:**
-```bash
-docker compose ps  # server, postgres, redis, worker, beat should be UP
-```
-
-**IMPORTANT:** Worker and beat services MUST be running for transcription processing:
-```bash
-docker compose up -d worker beat
-```
-
-**3. ngrok Tunnel for Webhooks:**
-```bash
-# Start ngrok (if not already running)
-ngrok http 1250 --log=stdout > /tmp/ngrok.log 2>&1 &
-
-# Get public URL
-curl -s http://localhost:4040/api/tunnels | python3 -c "import sys, json; data=json.load(sys.stdin); print(data['tunnels'][0]['public_url'])"
-```
-
-**Current ngrok URL:** `https://0503947384a3.ngrok-free.app` (as of last registration)
-
-**4. Webhook Created:**
-```bash
-cd server
-uv run python scripts/recreate_daily_webhook.py https://0503947384a3.ngrok-free.app/v1/daily/webhook
-# Verify: "Created webhook <uuid> (state: ACTIVE)"
-```
-
-**Current webhook status:** ✅ ACTIVE (webhook ID: dad5ad16-ceca-488e-8fc5-dae8650b51d0)
-
---
-
-## Test 1: Database Configuration
-
-**Check room platform:**
-```bash
-docker-compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT id, name, platform, recording_type FROM room WHERE name = 'test2';"
-```
-
-**Expected:**
-```
-id: 552640fd-16f2-4162-9526-8cf40cd2357e
-name: test2
-platform: whereby  # DB value (overridden by env var DAILY_MIGRATION_ROOM_IDS)
-recording_type: cloud
-```
-
-**Clear old meetings:**
-```bash
-docker-compose exec -T postgres psql -U reflector -d reflector -c \
-  "UPDATE meeting SET is_active = false WHERE room_id = '552640fd-16f2-4162-9526-8cf40cd2357e';"
-```
-
---
-
-## Test 2: Meeting Creation with Auto-Recording
-
-**Create meeting:**
-```bash
-curl -s -X POST http://localhost:1250/v1/rooms/test2/meeting \
-  -H "Content-Type: application/json" \
-  -d '{"allow_duplicated":false}' | python3 -m json.tool
-```
-
-**Expected Response:**
-```json
-{
-  "room_name": "test2-YYYYMMDDHHMMSS",  // Includes "test2" prefix!
-  "room_url": "https://monadical.daily.co/test2-...?t=<JWT_TOKEN>",  // Has token!
-  "platform": "daily",
-  "recording_type": "cloud"  // DB value (Whereby-specific)
-}
-```
-
-**Decode token to verify auto-recording:**
-```bash
-# Extract token from room_url, decode JWT payload
-echo "<token>" | python3 -c "
-import sys, json, base64
-token = sys.stdin.read().strip()
-payload = token.split('.')[1] + '=' * (4 - len(token.split('.')[1]) % 4)
-print(json.dumps(json.loads(base64.b64decode(payload)), indent=2))
-"
-```
-
-**Expected token payload:**
-```json
-{
-  "r": "test2-YYYYMMDDHHMMSS",  // Room name
-  "sr": true,  // start_recording: true ✅
-  "d": "...",  // Domain ID
-  "iat": 1234567890
-}
-```
-
---
-
-## Test 3: Daily.co API Verification
-
-**Check room configuration:**
-```bash
-ROOM_NAME="<from previous step>"
-curl -s -X GET "https://api.daily.co/v1/rooms/$ROOM_NAME" \
-  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -m json.tool
-```
-
-**Expected config:**
-```json
-{
-  "config": {
-    "enable_recording": "raw-tracks",  // ✅
-    "recordings_bucket": {
-      "bucket_name": "reflector-dailyco-local",
-      "bucket_region": "us-east-1",
-      "assume_role_arn": "arn:aws:iam::950402358378:role/DailyCo"
-    }
-  }
-}
-```
-
---
-
-## Test 4: Browser UI Test (Playwright MCP)
-
-**Using Claude Code MCP tools:**
-
-**Load room:**
-```
-Use: mcp__playwright__browser_navigate
-Input: {"url": "http://localhost:3000/test2"}
-
-Then wait 12 seconds for iframe to load
-```
-
-**Verify Daily.co iframe loaded:**
-```
-Use: mcp__playwright__browser_snapshot
-
-Expected in snapshot:
- iframe element with src containing "monadical.daily.co"
- Daily.co pre-call UI visible
-```
-
-**Take screenshot:**
-```
-Use: mcp__playwright__browser_take_screenshot
-Input: {"filename": "test2-before-join.png"}
-
-Expected: Daily.co pre-call UI with "Join" button visible
-```
-
-**Join meeting:**
-```
-Note: Daily.co iframe interaction requires clicking inside iframe.
-Use: mcp__playwright__browser_click
-Input: {"element": "Join button in Daily.co iframe", "ref": "<ref-from-snapshot>"}
-
-Then wait 5 seconds for call to connect
-```
-
-**Verify in-call:**
-```
-Use: mcp__playwright__browser_take_screenshot
-Input: {"filename": "test2-in-call.png"}
-
-Expected: "Waiting for others to join" or participant video visible
-```
-
-**Leave meeting:**
-```
-Use: mcp__playwright__browser_click
-Input: {"element": "Leave button in Daily.co iframe", "ref": "<ref-from-snapshot>"}
-```
-
---
-
-**Alternative: JavaScript snippets (for manual testing):**
-
-```javascript
-await page.goto('http://localhost:3000/test2');
-await new Promise(f => setTimeout(f, 12000));  // Wait for load
-
-// Verify iframe
-const iframes = document.querySelectorAll('iframe');
-// Expected: 1 iframe with src containing "monadical.daily.co"
-
-// Screenshot
-await page.screenshot({ path: 'test2-before-join.png' });
-
-// Join
-await page.locator('iframe').contentFrame().getByRole('button', { name: 'Join' }).click();
-await new Promise(f => setTimeout(f, 5000));
-
-// In-call screenshot
-await page.screenshot({ path: 'test2-in-call.png' });
-
-// Leave
-await page.locator('iframe').contentFrame().getByRole('button', { name: 'Leave' }).click();
-```
-
---
-
-## Test 5: Webhook Verification
-
-**Check server logs for webhooks:**
-```bash
-docker-compose logs --since 15m server 2>&1 | grep -i "participant joined\|recording started"
-```
-
-**Expected logs:**
-```
-[info] Participant joined | meeting_id=... | num_clients=1 | recording_type=cloud | recording_trigger=automatic-2nd-participant
-[info] Recording started | meeting_id=... | recording_id=... | platform=daily
-```
-
-**Check Daily.co webhook delivery logs:**
-```bash
-curl -s -X GET "https://api.daily.co/v1/logs/webhooks?limit=20" \
-  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -c "
-import sys, json
-logs = json.load(sys.stdin)
-for log in logs[:10]:
-    req = json.loads(log['request'])
-    room = req.get('payload', {}).get('room') or req.get('payload', {}).get('room_name', 'N/A')
-    print(f\"{req['type']:30s} | room: {room:30s} | status: {log['status']}\")
-"
-```
-
-**Expected output:**
-```
-participant.joined             | room: test2-YYYYMMDDHHMMSS       | status: 200
-recording.started              | room: test2-YYYYMMDDHHMMSS       | status: 200
-participant.left               | room: test2-YYYYMMDDHHMMSS       | status: 200
-recording.ready-to-download    | room: test2-YYYYMMDDHHMMSS       | status: 200
-```
-
-**Check database updated:**
-```bash
-docker-compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT room_name, num_clients FROM meeting WHERE room_name LIKE 'test2-%' ORDER BY end_date DESC LIMIT 1;"
-```
-
-**Expected:**
-```
-room_name: test2-YYYYMMDDHHMMSS
-num_clients: 0  // After participant left
-```
-
---
-
-## Test 6: Recording in S3
-
-**List recent recordings:**
-```bash
-curl -s -X GET "https://api.daily.co/v1/recordings" \
-  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -c "
-import sys, json
-data = json.load(sys.stdin)
-for rec in data.get('data', [])[:5]:
-    if 'test2-' in rec.get('room_name', ''):
-        print(f\"Room: {rec['room_name']}\")
-        print(f\"Status: {rec['status']}\")
-        print(f\"Duration: {rec.get('duration', 0)}s\")
-        print(f\"S3 key: {rec.get('s3key', 'N/A')}\")
-        print(f\"Tracks: {len(rec.get('tracks', []))} files\")
-        for track in rec.get('tracks', []):
-            print(f\"  - {track['type']}: {track['s3Key'].split('/')[-1]} ({track['size']} bytes)\")
-        print()
-"
-```
-
-**Expected output:**
-```
-Room: test2-20251009192341
-Status: finished
-Duration: ~30-120s
-S3 key: monadical/test2-20251009192341/1760037914930
-Tracks: 2 files
-  - audio: 1760037914930-<uuid>-cam-audio-1760037915265 (~400 KB)
-  - video: 1760037914930-<uuid>-cam-video-1760037915269 (~10-30 MB)
-```
-
-**Verify S3 path structure:**
- `monadical/` - Daily.co subdomain
- `test2-20251009192341/` - Reflector room name + timestamp
- `<timestamp>-<participant-uuid>-<media-type>-<track-start>.webm` - Individual track files
-
---
-
-## Test 7: Database Check - Recording and Transcript
-
-**Check recording created:**
-```bash
-docker-compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT id, bucket_name, object_key, status, meeting_id, recorded_at
-   FROM recording
-   ORDER BY recorded_at DESC LIMIT 1;"
-```
-
-**Expected:**
-```
-id: <recording-id-from-webhook>
-bucket_name: reflector-dailyco-local
-object_key: monadical/test2-<timestamp>/<recording-timestamp>-<uuid>-cam-audio-<track-start>.webm
-status: completed
-meeting_id: <meeting-id>
-recorded_at: <recent-timestamp>
-```
-
-**Check transcript created:**
-```bash
-docker compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT id, title, status, duration, recording_id, meeting_id, room_id
-   FROM transcript
-   ORDER BY created_at DESC LIMIT 1;"
-```
-
-**Expected (REAL transcription):**
-```
-id: <transcript-id>
-title: <AI-generated title based on actual conversation content>
-status: uploaded  (audio file processed and available)
-duration: <actual meeting duration in seconds>
-recording_id: <same-as-recording-id-above>
-meeting_id: <meeting-id>
-room_id: 552640fd-16f2-4162-9526-8cf40cd2357e
-```
-
-**Note:** Title and content will reflect the ACTUAL conversation, not mock data. Processing time depends on recording length and GPU backend availability (Modal).
-
-**Verify audio file exists:**
-```bash
-ls -lh data/<transcript-id>/upload.webm
-```
-
-**Expected:**
-```
-rw-r--r-- 1 user staff ~100-200K Oct 10 18:48 upload.webm
-```
-
-**Check transcript topics (REAL transcription):**
-```bash
-TRANSCRIPT_ID=$(docker compose exec -T postgres psql -U reflector -d reflector -t -c \
-  "SELECT id FROM transcript ORDER BY created_at DESC LIMIT 1;")
-
-docker compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT
-     jsonb_array_length(topics) as num_topics,
-     jsonb_array_length(participants) as num_participants,
-     short_summary,
-     title
-   FROM transcript
-   WHERE id = '$TRANSCRIPT_ID';"
-```
-
-**Expected (REAL data):**
-```
-num_topics: <varies based on conversation>
-num_participants: <actual number of participants who spoke>
-short_summary: <AI-generated summary of actual conversation>
-title: <AI-generated title based on content>
-```
-
-**Check topics contain actual transcription:**
-```bash
-docker compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT topics->0->'title', topics->0->'summary', topics->0->'transcript'
-   FROM transcript
-   ORDER BY created_at DESC LIMIT 1;" | head -20
-```
-
-**Expected output:** Will contain the ACTUAL transcribed conversation from the Daily.co meeting, not mock data.
-
-**Check participants:**
-```bash
-docker compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT participants FROM transcript ORDER BY created_at DESC LIMIT 1;" \
-  | python3 -c "import sys, json; data=json.loads(sys.stdin.read()); print(json.dumps(data, indent=2))"
-```
-
-**Expected (REAL diarization):**
-```json
-[
-  {
-    "id": "<uuid>",
-    "speaker": 0,
-    "name": "Speaker 1"
-  },
-  {
-    "id": "<uuid>",
-    "speaker": 1,
-    "name": "Speaker 2"
-  }
-]
-```
-
-**Note:** Speaker names will be generic ("Speaker 1", "Speaker 2", etc.) as determined by the diarization backend. Number of participants depends on how many actually spoke during the meeting.
-
-**Check word-level data:**
-```bash
-docker compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT jsonb_array_length(topics->0->'words') as num_words_first_topic
-   FROM transcript
-   ORDER BY created_at DESC LIMIT 1;"
-```
-
-**Expected:**
-```
-num_words_first_topic: <varies based on actual conversation length and topic chunking>
-```
-
-**Verify speaker diarization in words:**
-```bash
-docker compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT
-     topics->0->'words'->0->>'text' as first_word,
-     topics->0->'words'->0->>'speaker' as speaker,
-     topics->0->'words'->0->>'start' as start_time,
-     topics->0->'words'->0->>'end' as end_time
-   FROM transcript
-   ORDER BY created_at DESC LIMIT 1;"
-```
-
-**Expected (REAL transcription):**
-```
-first_word: <actual first word from transcription>
-speaker: 0, 1, 2, ... (actual speaker ID from diarization)
-start_time: <actual timestamp in seconds>
-end_time: <actual end timestamp>
-```
-
-**Note:** All timestamps and speaker IDs are from real transcription/diarization, synchronized across tracks.
-
---
-
-## Test 8: Recording Type Verification
-
-**Check what Daily.co received:**
-```bash
-curl -s -X GET "https://api.daily.co/v1/rooms/test2-<timestamp>" \
-  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -m json.tool | grep "enable_recording"
-```
-
-**Expected:**
-```json
-"enable_recording": "raw-tracks"
-```
-
-**NOT:** `"enable_recording": "cloud"` (that would be wrong - we want raw tracks)
-
---
-
-## Troubleshooting
-
-### Issue: No webhooks received
-
-**Check webhook state:**
-```bash
-curl -s -X GET "https://api.daily.co/v1/webhooks" \
-  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -m json.tool
-```
-
-**If state is FAILED:**
-```bash
-cd server
-uv run python scripts/recreate_daily_webhook.py https://<ngrok-url>/v1/daily/webhook
-```
-
-### Issue: Webhooks return 422
-
-**Check server logs:**
-```bash
-docker-compose logs --tail=50 server | grep "Failed to parse webhook event"
-```
-
-**Common cause:** Event structure mismatch. Daily.co events use:
-```json
-{
-  "version": "1.0.0",
-  "type": "participant.joined",
-  "payload": {...},  // NOT "data"
-  "event_ts": 123.456  // NOT "ts"
-}
-```
-
-### Issue: Recording not starting
-
-1. **Check token has `sr: true`:**
-   - Decode JWT token from room_url query param
-   - Should contain `"sr": true`
-
-2. **Check Daily.co room config:**
-   - `enable_recording` must be set (not false)
-   - For raw-tracks: must be exactly `"raw-tracks"`
-
-3. **Check participant actually joined:**
-   - Logs should show "Participant joined"
-   - Must click "Join" button, not just pre-call screen
-
-### Issue: Recording in S3 but wrong format
-
-**Daily.co recording types:**
- `"cloud"` → Single MP4 file (`download_link` in webhook)
- `"raw-tracks"` → Multiple WebM files (`tracks` array in webhook)
- `"raw-tracks-audio-only"` → Only audio WebM files
-
-**Current implementation:** Always uses `"raw-tracks"` (better for transcription)
-
---
-
-## Quick Validation Commands
-
-**One-liner to verify everything:**
-```bash
-# 1. Check room exists
-docker-compose exec -T postgres psql -U reflector -d reflector -c \
-  "SELECT name, platform FROM room WHERE name = 'test2';" && \
-
-# 2. Create meeting
-MEETING=$(curl -s -X POST http://localhost:1250/v1/rooms/test2/meeting \
-  -H "Content-Type: application/json" -d '{"allow_duplicated":false}') && \
-echo "$MEETING" | python3 -c "import sys,json; m=json.load(sys.stdin); print(f'Room: {m[\"room_name\"]}\nURL: {m[\"room_url\"][:80]}...')" && \
-
-# 3. Check Daily.co config
-ROOM_NAME=$(echo "$MEETING" | python3 -c "import sys,json; print(json.load(sys.stdin)['room_name'])") && \
-curl -s -X GET "https://api.daily.co/v1/rooms/$ROOM_NAME" \
-  -H "Authorization: Bearer $DAILY_API_KEY" | python3 -c "import sys,json; print(f'Recording: {json.load(sys.stdin)[\"config\"][\"enable_recording\"]}')"
-```
-
-**Expected output:**
-```
-name: test2, platform: whereby
-Room: test2-20251009192341
-URL: https://monadical.daily.co/test2-20251009192341?t=eyJhbGc...
-Recording: raw-tracks
-```
-
---
-
-## Success Criteria Checklist
-
- [x] Room name includes Reflector room prefix (`test2-...`)
- [x] Meeting URL contains JWT token (`?t=...`)
- [x] Token has `sr: true` (auto-recording enabled)
- [x] Daily.co room config: `enable_recording: "raw-tracks"`
- [x] Browser loads Daily.co interface (not Whereby)
- [x] Recording auto-starts when participant joins
- [x] Webhooks received: participant.joined, recording.started, participant.left, recording.ready-to-download
- [x] Recording status: `finished`
- [x] S3 contains 2 files: audio (.webm) and video (.webm)
- [x] S3 path: `monadical/test2-{timestamp}/{recording-start-ts}-{participant-uuid}-cam-{audio|video}-{track-start-ts}`
- [x] Database `num_clients` increments/decrements correctly
- [x] **Database recording entry created** with correct S3 path and status `completed`
- [ ] **Database transcript entry created** with status `uploaded`
- [ ] **Audio file downloaded** to `data/{transcript_id}/upload.webm`
- [ ] **Transcript has REAL data**: AI-generated title based on conversation
- [ ] **Transcript has topics** generated from actual content
- [ ] **Transcript has participants** with proper speaker diarization
- [ ] **Topics contain word-level data** with accurate timestamps and speaker IDs
- [ ] **Total duration** matches actual meeting length
- [ ] **MP3 and waveform files generated** by file processing pipeline
- [ ] **Frontend transcript page loads** without "Failed to load audio" error
- [ ] **Audio player functional** with working playback and waveform visualization
- [ ] **Multitrack processing completed** without errors in worker logs
- [ ] **Modal GPU backends accessible** (transcription and diarization)
--- a/server/Dockerfile
+++ b/server/Dockerfile
@@ -1,12 +1,11 @@
 FROM python:3.12-slim

 ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy \
-    UV_NO_CACHE=1
+    UV_LINK_MODE=copy

 # builder install base dependencies
 WORKDIR /tmp
-RUN apt-get update && apt-get install -y curl ffmpeg && apt-get clean
+RUN apt-get update && apt-get install -y curl && apt-get clean
 ADD https://astral.sh/uv/install.sh /uv-installer.sh
 RUN sh /uv-installer.sh && rm /uv-installer.sh
 ENV PATH="/root/.local/bin/:$PATH"
@@ -14,8 +13,8 @@ ENV PATH="/root/.local/bin/:$PATH"
 # install application dependencies
 RUN mkdir -p /app
 WORKDIR /app
-COPY pyproject.toml uv.lock README.md /app/
-RUN uv sync --compile-bytecode --locked
+COPY pyproject.toml uv.lock /app/
+RUN touch README.md && env uv sync --compile-bytecode --locked

 # pre-download nltk packages
 RUN uv run python -c "import nltk; nltk.download('punkt_tab'); nltk.download('averaged_perceptron_tagger_eng')"
@@ -27,15 +26,4 @@ COPY migrations /app/migrations
 COPY reflector /app/reflector
 WORKDIR /app

-# Create symlink for libgomp if it doesn't exist (for ARM64 compatibility)
-RUN if [ "$(uname -m)" = "aarch64" ] && [ ! -f /usr/lib/libgomp.so.1 ]; then \
-    LIBGOMP_PATH=$(find /app/.venv/lib -path "*/torch.libs/libgomp*.so.*" 2>/dev/null | head -n1); \
-    if [ -n "$LIBGOMP_PATH" ]; then \
-    ln -sf "$LIBGOMP_PATH" /usr/lib/libgomp.so.1; \
-    fi \
-    fi
-
-# Pre-check just to make sure the image will not fail
-RUN uv run python -c "import silero_vad.model"
-
 CMD ["./runserver.sh"]
--- a/server/README.md
+++ b/server/README.md
@@ -20,25 +20,3 @@ Polls SQS every 60 seconds via /server/reflector/worker/process.py:24-62:
 # Every 60 seconds, check for new recordings
 sqs = boto3.client("sqs", ...)
 response = sqs.receive_message(QueueUrl=queue_url, ...)
-
-# Requeue
-
-```bash
-uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID
-```
-
-## Pipeline Management
-
-### Continue stuck pipeline from final summaries (identify_participants) step:
-
-```bash
-uv run python -c "from reflector.pipelines.main_live_pipeline import task_pipeline_final_summaries; result = task_pipeline_final_summaries.delay(transcript_id='TRANSCRIPT_ID'); print(f'Task queued: {result.id}')"
-```
-
-### Run full post-processing pipeline (continues to completion):
-
-```bash
-uv run python -c "from reflector.pipelines.main_live_pipeline import pipeline_post; pipeline_post(transcript_id='TRANSCRIPT_ID')"
-```
-
-.
--- a/server/docs/data_retention.md
+++ b/server/docs/data_retention.md
@@ -1,95 +0,0 @@
-# Data Retention and Cleanup
-
-## Overview
-
-For public instances of Reflector, a data retention policy is automatically enforced to delete anonymous user data after a configurable period (default: 7 days). This ensures compliance with privacy expectations and prevents unbounded storage growth.
-
-## Configuration
-
-### Environment Variables
-
- `PUBLIC_MODE` (bool): Must be set to `true` to enable automatic cleanup
- `PUBLIC_DATA_RETENTION_DAYS` (int): Number of days to retain anonymous data (default: 7)
-
-### What Gets Deleted
-
-When data reaches the retention period, the following items are automatically removed:
-
-1. **Transcripts** from anonymous users (where `user_id` is NULL):
-   - Database records
-   - Local files (audio.wav, audio.mp3, audio.json waveform)
-   - Storage files (cloud storage if configured)
-
-## Automatic Cleanup
-
-### Celery Beat Schedule
-
-When `PUBLIC_MODE=true`, a Celery beat task runs daily at 3 AM to clean up old data:
-
-```python
-# Automatically scheduled when PUBLIC_MODE=true
-"cleanup_old_public_data": {
-    "task": "reflector.worker.cleanup.cleanup_old_public_data",
-    "schedule": crontab(hour=3, minute=0),  # Daily at 3 AM
-}
-```
-
-### Running the Worker
-
-Ensure both Celery worker and beat scheduler are running:
-
-```bash
-# Start Celery worker
-uv run celery -A reflector.worker.app worker --loglevel=info
-
-# Start Celery beat scheduler (in another terminal)
-uv run celery -A reflector.worker.app beat
-```
-
-## Manual Cleanup
-
-For testing or manual intervention, use the cleanup tool:
-
-```bash
-# Delete data older than 7 days (default)
-uv run python -m reflector.tools.cleanup_old_data
-
-# Delete data older than 30 days
-uv run python -m reflector.tools.cleanup_old_data --days 30
-```
-
-Note: The manual tool uses the same implementation as the Celery worker task to ensure consistency.
-
-## Important Notes
-
-1. **User Data Deletion**: Only anonymous data (where `user_id` is NULL) is deleted. Authenticated user data is preserved.
-
-2. **Storage Cleanup**: The system properly cleans up both local files and cloud storage when configured.
-
-3. **Error Handling**: If individual deletions fail, the cleanup continues and logs errors. Failed deletions are reported in the task output.
-
-4. **Public Instance Only**: The automatic cleanup task only runs when `PUBLIC_MODE=true` to prevent accidental data loss in private deployments.
-
-## Testing
-
-Run the cleanup tests:
-
-```bash
-uv run pytest tests/test_cleanup.py -v
-```
-
-## Monitoring
-
-Check Celery logs for cleanup task execution:
-
-```bash
-# Look for cleanup task logs
-grep "cleanup_old_public_data" celery.log
-grep "Starting cleanup of old public data" celery.log
-```
-
-Task statistics are logged after each run:
- Number of transcripts deleted
- Number of meetings deleted
- Number of orphaned recordings deleted
- Any errors encountered
--- a/server/docs/gpu/api-transcription.md
+++ b/server/docs/gpu/api-transcription.md
@@ -1,194 +0,0 @@
-## Reflector GPU Transcription API (Specification)
-
-This document defines the Reflector GPU transcription API that all implementations must adhere to. Current implementations include NVIDIA Parakeet (NeMo) and Whisper (faster-whisper), both deployed on Modal.com. The API surface and response shapes are OpenAI/Whisper-compatible, so clients can switch implementations by changing only the base URL.
-
-### Base URL and Authentication
-
- Example base URLs (Modal web endpoints):
-
-  - Parakeet: `https://<account>--reflector-transcriber-parakeet-web.modal.run`
-  - Whisper: `https://<account>--reflector-transcriber-web.modal.run`
-
- All endpoints are served under `/v1` and require a Bearer token:
-
-```
-Authorization: Bearer <REFLECTOR_GPU_APIKEY>
-```
-
-Note: To switch implementations, deploy the desired variant and point `TRANSCRIPT_URL` to its base URL. The API is identical.
-
-### Supported file types
-
-`mp3, mp4, mpeg, mpga, m4a, wav, webm`
-
-### Models and languages
-
- Parakeet (NVIDIA NeMo): default `nvidia/parakeet-tdt-0.6b-v2`
-  - Language support: only `en`. Other languages return HTTP 400.
- Whisper (faster-whisper): default `large-v2` (or deployment-specific)
-  - Language support: multilingual (per Whisper model capabilities).
-
-Note: The `model` parameter is accepted by all implementations for interface parity. Some backends may treat it as informational.
-
-### Endpoints
-
-#### POST /v1/audio/transcriptions
-
-Transcribe one or more uploaded audio files.
-
-Request: multipart/form-data
-
- `file` (File) — optional. Single file to transcribe.
- `files` (File[]) — optional. One or more files to transcribe.
- `model` (string) — optional. Defaults to the implementation-specific model (see above).
- `language` (string) — optional, defaults to `en`.
-  - Parakeet: only `en` is accepted; other values return HTTP 400
-  - Whisper: model-dependent; typically multilingual
- `batch` (boolean) — optional, defaults to `false`.
-
-Notes:
-
- Provide either `file` or `files`, not both. If neither is provided, HTTP 400.
- `batch` requires `files`; using `batch=true` without `files` returns HTTP 400.
- Response shape for multiple files is the same regardless of `batch`.
- Files sent to this endpoint are processed in a single pass (no VAD/chunking). This is intended for short clips (roughly ≤ 30s; depends on GPU memory/model). For longer audio, prefer `/v1/audio/transcriptions-from-url` which supports VAD-based chunking.
-
-Responses
-
-Single file 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"
-}
-```
-
-Multiple files response:
-
-```json
-{
-  "results": [
-    {"filename": "a1.mp3", "text": "...", "words": [...]},
-    {"filename": "a2.mp3", "text": "...", "words": [...]}]
-}
-```
-
-Notes:
-
- Word objects always include keys: `word`, `start`, `end`.
- Some implementations may include a trailing space in `word` to match Whisper tokenization behavior; clients should trim if needed.
-
-Example curl (single file):
-
-```bash
-curl -X POST \
-  -H "Authorization: Bearer $REFLECTOR_GPU_APIKEY" \
-  -F "file=@/path/to/audio.mp3" \
-  -F "language=en" \
-  "$BASE_URL/v1/audio/transcriptions"
-```
-
-Example curl (multiple files, batch):
-
-```bash
-curl -X POST \
-  -H "Authorization: Bearer $REFLECTOR_GPU_APIKEY" \
-  -F "files=@/path/a1.mp3" -F "files=@/path/a2.mp3" \
-  -F "batch=true" -F "language=en" \
-  "$BASE_URL/v1/audio/transcriptions"
-```
-
-#### POST /v1/audio/transcriptions-from-url
-
-Transcribe a single remote audio file by URL.
-
-Request: application/json
-
-Body parameters:
-
- `audio_file_url` (string) — required. URL of the audio file to transcribe.
- `model` (string) — optional. Defaults to the implementation-specific model (see above).
- `language` (string) — optional, defaults to `en`. Parakeet only accepts `en`.
- `timestamp_offset` (number) — optional, defaults to `0.0`. Added to each word's `start`/`end` in the response.
-
-```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",
-  "words": [
-    { "word": "hello", "start": 10.0, "end": 10.5 },
-    { "word": "world", "start": 10.5, "end": 11.0 }
-  ]
-}
-```
-
-Notes:
-
- `timestamp_offset` is added to each word’s `start`/`end` in the response.
- Implementations may perform VAD-based chunking and batching for long-form audio; word timings are adjusted accordingly.
-
-Example curl:
-
-```bash
-curl -X POST \
-  -H "Authorization: Bearer $REFLECTOR_GPU_APIKEY" \
-  -H "Content-Type: application/json" \
-  -d '{
-        "audio_file_url": "https://example.com/audio.mp3",
-        "language": "en",
-        "timestamp_offset": 0
-      }' \
-  "$BASE_URL/v1/audio/transcriptions-from-url"
-```
-
-### Error handling
-
- 400 Bad Request
-  - Parakeet: `language` other than `en`
-  - Missing required parameters (`file`/`files` for upload; `audio_file_url` for URL endpoint)
-  - Unsupported file extension
- 401 Unauthorized
-  - Missing or invalid Bearer token
- 404 Not Found
-  - `audio_file_url` does not exist
-
-### Implementation details
-
- GPUs: A10G for small-file/live, L40S for large-file URL transcription (subject to deployment)
- VAD chunking and segment batching; word timings adjusted and overlapping ends constrained
- Pads very short segments (< 0.5s) to avoid model crashes on some backends
-
-### Server configuration (Reflector API)
-
-Set the Reflector server to use the Modal backend and point `TRANSCRIPT_URL` to your chosen deployment:
-
-```
-TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://<account>--reflector-transcriber-parakeet-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=<REFLECTOR_GPU_APIKEY>
-```
-
-### Conformance tests
-
-Use the pytest-based conformance tests to validate any new implementation (including self-hosted) against this spec:
-
-```
-TRANSCRIPT_URL=https://<your-deployment-base> \
-TRANSCRIPT_MODAL_API_KEY=your-api-key \
-uv run -m pytest -m model_api --no-cov server/tests/test_model_api_transcript.py
-```
--- a/server/docs/webhook.md
+++ b/server/docs/webhook.md
@@ -1,233 +0,0 @@
-# Reflector Webhook Documentation
-
-## Overview
-
-Reflector supports webhook notifications to notify external systems when transcript processing is completed. Webhooks can be configured per room and are triggered automatically after a transcript is successfully processed.
-
-## Configuration
-
-Webhooks are configured at the room level with two fields:
- `webhook_url`: The HTTPS endpoint to receive webhook notifications
- `webhook_secret`: Optional secret key for HMAC signature verification (auto-generated if not provided)
-
-## Events
-
-### `transcript.completed`
-
-Triggered when a transcript has been fully processed, including transcription, diarization, summarization, topic detection and calendar event integration.
-
-### `test`
-
-A test event that can be triggered manually to verify webhook configuration.
-
-## Webhook Request Format
-
-### Headers
-
-All webhook requests include the following headers:
-
-| Header | Description | Example |
-|--------|-------------|---------|
-| `Content-Type` | Always `application/json` | `application/json` |
-| `User-Agent` | Identifies Reflector as the source | `Reflector-Webhook/1.0` |
-| `X-Webhook-Event` | The event type | `transcript.completed` or `test` |
-| `X-Webhook-Retry` | Current retry attempt number | `0`, `1`, `2`... |
-| `X-Webhook-Signature` | HMAC signature (if secret configured) | `t=1735306800,v1=abc123...` |
-
-### Signature Verification
-
-If a webhook secret is configured, Reflector includes an HMAC-SHA256 signature in the `X-Webhook-Signature` header to verify the webhook authenticity.
-
-The signature format is: `t={timestamp},v1={signature}`
-
-To verify the signature:
-1. Extract the timestamp and signature from the header
-2. Create the signed payload: `{timestamp}.{request_body}`
-3. Compute HMAC-SHA256 of the signed payload using your webhook secret
-4. Compare the computed signature with the received signature
-
-Example verification (Python):
-```python
-import hmac
-import hashlib
-
-def verify_webhook_signature(payload: bytes, signature_header: str, secret: str) -> bool:
-    # Parse header: "t=1735306800,v1=abc123..."
-    parts = dict(part.split("=") for part in signature_header.split(","))
-    timestamp = parts["t"]
-    received_signature = parts["v1"]
-
-    # Create signed payload
-    signed_payload = f"{timestamp}.{payload.decode('utf-8')}"
-
-    # Compute expected signature
-    expected_signature = hmac.new(
-        secret.encode("utf-8"),
-        signed_payload.encode("utf-8"),
-        hashlib.sha256
-    ).hexdigest()
-
-    # Compare signatures
-    return hmac.compare_digest(expected_signature, received_signature)
-```
-
-## Event Payloads
-
-### `transcript.completed` Event
-
-This event includes a convenient URL for accessing the transcript:
- `frontend_url`: Direct link to view the transcript in the web interface
-
-```json
-{
-  "event": "transcript.completed",
-  "event_id": "transcript.completed-abc-123-def-456",
-  "timestamp": "2025-08-27T12:34:56.789012Z",
-  "transcript": {
-    "id": "abc-123-def-456",
-    "room_id": "room-789",
-    "created_at": "2025-08-27T12:00:00Z",
-    "duration": 1800.5,
-    "title": "Q3 Product Planning Meeting",
-    "short_summary": "Team discussed Q3 product roadmap, prioritizing mobile app features and API improvements.",
-    "long_summary": "The product team met to finalize the Q3 roadmap. Key decisions included...",
-    "webvtt": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v Speaker 1>Welcome everyone to today's meeting...",
-    "topics": [
-      {
-        "title": "Introduction and Agenda",
-        "summary": "Meeting kickoff with agenda review",
-        "timestamp": 0.0,
-        "duration": 120.0,
-        "webvtt": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v Speaker 1>Welcome everyone..."
-      },
-      {
-        "title": "Mobile App Features Discussion",
-        "summary": "Team reviewed proposed mobile app features for Q3",
-        "timestamp": 120.0,
-        "duration": 600.0,
-        "webvtt": "WEBVTT\n\n00:02:00.000 --> 00:02:10.000\n<v Speaker 2>Let's talk about the mobile app..."
-      }
-    ],
-    "participants": [
-      {
-        "id": "participant-1",
-        "name": "John Doe",
-        "speaker": "Speaker 1"
-      },
-      {
-        "id": "participant-2",
-        "name": "Jane Smith",
-        "speaker": "Speaker 2"
-      }
-    ],
-    "source_language": "en",
-    "target_language": "en",
-    "status": "completed",
-    "frontend_url": "https://app.reflector.com/transcripts/abc-123-def-456"
-  },
-  "room": {
-    "id": "room-789",
-    "name": "Product Team Room"
-  },
-  "calendar_event": {
-    "id": "calendar-event-123",
-    "ics_uid": "event-123",
-    "title": "Q3 Product Planning Meeting",
-    "start_time": "2025-08-27T12:00:00Z",
-    "end_time": "2025-08-27T12:30:00Z",
-    "description": "Team discussed Q3 product roadmap, prioritizing mobile app features and API improvements.",
-    "location": "Conference Room 1",
-    "attendees": [
-      {
-        "id": "participant-1",
-        "name": "John Doe",
-        "speaker": "Speaker 1"
-      },
-      {
-        "id": "participant-2",
-        "name": "Jane Smith",
-        "speaker": "Speaker 2"
-      }
-    ]
-  }
-}
-```
-
-### `test` Event
-
-```json
-{
-  "event": "test",
-  "event_id": "test.2025-08-27T12:34:56.789012Z",
-  "timestamp": "2025-08-27T12:34:56.789012Z",
-  "message": "This is a test webhook from Reflector",
-  "room": {
-    "id": "room-789",
-    "name": "Product Team Room"
-  }
-}
-```
-
-## Retry Policy
-
-Webhooks are delivered with automatic retry logic to handle transient failures. When a webhook delivery fails due to server errors or network issues, Reflector will automatically retry the delivery multiple times over an extended period.
-
-### Retry Mechanism
-
-Reflector implements an exponential backoff strategy for webhook retries:
-
- **Initial retry delay**: 60 seconds after the first failure
- **Exponential backoff**: Each subsequent retry waits approximately twice as long as the previous one
- **Maximum retry interval**: 1 hour (backoff is capped at this duration)
- **Maximum retry attempts**: 30 attempts total
- **Total retry duration**: Retries continue for approximately 24 hours
-
-### How Retries Work
-
-When a webhook fails, Reflector will:
-1. Wait 60 seconds, then retry (attempt #1)
-2. If it fails again, wait ~2 minutes, then retry (attempt #2)
-3. Continue doubling the wait time up to a maximum of 1 hour between attempts
-4. Keep retrying at 1-hour intervals until successful or 30 attempts are exhausted
-
-The `X-Webhook-Retry` header indicates the current retry attempt number (0 for the initial attempt, 1 for first retry, etc.), allowing your endpoint to track retry attempts.
-
-### Retry Behavior by HTTP Status Code
-
-| Status Code | Behavior |
-|-------------|----------|
-| 2xx (Success) | No retry, webhook marked as delivered |
-| 4xx (Client Error) | No retry, request is considered permanently failed |
-| 5xx (Server Error) | Automatic retry with exponential backoff |
-| Network/Timeout Error | Automatic retry with exponential backoff |
-
-**Important Notes:**
- Webhooks timeout after 30 seconds. If your endpoint takes longer to respond, it will be considered a timeout error and retried.
- During the retry period (~24 hours), you may receive the same webhook multiple times if your endpoint experiences intermittent failures.
- There is no mechanism to manually retry failed webhooks after the retry period expires.
-
-## Testing Webhooks
-
-You can test your webhook configuration before processing transcripts:
-
-```http
-POST /v1/rooms/{room_id}/webhook/test
-```
-
-Response:
-```json
-{
-  "success": true,
-  "status_code": 200,
-  "message": "Webhook test successful",
-  "response_preview": "OK"
-}
-```
-
-Or in case of failure:
-```json
-{
-  "success": false,
-  "error": "Webhook request timed out (10 seconds)"
-}
-```
--- a/server/env.example
+++ b/server/env.example
@@ -20,24 +20,24 @@ AUTH_JWT_AUDIENCE=

 ## Using local whisper
 #TRANSCRIPT_BACKEND=whisper
+#WHISPER_MODEL_SIZE=tiny

 ## Using serverless modal.com (require reflector-gpu-modal deployed)
 #TRANSCRIPT_BACKEND=modal
 #TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
+#TRANSLATE_URL=https://xxxxx--reflector-translator-web.modal.run
 #TRANSCRIPT_MODAL_API_KEY=xxxxx

 TRANSCRIPT_BACKEND=modal
-TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
-TRANSCRIPT_MODAL_API_KEY=
+TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=***REMOVED***

 ## =======================================================
-## Translation backend
+## Transcription backend
 ##
 ## Only available in modal atm
 ## =======================================================
-TRANSLATION_BACKEND=modal
 TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
-#TRANSLATION_MODAL_API_KEY=xxxxx

 ## =======================================================
 ## LLM backend
@@ -47,11 +47,28 @@ TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
 ## llm backend implementation
 ## =======================================================

-## Context size for summary generation (tokens)
-# LLM_MODEL=microsoft/phi-4
-LLM_CONTEXT_WINDOW=16000
-LLM_URL=
-LLM_API_KEY=sk-
+## Using serverless modal.com (require reflector-gpu-modal deployed)
+LLM_BACKEND=modal
+LLM_URL=https://monadical-sas--reflector-llm-web.modal.run
+LLM_MODAL_API_KEY=***REMOVED***
+ZEPHYR_LLM_URL=https://monadical-sas--reflector-llm-zephyr-web.modal.run
+
+
+## Using OpenAI
+#LLM_BACKEND=openai
+#LLM_OPENAI_KEY=xxx
+#LLM_OPENAI_MODEL=gpt-3.5-turbo
+
+## Using GPT4ALL
+#LLM_BACKEND=openai
+#LLM_URL=http://localhost:4891/v1/completions
+#LLM_OPENAI_MODEL="GPT4All Falcon"
+
+## Default LLM MODEL NAME
+#DEFAULT_LLM=lmsys/vicuna-13b-v1.5
+
+## Cache directory to store models
+CACHE_DIR=data

 ## =======================================================
 ## Diarization
@@ -60,9 +77,7 @@ LLM_API_KEY=sk-
 ## To allow diarization, you need to expose expose the files to be dowloded by the pipeline
 ## =======================================================
 DIARIZATION_ENABLED=false
-DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
-#DIARIZATION_MODAL_API_KEY=xxxxx


 ## =======================================================
@@ -71,27 +86,3 @@ DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run

 ## Sentry DSN configuration
 #SENTRY_DSN=
-
-## =======================================================
-## Video Platform Configuration
-## =======================================================
-
-## Whereby
-#WHEREBY_API_KEY=your-whereby-api-key
-#WHEREBY_WEBHOOK_SECRET=your-whereby-webhook-secret
-#AWS_WHEREBY_ACCESS_KEY_ID=your-aws-key
-#AWS_WHEREBY_ACCESS_KEY_SECRET=your-aws-secret
-#AWS_PROCESS_RECORDING_QUEUE_URL=https://sqs.us-west-2.amazonaws.com/...
-
-## Daily.co
-#DAILY_API_KEY=your-daily-api-key
-#DAILY_WEBHOOK_SECRET=your-daily-webhook-secret
-#DAILY_SUBDOMAIN=your-subdomain
-#AWS_DAILY_S3_BUCKET=your-daily-bucket
-#AWS_DAILY_S3_REGION=us-west-2
-#AWS_DAILY_ROLE_ARN=arn:aws:iam::ACCOUNT:role/DailyRecording
-
-## Platform Selection
-#DAILY_MIGRATION_ENABLED=false           # Enable Daily.co support
-#DAILY_MIGRATION_ROOM_IDS=[]            # Specific rooms to use Daily
-#DEFAULT_VIDEO_PLATFORM=whereby          # Default platform for new rooms
--- a/server/gpu/modal_deployments/README.md
+++ b/server/gpu/modal_deployments/README.md
@@ -0,0 +1,81 @@
+# 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_llm.py` - LLM API
+- `reflector_transcriber.py` - Transcription 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_llm.py
+...
+└── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
+```
+
+Then in your reflector api configuration `.env`, you can set theses keys:
+
+```
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
+
+LLM_BACKEND=modal
+LLM_URL=https://xxxx--reflector-llm-web.modal.run
+LLM_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
+
+`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/server/gpu/modal_deployments/reflector_diarizer.py
+++ b/server/gpu/modal_deployments/reflector_diarizer.py
@@ -0,0 +1,187 @@
+"""
+Reflector GPU backend - diarizer
+===================================
+"""
+
+import os
+
+import modal.gpu
+from modal import App, Image, Secret, asgi_app, enter, method
+from pydantic import BaseModel
+
+PYANNOTE_MODEL_NAME: str = "pyannote/speaker-diarization-3.1"
+MODEL_DIR = "/root/diarization_models"
+app = App(name="reflector-diarizer")
+
+
+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 = (
+    Image.debian_slim(python_version="3.10.8")
+    .pip_install(
+        "pyannote.audio==3.1.0",
+        "requests",
+        "onnx",
+        "torchaudio",
+        "onnxruntime-gpu",
+        "torch==2.0.0",
+        "transformers==4.34.0",
+        "sentencepiece",
+        "protobuf",
+        "numpy",
+        "huggingface_hub",
+        "hf-transfer",
+    )
+    .run_function(
+        download_pyannote_audio, secrets=[Secret.from_name("my-huggingface-secret")]
+    )
+    .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=modal.gpu.A100(size="40GB"),
+    timeout=60 * 30,
+    scaledown_window=60,
+    allow_concurrent_inputs=1,
+    image=diarizer_image,
+)
+class Diarizer:
+    @enter()
+    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"
+        self.diarization_pipeline = Pipeline.from_pretrained(
+            PYANNOTE_MODEL_NAME, cache_dir=MODEL_DIR
+        )
+        self.diarization_pipeline.to(torch.device(self.device))
+
+    @method()
+    def diarize(self, audio_data: str, audio_suffix: str, timestamp: float):
+        import tempfile
+
+        import torchaudio
+
+        with tempfile.NamedTemporaryFile("wb+", suffix=f".{audio_suffix}") as fp:
+            fp.write(audio_data)
+
+            print("Diarizing audio")
+            waveform, sample_rate = torchaudio.load(fp.name)
+            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,
+    allow_concurrent_inputs=40,
+    secrets=[
+        Secret.from_name("reflector-gpu"),
+    ],
+    image=diarizer_image,
+)
+@asgi_app()
+def web():
+    import requests
+    from fastapi import Depends, FastAPI, HTTPException, status
+    from fastapi.security import OAuth2PasswordBearer
+
+    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"},
+            )
+
+    def validate_audio_file(audio_file_url: str):
+        # Check if the audio file exists
+        response = requests.head(audio_file_url, allow_redirects=True)
+        if response.status_code == 404:
+            raise HTTPException(
+                status_code=response.status_code,
+                detail="The audio file does not exist.",
+            )
+
+    class DiarizationResponse(BaseModel):
+        result: dict
+
+    @app.post(
+        "/diarize", dependencies=[Depends(apikey_auth), Depends(validate_audio_file)]
+    )
+    def diarize(
+        audio_file_url: str, timestamp: float = 0.0
+    ) -> HTTPException | DiarizationResponse:
+        # Currently the uploaded files are in mp3 format
+        audio_suffix = "mp3"
+
+        print("Downloading audio file")
+        response = requests.get(audio_file_url, allow_redirects=True)
+        print("Audio file downloaded successfully")
+
+        func = diarizerstub.diarize.spawn(
+            audio_data=response.content, audio_suffix=audio_suffix, timestamp=timestamp
+        )
+        result = func.get()
+        return result
+
+    return app
--- a/server/gpu/modal_deployments/reflector_llm.py
+++ b/server/gpu/modal_deployments/reflector_llm.py
@@ -0,0 +1,214 @@
+"""
+Reflector GPU backend - LLM
+===========================
+
+"""
+
+import json
+import os
+import threading
+from typing import Optional
+
+import modal
+from modal import App, Image, Secret, asgi_app, enter, exit, method
+
+# LLM
+LLM_MODEL: str = "lmsys/vicuna-13b-v1.5"
+LLM_LOW_CPU_MEM_USAGE: bool = True
+LLM_TORCH_DTYPE: str = "bfloat16"
+LLM_MAX_NEW_TOKENS: int = 300
+
+IMAGE_MODEL_DIR = "/root/llm_models"
+
+app = App(name="reflector-llm")
+
+
+def download_llm():
+    from huggingface_hub import snapshot_download
+
+    print("Downloading LLM model")
+    snapshot_download(LLM_MODEL, cache_dir=IMAGE_MODEL_DIR)
+    print("LLM model downloaded")
+
+
+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=IMAGE_MODEL_DIR, new_cache_dir=IMAGE_MODEL_DIR)
+    print("LLM cache moved")
+
+
+llm_image = (
+    Image.debian_slim(python_version="3.10.8")
+    .apt_install("git")
+    .pip_install(
+        "transformers",
+        "torch",
+        "sentencepiece",
+        "protobuf",
+        "jsonformer==0.12.0",
+        "accelerate==0.21.0",
+        "einops==0.6.1",
+        "hf-transfer~=0.1",
+        "huggingface_hub==0.16.4",
+    )
+    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
+    .run_function(download_llm)
+    .run_function(migrate_cache_llm)
+)
+
+
+@app.cls(
+    gpu="A100",
+    timeout=60 * 5,
+    scaledown_window=60 * 5,
+    allow_concurrent_inputs=15,
+    image=llm_image,
+)
+class LLM:
+    @enter()
+    def enter(self):
+        import torch
+        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+        print("Instance llm model")
+        model = AutoModelForCausalLM.from_pretrained(
+            LLM_MODEL,
+            torch_dtype=getattr(torch, LLM_TORCH_DTYPE),
+            low_cpu_mem_usage=LLM_LOW_CPU_MEM_USAGE,
+            cache_dir=IMAGE_MODEL_DIR,
+            local_files_only=True,
+        )
+
+        # JSONFormer doesn't yet support generation configs
+        print("Instance llm generation config")
+        model.config.max_new_tokens = LLM_MAX_NEW_TOKENS
+
+        # generation configuration
+        gen_cfg = GenerationConfig.from_model_config(model.config)
+        gen_cfg.max_new_tokens = LLM_MAX_NEW_TOKENS
+
+        # load tokenizer
+        print("Instance llm tokenizer")
+        tokenizer = AutoTokenizer.from_pretrained(
+            LLM_MODEL, cache_dir=IMAGE_MODEL_DIR, local_files_only=True
+        )
+
+        # move model to gpu
+        print("Move llm model to GPU")
+        model = model.cuda()
+
+        print("Warmup llm done")
+        self.model = model
+        self.tokenizer = tokenizer
+        self.gen_cfg = gen_cfg
+        self.GenerationConfig = GenerationConfig
+
+        self.lock = threading.Lock()
+
+    @exit()
+    def exit():
+        print("Exit llm")
+
+    @method()
+    def generate(
+        self, prompt: str, gen_schema: str | None, gen_cfg: str | None
+    ) -> dict:
+        """
+        Perform a generation action using the LLM
+        """
+        print(f"Generate {prompt=}")
+        if gen_cfg:
+            gen_cfg = self.GenerationConfig.from_dict(json.loads(gen_cfg))
+        else:
+            gen_cfg = self.gen_cfg
+
+        # If a gen_schema is given, conform to gen_schema
+        with self.lock:
+            if gen_schema:
+                import jsonformer
+
+                print(f"Schema {gen_schema=}")
+                jsonformer_llm = jsonformer.Jsonformer(
+                    model=self.model,
+                    tokenizer=self.tokenizer,
+                    json_schema=json.loads(gen_schema),
+                    prompt=prompt,
+                    max_string_token_length=gen_cfg.max_new_tokens,
+                )
+                response = jsonformer_llm()
+            else:
+                # If no gen_schema, perform prompt only generation
+
+                # tokenize prompt
+                input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
+                    self.model.device
+                )
+                output = self.model.generate(input_ids, generation_config=gen_cfg)
+
+                # decode output
+                response = self.tokenizer.decode(
+                    output[0].cpu(), skip_special_tokens=True
+                )
+                response = response[len(prompt) :]
+        print(f"Generated {response=}")
+        return {"text": response}
+
+
+# -------------------------------------------------------------------
+# Web API
+# -------------------------------------------------------------------
+
+
+@app.function(
+    scaledown_window=60 * 10,
+    timeout=60 * 5,
+    allow_concurrent_inputs=45,
+    secrets=[
+        Secret.from_name("reflector-gpu"),
+    ],
+)
+@asgi_app()
+def web():
+    from fastapi import Depends, FastAPI, HTTPException, status
+    from fastapi.security import OAuth2PasswordBearer
+    from pydantic import BaseModel
+
+    llmstub = LLM()
+
+    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 LLMRequest(BaseModel):
+        prompt: str
+        gen_schema: Optional[dict] = None
+        gen_cfg: Optional[dict] = None
+
+    @app.post("/llm", dependencies=[Depends(apikey_auth)])
+    def llm(
+        req: LLMRequest,
+    ):
+        gen_schema = json.dumps(req.gen_schema) if req.gen_schema else None
+        gen_cfg = json.dumps(req.gen_cfg) if req.gen_cfg else None
+        func = llmstub.generate.spawn(
+            prompt=req.prompt, gen_schema=gen_schema, gen_cfg=gen_cfg
+        )
+        result = func.get()
+        return result
+
+    return app
--- a/server/gpu/modal_deployments/reflector_llm_zephyr.py
+++ b/server/gpu/modal_deployments/reflector_llm_zephyr.py
@@ -0,0 +1,220 @@
+"""
+Reflector GPU backend - LLM
+===========================
+
+"""
+
+import json
+import os
+import threading
+from typing import Optional
+
+import modal
+from modal import App, Image, Secret, asgi_app, enter, exit, method
+
+# LLM
+LLM_MODEL: str = "HuggingFaceH4/zephyr-7b-alpha"
+LLM_LOW_CPU_MEM_USAGE: bool = True
+LLM_TORCH_DTYPE: str = "bfloat16"
+LLM_MAX_NEW_TOKENS: int = 300
+
+IMAGE_MODEL_DIR = "/root/llm_models/zephyr"
+
+app = App(name="reflector-llm-zephyr")
+
+
+def download_llm():
+    from huggingface_hub import snapshot_download
+
+    print("Downloading LLM model")
+    snapshot_download(LLM_MODEL, cache_dir=IMAGE_MODEL_DIR)
+    print("LLM model downloaded")
+
+
+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=IMAGE_MODEL_DIR, new_cache_dir=IMAGE_MODEL_DIR)
+    print("LLM cache moved")
+
+
+llm_image = (
+    Image.debian_slim(python_version="3.10.8")
+    .apt_install("git")
+    .pip_install(
+        "transformers==4.34.0",
+        "torch",
+        "sentencepiece",
+        "protobuf",
+        "jsonformer==0.12.0",
+        "accelerate==0.21.0",
+        "einops==0.6.1",
+        "hf-transfer~=0.1",
+        "huggingface_hub==0.16.4",
+    )
+    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
+    .run_function(download_llm)
+    .run_function(migrate_cache_llm)
+)
+
+
+@app.cls(
+    gpu="A10G",
+    timeout=60 * 5,
+    scaledown_window=60 * 5,
+    allow_concurrent_inputs=10,
+    image=llm_image,
+)
+class LLM:
+    @enter()
+    def enter(self):
+        import torch
+        from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+        print("Instance llm model")
+        model = AutoModelForCausalLM.from_pretrained(
+            LLM_MODEL,
+            torch_dtype=getattr(torch, LLM_TORCH_DTYPE),
+            low_cpu_mem_usage=LLM_LOW_CPU_MEM_USAGE,
+            cache_dir=IMAGE_MODEL_DIR,
+            local_files_only=True,
+        )
+
+        # JSONFormer doesn't yet support generation configs
+        print("Instance llm generation config")
+        model.config.max_new_tokens = LLM_MAX_NEW_TOKENS
+
+        # generation configuration
+        gen_cfg = GenerationConfig.from_model_config(model.config)
+        gen_cfg.max_new_tokens = LLM_MAX_NEW_TOKENS
+
+        # load tokenizer
+        print("Instance llm tokenizer")
+        tokenizer = AutoTokenizer.from_pretrained(
+            LLM_MODEL, cache_dir=IMAGE_MODEL_DIR, local_files_only=True
+        )
+        gen_cfg.pad_token_id = tokenizer.eos_token_id
+        gen_cfg.eos_token_id = tokenizer.eos_token_id
+        tokenizer.pad_token = tokenizer.eos_token
+        model.config.pad_token_id = tokenizer.eos_token_id
+
+        # move model to gpu
+        print("Move llm model to GPU")
+        model = model.cuda()
+
+        print("Warmup llm done")
+        self.model = model
+        self.tokenizer = tokenizer
+        self.gen_cfg = gen_cfg
+        self.GenerationConfig = GenerationConfig
+        self.lock = threading.Lock()
+
+    @exit()
+    def exit():
+        print("Exit llm")
+
+    @method()
+    def generate(
+        self, prompt: str, gen_schema: str | None, gen_cfg: str | None
+    ) -> dict:
+        """
+        Perform a generation action using the LLM
+        """
+        print(f"Generate {prompt=}")
+        if gen_cfg:
+            gen_cfg = self.GenerationConfig.from_dict(json.loads(gen_cfg))
+            gen_cfg.pad_token_id = self.tokenizer.eos_token_id
+            gen_cfg.eos_token_id = self.tokenizer.eos_token_id
+        else:
+            gen_cfg = self.gen_cfg
+
+        # If a gen_schema is given, conform to gen_schema
+        with self.lock:
+            if gen_schema:
+                import jsonformer
+
+                print(f"Schema {gen_schema=}")
+                jsonformer_llm = jsonformer.Jsonformer(
+                    model=self.model,
+                    tokenizer=self.tokenizer,
+                    json_schema=json.loads(gen_schema),
+                    prompt=prompt,
+                    max_string_token_length=gen_cfg.max_new_tokens,
+                )
+                response = jsonformer_llm()
+            else:
+                # If no gen_schema, perform prompt only generation
+
+                # tokenize prompt
+                input_ids = self.tokenizer.encode(prompt, return_tensors="pt").to(
+                    self.model.device
+                )
+                output = self.model.generate(input_ids, generation_config=gen_cfg)
+
+                # decode output
+                response = self.tokenizer.decode(
+                    output[0].cpu(), skip_special_tokens=True
+                )
+                response = response[len(prompt) :]
+                response = {"long_summary": response}
+        print(f"Generated {response=}")
+        return {"text": response}
+
+
+# -------------------------------------------------------------------
+# Web API
+# -------------------------------------------------------------------
+
+
+@app.function(
+    scaledown_window=60 * 10,
+    timeout=60 * 5,
+    allow_concurrent_inputs=30,
+    secrets=[
+        Secret.from_name("reflector-gpu"),
+    ],
+)
+@asgi_app()
+def web():
+    from fastapi import Depends, FastAPI, HTTPException, status
+    from fastapi.security import OAuth2PasswordBearer
+    from pydantic import BaseModel
+
+    llmstub = LLM()
+
+    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 LLMRequest(BaseModel):
+        prompt: str
+        gen_schema: Optional[dict] = None
+        gen_cfg: Optional[dict] = None
+
+    @app.post("/llm", dependencies=[Depends(apikey_auth)])
+    def llm(
+        req: LLMRequest,
+    ):
+        gen_schema = json.dumps(req.gen_schema) if req.gen_schema else None
+        gen_cfg = json.dumps(req.gen_cfg) if req.gen_cfg else None
+        func = llmstub.generate.spawn(
+            prompt=req.prompt, gen_schema=gen_schema, gen_cfg=gen_cfg
+        )
+        result = func.get()
+        return result
+
+    return app
--- a/server/gpu/modal_deployments/reflector_transcriber.py
+++ b/server/gpu/modal_deployments/reflector_transcriber.py
@@ -0,0 +1,161 @@
+import os
+import tempfile
+import threading
+
+import modal
+from pydantic import BaseModel
+
+MODELS_DIR = "/models"
+
+MODEL_NAME = "large-v2"
+MODEL_COMPUTE_TYPE: str = "float16"
+MODEL_NUM_WORKERS: int = 1
+
+MINUTES = 60  # seconds
+
+volume = modal.Volume.from_name("models", create_if_missing=True)
+
+app = modal.App("reflector-transcriber")
+
+
+def download_model():
+    from faster_whisper import download_model
+
+    volume.reload()
+
+    download_model(MODEL_NAME, cache_dir=MODELS_DIR)
+
+    volume.commit()
+
+
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .pip_install(
+        "huggingface_hub==0.27.1",
+        "hf-transfer==0.1.9",
+        "torch==2.5.1",
+        "faster-whisper==1.1.1",
+    )
+    .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/"
+            ),
+        }
+    )
+    .run_function(download_model, volumes={MODELS_DIR: volume})
+)
+
+
+@app.cls(
+    gpu="A10G",
+    timeout=5 * MINUTES,
+    scaledown_window=5 * MINUTES,
+    allow_concurrent_inputs=6,
+    image=image,
+    volumes={MODELS_DIR: volume},
+)
+class Transcriber:
+    @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=MODELS_DIR,
+            local_files_only=True,
+        )
+
+    @modal.method()
+    def transcribe_segment(
+        self,
+        audio_data: str,
+        audio_suffix: str,
+        language: str,
+    ):
+        with tempfile.NamedTemporaryFile("wb+", suffix=f".{audio_suffix}") as fp:
+            fp.write(audio_data)
+
+            with self.lock:
+                segments, _ = self.model.transcribe(
+                    fp.name,
+                    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)
+            words = [
+                {"word": word.word, "start": word.start, "end": word.end}
+                for segment in segments
+                for word in segment.words
+            ]
+
+            return {"text": text, "words": words}
+
+
+@app.function(
+    scaledown_window=60,
+    timeout=60,
+    allow_concurrent_inputs=40,
+    secrets=[
+        modal.Secret.from_name("reflector-gpu"),
+    ],
+    volumes={MODELS_DIR: volume},
+)
+@modal.asgi_app()
+def web():
+    from fastapi import Body, Depends, FastAPI, HTTPException, UploadFile, status
+    from fastapi.security import OAuth2PasswordBearer
+    from typing_extensions import Annotated
+
+    transcriber = Transcriber()
+
+    app = FastAPI()
+
+    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+    supported_file_types = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
+
+    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 TranscriptResponse(BaseModel):
+        result: dict
+
+    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
+    def transcribe(
+        file: UploadFile,
+        model: str = "whisper-1",
+        language: Annotated[str, Body(...)] = "en",
+    ) -> TranscriptResponse:
+        audio_data = file.file.read()
+        audio_suffix = file.filename.split(".")[-1]
+        assert audio_suffix in supported_file_types
+
+        func = transcriber.transcribe_segment.spawn(
+            audio_data=audio_data,
+            audio_suffix=audio_suffix,
+            language=language,
+        )
+        result = func.get()
+        return result
+
+    return app
--- a/server/gpu/modal_deployments/reflector_translator.py
+++ b/server/gpu/modal_deployments/reflector_translator.py
--- a/server/gpu/modal_deployments/reflector_vllm_hermes3.py
+++ b/server/gpu/modal_deployments/reflector_vllm_hermes3.py
@@ -0,0 +1,171 @@
+# # Run an OpenAI-Compatible vLLM Server
+
+import modal
+
+MODELS_DIR = "/llamas"
+MODEL_NAME = "NousResearch/Hermes-3-Llama-3.1-8B"
+N_GPU = 1
+
+
+def download_llm():
+    from huggingface_hub import snapshot_download
+
+    print("Downloading LLM model")
+    snapshot_download(
+        MODEL_NAME,
+        local_dir=f"{MODELS_DIR}/{MODEL_NAME}",
+        ignore_patterns=[
+            "*.pt",
+            "*.bin",
+            "*.pth",
+            "original/*",
+        ],  # Ensure safetensors
+    )
+    print("LLM model downloaded")
+
+
+def move_cache():
+    from transformers.utils import move_cache as transformers_move_cache
+
+    transformers_move_cache()
+
+
+vllm_image = (
+    modal.Image.debian_slim(python_version="3.10")
+    .pip_install("vllm==0.5.3post1")
+    .env({"HF_HUB_ENABLE_HF_TRANSFER": "1"})
+    .pip_install(
+        # "accelerate==0.34.2",
+        "einops==0.8.0",
+        "hf-transfer~=0.1",
+    )
+    .run_function(download_llm)
+    .run_function(move_cache)
+    .pip_install(
+        "bitsandbytes>=0.42.9",
+    )
+)
+
+app = modal.App("reflector-vllm-hermes3")
+
+
+@app.function(
+    image=vllm_image,
+    gpu=modal.gpu.A100(count=N_GPU, size="40GB"),
+    timeout=60 * 5,
+    scaledown_window=60 * 5,
+    allow_concurrent_inputs=100,
+    secrets=[
+        modal.Secret.from_name("reflector-gpu"),
+    ],
+)
+@modal.asgi_app()
+def serve():
+    import os
+
+    import fastapi
+    import vllm.entrypoints.openai.api_server as api_server
+    from vllm.engine.arg_utils import AsyncEngineArgs
+    from vllm.engine.async_llm_engine import AsyncLLMEngine
+    from vllm.entrypoints.logger import RequestLogger
+    from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+    from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
+    from vllm.usage.usage_lib import UsageContext
+
+    TOKEN = os.environ["REFLECTOR_GPU_APIKEY"]
+
+    # create a fastAPI app that uses vLLM's OpenAI-compatible router
+    web_app = fastapi.FastAPI(
+        title=f"OpenAI-compatible {MODEL_NAME} server",
+        description="Run an OpenAI-compatible LLM server with vLLM on modal.com",
+        version="0.0.1",
+        docs_url="/docs",
+    )
+
+    # security: CORS middleware for external requests
+    http_bearer = fastapi.security.HTTPBearer(
+        scheme_name="Bearer Token",
+        description="See code for authentication details.",
+    )
+    web_app.add_middleware(
+        fastapi.middleware.cors.CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+    # security: inject dependency on authed routes
+    async def is_authenticated(api_key: str = fastapi.Security(http_bearer)):
+        if api_key.credentials != TOKEN:
+            raise fastapi.HTTPException(
+                status_code=fastapi.status.HTTP_401_UNAUTHORIZED,
+                detail="Invalid authentication credentials",
+            )
+        return {"username": "authenticated_user"}
+
+    router = fastapi.APIRouter(dependencies=[fastapi.Depends(is_authenticated)])
+
+    # wrap vllm's router in auth router
+    router.include_router(api_server.router)
+    # add authed vllm to our fastAPI app
+    web_app.include_router(router)
+
+    engine_args = AsyncEngineArgs(
+        model=MODELS_DIR + "/" + MODEL_NAME,
+        tensor_parallel_size=N_GPU,
+        gpu_memory_utilization=0.90,
+        # max_model_len=8096,
+        enforce_eager=False,  # capture the graph for faster inference, but slower cold starts (30s > 20s)
+        # --- 4 bits load
+        # quantization="bitsandbytes",
+        # load_format="bitsandbytes",
+    )
+
+    engine = AsyncLLMEngine.from_engine_args(
+        engine_args, usage_context=UsageContext.OPENAI_API_SERVER
+    )
+
+    model_config = get_model_config(engine)
+
+    request_logger = RequestLogger(max_log_len=2048)
+
+    api_server.openai_serving_chat = OpenAIServingChat(
+        engine,
+        model_config=model_config,
+        served_model_names=[MODEL_NAME],
+        chat_template=None,
+        response_role="assistant",
+        lora_modules=[],
+        prompt_adapters=[],
+        request_logger=request_logger,
+    )
+    api_server.openai_serving_completion = OpenAIServingCompletion(
+        engine,
+        model_config=model_config,
+        served_model_names=[MODEL_NAME],
+        lora_modules=[],
+        prompt_adapters=[],
+        request_logger=request_logger,
+    )
+
+    return web_app
+
+
+def get_model_config(engine):
+    import asyncio
+
+    try:  # adapted from vLLM source -- https://github.com/vllm-project/vllm/blob/507ef787d85dec24490069ffceacbd6b161f4f72/vllm/entrypoints/openai/api_server.py#L235C1-L247C1
+        event_loop = asyncio.get_running_loop()
+    except RuntimeError:
+        event_loop = None
+
+    if event_loop is not None and event_loop.is_running():
+        # If the current is instanced by Ray Serve,
+        # there is already a running event loop
+        model_config = event_loop.run_until_complete(engine.get_model_config())
+    else:
+        # When using single vLLM without engine_use_ray
+        model_config = asyncio.run(engine.get_model_config())
+
+    return model_config
--- a/server/migration.load
+++ b/server/migration.load
@@ -0,0 +1,16 @@
+LOAD DATABASE
+     FROM sqlite:///app/reflector.sqlite3
+     INTO pgsql://reflector:reflector@postgres:5432/reflector
+WITH
+    include drop,
+    create tables,
+    create indexes,
+    reset sequences,
+    preserve index names,
+    prefetch rows = 10
+SET
+    work_mem to '512MB',
+    maintenance_work_mem to '1024MB'
+CAST
+    column transcript.duration to float using (lambda (val) (when val (format nil "~f" val)))
+;
--- a/server/migrations/README
+++ b/server/migrations/README
@@ -1,3 +1 @@
-Generic single-database configuration.
-
-Both data migrations and schema migrations must be in migrations.
+Generic single-database configuration.
--- a/server/migrations/env.py
+++ b/server/migrations/env.py
@@ -1,10 +1,9 @@
 from logging.config import fileConfig

 from alembic import context
-from sqlalchemy import engine_from_config, pool
-
 from reflector.db import metadata
 from reflector.settings import settings
+from sqlalchemy import engine_from_config, pool

 # this is the Alembic Config object, which provides
 # access to the values within the .ini file in use.
--- a/server/migrations/versions/0194f65cd6d3_add_webhook_fields_to_rooms.py
+++ b/server/migrations/versions/0194f65cd6d3_add_webhook_fields_to_rooms.py
@@ -1,36 +0,0 @@
-"""Add webhook fields to rooms
-
-Revision ID: 0194f65cd6d3
-Revises: 5a8907fd1d78
-Create Date: 2025-08-27 09:03:19.610995
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "0194f65cd6d3"
-down_revision: Union[str, None] = "5a8907fd1d78"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.add_column(sa.Column("webhook_url", sa.String(), nullable=True))
-        batch_op.add_column(sa.Column("webhook_secret", sa.String(), nullable=True))
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.drop_column("webhook_secret")
-        batch_op.drop_column("webhook_url")
-
-    # ### end Alembic commands ###
--- a/server/migrations/versions/082fa608201c_add_room_background_information.py
+++ b/server/migrations/versions/082fa608201c_add_room_background_information.py
@@ -0,0 +1,26 @@
+"""add_room_background_information
+
+Revision ID: 082fa608201c
+Revises: b7df9609542c
+Create Date: 2025-07-29 01:41:37.912195
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '082fa608201c'
+down_revision: Union[str, None] = 'b7df9609542c'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column('room', sa.Column('background_information', sa.Text(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column('room', 'background_information')
--- a/server/migrations/versions/0925da921477_unique_room_names.py
+++ b/server/migrations/versions/0925da921477_unique_room_names.py
@@ -8,6 +8,7 @@ Create Date: 2024-09-24 16:12:56.944133

 from typing import Sequence, Union

+import sqlalchemy as sa
 from alembic import op

 # revision identifiers, used by Alembic.
--- a/server/migrations/versions/0ab2d7ffaa16_add_long_summary_to_search_vector.py
+++ b/server/migrations/versions/0ab2d7ffaa16_add_long_summary_to_search_vector.py
@@ -1,64 +0,0 @@
-"""add_long_summary_to_search_vector
-
-Revision ID: 0ab2d7ffaa16
-Revises: b1c33bd09963
-Create Date: 2025-08-15 13:27:52.680211
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "0ab2d7ffaa16"
-down_revision: Union[str, None] = "b1c33bd09963"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # Drop the existing search vector column and index
-    op.drop_index("idx_transcript_search_vector_en", table_name="transcript")
-    op.drop_column("transcript", "search_vector_en")
-
-    # Recreate the search vector column with long_summary included
-    op.execute("""
-        ALTER TABLE transcript ADD COLUMN search_vector_en tsvector
-        GENERATED ALWAYS AS (
-            setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
-            setweight(to_tsvector('english', coalesce(long_summary, '')), 'B') ||
-            setweight(to_tsvector('english', coalesce(webvtt, '')), 'C')
-        ) STORED
-    """)
-
-    # Recreate the GIN index for the search vector
-    op.create_index(
-        "idx_transcript_search_vector_en",
-        "transcript",
-        ["search_vector_en"],
-        postgresql_using="gin",
-    )
-
-
-def downgrade() -> None:
-    # Drop the updated search vector column and index
-    op.drop_index("idx_transcript_search_vector_en", table_name="transcript")
-    op.drop_column("transcript", "search_vector_en")
-
-    # Recreate the original search vector column without long_summary
-    op.execute("""
-        ALTER TABLE transcript ADD COLUMN search_vector_en tsvector
-        GENERATED ALWAYS AS (
-            setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
-            setweight(to_tsvector('english', coalesce(webvtt, '')), 'B')
-        ) STORED
-    """)
-
-    # Recreate the GIN index for the search vector
-    op.create_index(
-        "idx_transcript_search_vector_en",
-        "transcript",
-        ["search_vector_en"],
-        postgresql_using="gin",
-    )
--- a/server/migrations/versions/0bc0f3ff0111_add_webvtt_field_to_transcript.py
+++ b/server/migrations/versions/0bc0f3ff0111_add_webvtt_field_to_transcript.py
@@ -1,25 +0,0 @@
-"""add_webvtt_field_to_transcript
-
-Revision ID: 0bc0f3ff0111
-Revises: b7df9609542c
-Create Date: 2025-08-05 19:36:41.740957
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-revision: str = "0bc0f3ff0111"
-down_revision: Union[str, None] = "b7df9609542c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    op.add_column("transcript", sa.Column("webvtt", sa.Text(), nullable=True))
-
-
-def downgrade() -> None:
-    op.drop_column("transcript", "webvtt")
--- a/server/migrations/versions/0ce521cda2ee_remove_user_id_from_meeting_table.py
+++ b/server/migrations/versions/0ce521cda2ee_remove_user_id_from_meeting_table.py
@@ -1,36 +0,0 @@
-"""remove user_id from meeting table
-
-Revision ID: 0ce521cda2ee
-Revises: 6dec9fb5b46c
-Create Date: 2025-09-10 12:40:55.688899
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "0ce521cda2ee"
-down_revision: Union[str, None] = "6dec9fb5b46c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.drop_column("user_id")
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column("user_id", sa.VARCHAR(), autoincrement=False, nullable=True)
-        )
-
-    # ### end Alembic commands ###
--- a/server/migrations/versions/0fea6d96b096_add_share_mode.py
+++ b/server/migrations/versions/0fea6d96b096_add_share_mode.py
@@ -5,11 +5,11 @@ Revises: f819277e5169
 Create Date: 2023-11-07 11:12:21.614198

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
 revision: str = "0fea6d96b096"
--- a/server/migrations/versions/116b2f287eab_add_full_text_search.py
+++ b/server/migrations/versions/116b2f287eab_add_full_text_search.py
@@ -1,46 +0,0 @@
-"""add_full_text_search
-
-Revision ID: 116b2f287eab
-Revises: 0bc0f3ff0111
-Create Date: 2025-08-07 11:27:38.473517
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-revision: str = "116b2f287eab"
-down_revision: Union[str, None] = "0bc0f3ff0111"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    conn = op.get_bind()
-    if conn.dialect.name != "postgresql":
-        return
-
-    op.execute("""
-        ALTER TABLE transcript ADD COLUMN search_vector_en tsvector
-        GENERATED ALWAYS AS (
-            setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
-            setweight(to_tsvector('english', coalesce(webvtt, '')), 'B')
-        ) STORED
-    """)
-
-    op.create_index(
-        "idx_transcript_search_vector_en",
-        "transcript",
-        ["search_vector_en"],
-        postgresql_using="gin",
-    )
-
-
-def downgrade() -> None:
-    conn = op.get_bind()
-    if conn.dialect.name != "postgresql":
-        return
-
-    op.drop_index("idx_transcript_search_vector_en", table_name="transcript")
-    op.drop_column("transcript", "search_vector_en")
--- a/server/migrations/versions/125031f7cb78_participants.py
+++ b/server/migrations/versions/125031f7cb78_participants.py
@@ -5,26 +5,26 @@ Revises: 0fea6d96b096
 Create Date: 2023-11-30 15:56:03.341466

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
-revision: str = "125031f7cb78"
-down_revision: Union[str, None] = "0fea6d96b096"
+revision: str = '125031f7cb78'
+down_revision: Union[str, None] = '0fea6d96b096'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column("transcript", sa.Column("participants", sa.JSON(), nullable=True))
+    op.add_column('transcript', sa.Column('participants', sa.JSON(), nullable=True))
    # ### end Alembic commands ###


 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_column("transcript", "participants")
+    op.drop_column('transcript', 'participants')
    # ### end Alembic commands ###
--- a/server/migrations/versions/1e49625677e4_add_platform_support.py
+++ b/server/migrations/versions/1e49625677e4_add_platform_support.py
@@ -1,50 +0,0 @@
-"""add_platform_support
-
-Revision ID: 1e49625677e4
-Revises: dc035ff72fd5
-Create Date: 2025-10-08 13:17:29.943612
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "1e49625677e4"
-down_revision: Union[str, None] = "dc035ff72fd5"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    """Add platform field with default 'whereby' for backward compatibility."""
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column(
-                "platform",
-                sa.String(),
-                nullable=False,
-                server_default="whereby",
-            )
-        )
-
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column(
-                "platform",
-                sa.String(),
-                nullable=False,
-                server_default="whereby",
-            )
-        )
-
-
-def downgrade() -> None:
-    """Remove platform field."""
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.drop_column("platform")
-
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.drop_column("platform")
--- a/server/migrations/versions/20250617140003_add_meeting_consent_table.py
+++ b/server/migrations/versions/20250617140003_add_meeting_consent_table.py
@@ -5,7 +5,6 @@ Revises: f819277e5169
 Create Date: 2025-06-17 14:00:03.000000

 """
-
 from typing import Sequence, Union

 import sqlalchemy as sa
@@ -20,16 +19,16 @@ depends_on: Union[str, Sequence[str], None] = None

 def upgrade() -> None:
    op.create_table(
-        "meeting_consent",
-        sa.Column("id", sa.String(), nullable=False),
-        sa.Column("meeting_id", sa.String(), nullable=False),
-        sa.Column("user_id", sa.String(), nullable=True),
-        sa.Column("consent_given", sa.Boolean(), nullable=False),
-        sa.Column("consent_timestamp", sa.DateTime(), nullable=False),
-        sa.PrimaryKeyConstraint("id"),
-        sa.ForeignKeyConstraint(["meeting_id"], ["meeting.id"]),
+        'meeting_consent',
+        sa.Column('id', sa.String(), nullable=False),
+        sa.Column('meeting_id', sa.String(), nullable=False),
+        sa.Column('user_id', sa.String(), nullable=True),
+        sa.Column('consent_given', sa.Boolean(), nullable=False),
+        sa.Column('consent_timestamp', sa.DateTime(), nullable=False),
+        sa.PrimaryKeyConstraint('id'),
+        sa.ForeignKeyConstraint(['meeting_id'], ['meeting.id']),
    )


 def downgrade() -> None:
-    op.drop_table("meeting_consent")
+    op.drop_table('meeting_consent')
--- a/server/migrations/versions/20250618140000_add_audio_deleted_field_to_transcript.py
+++ b/server/migrations/versions/20250618140000_add_audio_deleted_field_to_transcript.py
@@ -5,7 +5,6 @@ Revises: 20250617140003
 Create Date: 2025-06-18 14:00:00.000000

 """
-
 from typing import Sequence, Union

 import sqlalchemy as sa
@@ -23,4 +22,4 @@ def upgrade() -> None:


 def downgrade() -> None:
-    op.drop_column("transcript", "audio_deleted")
+    op.drop_column("transcript", "audio_deleted")
--- a/server/migrations/versions/2ae3db106d4e_clean_up_orphaned_room_id_references_in_.py
+++ b/server/migrations/versions/2ae3db106d4e_clean_up_orphaned_room_id_references_in_.py
@@ -1,32 +0,0 @@
-"""clean up orphaned room_id references in meeting table
-
-Revision ID: 2ae3db106d4e
-Revises: def1b5867d4c
-Create Date: 2025-09-11 10:35:15.759967
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "2ae3db106d4e"
-down_revision: Union[str, None] = "def1b5867d4c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # Set room_id to NULL for meetings that reference non-existent rooms
-    op.execute("""
-        UPDATE meeting
-        SET room_id = NULL
-        WHERE room_id IS NOT NULL
-          AND room_id NOT IN (SELECT id FROM room WHERE id IS NOT NULL)
-    """)
-
-
-def downgrade() -> None:
-    # Cannot restore orphaned references - no operation needed
-    pass
--- a/server/migrations/versions/2cf0b60a9d34_fix_transcript_duration_type.py
+++ b/server/migrations/versions/2cf0b60a9d34_fix_transcript_duration_type.py
@@ -5,40 +5,36 @@ Revises: ccd68dc784ff
 Create Date: 2025-07-15 16:53:40.397394

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
-revision: str = "2cf0b60a9d34"
-down_revision: Union[str, None] = "ccd68dc784ff"
+revision: str = '2cf0b60a9d34'
+down_revision: Union[str, None] = 'ccd68dc784ff'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("transcript", schema=None) as batch_op:
-        batch_op.alter_column(
-            "duration",
-            existing_type=sa.INTEGER(),
-            type_=sa.Float(),
-            existing_nullable=True,
-        )
+    with op.batch_alter_table('transcript', schema=None) as batch_op:
+        batch_op.alter_column('duration',
+               existing_type=sa.INTEGER(),
+               type_=sa.Float(),
+               existing_nullable=True)

    # ### end Alembic commands ###


 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("transcript", schema=None) as batch_op:
-        batch_op.alter_column(
-            "duration",
-            existing_type=sa.Float(),
-            type_=sa.INTEGER(),
-            existing_nullable=True,
-        )
+    with op.batch_alter_table('transcript', schema=None) as batch_op:
+        batch_op.alter_column('duration',
+               existing_type=sa.Float(),
+               type_=sa.INTEGER(),
+               existing_nullable=True)

    # ### end Alembic commands ###
--- a/server/migrations/versions/38a927dcb099_rename_back_text_to_transcript.py
+++ b/server/migrations/versions/38a927dcb099_rename_back_text_to_transcript.py
@@ -5,17 +5,17 @@ Revises: 9920ecfe2735
 Create Date: 2023-11-02 19:53:09.116240

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.sql import table, column
 from sqlalchemy import select
-from sqlalchemy.sql import column, table
+

 # revision identifiers, used by Alembic.
-revision: str = "38a927dcb099"
-down_revision: Union[str, None] = "9920ecfe2735"
+revision: str = '38a927dcb099'
+down_revision: Union[str, None] = '9920ecfe2735'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None

--- a/server/migrations/versions/4814901632bc_fix_duration.py
+++ b/server/migrations/versions/4814901632bc_fix_duration.py
@@ -5,13 +5,13 @@ Revises: 38a927dcb099
 Create Date: 2023-11-10 18:12:17.886522

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.sql import table, column
 from sqlalchemy import select
-from sqlalchemy.sql import column, table
+

 # revision identifiers, used by Alembic.
 revision: str = "4814901632bc"
@@ -24,10 +24,8 @@ def upgrade() -> None:
    # for all the transcripts, calculate the duration from the mp3
    # and update the duration column
    from pathlib import Path
-
-    import av
-
    from reflector.settings import settings
+    import av

    bind = op.get_bind()
    transcript = table(
--- a/server/migrations/versions/543ed284d69a_init.py
+++ b/server/migrations/versions/543ed284d69a_init.py
@@ -5,11 +5,14 @@ Revises:
 Create Date: 2023-08-29 10:54:45.142974

 """
-
 from typing import Sequence, Union

+from alembic import op
+import sqlalchemy as sa
+
+
 # revision identifiers, used by Alembic.
-revision: str = "543ed284d69a"
+revision: str = '543ed284d69a'
 down_revision: Union[str, None] = None
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
--- a/server/migrations/versions/5a8907fd1d78_add_cascade_delete_to_meeting_consent_.py
+++ b/server/migrations/versions/5a8907fd1d78_add_cascade_delete_to_meeting_consent_.py
@@ -1,50 +0,0 @@
-"""add cascade delete to meeting consent foreign key
-
-Revision ID: 5a8907fd1d78
-Revises: 0ab2d7ffaa16
-Create Date: 2025-08-26 17:26:50.945491
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "5a8907fd1d78"
-down_revision: Union[str, None] = "0ab2d7ffaa16"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
-        batch_op.drop_constraint(
-            batch_op.f("meeting_consent_meeting_id_fkey"), type_="foreignkey"
-        )
-        batch_op.create_foreign_key(
-            batch_op.f("meeting_consent_meeting_id_fkey"),
-            "meeting",
-            ["meeting_id"],
-            ["id"],
-            ondelete="CASCADE",
-        )
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
-        batch_op.drop_constraint(
-            batch_op.f("meeting_consent_meeting_id_fkey"), type_="foreignkey"
-        )
-        batch_op.create_foreign_key(
-            batch_op.f("meeting_consent_meeting_id_fkey"),
-            "meeting",
-            ["meeting_id"],
-            ["id"],
-        )
-
-    # ### end Alembic commands ###
--- a/server/migrations/versions/6025e9b2bef2_remove_one_active_meeting_per_room_.py
+++ b/server/migrations/versions/6025e9b2bef2_remove_one_active_meeting_per_room_.py
@@ -1,53 +0,0 @@
-"""remove_one_active_meeting_per_room_constraint
-
-Revision ID: 6025e9b2bef2
-Revises: 2ae3db106d4e
-Create Date: 2025-08-18 18:45:44.418392
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "6025e9b2bef2"
-down_revision: Union[str, None] = "2ae3db106d4e"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # Remove the unique constraint that prevents multiple active meetings per room
-    # This is needed to support calendar integration with overlapping meetings
-    # Check if index exists before trying to drop it
-    from alembic import context
-
-    if context.get_context().dialect.name == "postgresql":
-        conn = op.get_bind()
-        result = conn.execute(
-            sa.text(
-                "SELECT 1 FROM pg_indexes WHERE indexname = 'idx_one_active_meeting_per_room'"
-            )
-        )
-        if result.fetchone():
-            op.drop_index("idx_one_active_meeting_per_room", table_name="meeting")
-    else:
-        # For SQLite, just try to drop it
-        try:
-            op.drop_index("idx_one_active_meeting_per_room", table_name="meeting")
-        except:
-            pass
-
-
-def downgrade() -> None:
-    # Restore the unique constraint
-    op.create_index(
-        "idx_one_active_meeting_per_room",
-        "meeting",
-        ["room_id"],
-        unique=True,
-        postgresql_where=sa.text("is_active = true"),
-        sqlite_where=sa.text("is_active = 1"),
-    )
--- a/server/migrations/versions/61882a919591_webhook_url_and_secret_null_by_default.py
+++ b/server/migrations/versions/61882a919591_webhook_url_and_secret_null_by_default.py
@@ -1,28 +0,0 @@
-"""webhook url and secret null by default
-
-
-Revision ID: 61882a919591
-Revises: 0194f65cd6d3
-Create Date: 2025-08-29 11:46:36.738091
-
-"""
-
-from typing import Sequence, Union
-
-# revision identifiers, used by Alembic.
-revision: str = "61882a919591"
-down_revision: Union[str, None] = "0194f65cd6d3"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    pass
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    pass
-    # ### end Alembic commands ###
--- a/server/migrations/versions/62dea3db63a5_add_room_options.py
+++ b/server/migrations/versions/62dea3db63a5_add_room_options.py
@@ -8,8 +8,9 @@ Create Date: 2025-06-27 09:04:21.006823

 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
 revision: str = "62dea3db63a5"
@@ -32,7 +33,7 @@ def upgrade() -> None:
        sa.Column("user_id", sa.String(), nullable=True),
        sa.Column("room_id", sa.String(), nullable=True),
        sa.Column(
-            "is_locked", sa.Boolean(), server_default=sa.text("false"), nullable=False
+            "is_locked", sa.Boolean(), server_default=sa.text("0"), nullable=False
        ),
        sa.Column("room_mode", sa.String(), server_default="normal", nullable=False),
        sa.Column(
@@ -53,15 +54,12 @@ def upgrade() -> None:
        sa.Column("user_id", sa.String(), nullable=False),
        sa.Column("created_at", sa.DateTime(), nullable=False),
        sa.Column(
-            "zulip_auto_post",
-            sa.Boolean(),
-            server_default=sa.text("false"),
-            nullable=False,
+            "zulip_auto_post", sa.Boolean(), server_default=sa.text("0"), nullable=False
        ),
        sa.Column("zulip_stream", sa.String(), nullable=True),
        sa.Column("zulip_topic", sa.String(), nullable=True),
        sa.Column(
-            "is_locked", sa.Boolean(), server_default=sa.text("false"), nullable=False
+            "is_locked", sa.Boolean(), server_default=sa.text("0"), nullable=False
        ),
        sa.Column("room_mode", sa.String(), server_default="normal", nullable=False),
        sa.Column(
--- a/server/migrations/versions/6dec9fb5b46c_make_meeting_room_id_required_and_add_.py
+++ b/server/migrations/versions/6dec9fb5b46c_make_meeting_room_id_required_and_add_.py
@@ -1,35 +0,0 @@
-"""make meeting room_id required and add foreign key
-
-Revision ID: 6dec9fb5b46c
-Revises: 61882a919591
-Create Date: 2025-09-10 10:47:06.006819
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "6dec9fb5b46c"
-down_revision: Union[str, None] = "61882a919591"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.create_foreign_key(
-            None, "room", ["room_id"], ["id"], ondelete="CASCADE"
-        )
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.drop_constraint("meeting_room_id_fkey", type_="foreignkey")
-
-    # ### end Alembic commands ###
--- a/server/migrations/versions/74b2b0236931_add_transcript_source_kind.py
+++ b/server/migrations/versions/74b2b0236931_add_transcript_source_kind.py
@@ -20,14 +20,11 @@ depends_on: Union[str, Sequence[str], None] = None

 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    sourcekind_enum = sa.Enum("room", "live", "file", name="sourcekind")
-    sourcekind_enum.create(op.get_bind())
-
    op.add_column(
        "transcript",
        sa.Column(
            "source_kind",
-            sourcekind_enum,
+            sa.Enum("ROOM", "LIVE", "FILE", name="sourcekind"),
            nullable=True,
        ),
    )
@@ -46,8 +43,6 @@ def upgrade() -> None:
 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
    op.drop_column("transcript", "source_kind")
-    sourcekind_enum = sa.Enum(name="sourcekind")
-    sourcekind_enum.drop(op.get_bind())


 # ### end Alembic commands ###
--- a/server/migrations/versions/764ce6db4388_add_zulip_message_id.py
+++ b/server/migrations/versions/764ce6db4388_add_zulip_message_id.py
@@ -5,28 +5,26 @@ Revises: 62dea3db63a5
 Create Date: 2024-09-06 14:02:06.649665

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
-revision: str = "764ce6db4388"
-down_revision: Union[str, None] = "62dea3db63a5"
+revision: str = '764ce6db4388'
+down_revision: Union[str, None] = '62dea3db63a5'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column(
-        "transcript", sa.Column("zulip_message_id", sa.Integer(), nullable=True)
-    )
+    op.add_column('transcript', sa.Column('zulip_message_id', sa.Integer(), nullable=True))
    # ### end Alembic commands ###


 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_column("transcript", "zulip_message_id")
+    op.drop_column('transcript', 'zulip_message_id')
    # ### end Alembic commands ###
--- a/server/migrations/versions/8120ebc75366_populate_webvtt_from_topics.py
+++ b/server/migrations/versions/8120ebc75366_populate_webvtt_from_topics.py
@@ -1,106 +0,0 @@
-"""populate_webvtt_from_topics
-
-Revision ID: 8120ebc75366
-Revises: 116b2f287eab
-Create Date: 2025-08-11 19:11:01.316947
-
-"""
-
-import json
-from typing import Sequence, Union
-
-from alembic import op
-from sqlalchemy import text
-
-# revision identifiers, used by Alembic.
-revision: str = "8120ebc75366"
-down_revision: Union[str, None] = "116b2f287eab"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def topics_to_webvtt(topics):
-    """Convert topics list to WebVTT format string."""
-    if not topics:
-        return None
-
-    lines = ["WEBVTT", ""]
-
-    for topic in topics:
-        start_time = format_timestamp(topic.get("start"))
-        end_time = format_timestamp(topic.get("end"))
-        text = topic.get("text", "").strip()
-
-        if start_time and end_time and text:
-            lines.append(f"{start_time} --> {end_time}")
-            lines.append(text)
-            lines.append("")
-
-    return "\n".join(lines).strip()
-
-
-def format_timestamp(seconds):
-    """Format seconds to WebVTT timestamp format (HH:MM:SS.mmm)."""
-    if seconds is None:
-        return None
-
-    hours = int(seconds // 3600)
-    minutes = int((seconds % 3600) // 60)
-    secs = seconds % 60
-
-    return f"{hours:02d}:{minutes:02d}:{secs:06.3f}"
-
-
-def upgrade() -> None:
-    """Populate WebVTT field for all transcripts with topics."""
-
-    # Get connection
-    connection = op.get_bind()
-
-    # Query all transcripts with topics
-    result = connection.execute(
-        text("SELECT id, topics FROM transcript WHERE topics IS NOT NULL")
-    )
-
-    rows = result.fetchall()
-    print(f"Found {len(rows)} transcripts with topics")
-
-    updated_count = 0
-    error_count = 0
-
-    for row in rows:
-        transcript_id = row[0]
-        topics_data = row[1]
-
-        if not topics_data:
-            continue
-
-        try:
-            # Parse JSON if it's a string
-            if isinstance(topics_data, str):
-                topics_data = json.loads(topics_data)
-
-            # Convert topics to WebVTT format
-            webvtt_content = topics_to_webvtt(topics_data)
-
-            if webvtt_content:
-                # Update the webvtt field
-                connection.execute(
-                    text("UPDATE transcript SET webvtt = :webvtt WHERE id = :id"),
-                    {"webvtt": webvtt_content, "id": transcript_id},
-                )
-                updated_count += 1
-                print(f"✓ Updated transcript {transcript_id}")
-
-        except Exception as e:
-            error_count += 1
-            print(f"✗ Error updating transcript {transcript_id}: {e}")
-
-    print(f"\nMigration complete!")
-    print(f"  Updated: {updated_count}")
-    print(f"  Errors: {error_count}")
-
-
-def downgrade() -> None:
-    """Clear WebVTT field for all transcripts."""
-    op.execute(text("UPDATE transcript SET webvtt = NULL"))
--- a/server/migrations/versions/88d292678ba2_fix_transcript_json_nan_values.py
+++ b/server/migrations/versions/88d292678ba2_fix_transcript_json_nan_values.py
@@ -9,6 +9,8 @@ Create Date: 2025-07-15 19:30:19.876332
 from typing import Sequence, Union

 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
 revision: str = "88d292678ba2"
@@ -19,7 +21,7 @@ depends_on: Union[str, Sequence[str], None] = None

 def upgrade() -> None:
    import json
-
+    import re
    from sqlalchemy import text

    # Get database connection
@@ -56,9 +58,7 @@ def upgrade() -> None:
            fixed_events = json.dumps(jevents)
            assert "NaN" not in fixed_events
        except (json.JSONDecodeError, AssertionError) as e:
-            print(
-                f"Warning: Invalid JSON for transcript {transcript_id}, skipping: {e}"
-            )
+            print(f"Warning: Invalid JSON for transcript {transcript_id}, skipping: {e}")
            continue

        # Update the record with fixed JSON
--- a/server/migrations/versions/9920ecfe2735_rename_transcript_to_text.py
+++ b/server/migrations/versions/9920ecfe2735_rename_transcript_to_text.py
@@ -5,13 +5,13 @@ Revises: 99365b0cd87b
 Create Date: 2023-11-02 18:55:17.019498

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.sql import table, column
 from sqlalchemy import select
-from sqlalchemy.sql import column, table
+

 # revision identifiers, used by Alembic.
 revision: str = "9920ecfe2735"
--- a/server/migrations/versions/99365b0cd87b_add_title_short_and_long_summary_and_.py
+++ b/server/migrations/versions/99365b0cd87b_add_title_short_and_long_summary_and_.py
@@ -8,8 +8,8 @@ Create Date: 2023-09-01 20:19:47.216334

 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa

 # revision identifiers, used by Alembic.
 revision: str = "99365b0cd87b"
@@ -22,7 +22,7 @@ def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
    op.execute(
        "UPDATE transcript SET events = "
-        'REPLACE(events::text, \'"event": "SUMMARY"\', \'"event": "LONG_SUMMARY"\')::json;'
+        'REPLACE(events, \'"event": "SUMMARY"\', \'"event": "LONG_SUMMARY"\');'
    )
    op.alter_column("transcript", "summary", new_column_name="long_summary")
    op.add_column("transcript", sa.Column("title", sa.String(), nullable=True))
@@ -34,7 +34,7 @@ def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
    op.execute(
        "UPDATE transcript SET events = "
-        'REPLACE(events::text, \'"event": "LONG_SUMMARY"\', \'"event": "SUMMARY"\')::json;'
+        'REPLACE(events, \'"event": "LONG_SUMMARY"\', \'"event": "SUMMARY"\');'
    )
    with op.batch_alter_table("transcript", schema=None) as batch_op:
        batch_op.alter_column("long_summary", nullable=True, new_column_name="summary")
--- a/server/migrations/versions/9f5c78d352d6_datetime_timezone.py
+++ b/server/migrations/versions/9f5c78d352d6_datetime_timezone.py
@@ -1,121 +0,0 @@
-"""datetime timezone
-
-Revision ID: 9f5c78d352d6
-Revises: 8120ebc75366
-Create Date: 2025-08-13 19:18:27.113593
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-from sqlalchemy.dialects import postgresql
-
-# revision identifiers, used by Alembic.
-revision: str = "9f5c78d352d6"
-down_revision: Union[str, None] = "8120ebc75366"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.alter_column(
-            "start_date",
-            existing_type=postgresql.TIMESTAMP(),
-            type_=sa.DateTime(timezone=True),
-            existing_nullable=True,
-        )
-        batch_op.alter_column(
-            "end_date",
-            existing_type=postgresql.TIMESTAMP(),
-            type_=sa.DateTime(timezone=True),
-            existing_nullable=True,
-        )
-
-    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
-        batch_op.alter_column(
-            "consent_timestamp",
-            existing_type=postgresql.TIMESTAMP(),
-            type_=sa.DateTime(timezone=True),
-            existing_nullable=False,
-        )
-
-    with op.batch_alter_table("recording", schema=None) as batch_op:
-        batch_op.alter_column(
-            "recorded_at",
-            existing_type=postgresql.TIMESTAMP(),
-            type_=sa.DateTime(timezone=True),
-            existing_nullable=False,
-        )
-
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.alter_column(
-            "created_at",
-            existing_type=postgresql.TIMESTAMP(),
-            type_=sa.DateTime(timezone=True),
-            existing_nullable=False,
-        )
-
-    with op.batch_alter_table("transcript", schema=None) as batch_op:
-        batch_op.alter_column(
-            "created_at",
-            existing_type=postgresql.TIMESTAMP(),
-            type_=sa.DateTime(timezone=True),
-            existing_nullable=True,
-        )
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("transcript", schema=None) as batch_op:
-        batch_op.alter_column(
-            "created_at",
-            existing_type=sa.DateTime(timezone=True),
-            type_=postgresql.TIMESTAMP(),
-            existing_nullable=True,
-        )
-
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.alter_column(
-            "created_at",
-            existing_type=sa.DateTime(timezone=True),
-            type_=postgresql.TIMESTAMP(),
-            existing_nullable=False,
-        )
-
-    with op.batch_alter_table("recording", schema=None) as batch_op:
-        batch_op.alter_column(
-            "recorded_at",
-            existing_type=sa.DateTime(timezone=True),
-            type_=postgresql.TIMESTAMP(),
-            existing_nullable=False,
-        )
-
-    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
-        batch_op.alter_column(
-            "consent_timestamp",
-            existing_type=sa.DateTime(timezone=True),
-            type_=postgresql.TIMESTAMP(),
-            existing_nullable=False,
-        )
-
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.alter_column(
-            "end_date",
-            existing_type=sa.DateTime(timezone=True),
-            type_=postgresql.TIMESTAMP(),
-            existing_nullable=True,
-        )
-        batch_op.alter_column(
-            "start_date",
-            existing_type=sa.DateTime(timezone=True),
-            type_=postgresql.TIMESTAMP(),
-            existing_nullable=True,
-        )
-
-    # ### end Alembic commands ###
--- a/server/migrations/versions/a7122bc0b2ca_add_shared_rooms.py
+++ b/server/migrations/versions/a7122bc0b2ca_add_shared_rooms.py
@@ -25,7 +25,7 @@ def upgrade() -> None:
        sa.Column(
            "is_shared",
            sa.Boolean(),
-            server_default=sa.text("false"),
+            server_default=sa.text("0"),
            nullable=False,
        ),
    )
--- a/server/migrations/versions/a9c9c229ee36_transcript_composite_index.py
+++ b/server/migrations/versions/a9c9c229ee36_transcript_composite_index.py
@@ -9,6 +9,8 @@ Create Date: 2025-07-15 20:09:40.253018
 from typing import Sequence, Union

 from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql

 # revision identifiers, used by Alembic.
 revision: str = "a9c9c229ee36"
--- a/server/migrations/versions/b0e5f7876032_add_meeting_is_active.py
+++ b/server/migrations/versions/b0e5f7876032_add_meeting_is_active.py
@@ -5,37 +5,30 @@ Revises: 6ea59639f30e
 Create Date: 2025-01-28 10:06:50.446233

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
-revision: str = "b0e5f7876032"
-down_revision: Union[str, None] = "6ea59639f30e"
+revision: str = 'b0e5f7876032'
+down_revision: Union[str, None] = '6ea59639f30e'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column(
-                "is_active",
-                sa.Boolean(),
-                server_default=sa.text("true"),
-                nullable=False,
-            )
-        )
+    with op.batch_alter_table('meeting', schema=None) as batch_op:
+        batch_op.add_column(sa.Column('is_active', sa.Boolean(), server_default=sa.text('1'), nullable=False))

    # ### end Alembic commands ###


 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.drop_column("is_active")
+    with op.batch_alter_table('meeting', schema=None) as batch_op:
+        batch_op.drop_column('is_active')

    # ### end Alembic commands ###
--- a/server/migrations/versions/b1c33bd09963_add_search_optimization_indexes.py
+++ b/server/migrations/versions/b1c33bd09963_add_search_optimization_indexes.py
@@ -1,41 +0,0 @@
-"""add_search_optimization_indexes
-
-Revision ID: b1c33bd09963
-Revises: 9f5c78d352d6
-Create Date: 2025-08-14 17:26:02.117408
-
-"""
-
-from typing import Sequence, Union
-
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "b1c33bd09963"
-down_revision: Union[str, None] = "9f5c78d352d6"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # Add indexes for actual search filtering patterns used in frontend
-    # Based on /browse page filters: room_id and source_kind
-
-    # Index for room_id + created_at (for room-specific searches with date ordering)
-    op.create_index(
-        "idx_transcript_room_id_created_at",
-        "transcript",
-        ["room_id", "created_at"],
-        if_not_exists=True,
-    )
-
-    # Index for source_kind alone (actively used filter in frontend)
-    op.create_index(
-        "idx_transcript_source_kind", "transcript", ["source_kind"], if_not_exists=True
-    )
-
-
-def downgrade() -> None:
-    # Remove the indexes in reverse order
-    op.drop_index("idx_transcript_source_kind", "transcript", if_exists=True)
-    op.drop_index("idx_transcript_room_id_created_at", "transcript", if_exists=True)
--- a/server/migrations/versions/b3df9681cae9_add_transcript_table.py
+++ b/server/migrations/versions/b3df9681cae9_add_transcript_table.py
@@ -8,8 +8,9 @@ Create Date: 2025-06-27 08:57:16.306940

 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
 revision: str = "b3df9681cae9"
--- a/server/migrations/versions/b469348df210_remove_viewer_room_url.py
+++ b/server/migrations/versions/b469348df210_remove_viewer_room_url.py
@@ -8,8 +8,9 @@ Create Date: 2024-10-11 13:45:28.914902

 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
 revision: str = "b469348df210"
--- a/server/migrations/versions/b7df9609542c_add_unique_constraint_one_active_.py
+++ b/server/migrations/versions/b7df9609542c_add_unique_constraint_one_active_.py
@@ -5,15 +5,15 @@ Revises: d7fbb74b673b
 Create Date: 2025-07-25 16:27:06.959868

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
-revision: str = "b7df9609542c"
-down_revision: Union[str, None] = "d7fbb74b673b"
+revision: str = 'b7df9609542c'
+down_revision: Union[str, None] = 'd7fbb74b673b'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None

@@ -22,14 +22,14 @@ def upgrade() -> None:
    # Create a partial unique index that ensures only one active meeting per room
    # This works for both PostgreSQL and SQLite
    op.create_index(
-        "idx_one_active_meeting_per_room",
-        "meeting",
-        ["room_id"],
+        'idx_one_active_meeting_per_room',
+        'meeting',
+        ['room_id'],
        unique=True,
-        postgresql_where=sa.text("is_active = true"),
-        sqlite_where=sa.text("is_active = 1"),
+        postgresql_where=sa.text('is_active = true'),
+        sqlite_where=sa.text('is_active = 1')
    )


 def downgrade() -> None:
-    op.drop_index("idx_one_active_meeting_per_room", table_name="meeting")
+    op.drop_index('idx_one_active_meeting_per_room', table_name='meeting')
--- a/server/migrations/versions/b9348748bbbc_reviewed.py
+++ b/server/migrations/versions/b9348748bbbc_reviewed.py
@@ -5,31 +5,25 @@ Revises: 125031f7cb78
 Create Date: 2023-12-13 15:37:51.303970

 """
-
 from typing import Sequence, Union

-import sqlalchemy as sa
 from alembic import op
+import sqlalchemy as sa

 # revision identifiers, used by Alembic.
-revision: str = "b9348748bbbc"
-down_revision: Union[str, None] = "125031f7cb78"
+revision: str = 'b9348748bbbc'
+down_revision: Union[str, None] = '125031f7cb78'
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None


 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    op.add_column(
-        "transcript",
-        sa.Column(
-            "reviewed", sa.Boolean(), server_default=sa.text("false"), nullable=False
-        ),
-    )
+    op.add_column('transcript', sa.Column('reviewed', sa.Boolean(), server_default=sa.text('0'), nullable=False))
    # ### end Alembic commands ###


 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
-    op.drop_column("transcript", "reviewed")
+    op.drop_column('transcript', 'reviewed')
    # ### end Alembic commands ###
--- a/server/migrations/versions/ccd68dc784ff_add_performance_indexes.py
+++ b/server/migrations/versions/ccd68dc784ff_add_performance_indexes.py
@@ -9,6 +9,8 @@ Create Date: 2025-07-15 11:48:42.854741
 from typing import Sequence, Union

 from alembic import op
+import sqlalchemy as sa
+

 # revision identifiers, used by Alembic.
 revision: str = "ccd68dc784ff"
--- a/Show More
+++ b/Show More
				`@@ -1 +0,0 @@`
				`b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15`