401 reauth experiments

doc
nextjs magic
2025-12-21 04:39:06 +00:00 · 2025-09-05 14:20:00 -04:00 · 2025-09-05 13:05:00 -04:00 · 2025-09-05 12:59:18 -04:00 · 2025-09-05 12:44:12 -04:00 · 2025-09-04 22:16:01 -04:00
337 changed files with 5047 additions and 65472 deletions
--- a/.github/workflows/deploy.yml
+++ b/.github/workflows/deploy.yml
@@ -0,0 +1,90 @@
 name: Deploy to Amazon ECS
 on: [workflow_dispatch]
 env:
  # 950402358378.dkr.ecr.us-east-1.amazonaws.com/reflector
  AWS_REGION: us-east-1
  ECR_REPOSITORY: reflector
 jobs:
  build:
    strategy:
      matrix:
        include:
          - platform: linux/amd64
            runner: linux-amd64
            arch: amd64
          - platform: linux/arm64
            runner: linux-arm64
            arch: arm64
    runs-on: ${{ matrix.runner }}
    permissions:
      contents: read
    outputs:
      registry: ${{ steps.login-ecr.outputs.registry }}
    steps:
      - uses: actions/checkout@v4
      - 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
        id: login-ecr
        uses: aws-actions/amazon-ecr-login@v2
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Build and push ${{ matrix.arch }}
        uses: docker/build-push-action@v5
        with:
          context: server
          platforms: ${{ matrix.platform }}
          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"
--- a/.github/workflows/dockerhub-backend.yml
+++ b/.github/workflows/dockerhub-backend.yml
@@ -1,53 +0,0 @@
 name: Build and Push Backend Docker Image (Docker Hub)
 on:
  push:
    tags:
      - "v*"
  workflow_dispatch:
 env:
  REGISTRY: docker.io
  IMAGE_NAME: monadicalsas/reflector-backend
 jobs:
  build-and-push:
    runs-on: ubuntu-latest
    permissions:
      contents: read
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Log in to Docker Hub
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: monadicalsas
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      - name: Extract metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=ref,event=branch
            type=ref,event=tag
            type=raw,value=latest,enable={{is_default_branch}}
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Build and push Docker image
        uses: docker/build-push-action@v5
        with:
          context: ./server
          file: ./server/Dockerfile
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          cache-from: type=gha
          cache-to: type=gha,mode=max
          platforms: linux/amd64,linux/arm64
--- a/.github/workflows/dockerhub-frontend.yml
+++ b/.github/workflows/dockerhub-frontend.yml
@@ -1,70 +0,0 @@
 name: Build and Push Frontend Docker Image
 on:
  push:
    tags:
      - "v*"
  workflow_dispatch:
 env:
  REGISTRY: docker.io
  IMAGE_NAME: monadicalsas/reflector-frontend
 jobs:
  build-and-push:
    runs-on: ubuntu-latest
    permissions:
      contents: read
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Log in to Docker Hub
        uses: docker/login-action@v3
        with:
          registry: ${{ env.REGISTRY }}
          username: monadicalsas
          password: ${{ secrets.DOCKERHUB_TOKEN }}
      - name: Extract metadata
        id: meta
        uses: docker/metadata-action@v5
        with:
          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
          tags: |
            type=ref,event=branch
            type=ref,event=tag
            type=raw,value=latest,enable={{is_default_branch}}
          github-token: ${{ secrets.GITHUB_TOKEN }}
      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3
      - name: Build and push Docker image
        uses: docker/build-push-action@v5
        with:
          context: ./www
          file: ./www/Dockerfile
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          cache-from: type=gha
          cache-to: type=gha,mode=max
          platforms: linux/amd64,linux/arm64
  deploy:
    needs: build-and-push
    runs-on: ubuntu-latest
    if: success()
    strategy:
      matrix:
        environment: [reflector-monadical, reflector-media]
    environment: ${{ matrix.environment }}
    steps:
      - name: Trigger Coolify deployment
        run: |
          curl -X POST "${{ secrets.COOLIFY_WEBHOOK_URL }}" \
            -H "Content-Type: application/json" \
            -H "Authorization: Bearer ${{ secrets.COOLIFY_WEBHOOK_TOKEN }}" \
            -f || (echo "Failed to trigger Coolify deployment for ${{ matrix.environment }}" && exit 1)
--- a/.gitignore
+++ b/.gitignore
@@ -1,7 +1,6 @@
 .DS_Store
 server/.env
 .env
 Caddyfile
 server/exportdanswer
 .vercel
 .env*.local
@@ -19,5 +18,3 @@ CLAUDE.local.md
 www/.env.development
 www/.env.production
 .playwright-mcp
 docs/pnpm-lock.yaml
 .secrets
--- a/.gitleaksignore
+++ b/.gitleaksignore
@@ -1,5 +1 @@
 b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15
 docs/docs/installation/auth-setup.md:curl-auth-header:250
 docs/docs/installation/daily-setup.md:curl-auth-header:277
 gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
 gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83
--- a/.secrets.example
+++ b/.secrets.example
@@ -1,24 +0,0 @@
 # Example secrets file for GitHub Actions workflows
 # Copy this to .secrets and fill in your values
 # These secrets should be configured in GitHub repository settings:
 # Settings > Secrets and variables > Actions
 # DockerHub Configuration (required for frontend and backend deployment)
 # Create a Docker Hub access token at https://hub.docker.com/settings/security
 # Username: monadicalsas
 DOCKERHUB_TOKEN=your-dockerhub-access-token
 # GitHub Token (required for frontend and backend deployment)
 # Used by docker/metadata-action for extracting image metadata
 # Can use the default GITHUB_TOKEN or create a personal access token
 GITHUB_TOKEN=your-github-token-or-use-default-GITHUB_TOKEN
 # Coolify Deployment Webhook (required for frontend deployment)
 # Used to trigger automatic deployment after image push
 # Configure these secrets in GitHub Environments:
 # Each environment should have:
 #   - COOLIFY_WEBHOOK_URL: The webhook URL for that specific deployment
 #   - COOLIFY_WEBHOOK_TOKEN: The webhook token (can be the same for both if using same token)
 # Optional: GitHub Actions Cache Token (for local testing with act)
 GHA_CACHE_TOKEN=your-github-token-or-empty
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,238 +1,5 @@
 # Changelog
 ## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
 ### Bug Fixes
 * build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
 ## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
 ### Bug Fixes
 * populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
 ## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
 ### Features
 * dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
 * llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
 ### Bug Fixes
 * celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
 * deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
 * hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
 * return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
 ## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
 ### Bug Fixes
 * Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
 ## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
 ### Bug Fixes
 * align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
 ## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
 ### Bug Fixes
 * daily auto refresh fix ([#755](https://github.com/Monadical-SAS/reflector/issues/755)) ([fe47c46](https://github.com/Monadical-SAS/reflector/commit/fe47c46489c5aa0cc538109f7559cc9accb35c01))
 * Skip mixdown for multitrack ([#760](https://github.com/Monadical-SAS/reflector/issues/760)) ([b51b7aa](https://github.com/Monadical-SAS/reflector/commit/b51b7aa9176c1a53ba57ad99f5e976c804a1e80c))
 ## [0.22.1](https://github.com/Monadical-SAS/reflector/compare/v0.22.0...v0.22.1) (2025-11-27)
 ### Bug Fixes
 * participants update from daily ([#749](https://github.com/Monadical-SAS/reflector/issues/749)) ([7f0b728](https://github.com/Monadical-SAS/reflector/commit/7f0b728991c1b9f9aae702c96297eae63b561ef5))
 ## [0.22.0](https://github.com/Monadical-SAS/reflector/compare/v0.21.0...v0.22.0) (2025-11-26)
 ### Features
 * Multitrack segmentation ([#747](https://github.com/Monadical-SAS/reflector/issues/747)) ([d63040e](https://github.com/Monadical-SAS/reflector/commit/d63040e2fdc07e7b272e85a39eb2411cd6a14798))
 ## [0.21.0](https://github.com/Monadical-SAS/reflector/compare/v0.20.0...v0.21.0) (2025-11-26)
 ### Features
 * add transcript format parameter to GET endpoint ([#709](https://github.com/Monadical-SAS/reflector/issues/709)) ([f6ca075](https://github.com/Monadical-SAS/reflector/commit/f6ca07505f34483b02270a2ef3bd809e9d2e1045))
 ## [0.20.0](https://github.com/Monadical-SAS/reflector/compare/v0.19.0...v0.20.0) (2025-11-25)
 ### Features
 * link transcript participants ([#737](https://github.com/Monadical-SAS/reflector/issues/737)) ([9bec398](https://github.com/Monadical-SAS/reflector/commit/9bec39808fc6322612d8b87e922a6f7901fc01c1))
 * transcript restart script ([#742](https://github.com/Monadical-SAS/reflector/issues/742)) ([86d5e26](https://github.com/Monadical-SAS/reflector/commit/86d5e26224bb55a0f1cc785aeda52065bb92ee6f))
 ## [0.19.0](https://github.com/Monadical-SAS/reflector/compare/v0.18.0...v0.19.0) (2025-11-25)
 ### Features
 * dailyco api module ([#725](https://github.com/Monadical-SAS/reflector/issues/725)) ([4287f8b](https://github.com/Monadical-SAS/reflector/commit/4287f8b8aeee60e51db7539f4dcbda5f6e696bd8))
 * dailyco poll ([#730](https://github.com/Monadical-SAS/reflector/issues/730)) ([8e438ca](https://github.com/Monadical-SAS/reflector/commit/8e438ca285152bd48fdc42767e706fb448d3525c))
 * multitrack cli ([#735](https://github.com/Monadical-SAS/reflector/issues/735)) ([11731c9](https://github.com/Monadical-SAS/reflector/commit/11731c9d38439b04e93b1c3afbd7090bad11a11f))
 ### Bug Fixes
 * default platform fix ([#736](https://github.com/Monadical-SAS/reflector/issues/736)) ([c442a62](https://github.com/Monadical-SAS/reflector/commit/c442a627873ca667656eeaefb63e54ab10b8d19e))
 * parakeet vad not getting the end timestamp ([#728](https://github.com/Monadical-SAS/reflector/issues/728)) ([18ed713](https://github.com/Monadical-SAS/reflector/commit/18ed7133693653ef4ddac6c659a8c14b320d1657))
 * start raw tracks recording ([#729](https://github.com/Monadical-SAS/reflector/issues/729)) ([3e47c2c](https://github.com/Monadical-SAS/reflector/commit/3e47c2c0573504858e0d2e1798b6ed31f16b4a5d))
 ## [0.18.0](https://github.com/Monadical-SAS/reflector/compare/v0.17.0...v0.18.0) (2025-11-14)
 ### Features
 * daily QOL: participants dictionary ([#721](https://github.com/Monadical-SAS/reflector/issues/721)) ([b20cad7](https://github.com/Monadical-SAS/reflector/commit/b20cad76e69fb6a76405af299a005f1ddcf60eae))
 ### Bug Fixes
 * add proccessing page to file upload and reprocessing ([#650](https://github.com/Monadical-SAS/reflector/issues/650)) ([28a7258](https://github.com/Monadical-SAS/reflector/commit/28a7258e45317b78e60e6397be2bc503647eaace))
 * copy transcript ([#674](https://github.com/Monadical-SAS/reflector/issues/674)) ([a9a4f32](https://github.com/Monadical-SAS/reflector/commit/a9a4f32324f66c838e081eee42bb9502f38c1db1))
 ## [0.17.0](https://github.com/Monadical-SAS/reflector/compare/v0.16.0...v0.17.0) (2025-11-13)
 ### Features
 * add API key management UI ([#716](https://github.com/Monadical-SAS/reflector/issues/716)) ([372202b](https://github.com/Monadical-SAS/reflector/commit/372202b0e1a86823900b0aa77be1bfbc2893d8a1))
 * daily.co support as alternative to whereby ([#691](https://github.com/Monadical-SAS/reflector/issues/691)) ([1473fd8](https://github.com/Monadical-SAS/reflector/commit/1473fd82dc472c394cbaa2987212ad662a74bcac))
 ## [0.16.0](https://github.com/Monadical-SAS/reflector/compare/v0.15.0...v0.16.0) (2025-10-24)
 ### Features
 * search date filter ([#710](https://github.com/Monadical-SAS/reflector/issues/710)) ([962c40e](https://github.com/Monadical-SAS/reflector/commit/962c40e2b6428ac42fd10aea926782d7a6f3f902))
 ## [0.15.0](https://github.com/Monadical-SAS/reflector/compare/v0.14.0...v0.15.0) (2025-10-20)
 ### Features
 * api tokens  ([#705](https://github.com/Monadical-SAS/reflector/issues/705)) ([9a258ab](https://github.com/Monadical-SAS/reflector/commit/9a258abc0209b0ac3799532a507ea6a9125d703a))
 ## [0.14.0](https://github.com/Monadical-SAS/reflector/compare/v0.13.1...v0.14.0) (2025-10-08)
 ### Features
 * Add calendar event data to transcript webhook payload ([#689](https://github.com/Monadical-SAS/reflector/issues/689)) ([5f6910e](https://github.com/Monadical-SAS/reflector/commit/5f6910e5131b7f28f86c9ecdcc57fed8412ee3cd))
 * container build for www / github ([#672](https://github.com/Monadical-SAS/reflector/issues/672)) ([969bd84](https://github.com/Monadical-SAS/reflector/commit/969bd84fcc14851d1a101412a0ba115f1b7cde82))
 * docker-compose for production frontend ([#664](https://github.com/Monadical-SAS/reflector/issues/664)) ([5bf64b5](https://github.com/Monadical-SAS/reflector/commit/5bf64b5a41f64535e22849b4bb11734d4dbb4aae))
 ### Bug Fixes
 * restore feature boolean logic ([#671](https://github.com/Monadical-SAS/reflector/issues/671)) ([3660884](https://github.com/Monadical-SAS/reflector/commit/36608849ec64e953e3be456172502762e3c33df9))
 * security review ([#656](https://github.com/Monadical-SAS/reflector/issues/656)) ([5d98754](https://github.com/Monadical-SAS/reflector/commit/5d98754305c6c540dd194dda268544f6d88bfaf8))
 * update transcript list on reprocess ([#676](https://github.com/Monadical-SAS/reflector/issues/676)) ([9a71af1](https://github.com/Monadical-SAS/reflector/commit/9a71af145ee9b833078c78d0c684590ab12e9f0e))
 * upgrade nemo toolkit ([#678](https://github.com/Monadical-SAS/reflector/issues/678)) ([eef6dc3](https://github.com/Monadical-SAS/reflector/commit/eef6dc39037329b65804297786d852dddb0557f9))
 ## [0.13.1](https://github.com/Monadical-SAS/reflector/compare/v0.13.0...v0.13.1) (2025-09-22)
 ### Bug Fixes
 * TypeError on not all arguments converted during string formatting in logger ([#667](https://github.com/Monadical-SAS/reflector/issues/667)) ([565a629](https://github.com/Monadical-SAS/reflector/commit/565a62900f5a02fc946b68f9269a42190ed70ab6))
 ## [0.13.0](https://github.com/Monadical-SAS/reflector/compare/v0.12.1...v0.13.0) (2025-09-19)
 ### Features
 * room form edit with enter ([#662](https://github.com/Monadical-SAS/reflector/issues/662)) ([47716f6](https://github.com/Monadical-SAS/reflector/commit/47716f6e5ddee952609d2fa0ffabdfa865286796))
 ### Bug Fixes
 * invalid cleanup call ([#660](https://github.com/Monadical-SAS/reflector/issues/660)) ([0abcebf](https://github.com/Monadical-SAS/reflector/commit/0abcebfc9491f87f605f21faa3e53996fafedd9a))
 ## [0.12.1](https://github.com/Monadical-SAS/reflector/compare/v0.12.0...v0.12.1) (2025-09-17)
 ### Bug Fixes
 * production blocked because having existing meeting with room_id null ([#657](https://github.com/Monadical-SAS/reflector/issues/657)) ([870e860](https://github.com/Monadical-SAS/reflector/commit/870e8605171a27155a9cbee215eeccb9a8d6c0a2))
 ## [0.12.0](https://github.com/Monadical-SAS/reflector/compare/v0.11.0...v0.12.0) (2025-09-17)
 ### Features
 * calendar integration ([#608](https://github.com/Monadical-SAS/reflector/issues/608)) ([6f680b5](https://github.com/Monadical-SAS/reflector/commit/6f680b57954c688882c4ed49f40f161c52a00a24))
 * self-hosted gpu api ([#636](https://github.com/Monadical-SAS/reflector/issues/636)) ([ab859d6](https://github.com/Monadical-SAS/reflector/commit/ab859d65a6bded904133a163a081a651b3938d42))
 ### Bug Fixes
 * ignore player hotkeys for text inputs ([#646](https://github.com/Monadical-SAS/reflector/issues/646)) ([fa049e8](https://github.com/Monadical-SAS/reflector/commit/fa049e8d068190ce7ea015fd9fcccb8543f54a3f))
 ## [0.11.0](https://github.com/Monadical-SAS/reflector/compare/v0.10.0...v0.11.0) (2025-09-16)
 ### Features
 * remove profanity filter that was there for conference ([#652](https://github.com/Monadical-SAS/reflector/issues/652)) ([b42f7cf](https://github.com/Monadical-SAS/reflector/commit/b42f7cfc606783afcee792590efcc78b507468ab))
 ### Bug Fixes
 * zulip and consent handler on the file pipeline ([#645](https://github.com/Monadical-SAS/reflector/issues/645)) ([5f143fe](https://github.com/Monadical-SAS/reflector/commit/5f143fe3640875dcb56c26694254a93189281d17))
 * zulip stream and topic selection in share dialog ([#644](https://github.com/Monadical-SAS/reflector/issues/644)) ([c546e69](https://github.com/Monadical-SAS/reflector/commit/c546e69739e68bb74fbc877eb62609928e5b8de6))
 ## [0.10.0](https://github.com/Monadical-SAS/reflector/compare/v0.9.0...v0.10.0) (2025-09-11)
 ### Features
 * replace nextjs-config with environment variables ([#632](https://github.com/Monadical-SAS/reflector/issues/632)) ([369ecdf](https://github.com/Monadical-SAS/reflector/commit/369ecdff13f3862d926a9c0b87df52c9d94c4dde))
 ### Bug Fixes
 * anonymous users transcript permissions ([#621](https://github.com/Monadical-SAS/reflector/issues/621)) ([f81fe99](https://github.com/Monadical-SAS/reflector/commit/f81fe9948a9237b3e0001b2d8ca84f54d76878f9))
 * auth post ([#624](https://github.com/Monadical-SAS/reflector/issues/624)) ([cde99ca](https://github.com/Monadical-SAS/reflector/commit/cde99ca2716f84ba26798f289047732f0448742e))
 * auth post ([#626](https://github.com/Monadical-SAS/reflector/issues/626)) ([3b85ff3](https://github.com/Monadical-SAS/reflector/commit/3b85ff3bdf4fb053b103070646811bc990c0e70a))
 * auth post ([#627](https://github.com/Monadical-SAS/reflector/issues/627)) ([962038e](https://github.com/Monadical-SAS/reflector/commit/962038ee3f2a555dc3c03856be0e4409456e0996))
 * missing follow_redirects=True on modal endpoint ([#630](https://github.com/Monadical-SAS/reflector/issues/630)) ([fc363bd](https://github.com/Monadical-SAS/reflector/commit/fc363bd49b17b075e64f9186e5e0185abc325ea7))
 * sync backend and frontend token refresh logic ([#614](https://github.com/Monadical-SAS/reflector/issues/614)) ([5a5b323](https://github.com/Monadical-SAS/reflector/commit/5a5b3233820df9536da75e87ce6184a983d4713a))
 ## [0.9.0](https://github.com/Monadical-SAS/reflector/compare/v0.8.2...v0.9.0) (2025-09-06)
 ### Features
 * frontend openapi react query ([#606](https://github.com/Monadical-SAS/reflector/issues/606)) ([c4d2825](https://github.com/Monadical-SAS/reflector/commit/c4d2825c81f81ad8835629fbf6ea8c7383f8c31b))
 ### Bug Fixes
 * align whisper transcriber api with parakeet ([#602](https://github.com/Monadical-SAS/reflector/issues/602)) ([0663700](https://github.com/Monadical-SAS/reflector/commit/0663700a615a4af69a03c96c410f049e23ec9443))
 * kv use tls explicit ([#610](https://github.com/Monadical-SAS/reflector/issues/610)) ([08d88ec](https://github.com/Monadical-SAS/reflector/commit/08d88ec349f38b0d13e0fa4cb73486c8dfd31836))
 * source kind for file processing ([#601](https://github.com/Monadical-SAS/reflector/issues/601)) ([dc82f8b](https://github.com/Monadical-SAS/reflector/commit/dc82f8bb3bdf3ab3d4088e592a30fd63907319e1))
 * token refresh locking ([#613](https://github.com/Monadical-SAS/reflector/issues/613)) ([7f5a4c9](https://github.com/Monadical-SAS/reflector/commit/7f5a4c9ddc7fd098860c8bdda2ca3b57f63ded2f))
 ## [0.8.2](https://github.com/Monadical-SAS/reflector/compare/v0.8.1...v0.8.2) (2025-08-29)
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -66,6 +66,7 @@ pnpm install
 # Copy configuration templates
 cp .env_template .env
 cp config-template.ts config.ts
 ```
 **Development:**
@@ -151,7 +152,7 @@ All endpoints prefixed `/v1/`:
 **Frontend** (`www/.env`):
 - `NEXTAUTH_URL`, `NEXTAUTH_SECRET` - Authentication configuration
- `REFLECTOR_API_URL` - Backend API endpoint
+- `NEXT_PUBLIC_REFLECTOR_API_URL` - Backend API endpoint
 - `REFLECTOR_DOMAIN_CONFIG` - Feature flags and domain settings
 ## Testing Strategy
--- a/Caddyfile.example
+++ b/Caddyfile.example
@@ -1,22 +0,0 @@
 # Reflector Caddyfile
 # Replace example.com with your actual domains
 # CORS is handled by the backend - Caddy just proxies
 #
 # For environment variable substitution, set:
 #   FRONTEND_DOMAIN=app.example.com
 #   API_DOMAIN=api.example.com
 #   AUTHENTIK_DOMAIN=authentik.example.com  (optional, for authentication)
 # Or edit this file directly with your domains.
 {$FRONTEND_DOMAIN:app.example.com} {
 	reverse_proxy web:3000
 }
 {$API_DOMAIN:api.example.com} {
 	reverse_proxy server:1250
 }
 # Uncomment if using Authentik for authentication (see auth-setup.md)
 # {$AUTHENTIK_DOMAIN:authentik.example.com} {
 # 	reverse_proxy authentik-server-1:9000
 # }
--- a/README.md
+++ b/README.md
@@ -99,10 +99,11 @@ Start with `cd www`.
 ```bash
 pnpm install
-cp .env.example .env
+cp .env_template .env
 cp config-template.ts config.ts
 ```
-Then, fill in the environment variables in `.env` as needed. If you are unsure on how to proceed, ask in Zulip.
+Then, fill in the environment variables in `.env` and the configuration in `config.ts` as needed. If you are unsure on how to proceed, ask in Zulip.
 **Run in development mode**
@@ -167,47 +168,3 @@ You can manually process an audio file by calling the process tool:
 ```bash
 uv run python -m reflector.tools.process path/to/audio.wav
 ```
 ## Reprocessing any transcription
 ```bash
 uv run -m reflector.tools.process_transcript 81ec38d1-9dd7-43d2-b3f8-51f4d34a07cd --sync
 ```
 ## Build-time env variables
 Next.js projects are more used to NEXT_PUBLIC_ prefixed buildtime vars. We don't have those for the reason we need to serve a ccustomizable prebuild docker container.
 Instead, all the variables are runtime. Variables needed to the frontend are served to the frontend app at initial render.
 It also means there's no static prebuild and no static files to serve for js/html.
 ## Feature Flags
 Reflector uses environment variable-based feature flags to control application functionality. These flags allow you to enable or disable features without code changes.
 ### Available Feature Flags
 | Feature Flag | Environment Variable |
 |-------------|---------------------|
 | `requireLogin` | `FEATURE_REQUIRE_LOGIN` |
 | `privacy` | `FEATURE_PRIVACY` |
 | `browse` | `FEATURE_BROWSE` |
 | `sendToZulip` | `FEATURE_SEND_TO_ZULIP` |
 | `rooms` | `FEATURE_ROOMS` |
 ### Setting Feature Flags
 Feature flags are controlled via environment variables using the pattern `FEATURE_{FEATURE_NAME}` where `{FEATURE_NAME}` is the SCREAMING_SNAKE_CASE version of the feature name.
 **Examples:**
 ```bash
 # Enable user authentication requirement
 FEATURE_REQUIRE_LOGIN=true
 # Disable browse functionality
 FEATURE_BROWSE=false
 # Enable Zulip integration
 FEATURE_SEND_TO_ZULIP=true
 ```
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -39,7 +39,7 @@ services:
    ports:
      - 6379:6379
  web:
-    image: node:22-alpine
+    image: node:18
    ports:
      - "3000:3000"
    command: sh -c "corepack enable && pnpm install && pnpm dev"
@@ -50,8 +50,6 @@ services:
      - /app/node_modules
    env_file:
      - ./www/.env.local
    environment:
      - NODE_ENV=development
  postgres:
    image: postgres:17
--- a/docker-compose.prod.yml
+++ b/docker-compose.prod.yml
@@ -1,113 +0,0 @@
 # Production Docker Compose configuration
 # Usage: docker compose -f docker-compose.prod.yml up -d
 #
 # Prerequisites:
 # 1. Copy .env.example to .env and configure for both server/ and www/
 # 2. Copy Caddyfile.example to Caddyfile and edit with your domains
 # 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
 services:
  web:
    image: monadicalsas/reflector-frontend:latest
    restart: unless-stopped
    env_file:
      - ./www/.env
    pull_policy: always
    environment:
      - KV_URL=redis://redis:6379
    depends_on:
      - redis
  server:
    image: monadicalsas/reflector-backend:latest
    restart: unless-stopped
    env_file:
      - ./server/.env
    environment:
      ENTRYPOINT: server
    depends_on:
      - postgres
      - redis
    volumes:
      - server_data:/app/data
      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
  worker:
    image: monadicalsas/reflector-backend:latest
    restart: unless-stopped
    env_file:
      - ./server/.env
    environment:
      ENTRYPOINT: worker
    depends_on:
      - postgres
      - redis
    volumes:
      - server_data:/app/data
      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
  beat:
    image: monadicalsas/reflector-backend:latest
    restart: unless-stopped
    env_file:
      - ./server/.env
    environment:
      ENTRYPOINT: beat
    depends_on:
      - postgres
      - redis
  redis:
    image: redis:7.2-alpine
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 30s
      timeout: 3s
      retries: 3
    volumes:
      - redis_data:/data
  postgres:
    image: postgres:17-alpine
    restart: unless-stopped
    environment:
      POSTGRES_USER: reflector
      POSTGRES_PASSWORD: reflector
      POSTGRES_DB: reflector
    volumes:
      - postgres_data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U reflector"]
      interval: 30s
      timeout: 3s
      retries: 3
  caddy:
    image: caddy:2-alpine
    restart: unless-stopped
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./Caddyfile:/etc/caddy/Caddyfile:ro
      - caddy_data:/data
      - caddy_config:/config
    depends_on:
      - web
      - server
  docs:
    build: ./docs
    restart: unless-stopped
 volumes:
  redis_data:
  postgres_data:
  server_data:
  caddy_data:
  caddy_config:
 networks:
  default:
    attachable: true
--- a/docs/.gitignore
+++ b/docs/.gitignore
@@ -1,20 +0,0 @@
 # Dependencies
 /node_modules
 # Production
 /build
 # Generated files
 .docusaurus
 .cache-loader
 # Misc
 .DS_Store
 .env.local
 .env.development.local
 .env.test.local
 .env.production.local
 npm-debug.log*
 yarn-debug.log*
 yarn-error.log*
--- a/docs/Dockerfile
+++ b/docs/Dockerfile
@@ -1,39 +0,0 @@
 FROM node:18-alpine AS builder
 WORKDIR /app
 # Install curl for fetching OpenAPI spec
 RUN apk add --no-cache curl
 # Copy package files
 COPY package*.json ./
 # Install dependencies
 RUN npm ci
 # Copy source
 COPY . .
 # Fetch OpenAPI spec from production API
 ARG OPENAPI_URL=https://api-reflector.monadical.com/openapi.json
 RUN mkdir -p ./static && curl -sf "${OPENAPI_URL}" -o ./static/openapi.json || echo '{}' > ./static/openapi.json
 # Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
 RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
 # Build static site (skip prebuild hook by calling docusaurus directly)
 RUN npx docusaurus build
 # Production image
 FROM nginx:alpine
 # Copy built static files
 COPY --from=builder /app/build /usr/share/nginx/html
 # Healthcheck for container orchestration
 HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
  CMD wget --no-verbose --tries=1 --spider http://localhost/ || exit 1
 # Expose port
 EXPOSE 80
 CMD ["nginx", "-g", "daemon off;"]
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,41 +0,0 @@
 # Website
 This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
 ### Installation
 ```
 $ yarn
 ```
 ### Local Development
 ```
 $ yarn start
 ```
 This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
 ### Build
 ```
 $ yarn build
 ```
 This command generates static content into the `build` directory and can be served using any static contents hosting service.
 ### Deployment
 Using SSH:
 ```
 $ USE_SSH=true yarn deploy
 ```
 Not using SSH:
 ```
 $ GIT_USER=<Your GitHub username> yarn deploy
 ```
 If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
--- a/docs/TODO.md
+++ b/docs/TODO.md
@@ -1,170 +0,0 @@
 # Documentation TODO List
 This file tracks information needed from the user to complete the documentation.
 ## Required Information
 ### Processing Times & Costs
 Please provide actual numbers for:
 - [ ] **Modal.com GPU Costs**
  - Cost per hour of audio for Whisper transcription
  - Cost per hour of audio for Pyannote diarization
  - Cost per hour of audio for Seamless-M4T translation
  - Typical GPU instance used (T4, A10, etc.)
 - [ ] **RunPod LLM Costs**
  - Cost per 1000 tokens for summarization
  - Model used (phi-4-unsloth-bnb-4bit)
  - RTX 4000 Ada instance cost per hour
 - [ ] **AWS S3 Storage**
  - Cost per GB per month
  - Data transfer costs
  - Typical storage requirements per hour of audio
 - [ ] **Whereby API**
  - Monthly cost structure
  - API call limits
  - Room participant limits
 - [ ] **Actual Processing Times**
  - Whisper tiny model: X minutes per hour of audio
  - Whisper base model: X minutes per hour of audio
  - Whisper large-v3 model: X minutes per hour of audio
  - Diarization: X minutes per hour of audio
  - Translation: X minutes per hour of audio
 ### Screenshots Needed
 Location: `/docs/static/screenshots/`
 Please provide screenshots of:
 - [ ] **Dashboard Overview** - Main dashboard showing recent transcripts
 - [ ] **Live Transcription** - Active transcription in progress
 - [ ] **Meeting Room Interface** - Whereby room with participants
 - [ ] **Transcript with Diarization** - Showing speaker labels
 - [ ] **Settings Page** - Configuration options
 - [ ] **API Documentation** - OpenAPI/Swagger interface
 - [ ] **File Upload Interface** - Drag and drop upload
 - [ ] **Translation View** - Showing original and translated text
 - [ ] **Summary View** - Generated summary and topics
 ### Setup Screenshots
 Please provide step-by-step screenshots for:
 - [ ] **Modal.com Setup**
  - Creating account
  - Getting API keys
  - Deploying functions
 - [ ] **Whereby Configuration**
  - Creating developer account
  - Getting API credentials
  - Setting up rooms
 - [ ] **AWS S3 Setup**
  - Creating bucket
  - Setting permissions
  - Getting access keys
 - [ ] **Authentik Integration**
  - Adding application
  - Configuring OAuth
  - Setting up users
 ### Technical Details
 Please provide specific values for:
 - [ ] **WebRTC Configuration**
  - Exact UDP port range used (e.g., 10000-20000)
  - STUN server configuration (if any)
  - ICE candidate gathering timeout
  - https://docs.daily.co/guides/privacy-and-security/corporate-firewalls-nats-allowed-ip-list
 - [ ] **Worker Configuration**
  - Default Celery worker count
  - Worker memory limits
  - Queue priorities
 - [ ] **Redis Requirements**
  - Typical memory usage
  - Persistence configuration
  - Eviction policies
 - [ ] **PostgreSQL**
  - Expected database growth (MB per hour of audio)
  - Recommended connection pool size
  - Backup strategy
 - [ ] **Performance Metrics**
  - Average transcription accuracy (WER)
  - Average diarization accuracy (DER)
  - Translation quality scores
  - Typical latency for live streaming
 ### Configuration Examples
 Please provide real-world examples for:
 - [ ] **Production .env file** (sanitized)
 - [ ] **Caddy configuration** for production
 - [ ] **Docker compose** for production deployment
 - [ ] **Nginx configuration** (if alternative to Caddy)
 ### API Examples
 Please provide:
 - [ ] **Sample API requests** for common operations
 - [ ] **WebSocket message examples**
 - [ ] **Webhook payload examples**
 - [ ] **Error response examples**
 ## How to Add Information
 1. **For text information**: Edit the relevant markdown files in `/docs/docs/`
 2. **For screenshots**: Add to `/docs/static/screenshots/` and reference in docs
 3. **For code examples**: Add to documentation with proper syntax highlighting
 ## Priority Items
 High priority (blocks documentation completeness):
 1. Modal.com costs and setup steps
 2. Basic screenshots (dashboard, transcription)
 3. Docker deployment configuration
 Medium priority (enhances documentation):
 1. Performance metrics
 2. Advanced configuration examples
 3. Troubleshooting scenarios
 Low priority (nice to have):
 1. Video tutorials
 2. Architecture diagrams
 3. Benchmark comparisons
 ## Documentation Structure
 Once information is provided, update these files:
 - `/docs/docs/installation/modal-setup.md` - Add Modal.com setup screenshots
 - `/docs/docs/installation/whereby-setup.md` - Add Whereby configuration steps
 - `/docs/docs/reference/configuration.md` - Add environment variable details
 - `/docs/docs/pipelines/file-pipeline.md` - Add actual processing times
 - `/docs/docs/pipelines/live-pipeline.md` - Add latency measurements
 ## Notes
 - Replace placeholder values with actual data
 - Ensure all sensitive information is sanitized
 - Test all configuration examples before documenting
 - Verify all costs are up-to-date
 ---
 Last updated: 2025-08-20
 Contact: [Your Email]
--- a/docs/create-docs.sh
+++ b/docs/create-docs.sh
@@ -1,777 +0,0 @@
 #!/bin/bash
 # Create directory structure
 mkdir -p docs/concepts
 mkdir -p docs/installation
 mkdir -p docs/pipelines
 mkdir -p docs/reference/architecture
 mkdir -p docs/reference/processors
 mkdir -p docs/reference/api
 # Create all documentation files with content
 echo "Creating documentation files..."
 # Concepts - Modes
 cat > docs/concepts/modes.md << 'EOF'
 ---
 sidebar_position: 2
 title: Operating Modes
 ---
 # Operating Modes
 Reflector operates in two distinct modes to accommodate different use cases and security requirements.
 ## Public Mode
 Public mode provides immediate access to core transcription features without requiring authentication.
 ### Features Available
 - **File Upload**: Process audio files up to 2GB
 - **Live Transcription**: Stream audio from microphone
 - **Basic Processing**: Transcription and diarization
 - **Temporary Storage**: Results available for 24 hours
 ### Limitations
 - No persistent storage
 - No meeting rooms
 - Limited to single-user sessions
 - No team collaboration features
 ### Use Cases
 - Quick transcription needs
 - Testing and evaluation
 - Individual users
 - Public demonstrations
 ## Private Mode
 Private mode unlocks the full potential of Reflector with authentication and persistent storage.
 ### Additional Features
 - **Virtual Meeting Rooms**: Whereby integration
 - **Team Collaboration**: Share transcripts with team
 - **Persistent Storage**: Long-term transcript archive
 - **Advanced Analytics**: Meeting insights and trends
 - **Custom Integration**: Webhooks and API access
 - **User Management**: Role-based access control
 ### Authentication Options
 #### Authentik Integration
 Enterprise-grade SSO with support for:
 - SAML 2.0
 - OAuth 2.0 / OIDC
 - LDAP / Active Directory
 - Multi-factor authentication
 #### JWT Authentication
 Stateless token-based auth for:
 - API access
 - Service-to-service communication
 - Mobile applications
 ### Room Management
 Virtual rooms provide dedicated spaces for meetings:
 - **Persistent URLs**: Same link for recurring meetings
 - **Access Control**: Invite-only or open rooms
 - **Recording Consent**: Automatic consent management
 - **Custom Settings**: Per-room configuration
 ## Mode Selection
 The mode is determined by your deployment configuration:
 ```yaml
 # Public Mode (no authentication)
 REFLECTOR_AUTH_BACKEND=none
 # Private Mode (with authentication)
 REFLECTOR_AUTH_BACKEND=jwt
 # or
 REFLECTOR_AUTH_BACKEND=authentik
 ```
 ## Feature Comparison
 | Feature | Public Mode | Private Mode |
 |---------|------------|--------------|
 | File Upload | ✅ | ✅ |
 | Live Transcription | ✅ | ✅ |
 | Speaker Diarization | ✅ | ✅ |
 | Translation | ✅ | ✅ |
 | Summarization | ✅ | ✅ |
 | Meeting Rooms | ❌ | ✅ |
 | Persistent Storage | ❌ | ✅ |
 | Team Collaboration | ❌ | ✅ |
 | API Access | Limited | Full |
 | User Management | ❌ | ✅ |
 | Custom Branding | ❌ | ✅ |
 | Analytics | ❌ | ✅ |
 | Webhooks | ❌ | ✅ |
 ## Security Considerations
 ### Public Mode Security
 - Rate limiting to prevent abuse
 - File size restrictions
 - Automatic cleanup of old data
 - No PII storage
 ### Private Mode Security
 - Encrypted data storage
 - Audit logging
 - Session management
 - Access control lists
 - Data retention policies
 ## Choosing the Right Mode
 ### Choose Public Mode if:
 - You need quick, one-time transcriptions
 - You're evaluating Reflector
 - You don't need persistent storage
 - You're processing non-sensitive content
 ### Choose Private Mode if:
 - You need team collaboration
 - You require persistent storage
 - You're processing sensitive content
 - You need meeting room functionality
 - You want advanced analytics
 EOF
 # Concepts - Independence
 cat > docs/concepts/independence.md << 'EOF'
 ---
 sidebar_position: 3
 title: Data Independence
 ---
 # Data Independence & Privacy
 Reflector is designed with privacy and data independence as core principles, giving you complete control over your data and processing.
 ## Privacy by Design
 ### No Third-Party Data Sharing
 Your audio and transcripts are never shared with third parties:
 - **Local Processing**: All ML models can run on your infrastructure
 - **No Training on User Data**: Your content is never used to improve models
 - **Isolated Processing**: Each transcript is processed in isolation
 - **No Analytics Tracking**: No usage analytics sent to external services
 ### Data Ownership
 You maintain complete ownership of all data:
 - **Export Anytime**: Download all your transcripts and audio
 - **Delete on Demand**: Permanent deletion with no recovery
 - **API Access**: Full programmatic access to your data
 - **No Vendor Lock-in**: Standard formats for easy migration
 ## Processing Transparency
 ### What Happens to Your Audio
 1. **Upload/Stream**: Audio received by your server
 2. **Temporary Storage**: Stored only for processing duration
 3. **Processing**: ML models process audio locally or on Modal
 4. **Results Storage**: Transcripts stored in your database
 5. **Cleanup**: Original audio deleted (unless configured otherwise)
 ### Local vs Cloud Processing
 #### Local Processing
 When configured for local processing:
 - All models run on your hardware
 - No data leaves your infrastructure
 - Complete air-gap capability
 - Higher hardware requirements
 #### Modal.com Processing
 When using Modal for GPU acceleration:
 - Audio chunks sent to Modal for processing
 - Processed immediately and deleted
 - No long-term storage on Modal
 - Modal's security: SOC 2 Type II compliant
 ### Data Retention
 Default retention policies:
 - **Public Mode**: 24 hours then automatic deletion
 - **Private Mode**: Configurable (default: indefinite)
 - **Audio Files**: Deleted after processing (configurable)
 - **Transcripts**: Retained based on policy
 ## Compliance Features
 ### GDPR Compliance
 - **Right to Access**: Export all user data
 - **Right to Deletion**: Permanent data removal
 - **Data Portability**: Standard export formats
 - **Privacy by Default**: Minimal data collection
 ### HIPAA Considerations
 For healthcare deployments:
 - **Self-hosted Option**: Complete infrastructure control
 - **Encryption**: At rest and in transit
 - **Audit Logging**: Complete access trail
 - **Access Controls**: Role-based permissions
 ### Industry Standards
 - **TLS 1.3**: Modern encryption for data in transit
 - **AES-256**: Encryption for data at rest
 - **JWT Tokens**: Secure, stateless authentication
 - **OWASP Guidelines**: Security best practices
 ## Self-Hosted Deployment
 ### Complete Independence
 Self-hosting provides maximum control:
 - **Your Infrastructure**: Run on your servers
 - **Your Network**: No external connections required
 - **Your Policies**: Implement custom retention
 - **Your Compliance**: Meet specific requirements
 ### Air-Gap Capability
 Reflector can run completely offline:
 1. Download all models during setup
 2. Configure for local processing only
 3. Disable all external integrations
 4. Run in isolated network environment
 ## Data Flow Control
 ### Configurable Processing
 Control where each step happens:
 ```yaml
 # All local processing
 TRANSCRIPT_BACKEND=local
 DIARIZATION_BACKEND=local
 TRANSLATION_BACKEND=local
 # Hybrid approach
 TRANSCRIPT_BACKEND=modal  # Fast GPU processing
 DIARIZATION_BACKEND=local # Sensitive speaker data
 TRANSLATION_BACKEND=modal  # Non-sensitive translation
 ```
 ### Storage Options
 Choose where data is stored:
 - **Local Filesystem**: Complete control
 - **PostgreSQL**: Self-hosted database
 - **S3-Compatible**: MinIO or AWS with encryption
 - **Hybrid**: Different storage for different data types
 ## Security Architecture
 ### Defense in Depth
 Multiple layers of security:
 1. **Network Security**: Firewalls and VPNs
 2. **Application Security**: Input validation and sanitization
 3. **Data Security**: Encryption and access controls
 4. **Operational Security**: Logging and monitoring
 ### Zero Trust Principles
 - **Verify Everything**: All requests authenticated
 - **Least Privilege**: Minimal permissions granted
 - **Assume Breach**: Design for compromise containment
 - **Encrypt Everything**: No plaintext transmission
 ## Audit and Compliance
 ### Audit Logging
 Comprehensive logging of:
 - **Access Events**: Who accessed what and when
 - **Processing Events**: What was processed and how
 - **Configuration Changes**: System modifications
 - **Security Events**: Failed authentication attempts
 ### Compliance Reporting
 Generate reports for:
 - **Data Processing**: What data was processed
 - **Data Access**: Who accessed the data
 - **Data Retention**: What was retained or deleted
 - **Security Events**: Security-related incidents
 ## Best Practices
 ### For Maximum Privacy
 1. **Self-host** all components
 2. **Use local processing** for all models
 3. **Implement short retention** periods
 4. **Encrypt all storage** at rest
 5. **Use VPN** for all connections
 6. **Regular audits** of access logs
 ### For Balanced Approach
 1. **Self-host core services** (database, API)
 2. **Use Modal for processing** (faster, cost-effective)
 3. **Implement encryption** everywhere
 4. **Regular backups** with encryption
 5. **Monitor access** patterns
 EOF
 # Concepts - Pipeline
 cat > docs/concepts/pipeline.md << 'EOF'
 ---
 sidebar_position: 4
 title: Processing Pipeline
 ---
 # Processing Pipeline
 Reflector uses a sophisticated pipeline architecture to process audio efficiently and accurately.
 ## Pipeline Overview
 The processing pipeline consists of modular components that can be combined and configured based on your needs:
 ```mermaid
 graph LR
    A[Audio Input] --> B[Pre-processing]
    B --> C[Chunking]
    C --> D[Transcription]
    D --> E[Diarization]
    E --> F[Alignment]
    F --> G[Post-processing]
    G --> H[Output]
 ```
 ## Pipeline Components
 ### Audio Input
 Accepts various input sources:
 - **File Upload**: MP3, WAV, M4A, WebM, MP4
 - **WebRTC Stream**: Live browser audio
 - **Recording Integration**: Whereby recordings
 - **API Upload**: Direct API submission
 ### Pre-processing
 Prepares audio for optimal processing:
 - **Format Conversion**: Convert to 16kHz mono WAV
 - **Normalization**: Adjust volume to -23 LUFS
 - **Noise Reduction**: Optional background noise removal
 - **Validation**: Check duration and quality
 ### Chunking
 Splits audio for parallel processing:
 - **Fixed Size**: 30-second chunks by default
 - **Overlap**: 1-second overlap for continuity
 - **Smart Boundaries**: Attempt to split at silence
 - **Metadata**: Track chunk positions
 ### Transcription
 Converts speech to text:
 - **Model Selection**: Whisper or Parakeet
 - **Language Detection**: Automatic or specified
 - **Timestamp Generation**: Word-level timing
 - **Confidence Scores**: Quality indicators
 ### Diarization
 Identifies different speakers:
 - **Voice Activity Detection**: Find speech segments
 - **Speaker Embedding**: Extract voice characteristics
 - **Clustering**: Group similar voices
 - **Label Assignment**: Assign speaker IDs
 ### Alignment
 Merges all processing results:
 - **Chunk Assembly**: Combine transcription chunks
 - **Speaker Mapping**: Align speakers with text
 - **Overlap Resolution**: Handle chunk boundaries
 - **Timeline Creation**: Build unified timeline
 ### Post-processing
 Enhances the final output:
 - **Formatting**: Apply punctuation and capitalization
 - **Translation**: Convert to target languages
 - **Summarization**: Generate concise summaries
 - **Topic Extraction**: Identify key themes
 - **Action Items**: Extract tasks and decisions
 ## Processing Modes
 ### Batch Processing
 For uploaded files:
 - Optimized for throughput
 - Parallel chunk processing
 - Higher accuracy models
 - Complete file analysis
 ### Stream Processing
 For live audio:
 - Optimized for latency
 - Sequential processing
 - Real-time feedback
 - Progressive results
 ### Hybrid Processing
 For meetings:
 - Stream during meeting
 - Batch after completion
 - Best of both modes
 - Maximum accuracy
 ## Pipeline Configuration
 ### Model Selection
 Choose models based on requirements:
 ```python
 # High accuracy (slower)
 config = {
    "transcription_model": "whisper-large-v3",
    "diarization_model": "pyannote-3.1",
    "translation_model": "seamless-m4t-large"
 }
 # Balanced (default)
 config = {
    "transcription_model": "whisper-base",
    "diarization_model": "pyannote-3.1",
    "translation_model": "seamless-m4t-medium"
 }
 # Fast processing
 config = {
    "transcription_model": "whisper-tiny",
    "diarization_model": "pyannote-3.1-fast",
    "translation_model": "seamless-m4t-small"
 }
 ```
 ### Processing Options
 Customize pipeline behavior:
 ```yaml
 # Parallel processing
 max_parallel_chunks: 10
 chunk_size_seconds: 30
 chunk_overlap_seconds: 1
 # Quality settings
 enable_noise_reduction: true
 enable_normalization: true
 min_speech_confidence: 0.5
 # Post-processing
 enable_translation: true
 target_languages: ["es", "fr", "de"]
 enable_summarization: true
 summary_length: "medium"
 ```
 ## Performance Characteristics
 ### Processing Times
 For 1 hour of audio:
 | Pipeline Config | Processing Time | Accuracy |
 |----------------|-----------------|----------|
 | Fast | 2-3 minutes | 85-90% |
 | Balanced | 5-8 minutes | 92-95% |
 | High Accuracy | 15-20 minutes | 95-98% |
 ### Resource Usage
 | Component | CPU Usage | Memory | GPU |
 |-----------|-----------|---------|-----|
 | Transcription | Medium | 2-4 GB | Required |
 | Diarization | High | 4-8 GB | Required |
 | Translation | Low | 2-3 GB | Optional |
 | Post-processing | Low | 1-2 GB | Not needed |
 ## Pipeline Orchestration
 ### Celery Task Chain
 The pipeline is orchestrated using Celery:
 ```python
 chain = (
    chunk_audio.s(audio_id) |
    group(transcribe_chunk.s(chunk) for chunk in chunks) |
    merge_transcriptions.s() |
    diarize_audio.s() |
    align_speakers.s() |
    post_process.s()
 )
 ```
 ### Error Handling
 Robust error recovery:
 - **Automatic Retry**: Failed tasks retry up to 3 times
 - **Partial Recovery**: Continue with successful chunks
 - **Fallback Models**: Use alternative models on failure
 - **Error Reporting**: Detailed error messages
 ### Progress Tracking
 Real-time progress updates:
 - **Chunk Progress**: Track individual chunk processing
 - **Overall Progress**: Percentage completion
 - **ETA Calculation**: Estimated completion time
 - **WebSocket Updates**: Live progress to clients
 ## Optimization Strategies
 ### GPU Utilization
 Maximize GPU efficiency:
 - **Batch Processing**: Process multiple chunks together
 - **Model Caching**: Keep models loaded in memory
 - **Dynamic Batching**: Adjust batch size based on GPU memory
 - **Multi-GPU Support**: Distribute across available GPUs
 ### Memory Management
 Efficient memory usage:
 - **Streaming Processing**: Process large files in chunks
 - **Garbage Collection**: Clean up after each chunk
 - **Memory Limits**: Prevent out-of-memory errors
 - **Disk Caching**: Use disk for large intermediate results
 ### Network Optimization
 Minimize network overhead:
 - **Compression**: Compress audio before transfer
 - **CDN Integration**: Use CDN for static assets
 - **Connection Pooling**: Reuse network connections
 - **Parallel Uploads**: Multiple concurrent uploads
 ## Quality Assurance
 ### Accuracy Metrics
 Monitor processing quality:
 - **Word Error Rate (WER)**: Transcription accuracy
 - **Diarization Error Rate (DER)**: Speaker identification accuracy
 - **Translation BLEU Score**: Translation quality
 - **Summary Coherence**: Summary quality metrics
 ### Validation Steps
 Ensure output quality:
 - **Confidence Thresholds**: Filter low-confidence segments
 - **Consistency Checks**: Verify timeline consistency
 - **Language Validation**: Ensure correct language detection
 - **Format Validation**: Check output format compliance
 ## Advanced Features
 ### Custom Models
 Use your own models:
 - **Fine-tuned Whisper**: Domain-specific models
 - **Custom Diarization**: Trained on your speakers
 - **Specialized Post-processing**: Industry-specific formatting
 ### Pipeline Extensions
 Add custom processing steps:
 - **Sentiment Analysis**: Analyze emotional tone
 - **Entity Extraction**: Identify people, places, organizations
 - **Custom Metrics**: Calculate domain-specific metrics
 - **Integration Hooks**: Call external services
 EOF
 # Create installation documentation
 cat > docs/installation/overview.md << 'EOF'
 ---
 sidebar_position: 1
 title: Installation Overview
 ---
 # Installation Overview
 Reflector is designed for self-hosted deployment, giving you complete control over your infrastructure and data.
 ## Deployment Options
 ### Docker Deployment (Recommended)
 The easiest way to deploy Reflector:
 - Pre-configured containers
 - Automated dependency management
 - Consistent environment
 - Easy updates
 ### Manual Installation
 For custom deployments:
 - Greater control over configuration
 - Integration with existing infrastructure
 - Custom optimization options
 - Development environments
 ## Requirements
 ### System Requirements
 **Minimum Requirements:**
 - CPU: 4 cores
 - RAM: 8 GB
 - Storage: 50 GB
 - OS: Ubuntu 20.04+ or similar Linux
 **Recommended Requirements:**
 - CPU: 8+ cores
 - RAM: 16 GB
 - Storage: 100 GB SSD
 - GPU: NVIDIA GPU with 8GB+ VRAM (for local processing)
 ### Network Requirements
 - Public IP address (for WebRTC)
 - Ports: 80, 443, 8000, 3000
 - Domain name (for SSL)
 - SSL certificate (Let's Encrypt supported)
 ## Required Services
 ### Core Services
 These services are required for basic operation:
 1. **PostgreSQL** - Primary database
 2. **Redis** - Message broker and cache
 3. **Docker** - Container runtime
 ### GPU Processing
 Choose one:
 - **Modal.com** - Serverless GPU (recommended)
 - **Local GPU** - Self-hosted GPU processing
 ### Optional Services
 Enhance functionality with:
 - **AWS S3** - Long-term storage
 - **Whereby** - Video conferencing rooms
 - **Authentik** - Enterprise authentication
 - **Zulip** - Chat integration
 ## Quick Start
 ### Using Docker Compose
 1. Clone the repository:
 ```bash
 git clone https://github.com/monadical-sas/reflector.git
 cd reflector
 ```
 2. Navigate to docker directory:
 ```bash
 cd docker
 ```
 3. Copy and configure environment:
 ```bash
 cp .env.example .env
 # Edit .env with your settings
 ```
 4. Start services:
 ```bash
 docker compose up -d
 ```
 5. Access Reflector:
 - Frontend: https://your-domain.com
 - API: https://your-domain.com/api
 ## Configuration Overview
 ### Essential Configuration
 ```env
 # Database
 DATABASE_URL=postgresql://user:pass@localhost/reflector
 # Redis
 REDIS_URL=redis://localhost:6379
 # Modal.com (for GPU processing)
 TRANSCRIPT_MODAL_API_KEY=your-key
 DIARIZATION_MODAL_API_KEY=your-key
 # Domain
 DOMAIN=your-domain.com
 ```
 ### Security Configuration
 ```env
 # Authentication
 REFLECTOR_AUTH_BACKEND=jwt
 NEXTAUTH_SECRET=generate-strong-secret
 # SSL (handled by Caddy)
 # Automatic with Let's Encrypt
 ```
 ## Service Architecture
 ```mermaid
 graph TD
    A[Caddy Reverse Proxy] --> B[Frontend - Next.js]
    A --> C[Backend - FastAPI]
    C --> D[PostgreSQL]
    C --> E[Redis]
    C --> F[Celery Workers]
    F --> G[Modal.com GPU]
 ```
 ## Next Steps
 1. **Review Requirements**: [System Requirements](./requirements)
 2. **Docker Setup**: [Docker Deployment Guide](./docker-setup)
 3. **Configure Services**:
   - [Modal.com Setup](./modal-setup)
   - [Whereby Setup](./whereby-setup)
   - [AWS S3 Setup](./aws-setup)
 4. **Optional Services**:
   - [Authentik Setup](./authentik-setup)
   - [Zulip Setup](./zulip-setup)
 ## Getting Help
 - [Troubleshooting Guide](../reference/troubleshooting)
 - [GitHub Issues](https://github.com/monadical-sas/reflector/issues)
 - [Community Discord](#)
 EOF
 chmod +x create-docs.sh
 echo "Documentation creation script ready. Run ./create-docs.sh to generate all docs."
--- a/docs/docs/concepts/modes.md
+++ b/docs/docs/concepts/modes.md
@@ -1,125 +0,0 @@
 ---
 sidebar_position: 2
 title: Operating Modes
 ---
 # Operating Modes
 Reflector operates in two distinct modes to accommodate different use cases and security requirements.
 ## Public Mode
 Public mode provides immediate access to core transcription features without requiring authentication.
 ### Features Available
 - **File Upload**: Process audio files up to 2GB
 - **Live Transcription**: Stream audio from microphone
 - **Basic Processing**: Transcription and diarization
 - **Temporary Storage**: Results available for 24 hours
 ### Limitations
 - No persistent storage
 - No meeting rooms
 - Limited to single-user sessions
 - No team collaboration features
 ### Use Cases
 - Quick transcription needs
 - Testing and evaluation
 - Individual users
 - Public demonstrations
 ## Private Mode
 Private mode unlocks the full potential of Reflector with authentication and persistent storage.
 ### Additional Features
 - **Virtual Meeting Rooms**: Whereby and Daily.co integration
 - **Team Collaboration**: Share transcripts with team
 - **Persistent Storage**: Long-term transcript archive
 - **Advanced Analytics**: Meeting insights and trends
 - **Custom Integration**: Webhooks and API access
 - **User Management**: Role-based access control
 ### Authentication Options
 #### Authentik Integration
 Enterprise-grade SSO with support for:
 - SAML 2.0
 - OAuth 2.0 / OIDC
 - LDAP / Active Directory
 - Multi-factor authentication
 #### JWT Authentication
 Stateless token-based auth for:
 - API access
 - Service-to-service communication
 - Mobile applications
 ### Room Management
 Virtual rooms provide dedicated spaces for meetings:
 - **Persistent URLs**: Same link for recurring meetings
 - **Access Control**: Invite-only or open rooms
 - **Recording Consent**: Automatic consent management
 - **Custom Settings**: Per-room configuration
 ## Mode Selection
 The mode is determined by your deployment configuration:
 ```yaml
 # Public Mode (no authentication)
 REFLECTOR_AUTH_BACKEND=none
 # Private Mode (with authentication)
 REFLECTOR_AUTH_BACKEND=jwt
 # or
 REFLECTOR_AUTH_BACKEND=authentik
 ```
 ## Feature Comparison
 | Feature | Public Mode | Private Mode |
 |---------|------------|--------------|
 | File Upload | ✅ | ✅ |
 | Live Transcription | ✅ | ✅ |
 | Speaker Diarization | ✅ | ✅ |
 | Translation | ✅ | ✅ |
 | Summarization | ✅ | ✅ |
 | Meeting Rooms | ❌ | ✅ |
 | Persistent Storage | ❌ | ✅ |
 | Team Collaboration | ❌ | ✅ |
 | API Access | Limited | Full |
 | User Management | ❌ | ✅ |
 | Custom Branding | ❌ | ✅ |
 | Analytics | ❌ | ✅ |
 | Webhooks | ❌ | ✅ |
 ## Security Considerations
 ### Public Mode Security
 - File size restrictions
 - Automatic cleanup of old data
 ### Private Mode Security
 - Encrypted data storage
 - Audit logging
 - Session management
 - Access control lists
 - Data retention policies
 ## Choosing the Right Mode
 ### Choose Public Mode if:
 - You need quick, one-time transcriptions
 - You're evaluating Reflector
 - You don't need persistent storage
 - You're processing non-sensitive content
 ### Choose Private Mode if:
 - You need team collaboration
 - You require persistent storage
 - You're processing sensitive content
 - You need meeting room functionality
 - You want advanced analytics
--- a/docs/docs/concepts/overview.md
+++ b/docs/docs/concepts/overview.md
@@ -1,194 +0,0 @@
 ---
 sidebar_position: 1
 title: Architecture Overview
 ---
 # Architecture Overview
 Reflector is built as a modern, scalable, microservices-based application designed to handle audio processing workloads efficiently while maintaining data privacy and control.
 ## System Components
 ### Frontend Application
 The user interface is built with **Next.js 14** using the App Router pattern, providing:
 - Server-side rendering for optimal performance
 - Real-time WebSocket connections for live transcription
 - WebRTC support for audio streaming and live meetings
 - Responsive design with Chakra UI components
 ### Backend API Server
 The core API is powered by **FastAPI**, a modern Python framework that provides:
 - High-performance async request handling
 - Automatic OpenAPI documentation generation
 - Type safety with Pydantic models
 - WebSocket support for real-time updates
 ### Processing Pipeline
 Audio processing is handled through a modular pipeline architecture:
 ```
 Audio Input → Chunking → Transcription → Diarization → Post-Processing → Storage
 ```
 Each step can run independently and in parallel, allowing for:
 - Scalable processing of large files
 - Real-time streaming capabilities
 - Fault tolerance and retry mechanisms
 ### Worker Architecture
 Background tasks are managed by **Celery** workers with **Redis** as the message broker:
 - Distributed task processing
 - Priority queues for time-sensitive operations
 - Automatic retry on failure
 - Progress tracking and notifications
 ### GPU Acceleration
 ML models run on GPU-accelerated infrastructure:
 - **Modal.com** for serverless GPU processing
 - Support for local GPU deployment (coming soon)
 - Automatic scaling based on demand
 - Cost-effective pay-per-use model
 ## Data Flow
 ### File Processing Flow
 1. **Upload**: User uploads audio file through web interface
 2. **Storage**: File stored temporarily or in S3
 3. **Queue**: Processing job added to Celery queue
 4. **Chunking**: Audio split into 30-second segments
 5. **Parallel Processing**: Chunks processed simultaneously
 6. **Assembly**: Results merged and aligned
 7. **Post-Processing**: Summary, topics, translation
 8. **Delivery**: Results stored and user notified
 ### Live Streaming Flow
 1. **WebRTC Connection**: Browser establishes peer connection
 2. **Audio Capture**: Microphone audio streamed to server
 3. **Buffering**: Audio buffered for processing
 4. **VAD**: Voice activity detection segments speech
 5. **Real-time Processing**: Segments transcribed immediately
 6. **WebSocket Updates**: Results streamed back to client
 7. **Continuous Assembly**: Full transcript built progressively
 ## Deployment Architecture
 ### Container-Based Deployment
 All components are containerized for consistent deployment:
 ```yaml
 services:
  frontend:    # Next.js application
  backend:     # FastAPI server
  worker:      # Celery workers
  redis:       # Message broker
  postgres:    # Database
  caddy:       # Reverse proxy
 ```
 ### Networking
 - **Host Network Mode**: Required for WebRTC/ICE compatibility
 - **Caddy Reverse Proxy**: Handles SSL termination and routing
 - **WebSocket Upgrade**: Supports real-time connections
 ## Scalability Considerations
 ### Horizontal Scaling
 - **Stateless Backend**: Multiple API server instances
 - **Worker Pools**: Add workers based on queue depth
 - **Database Pooling**: Connection management for concurrent access
 ### Vertical Scaling
 - **GPU Workers**: Scale up for faster model inference
 - **Memory Optimization**: Efficient audio buffering
 - **CPU Optimization**: Multi-threaded processing where applicable
 ## Security Architecture
 ### Authentication & Authorization
 - **JWT Tokens**: Stateless authentication
 - **Authentik Integration**: Enterprise SSO support
 - **Role-Based Access**: Granular permissions
 ### Data Protection
 - **Encryption at Rest**: Database and S3 encryption
 - **Encryption in Transit**: TLS for all connections
 - **Temporary Storage**: Automatic cleanup of processed files
 ### Privacy by Design
 - **Local Processing**: Option to process entirely on-premises
 - **No Training on User Data**: Models are pre-trained
 - **Data Isolation**: Multi-tenant data separation
 ## Integration Points
 ### External Services
 - **Modal.com**: GPU processing
 - **AWS S3**: Long-term storage
 - **Whereby**: Video conferencing rooms
 - **Zulip**: Chat integration (optional)
 ### APIs and Webhooks
 - **RESTful API**: Standard CRUD operations
 - **WebSocket API**: Real-time updates
 - **Webhook Notifications**: Processing completion events
 - **OpenAPI Specification**: Machine-readable API definition
 ## Performance Optimization
 ### Caching Strategy
 - **Redis Cache**: Frequently accessed data
 - **CDN**: Static asset delivery
 - **Browser Cache**: Client-side optimization
 ### Database Optimization
 - **Indexed Queries**: Fast search and retrieval
 - **Connection Pooling**: Efficient resource usage
 - **Query Optimization**: N+1 query prevention
 ### Processing Optimization
 - **Batch Processing**: Efficient GPU utilization
 - **Parallel Execution**: Multi-core CPU usage
 - **Stream Processing**: Reduced memory footprint
 ## Monitoring and Observability
 ### Metrics Collection
 - **Application Metrics**: Request rates, response times
 - **System Metrics**: CPU, memory, disk usage
 - **Business Metrics**: Transcription accuracy, processing times
 ### Logging
 - **Structured Logging**: JSON format for analysis
 - **Log Aggregation**: Centralized log management
 - **Error Tracking**: Sentry integration
 ### Health Checks
 - **Liveness Probes**: Component availability
 - **Readiness Probes**: Service readiness
 - **Dependency Checks**: External service status
--- a/docs/docs/concepts/pipeline.md
+++ b/docs/docs/concepts/pipeline.md
@@ -1,272 +0,0 @@
 ---
 sidebar_position: 4
 title: Processing Pipeline
 ---
 # Processing Pipeline
 Reflector uses a modular pipeline architecture to process audio efficiently and accurately.
 ## Pipeline Overview
 The processing pipeline consists of modular components that can be combined and configured based on your needs:
 ```mermaid
 graph LR
    A[Audio Input] --> B[Pre-processing]
    B --> C[Chunking]
    C --> D[Transcription]
    D --> E[Diarization]
    E --> F[Alignment]
    F --> G[Post-processing]
    G --> H[Output]
 ```
 ## Pipeline Components
 ### Audio Input
 Accepts various input sources:
 - **File Upload**: MP3, WAV, M4A, WebM, MP4
 - **WebRTC Stream**: Live browser audio
 - **Recording Integration**: Whereby recordings
 - **API Upload**: Direct API submission
 ### Pre-processing
 Prepares audio for optimal processing:
 - **Format Conversion**: Convert to 16kHz mono WAV
 - **Noise Reduction**: Optional background noise removal
 - **Validation**: Check duration and quality
 ### Chunking
 Splits audio for parallel processing:
 - **Fixed Size**: 30-second chunks by default
 - **Overlap**: 1-second overlap for continuity
 - **Silence Detection**: Attempt to split at silence
 - **Metadata**: Track chunk positions
 ### Transcription
 Converts speech to text:
 - **Model Selection**: Whisper or Parakeet
 - **Language Detection**: Automatic or specified
 - **Timestamp Generation**: Word-level timing
 - **Confidence Scores**: Quality indicators
 ### Diarization
 Identifies different speakers:
 - **Voice Activity Detection**: Find speech segments
 - **Speaker Embedding**: Extract voice characteristics
 - **Clustering**: Group similar voices
 - **Label Assignment**: Assign speaker IDs
 ### Alignment
 Merges all processing results:
 - **Chunk Assembly**: Combine transcription chunks
 - **Speaker Mapping**: Align speakers with text
 - **Overlap Resolution**: Handle chunk boundaries
 - **Timeline Creation**: Build unified timeline
 ### Post-processing
 Enhances the final output:
 - **Formatting**: Apply punctuation and capitalization
 - **Translation**: Convert to target languages
 - **Summarization**: Generate concise summaries
 - **Topic Extraction**: Identify key themes
 - **Action Items**: Extract tasks and decisions
 ## Processing Modes
 ### Batch Processing
 For uploaded files:
 - Optimized for throughput
 - Parallel chunk processing
 - Higher accuracy models
 - Complete file analysis
 ### Stream Processing
 For live audio:
 - Optimized for latency
 - Sequential processing
 - Real-time feedback
 - Progressive results
 ### Hybrid Processing
 For meetings:
 - Stream during meeting
 - Batch after completion
 - Best of both modes
 - Maximum accuracy
 ## Pipeline Configuration
 ### Model Selection
 Choose models based on requirements:
 ```python
 # High accuracy (slower)
 config = {
    "transcription_model": "whisper-large-v3",
    "diarization_model": "pyannote-3.1",
    "translation_model": "seamless-m4t-large"
 }
 # Balanced (default)
 config = {
    "transcription_model": "whisper-base",
    "diarization_model": "pyannote-3.1",
    "translation_model": "seamless-m4t-medium"
 }
 # Fast processing
 config = {
    "transcription_model": "whisper-tiny",
    "diarization_model": "pyannote-3.1-fast",
    "translation_model": "seamless-m4t-small"
 }
 ```
 ### Processing Options
 Customize pipeline behavior:
 ```yaml
 # Parallel processing
 max_parallel_chunks: 10
 chunk_size_seconds: 30
 chunk_overlap_seconds: 1
 # Quality settings
 enable_noise_reduction: true
 min_speech_confidence: 0.5
 # Post-processing
 enable_translation: true
 target_languages: ["es", "fr", "de"]
 enable_summarization: true
 summary_length: "medium"
 ```
 ## Performance Characteristics
 ### Processing Times
 For 1 hour of audio:
 | Pipeline Config | Processing Time | Accuracy |
 |----------------|-----------------|----------|
 | Fast | 2-3 minutes | 85-90% |
 | Balanced | 5-8 minutes | 92-95% |
 | High Accuracy | 15-20 minutes | 95-98% |
 ### Resource Usage
 | Component | CPU Usage | Memory | GPU |
 |-----------|-----------|---------|-----|
 | Transcription | Medium | 2-4 GB | Required |
 | Diarization | High | 4-8 GB | Required |
 | Translation | Low | 2-3 GB | Optional |
 | Post-processing | Low | 1-2 GB | Not needed |
 ## Pipeline Orchestration
 ### Celery Task Chain
 The pipeline is orchestrated using Celery:
 ```python
 chain = (
    chunk_audio.s(audio_id) |
    group(transcribe_chunk.s(chunk) for chunk in chunks) |
    merge_transcriptions.s() |
    diarize_audio.s() |
    align_speakers.s() |
    post_process.s()
 )
 ```
 ### Error Handling
 Error recovery:
 - **Automatic Retry**: Failed tasks retry up to 3 times
 - **Partial Recovery**: Continue with successful chunks
 - **Fallback Models**: Use alternative models on failure
 - **Error Reporting**: Detailed error messages
 ### Progress Tracking
 Real-time progress updates:
 - **Chunk Progress**: Track individual chunk processing
 - **Overall Progress**: Percentage completion
 - **ETA Calculation**: Estimated completion time
 - **WebSocket Updates**: Live progress to clients
 ## Optimization Strategies
 ### GPU Utilization
 Maximize GPU efficiency:
 - **Batch Processing**: Process multiple chunks together
 - **Model Caching**: Keep models loaded in memory
 - **Dynamic Batching**: Adjust batch size based on GPU memory
 - **Multi-GPU Support**: Distribute across available GPUs
 ### Memory Management
 Efficient memory usage:
 - **Streaming Processing**: Process large files in chunks
 - **Garbage Collection**: Clean up after each chunk
 - **Memory Limits**: Prevent out-of-memory errors
 - **Disk Caching**: Use disk for large intermediate results
 ### Network Optimization
 Minimize network overhead:
 - **Compression**: Compress audio before transfer
 - **CDN Integration**: Use CDN for static assets
 - **Connection Pooling**: Reuse network connections
 - **Parallel Uploads**: Multiple concurrent uploads
 ## Quality Assurance
 ### Accuracy Metrics
 Monitor processing quality:
 - **Word Error Rate (WER)**: Transcription accuracy
 - **Diarization Error Rate (DER)**: Speaker identification accuracy
 - **Translation BLEU Score**: Translation quality
 - **Summary Coherence**: Summary quality metrics
 ### Validation Steps
 Ensure output quality:
 - **Confidence Thresholds**: Filter low-confidence segments
 - **Consistency Checks**: Verify timeline consistency
 - **Language Validation**: Ensure correct language detection
 - **Format Validation**: Check output format compliance
 ## Advanced Features
 ### Custom Models
 Use your own models:
 - **Fine-tuned Whisper**: Domain-specific models
 - **Custom Diarization**: Trained on your speakers
 - **Specialized Post-processing**: Industry-specific formatting
 ### Pipeline Extensions
 Add custom processing steps:
 - **Sentiment Analysis**: Analyze emotional tone
 - **Entity Extraction**: Identify people, places, organizations
 - **Custom Metrics**: Calculate domain-specific metrics
 - **Integration Hooks**: Call external services
--- a/docs/docs/installation/auth-setup.md
+++ b/docs/docs/installation/auth-setup.md
@@ -1,285 +0,0 @@
 ---
 sidebar_position: 5
 title: Authentication Setup
 ---
 # Authentication Setup
 This page covers authentication setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
 Reflector uses [Authentik](https://goauthentik.io/) for OAuth/OIDC authentication. This guide walks you through setting up Authentik and connecting it to Reflector.
 The guide simplistically sets Authentic on the same server as Reflector. You can use your own Authentic instance instead.
 ## Overview
 Reflector's authentication flow:
 1. User clicks "Sign In" on frontend
 2. Frontend redirects to Authentik login page
 3. User authenticates with Authentik
 4. Authentik redirects back with OAuth tokens
 5. Frontend stores tokens, backends verify JWT signature
 ## Option 1: Self-Hosted Authentik (Same Server)
 This setup runs Authentik on the same server as Reflector, with Caddy proxying to both.
 ### Deploy Authentik
 ```bash
 # Create directory for Authentik
 mkdir -p ~/authentik && cd ~/authentik
 # Download docker-compose file
 curl -O https://goauthentik.io/docker-compose.yml
 # Generate secrets and bootstrap credentials
 cat > .env << 'EOF'
 PG_PASS=$(openssl rand -base64 36 | tr -d '\n')
 AUTHENTIK_SECRET_KEY=$(openssl rand -base64 60 | tr -d '\n')
 # Privacy-focused choice for self-hosted deployments
 AUTHENTIK_ERROR_REPORTING__ENABLED=false
 AUTHENTIK_BOOTSTRAP_PASSWORD=YourSecurePassword123
 AUTHENTIK_BOOTSTRAP_EMAIL=admin@example.com
 EOF
 # Start Authentik
 sudo docker compose up -d
 ```
 Authentik takes ~2 minutes to run migrations and apply blueprints on first start.
 ### Connect Authentik to Reflector's Network
 If Authentik runs in a separate Docker Compose project, connect it to Reflector's network so Caddy can proxy to it:
 ```bash
 # Wait for Authentik to be healthy
 # Connect Authentik server to Reflector's network
 sudo docker network connect reflector_default authentik-server-1
 ```
 **Important:** This step must be repeated if you restart Authentik with `docker compose down`. Add it to your deployment scripts or use `docker compose up -d` (which preserves containers) instead of down/up.
 ### Add Authentik to Caddy
 Uncomment the Authentik section in your `Caddyfile` and set your domain:
 ```bash
 nano Caddyfile
 ```
 Uncomment and edit:
 ```
 {$AUTHENTIK_DOMAIN:authentik.example.com} {
    reverse_proxy authentik-server-1:9000
 }
 ```
 Reload Caddy:
 ```bash
 docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
 ```
 ### Create OAuth2 Provider in Authentik
 **Option A: Automated Setup (Recommended)**
 **Location: Reflector server**
 Run the setup script from the Reflector repository:
 ```bash
 ssh user@your-server-ip
 cd ~/reflector
 ./scripts/setup-authentik-oauth.sh https://authentik.example.com YourSecurePassword123 https://app.example.com
 ```
 **Important:** The script must be run from the `~/reflector` directory on your server, as it creates files using relative paths.
 The script will output the configuration values to add to your `.env` files. Skip to "Update docker-compose.prod.yml".
 **Option B: Manual Setup**
 1. **Login to Authentik Admin** at `https://authentik.example.com/`
   - Username: `akadmin`
   - Password: The `AUTHENTIK_BOOTSTRAP_PASSWORD` you set in .env
 2. **Create OAuth2 Provider:**
   - Go to **Applications > Providers > Create**
   - Select **OAuth2/OpenID Provider**
   - Configure:
     - **Name**: `Reflector`
     - **Authorization flow**: `default-provider-authorization-implicit-consent`
     - **Client type**: `Confidential`
     - **Client ID**: Note this value (auto-generated)
     - **Client Secret**: Note this value (auto-generated)
     - **Redirect URIs**: Add entry with:
       ```
       https://app.example.com/api/auth/callback/authentik
       ```
   - Scroll down to **Advanced protocol settings**
   - In **Scopes**, add these three mappings:
     - `authentik default OAuth Mapping: OpenID 'email'`
     - `authentik default OAuth Mapping: OpenID 'openid'`
     - `authentik default OAuth Mapping: OpenID 'profile'`
   - Click **Finish**
 3. **Create Application:**
   - Go to **Applications > Applications > Create**
   - Configure:
     - **Name**: `Reflector`
     - **Slug**: `reflector` (auto-filled)
     - **Provider**: Select the `Reflector` provider you just created
   - Click **Create**
 ### Get Public Key for JWT Verification
 **Location: Reflector server**
 Extract the public key from Authentik's JWKS endpoint:
 ```bash
 mkdir -p ~/reflector/server/reflector/auth/jwt/keys
 curl -s https://authentik.example.com/application/o/reflector/jwks/ | \
  jq -r '.keys[0].x5c[0]' | base64 -d | openssl x509 -pubkey -noout \
  > ~/reflector/server/reflector/auth/jwt/keys/authentik_public.pem
 ```
 ### Update docker-compose.prod.yml
 **Location: Reflector server**
 **Note:** This step is already done in the current `docker-compose.prod.yml`. Verify the volume mounts exist:
 ```yaml
 server:
  image: monadicalsas/reflector-backend:latest
  # ... other config ...
  volumes:
    - server_data:/app/data
    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
 worker:
  image: monadicalsas/reflector-backend:latest
  # ... other config ...
  volumes:
    - server_data:/app/data
    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
 ```
 ### Configure Reflector Backend
 **Location: Reflector server**
 Update `server/.env`:
 ```env
 # Authentication
 AUTH_BACKEND=jwt
 AUTH_JWT_PUBLIC_KEY=authentik_public.pem
 AUTH_JWT_AUDIENCE=<your-client-id>
 CORS_ALLOW_CREDENTIALS=true
 ```
 Replace `<your-client-id>` with the Client ID from previous steps.
 ### Configure Reflector Frontend
 **Location: Reflector server**
 Update `www/.env`:
 ```env
 # Authentication
 FEATURE_REQUIRE_LOGIN=true
 # Authentik OAuth
 AUTHENTIK_ISSUER=https://authentik.example.com/application/o/reflector
 AUTHENTIK_REFRESH_TOKEN_URL=https://authentik.example.com/application/o/token/
 AUTHENTIK_CLIENT_ID=<your-client-id>
 AUTHENTIK_CLIENT_SECRET=<your-client-secret>
 # NextAuth
 NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
 ```
 ### Restart Services
 **Location: Reflector server**
 ```bash
 cd ~/reflector
 sudo docker compose -f docker-compose.prod.yml up -d --force-recreate server worker web
 ```
 ### Verify Authentication
 1. Visit `https://app.example.com`
 2. Click "Log in" or navigate to `/api/auth/signin`
 3. Click "Sign in with Authentik"
 4. Login with your Authentik credentials
 5. You should be redirected back and see "Log out" in the header
 ## Option 2: Disable Authentication
 For testing or internal deployments where authentication isn't needed:
 **Backend `server/.env`:**
 ```env
 AUTH_BACKEND=none
 ```
 **Frontend `www/.env`:**
 ```env
 FEATURE_REQUIRE_LOGIN=false
 ```
 **Note:** The pre-built Docker images have `FEATURE_REQUIRE_LOGIN=true` baked in. To disable auth, you'll need to rebuild the frontend image with the env var set at build time, or set up Authentik.
 ## Troubleshooting
 ### "Invalid redirect URI" error
 - Verify the redirect URI in Authentik matches exactly:
  ```
  https://app.example.com/api/auth/callback/authentik
  ```
 - Check for trailing slashes - they must match exactly
 ### "Invalid audience" JWT error
 - Ensure `AUTH_JWT_AUDIENCE` in `server/.env` matches the Client ID from Authentik
 - The audience value is the OAuth Client ID, not the issuer URL
 ### "JWT verification failed" error
 - Verify the public key file is mounted in the container
 - Check `AUTH_JWT_PUBLIC_KEY` points to the correct filename
 - Ensure the key was extracted from the correct provider's JWKS endpoint
 ### Caddy returns 503 for Authentik
 - Verify Authentik container is connected to Reflector's network:
  ```bash
  sudo docker network connect reflector_default authentik-server-1
  ```
 - Check Authentik is healthy: `cd ~/authentik && sudo docker compose ps`
 ### Users can't access protected pages
 - Verify `FEATURE_REQUIRE_LOGIN=true` in frontend
 - Check `AUTH_BACKEND=jwt` in backend
 - Verify CORS settings allow credentials
 ### Token refresh errors
 - Ensure Redis is running (frontend uses Redis for token caching)
 - Verify `KV_URL` is set correctly in frontend env
 - Check `AUTHENTIK_REFRESH_TOKEN_URL` is correct
 ## API Key Authentication
 For programmatic access (scripts, integrations), users can generate API keys:
 1. Login to Reflector
 2. Go to Settings > API Keys
 3. Click "Generate New Key"
 4. Use the key in requests:
   ```bash
   curl -H "X-API-Key: your-api-key" https://api.example.com/v1/transcripts
   ```
 API keys are stored hashed and can be revoked at any time.
--- a/docs/docs/installation/daily-setup.md
+++ b/docs/docs/installation/daily-setup.md
@@ -1,165 +0,0 @@
 ---
 sidebar_position: 6
 title: Daily.co Setup
 ---
 # Daily.co Setup
 This page covers Daily.co video platform setup for live meeting rooms. For the complete deployment guide, see [Deployment Guide](./overview).
 Daily.co enables live video meetings with automatic recording and transcription.
 ## What You'll Set Up
 ```
 User joins meeting → Daily.co video room → Recording to S3 → [Webhook] → Reflector transcribes
 ```
 ## Prerequisites
 - [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
 - [ ] **AWS account** - For S3 storage
 - [ ] **Reflector deployed** - Complete steps from [Deployment Guide](./overview)
 ---
 ## Create Daily.co Account
 1. Visit https://dashboard.daily.co and sign up
 2. Verify your email
 3. Note your subdomain (e.g., `yourname.daily.co` → subdomain is `yourname`)
 ---
 ## Get Daily.co API Key
 1. In Daily.co dashboard, go to **Developers**
 2. Click **API Keys**
 3. Click **Create API Key**
 4. Copy the key (starts with a long string)
 Save this for later.
 ---
 ## Create AWS S3 Bucket
 Daily.co needs somewhere to store recordings before Reflector processes them.
 ```bash
 # Choose a unique bucket name
 BUCKET_NAME="reflector-dailyco-yourname" # -yourname is not a requirement, you can name the bucket as you wish
 AWS_REGION="us-east-1"
 # Create bucket
 aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
 # Enable versioning (required)
 aws s3api put-bucket-versioning \
  --bucket $BUCKET_NAME \
  --versioning-configuration Status=Enabled
 ```
 ---
 ## Create IAM Role for Daily.co
 Daily.co needs permission to write recordings to your S3 bucket.
 Follow the guide https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket
 Save the role ARN - you'll need it soon.
 It looks like: `arn:aws:iam::123456789012:role/DailyCo`
 Shortly, you'll need to set up a role and give this role your s3 bucket access
 No additional setup is required from Daily.co settings website side: the app code takes care of letting Daily know where to save the recordings.
 ---
 ## Configure Reflector
 **Location: Reflector server**
 Add to `server/.env`:
 ```env
 # Daily.co Configuration
 DEFAULT_VIDEO_PLATFORM=daily
 DAILY_API_KEY=<your-api-key-from-daily-setup>
 DAILY_SUBDOMAIN=<your-subdomain-from-daily-setup>
 # S3 Storage for Daily.co recordings
 DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket-from-daily-setup>
 DAILYCO_STORAGE_AWS_REGION=us-east-1
 DAILYCO_STORAGE_AWS_ROLE_ARN=<your-role-arn-from-daily-setup>
 # Transcript storage (should already be configured from main setup)
 # TRANSCRIPT_STORAGE_BACKEND=aws
 # TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=<your-key>
 # TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=<your-secret>
 # TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=<your-bucket-name>
 # TRANSCRIPT_STORAGE_AWS_REGION=<your-bucket-region>
 ```
 ---
 ## Restart Services
 After changing `.env` files, reload with `up -d`:
 ```bash
 sudo docker compose -f docker-compose.prod.yml up -d server worker
 ```
 **Note**: `docker compose up -d` detects env changes and recreates containers automatically.
 ---
 ## Test Live Room
 1. Visit your Reflector frontend: `https://app.example.com`
 2. Go to **Rooms**
 3. Click **Create Room**
 4. Select **Daily** as the platform
 5. Allow camera/microphone access
 6. You should see Daily.co video interface
 7. Speak for 10-20 seconds
 8. Leave the meeting
 9. Recording should appear in **Transcripts** within 5 minutes (if webhooks aren't set up yet, see [Webhook Configuration](#webhook-configuration-optional) below)
 ---
 ## Troubleshooting
 ### Recording doesn't appear in S3
 1. Check Daily.co dashboard → **Logs** for errors
 2. Verify IAM role trust policy has correct Daily.co account ID and your Daily.co subdomain
 3. Verify that the bucket has
 ### Recording in S3 but not transcribed
 1. Check webhook is configured (Reflector should auto-create it)
 2. Check worker logs:
   ```bash
   docker compose -f docker-compose.prod.yml logs worker --tail 50
   ```
 3. Verify `DAILYCO_STORAGE_AWS_*` vars in `server/.env`
 ### "Access Denied" when Daily.co tries to write to S3
 1. Double-check IAM role ARN in Daily.co settings
 2. Verify bucket name matches exactly
 3. Check IAM policy has `s3:PutObject` permission
 ---
 ## Webhook Configuration [optional]
 `manage_daily_webhook.py` script guides you through creating a webhook for Daily recordings.
 The webhook isn't required - polling mechanism is the default and performed automatically.
 This guide won't go deep into webhook setup.
--- a/docs/docs/installation/docker-setup.md
+++ b/docs/docs/installation/docker-setup.md
@@ -1,192 +0,0 @@
 ---
 sidebar_position: 3
 title: Docker Reference
 ---
 # Docker Reference
 This page documents the Docker Compose configuration for Reflector. For the complete deployment guide, see [Deployment Guide](./overview).
 ## Services
 The `docker-compose.prod.yml` includes these services:
 | Service | Image | Purpose |
 |---------|-------|---------|
 | `web` | `monadicalsas/reflector-frontend` | Next.js frontend |
 | `server` | `monadicalsas/reflector-backend` | FastAPI backend |
 | `worker` | `monadicalsas/reflector-backend` | Celery worker for background tasks |
 | `beat` | `monadicalsas/reflector-backend` | Celery beat scheduler |
 | `redis` | `redis:7.2-alpine` | Message broker and cache |
 | `postgres` | `postgres:17-alpine` | Primary database |
 | `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
 ## Environment Files
 Reflector uses two separate environment files:
 ### Backend (`server/.env`)
 Used by: `server`, `worker`, `beat`
 Key variables:
 ```env
 # Database connection
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
 # Redis
 REDIS_HOST=redis
 CELERY_BROKER_URL=redis://redis:6379/1
 CELERY_RESULT_BACKEND=redis://redis:6379/1
 # API domain and CORS
 BASE_URL=https://api.example.com
 CORS_ORIGIN=https://app.example.com
 # Modal GPU processing
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://...
 TRANSCRIPT_MODAL_API_KEY=...
 ```
 ### Frontend (`www/.env`)
 Used by: `web`
 Key variables:
 ```env
 # Domain configuration
 SITE_URL=https://app.example.com
 API_URL=https://api.example.com
 WEBSOCKET_URL=wss://api.example.com
 SERVER_API_URL=http://server:1250
 # Authentication
 NEXTAUTH_URL=https://app.example.com
 NEXTAUTH_SECRET=...
 ```
 Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-side (SSR).
 ## Volumes
 | Volume | Purpose |
 |--------|---------|
 | `redis_data` | Redis persistence |
 | `postgres_data` | PostgreSQL data |
 | `server_data` | Uploaded files, local storage |
 | `caddy_data` | SSL certificates |
 | `caddy_config` | Caddy configuration |
 ## Network
 All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
 ## Common Commands
 ### Start all services
 ```bash
 docker compose -f docker-compose.prod.yml up -d
 ```
 ### View logs
 ```bash
 # All services
 docker compose -f docker-compose.prod.yml logs -f
 # Specific service
 docker compose -f docker-compose.prod.yml logs server --tail 50
 ```
 ### Restart a service
 ```bash
 # Quick restart (doesn't reload .env changes)
 docker compose -f docker-compose.prod.yml restart server
 # Reload .env and restart
 docker compose -f docker-compose.prod.yml up -d server
 ```
 ### Run database migrations
 ```bash
 docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
 ```
 ### Access database
 ```bash
 docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
 ```
 ### Pull latest images
 ```bash
 docker compose -f docker-compose.prod.yml pull
 docker compose -f docker-compose.prod.yml up -d
 ```
 ### Stop all services
 ```bash
 docker compose -f docker-compose.prod.yml down
 ```
 ### Full reset (WARNING: deletes data)
 ```bash
 docker compose -f docker-compose.prod.yml down -v
 ```
 ## Customization
 ### Using a different database
 To use an external PostgreSQL:
 1. Remove `postgres` service from compose file
 2. Update `DATABASE_URL` in `server/.env`:
   ```env
   DATABASE_URL=postgresql+asyncpg://user:pass@external-host:5432/reflector
   ```
 ### Using external Redis
 1. Remove `redis` service from compose file
 2. Update Redis settings in `server/.env`:
   ```env
   REDIS_HOST=external-redis-host
   CELERY_BROKER_URL=redis://external-redis-host:6379/1
   ```
 ### Adding Authentik
 To add Authentik for authentication, see [Authentication Setup](./auth-setup). Quick steps:
 1. Deploy Authentik separately
 2. Connect to Reflector's network:
   ```bash
   docker network connect reflector_default authentik-server-1
   ```
 3. Add to Caddyfile:
   ```
   authentik.example.com {
       reverse_proxy authentik-server-1:9000
   }
   ```
 ## Caddyfile Reference
 The Caddyfile supports environment variable substitution:
 ```
 {$FRONTEND_DOMAIN:app.example.com} {
    reverse_proxy web:3000
 }
 {$API_DOMAIN:api.example.com} {
    reverse_proxy server:1250
 }
 ```
 Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
 ### Reload Caddy after changes
 ```bash
 docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
 ```
--- a/docs/docs/installation/docs-deployment.md
+++ b/docs/docs/installation/docs-deployment.md
@@ -1,139 +0,0 @@
 ---
 sidebar_position: 10
 title: Docs Website Deployment
 ---
 # Docs Website Deployment
 This guide covers deploying the Reflector documentation website. **This is optional and intended for internal/experimental use only.**
 ## Overview
 The documentation is built using Docusaurus and deployed as a static nginx-served site.
 ## Prerequisites
 - Reflector already deployed (Steps 1-7 from [Deployment Guide](./overview))
 - DNS A record for docs subdomain (e.g., `docs.example.com`)
 ## Deployment Steps
 ### Step 1: Pre-fetch OpenAPI Spec
 The docs site includes API reference from your running backend. Fetch it before building:
 ```bash
 cd ~/reflector
 docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
 ```
 This creates `docs/static/openapi.json` (should be ~70KB) which will be copied during Docker build.
 **Why not fetch during build?** Docker build containers are network-isolated and can't access the running backend services.
 ### Step 2: Verify Dockerfile
 The Dockerfile is already in `docs/Dockerfile`:
 ```dockerfile
 FROM node:18-alpine AS builder
 WORKDIR /app
 # Copy package files
 COPY package*.json ./
 # Inshall dependencies
 RUN npm ci
 # Copy source (includes static/openapi.json if pre-fetched)
 COPY . .
 # Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
 RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
 # Build static site
 RUN npx docusaurus build
 FROM nginx:alpine
 COPY --from=builder /app/build /usr/share/nginx/html
 EXPOSE 80
 CMD ["nginx", "-g", "daemon off;"]
 ```
 ### Step 3: Add Docs Service to docker-compose.prod.yml
 Add this service to `docker-compose.prod.yml`:
 ```yaml
 docs:
  build: ./docs
  restart: unless-stopped
  networks:
    - default
 ```
 ### Step 4: Add Caddy Route
 Add to `Caddyfile`:
 ```
 {$DOCS_DOMAIN:docs.example.com} {
    reverse_proxy docs:80
 }
 ```
 ### Step 5: Build and Deploy
 ```bash
 cd ~/reflector
 docker compose -f docker-compose.prod.yml up -d --build docs
 docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
 ```
 ### Step 6: Verify
 ```bash
 # Check container status
 docker compose -f docker-compose.prod.yml ps docs
 # Should show "Up"
 # Test URL
 curl -I https://docs.example.com
 # Should return HTTP/2 200
 ```
 Visit `https://docs.example.com` in your browser
 ## Updating Documentation
 When docs are updated:
 ```bash
 cd ~/reflector
 git pull
 # Refresh OpenAPI spec from backend
 docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
 # Rebuild docs
 docker compose -f docker-compose.prod.yml up -d --build docs
 ```
 ## Troubleshooting
 ### Missing openapi.json during build
 - Make sure you ran the pre-fetch step first (Step 1)
 - Verify `docs/static/openapi.json` exists and is ~70KB
 - Re-run: `docker compose exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json`
 ### Build fails with "Docusaurus found broken links"
 - This happens if `onBrokenLinks: 'throw'` is set in docusaurus.config.ts
 - Solution is already in Dockerfile: uses `sed` to change to `'warn'` during build
 ### 404 on all pages
 - Docusaurus baseUrl might be wrong - should be `/` for custom domain
 - Check `docs/docusaurus.config.ts`: `baseUrl: '/'`
 ### Docs not updating after rebuild
 - Force rebuild: `docker compose -f docker-compose.prod.yml build --no-cache docs`
 - Then: `docker compose -f docker-compose.prod.yml up -d docs`
--- a/docs/docs/installation/modal-setup.md
+++ b/docs/docs/installation/modal-setup.md
@@ -1,171 +0,0 @@
 ---
 sidebar_position: 4
 title: Modal.com Setup
 ---
 # Modal.com Setup
 This page covers Modal.com GPU setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
 Reflector uses [Modal.com](https://modal.com) for GPU-accelerated audio processing. This guide walks you through deploying the required GPU functions.
 ## What is Modal.com?
 Modal is a serverless GPU platform. You deploy Python code that runs on their GPUs, and pay only for actual compute time. Reflector uses Modal for:
 - **Transcription**: Whisper model for speech-to-text
 - **Diarization**: Pyannote model for speaker identification
 ## Prerequisites
 1. **Modal.com account** - Sign up at https://modal.com (free tier available)
 2. **HuggingFace account** - Required for Pyannote diarization models:
   - Create account at https://huggingface.co
   - Accept **both** Pyannote licenses:
     - https://huggingface.co/pyannote/speaker-diarization-3.1
     - https://huggingface.co/pyannote/segmentation-3.0
   - Generate access token at https://huggingface.co/settings/tokens
 ## Deployment
 **Location: YOUR LOCAL COMPUTER (laptop/desktop)**
 Modal CLI requires browser authentication, so this must run on a machine with a browser - not on a headless server.
 ### Install Modal CLI
 ```bash
 pip install modal
 ```
 ### Authenticate with Modal
 ```bash
 modal setup
 ```
 This opens your browser for authentication. Complete the login flow.
 ### Clone Repository and Deploy
 ```bash
 git clone https://github.com/monadical-sas/reflector.git
 cd reflector/gpu/modal_deployments
 ./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
 ```
 Or run interactively (script will prompt for token):
 ```bash
 ./deploy-all.sh
 ```
 ### What the Script Does
 1. **Prompts for HuggingFace token** - Needed to download the Pyannote diarization model
 2. **Generates API key** - Creates a secure random key for authenticating requests to GPU functions
 3. **Creates Modal secrets**:
   - `hf_token` - Your HuggingFace token
   - `reflector-gpu` - The generated API key
 4. **Deploys GPU functions** - Transcriber (Whisper) and Diarizer (Pyannote)
 5. **Outputs configuration** - Prints URLs and API key to console
 ### Example Output
 ```
 ==========================================
 Reflector GPU Functions Deployment
 ==========================================
 Generating API key for GPU services...
 Creating Modal secrets...
  -> Creating secret: hf_token
  -> Creating secret: reflector-gpu
 Deploying transcriber (Whisper)...
  -> https://yourname--reflector-transcriber-web.modal.run
 Deploying diarizer (Pyannote)...
  -> https://yourname--reflector-diarizer-web.modal.run
 ==========================================
 Deployment complete!
 ==========================================
 Copy these values to your server's server/.env file:
 # --- Modal GPU Configuration ---
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=abc123...
 DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
 DIARIZATION_MODAL_API_KEY=abc123...
 # --- End Modal Configuration ---
 ```
 Copy the output and paste it into your `server/.env` file on your server.
 ## Costs
 Modal charges based on GPU compute time:
 - Functions scale to zero when not in use (no cost when idle)
 - You only pay for actual processing time
 - Free tier includes $30/month of credits
 Typical costs for audio processing:
 - Transcription: ~$0.01-0.05 per minute of audio
 - Diarization: ~$0.02-0.10 per minute of audio
 ## Troubleshooting
 ### "Modal CLI not installed"
 ```bash
 pip install modal
 ```
 ### "Not authenticated with Modal"
 ```bash
 modal setup
 # Complete browser authentication
 ```
 ### "Failed to create secret hf_token"
 - Verify your HuggingFace token is valid
 - Ensure you've accepted the Pyannote license
 - Token needs `read` permission
 ### Deployment fails
 Check the Modal dashboard for detailed error logs:
 - Visit https://modal.com/apps
 - Click on the failed function
 - View build and runtime logs
 ### Re-running deployment
 The script is safe to re-run. It will:
 - Update existing secrets if they exist
 - Redeploy functions with latest code
 - Output new configuration (API key stays the same if secret exists)
 ## Manual Deployment (Advanced)
 If you prefer to deploy functions individually:
 ```bash
 cd gpu/modal_deployments
 # Create secrets manually
 modal secret create hf_token HF_TOKEN=your-hf-token
 modal secret create reflector-gpu REFLECTOR_GPU_APIKEY=$(openssl rand -hex 32)
 # Deploy each function
 modal deploy reflector_transcriber.py
 modal deploy reflector_diarizer.py
 ```
 ## Monitoring
 View your deployed functions and their usage:
 - **Modal Dashboard**: https://modal.com/apps
 - **Function logs**: Click on any function to view logs
 - **Usage**: View compute time and costs in the dashboard
--- a/docs/docs/installation/overview.md
+++ b/docs/docs/installation/overview.md
@@ -1,401 +0,0 @@
 ---
 sidebar_position: 1
 title: Deployment Guide
 ---
 # Deployment Guide
 This guide walks you through deploying Reflector from scratch. Follow these steps in order.
 ## What You'll Set Up
 ```mermaid
 flowchart LR
    User --> Caddy["Caddy (auto-SSL)"]
    Caddy --> Frontend["Frontend (Next.js)"]
    Caddy --> Backend["Backend (FastAPI)"]
    Backend --> PostgreSQL
    Backend --> Redis
    Backend --> Workers["Celery Workers"]
    Workers --> PostgreSQL
    Workers --> Redis
    Workers --> GPU["GPU Processing<br/>(Modal.com OR Self-hosted)"]
 ```
 ## Prerequisites
 Before starting, you need:
 - **Production server** -  4+ cores, 8GB+ RAM, public IP
 - **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
 - **GPU processing** - Choose one:
  - Modal.com account, OR
  - GPU server with NVIDIA GPU (8GB+ VRAM)
 - **HuggingFace account** - Free at https://huggingface.co
  - Accept both Pyannote licenses (required for speaker diarization):
    - https://huggingface.co/pyannote/speaker-diarization-3.1
    - https://huggingface.co/pyannote/segmentation-3.0
 - **LLM API** - For summaries and topic detection. Choose one:
  - OpenAI API key at https://platform.openai.com/account/api-keys, OR
  - Any OpenAI-compatible endpoint (vLLM, LiteLLM, Ollama, etc.)
 - **AWS S3 bucket** - For storing audio files and transcripts (see [S3 Setup](#create-s3-bucket-for-transcript-storage) below)
 ### Optional (for live meeting rooms)
 - [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
 - [ ] **AWS S3 bucket + IAM Role** - For Daily.co recording storage (separate from transcript storage)
 ---
 ## Configure DNS
 ```
 Type: A    Name: app    Value: <your-server-ip>
 Type: A    Name: api    Value: <your-server-ip>
 ```
 ---
 ## Deploy GPU Processing
 Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
 | | **Modal.com (Cloud)** | **Self-Hosted GPU** |
 |---|---|---|
 | **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
 | **Pricing** | Pay-per-use | Fixed infrastructure cost |
 ### Option A: Modal.com (Serverless Cloud GPU)
 #### Accept HuggingFace Licenses
 Visit both pages and click "Accept":
 - https://huggingface.co/pyannote/speaker-diarization-3.1
 - https://huggingface.co/pyannote/segmentation-3.0
 Generate a token at https://huggingface.co/settings/tokens
 #### Deploy to Modal
 There's an install script to help with this setup. It's using modal API to set all necessary moving parts.
 As an alternative, all those operations that script does could be performed in modal settings in modal UI.
 ```bash
 pip install modal
 modal setup  # opens browser for authentication
 git clone https://github.com/monadical-sas/reflector.git
 cd reflector/gpu/modal_deployments
 ./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
 ```
 **Save the output** - copy the configuration block, you'll need it soon.
 See [Modal Setup](./modal-setup) for troubleshooting and details.
 ### Option B: Self-Hosted GPU
 **Location: YOUR GPU SERVER**
 Requires: NVIDIA GPU with 8GB+ VRAM, Ubuntu 22.04+, 40-50GB disk (Docker) or 25-30GB (Systemd).
 See [Self-Hosted GPU Setup](./self-hosted-gpu-setup) for complete instructions. Quick summary:
 1. Install NVIDIA drivers and Docker (or uv for systemd)
 2. Clone repository: `git clone https://github.com/monadical-sas/reflector.git`
 3. Configure `.env` with HuggingFace token
 4. Start service (Docker compose or systemd)
 5. Set up Caddy reverse proxy for HTTPS
 **Save your API key and HTTPS URL** - you'll need them soon.
 ---
 ## Prepare Server
 **Location: dedicated reflector server**
 ### Install Docker
 ```bash
 ssh user@your-server-ip
 curl -fsSL https://get.docker.com | sh
 sudo usermod -aG docker $USER
 # Log out and back in for group changes
 exit
 ssh user@your-server-ip
 docker --version  # verify
 ```
 ### Firewall
 Ensure ports 80 (HTTP) and 443 (HTTPS) are open for inbound traffic. The method varies by cloud provider and OS configuration.
 ### Clone Repository
 The Docker images contain all application code. You clone the repository for configuration files and the compose definition:
 ```bash
 git clone https://github.com/monadical-sas/reflector.git
 cd reflector
 ```
 ---
 ## Create S3 Bucket for Transcript Storage
 Reflector requires AWS S3 to store audio files during processing.
 ### Create Bucket
 ```bash
 # Choose a unique bucket name
 BUCKET_NAME="reflector-transcripts-yourname"
 AWS_REGION="us-east-1"
 # Create bucket
 aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
 ```
 ### Create IAM User
 Create an IAM user with S3 access for Reflector:
 1. Go to AWS IAM Console → Users → Create User
 2. Name: `reflector-transcripts`
 3. Attach policy: `AmazonS3FullAccess` (or create a custom policy for just your bucket)
 4. Create access key (Access key ID + Secret access key)
 Save these credentials - you'll need them in the next step.
 ---
 ## Configure Environment
 Reflector has two env files:
 - `server/.env` - Backend configuration
 - `www/.env` - Frontend configuration
 ### Backend Configuration
 ```bash
 cp server/.env.example server/.env
 nano server/.env
 ```
 **Required settings:**
 ```env
 # Database (defaults work with docker-compose.prod.yml)
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
 # Redis
 REDIS_HOST=redis
 CELERY_BROKER_URL=redis://redis:6379/1
 CELERY_RESULT_BACKEND=redis://redis:6379/1
 # Your domains
 BASE_URL=https://api.example.com
 CORS_ORIGIN=https://app.example.com
 CORS_ALLOW_CREDENTIALS=true
 # Secret key - generate with: openssl rand -hex 32
 SECRET_KEY=<your-generated-secret>
 # GPU Processing - choose ONE option:
 # Option A: Modal.com (paste from deploy-all.sh output)
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=<from-deploy-all.sh-output>
 DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
 DIARIZATION_MODAL_API_KEY=<from-deploy-all.sh-output>
 # Option B: Self-hosted GPU (use your GPU server URL and API key)
 # TRANSCRIPT_BACKEND=modal
 # TRANSCRIPT_URL=https://gpu.example.com
 # TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
 # DIARIZATION_BACKEND=modal
 # DIARIZATION_URL=https://gpu.example.com
 # DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
 # Storage - where to store audio files and transcripts (requires AWS S3)
 TRANSCRIPT_STORAGE_BACKEND=aws
 TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
 TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
 TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
 TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
 # LLM - for generating titles, summaries, and topics
 LLM_API_KEY=sk-your-openai-api-key
 LLM_MODEL=gpt-4o-mini
 # LLM_URL=https://api.openai.com/v1  # Optional: custom endpoint (vLLM, LiteLLM, Ollama, etc.)
 # Auth - disable for initial setup (see a dedicated step for authentication)
 AUTH_BACKEND=none
 ```
 ### Frontend Configuration
 ```bash
 cp www/.env.example www/.env
 nano www/.env
 ```
 **Required settings:**
 ```env
 # Your domains
 SITE_URL=https://app.example.com
 API_URL=https://api.example.com
 WEBSOCKET_URL=wss://api.example.com
 SERVER_API_URL=http://server:1250
 # NextAuth
 NEXTAUTH_URL=https://app.example.com
 NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
 # Disable login requirement for initial setup
 FEATURE_REQUIRE_LOGIN=false
 ```
 ---
 ## Configure Caddy
 ```bash
 cp Caddyfile.example Caddyfile
 nano Caddyfile
 ```
 Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy's env var substitution - you can either edit the file directly or set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables.
 ```
 {$FRONTEND_DOMAIN:app.example.com} {
    reverse_proxy web:3000
 }
 {$API_DOMAIN:api.example.com} {
    reverse_proxy server:1250
 }
 ```
 ---
 ## Start Services
 ```bash
 docker compose -f docker-compose.prod.yml up -d
 ```
 Wait for PostgreSQL to be ready, then run migrations:
 ```bash
 # Wait for postgres to be healthy (may take 30-60 seconds on first run)
 docker compose -f docker-compose.prod.yml exec postgres pg_isready -U reflector
 # Run database migrations
 docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
 ```
 ---
 ## Verify Deployment
 ### Check services
 ```bash
 docker compose -f docker-compose.prod.yml ps
 # All should show "Up"
 ```
 ### Test API
 ```bash
 curl https://api.example.com/health
 # Should return: {"status":"healthy"}
 ```
 ### Test Frontend
 - Visit https://app.example.com
 - You should see the Reflector interface
 - Try uploading an audio file to test transcription
 ---
 ## Enable Authentication (Required for Live Rooms)
 By default, Reflector is open (no login required). **Authentication is required if you want to use Live Meeting Rooms.**
 See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
 Quick summary:
 1. Deploy Authentik on your server
 2. Create OAuth provider in Authentik
 3. Extract public key for JWT verification
 4. Update `server/.env`: `AUTH_BACKEND=jwt` + `AUTH_JWT_AUDIENCE`
 5. Update `www/.env`: `FEATURE_REQUIRE_LOGIN=true` + Authentik credentials
 6. Mount JWT keys volume and restart services
 ---
 ## Enable Live Meeting Rooms
 **Requires: Authentication Step**
 Live rooms require Daily.co and AWS S3. See [Daily.co Setup](./daily-setup) for complete S3/IAM configuration instructions.
 Note that Reflector also supports Whereby as a call provider - this doc doesn't cover its setup yet.
 Quick config - Add to `server/.env`:
 ```env
 DEFAULT_VIDEO_PLATFORM=daily
 DAILY_API_KEY=<from-daily.co-dashboard>
 DAILY_SUBDOMAIN=<your-daily-subdomain>
 # S3 for recording storage
 DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket>
 DAILYCO_STORAGE_AWS_REGION=us-east-1
 DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
 ```
 Reload env and restart:
 ```bash
 docker compose -f docker-compose.prod.yml up -d server worker
 ```
 ---
 ## Troubleshooting
 ### Check logs for errors
 ```bash
 docker compose -f docker-compose.prod.yml logs server --tail 20
 docker compose -f docker-compose.prod.yml logs worker --tail 20
 ```
 ### Services won't start
 ```bash
 docker compose -f docker-compose.prod.yml logs
 ```
 ### CORS errors in browser
 - Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
 - Reload env: `docker compose -f docker-compose.prod.yml up -d server`
 ### SSL certificate errors
 - Caddy auto-provisions Let's Encrypt certificates
 - Ensure ports 80 and 443 are open
 - Check: `docker compose -f docker-compose.prod.yml logs caddy`
 ### Transcription not working
 - Check Modal dashboard: https://modal.com/apps
 - Verify URLs in `server/.env` match deployed functions
 - Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
 ### "Login required" but auth not configured
 - Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
 - Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
--- a/docs/docs/installation/requirements.md
+++ b/docs/docs/installation/requirements.md
@@ -1,60 +0,0 @@
 ---
 sidebar_position: 2
 title: System Requirements
 ---
 # System Requirements
 This page lists hardware and software requirements. For the complete deployment guide, see [Deployment Guide](./overview).
 ## Server Requirements
 ### Minimum Requirements
 - **CPU**: 4 cores
 - **RAM**: 8 GB
 - **Storage**: 50 GB SSD
 - **OS**: Ubuntu 22.04+ or compatible Linux
 - **Network**: Public IP address
 ### Recommended Requirements
 - **CPU**: 8+ cores
 - **RAM**: 16 GB
 - **Storage**: 100 GB SSD
 - **Network**: 1 Gbps connection
 ## Software Requirements
 - Docker Engine 20.10+
 - Docker Compose 2.0+
 ## External Services
 ### Required
 - **Two domain names** - One for frontend (e.g., `app.example.com`), one for API (e.g., `api.example.com`)
 - **Modal.com account** - For GPU-accelerated transcription and diarization (free tier available)
 - **HuggingFace account** - For Pyannote diarization model access
 - **OpenAI API key** - For generating summaries and topic detection (https://platform.openai.com/account/api-keys)
 ### Required for Live Meeting Rooms
 - **Daily.co account** - For video conferencing (free tier available at https://dashboard.daily.co)
 - **AWS S3 bucket + IAM Role** - For Daily.co to store recordings
 - **Another AWS S3 bucket (optional, can reuse the one above)** - For Reflector to store "compiled" mp3 files and transient diarization process temporary files
 ### Optional
 - **AWS S3** - For cloud storage of recordings and transcripts
 - **Authentik** - For SSO/OIDC authentication
 - **Sentry** - For error tracking
 ## Development Requirements
 For local development only (not required for production deployment):
 - Node.js 22+ (for frontend development)
 - Python 3.12+ (for backend development)
 - pnpm (for frontend package management)
 - uv (for Python package management)
--- a/docs/docs/installation/self-hosted-gpu-setup.md
+++ b/docs/docs/installation/self-hosted-gpu-setup.md
@@ -1,465 +0,0 @@
 ---
 sidebar_position: 5
 title: Self-Hosted GPU Setup
 ---
 # Self-Hosted GPU Setup
 This guide covers deploying Reflector's GPU processing on your own server instead of Modal.com. For the complete deployment guide, see [Deployment Guide](./overview).
 ## When to Use Self-Hosted GPU
 **Choose self-hosted GPU if you:**
 - Have GPU hardware available (NVIDIA required)
 - Want full control over processing
 - Prefer fixed infrastructure costs over pay-per-use
 - Have privacy or data locality requirements
 - Need to process audio without external API calls
 **Choose Modal.com instead if you:**
 - Don't have GPU hardware
 - Want zero infrastructure management
 - Prefer pay-per-use pricing
 - Need instant scaling for variable workloads
 See [Modal.com Setup](./modal-setup) for cloud GPU deployment.
 ## What Gets Deployed
 The self-hosted GPU service provides the same API endpoints as Modal:
 - `POST /v1/audio/transcriptions` - Whisper transcription
 - `POST /diarize` - Pyannote speaker diarization
 Your main Reflector server connects to this service exactly like it connects to Modal - only the URL changes.
 ## Prerequisites
 ### Hardware
 - **GPU**: NVIDIA GPU with 8GB+ VRAM (tested on Tesla T4 with 15GB)
 - **CPU**: 4+ cores recommended
 - **RAM**: 8GB minimum, 16GB recommended
 - **Disk**:
  - Docker method: 40-50GB minimum
  - Systemd method: 25-30GB minimum
 ### Software
 - Public IP address
 - Domain name with DNS A record pointing to server
 ### Accounts
 - **HuggingFace account** with accepted Pyannote licenses:
  - https://huggingface.co/pyannote/speaker-diarization-3.1
  - https://huggingface.co/pyannote/segmentation-3.0
 - **HuggingFace access token** from https://huggingface.co/settings/tokens
 ## Choose Deployment Method
 ---
 ## Docker Deployment
 ### Step 1: Install NVIDIA Driver
 ```bash
 sudo apt update
 sudo apt install -y nvidia-driver-535
 # Load kernel modules
 sudo modprobe nvidia
 # Verify installation
 nvidia-smi
 ```
 Expected output: GPU details with driver version and CUDA version.
 ### Step 2: Install Docker
 ```bash
 curl -fsSL https://get.docker.com | sudo sh
 sudo usermod -aG docker $USER
 # Log out and back in for group changes
 exit
 # SSH back in
 ```
 ### Step 3: Install NVIDIA Container Toolkit
 ```bash
 # Add NVIDIA repository
 curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
 curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
 # Install toolkit
 sudo apt-get update
 sudo apt-get install -y nvidia-container-toolkit
 # Configure Docker runtime
 sudo nvidia-ctk runtime configure --runtime=docker
 sudo systemctl restart docker
 ```
 ### Step 4: Clone Repository and Configure
 ```bash
 git clone https://github.com/monadical-sas/reflector.git
 cd reflector/gpu/self_hosted
 # Create environment file
 cat > .env << EOF
 REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
 HF_TOKEN=your_huggingface_token_here
 EOF
 # Note the generated API key - you'll need it for main server config
 cat .env
 ```
 ### Step 5: Create Docker Compose File
 ```bash
 cat > compose.yml << 'EOF'
 services:
  reflector_gpu:
    build:
      context: .
    ports:
      - "8000:8000"
    env_file:
      - .env
    volumes:
      - ./cache:/root/.cache
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]
    restart: unless-stopped
 EOF
 ```
 ### Step 6: Build and Start
 ```bash
 # Build image (takes ~5 minutes, downloads ~10GB)
 sudo docker compose build
 # Start service
 sudo docker compose up -d
 # Wait for startup and verify
 sleep 30
 sudo docker compose logs
 ```
 Look for: `INFO: Application startup complete. Uvicorn running on http://0.0.0.0:8000`
 ### Step 7: Verify GPU Access
 ```bash
 # Check GPU is accessible from container
 sudo docker exec $(sudo docker ps -q) nvidia-smi
 ```
 Should show GPU with ~3GB VRAM used (models loaded).
 ---
 ## Systemd Deployment
 ### Step 1: Install NVIDIA Driver
 ```bash
 sudo apt update
 sudo apt install -y nvidia-driver-535
 # Load kernel modules
 sudo modprobe nvidia
 # Verify installation
 nvidia-smi
 ```
 ### Step 2: Install Dependencies
 ```bash
 # Install ffmpeg
 sudo apt install -y ffmpeg
 # Install uv package manager
 curl -LsSf https://astral.sh/uv/install.sh | sh
 source ~/.local/bin/env
 # Clone repository
 git clone https://github.com/monadical-sas/reflector.git
 cd reflector/gpu/self_hosted
 ```
 ### Step 3: Configure Environment
 ```bash
 # Create environment file
 cat > .env << EOF
 REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
 HF_TOKEN=your_huggingface_token_here
 EOF
 # Note the generated API key
 cat .env
 ```
 ### Step 4: Install Python Packages
 ```bash
 # Install dependencies (~3GB download)
 uv sync
 ```
 ### Step 5: Create Systemd Service
 ```bash
 # Generate library paths for NVIDIA packages
 export NVIDIA_LIBS=$(find ~/reflector/gpu/self_hosted/.venv/lib/python3.12/site-packages/nvidia -name lib -type d | tr '\n' ':')
 # Load environment variables
 source ~/reflector/gpu/self_hosted/.env
 # Create service file
 sudo tee /etc/systemd/system/reflector-gpu.service << EOFSVC
 [Unit]
 Description=Reflector GPU Service (Transcription & Diarization)
 After=network.target
 [Service]
 Type=simple
 User=$USER
 WorkingDirectory=$HOME/reflector/gpu/self_hosted
 Environment="PATH=$HOME/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
 Environment="HF_TOKEN=${HF_TOKEN}"
 Environment="REFLECTOR_GPU_APIKEY=${REFLECTOR_GPU_APIKEY}"
 Environment="LD_LIBRARY_PATH=${NVIDIA_LIBS}"
 ExecStart=$HOME/reflector/gpu/self_hosted/.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
 Restart=always
 RestartSec=10
 [Install]
 WantedBy=multi-user.target
 EOFSVC
 # Enable and start
 sudo systemctl daemon-reload
 sudo systemctl enable reflector-gpu
 sudo systemctl start reflector-gpu
 ```
 ### Step 6: Verify Service
 ```bash
 # Check status
 sudo systemctl status reflector-gpu
 # View logs
 sudo journalctl -u reflector-gpu -f
 ```
 Look for: `INFO: Application startup complete.`
 ---
 ## Configure HTTPS with Caddy
 Both deployment methods need HTTPS for production. Caddy handles SSL automatically.
 ### Install Caddy
 ```bash
 sudo apt install -y debian-keyring debian-archive-keyring apt-transport-https curl
 curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | \
  sudo gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg
 curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | \
  sudo tee /etc/apt/sources.list.d/caddy-stable.list
 sudo apt update
 sudo apt install -y caddy
 ```
 ### Configure Reverse Proxy
 ```bash
 sudo tee /etc/caddy/Caddyfile << 'EOF'
 gpu.example.com {
    reverse_proxy localhost:8000
 }
 EOF
 # Reload Caddy (auto-provisions SSL certificate)
 sudo systemctl reload caddy
 ```
 Replace `gpu.example.com` with your domain.
 ### Verify HTTPS
 ```bash
 curl -I https://gpu.example.com/docs
 # Should return HTTP/2 200
 ```
 ---
 ## Configure Main Reflector Server
 On your main Reflector server, update `server/.env`:
 ```env
 # GPU Processing - Self-hosted
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://gpu.example.com
 TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
 DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://gpu.example.com
 DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
 ```
 **Note:** The backend type is `modal` because the self-hosted GPU service implements the same API contract as Modal.com. This allows you to switch between cloud and self-hosted GPU processing by only changing the URL and API key.
 Restart services to apply:
 ```bash
 docker compose -f docker-compose.prod.yml restart server worker
 ```
 ---
 ## Service Management
 All commands in this section assume you're in `~/reflector/gpu/self_hosted/`.
 ### Docker
 ```bash
 # View logs
 sudo docker compose logs -f
 # Restart service
 sudo docker compose restart
 # Stop service
 sudo docker compose down
 # Check status
 sudo docker compose ps
 ```
 ### Systemd
 ```bash
 # View logs
 sudo journalctl -u reflector-gpu -f
 # Restart service
 sudo systemctl restart reflector-gpu
 # Stop service
 sudo systemctl stop reflector-gpu
 # Check status
 sudo systemctl status reflector-gpu
 ```
 ### Monitor GPU
 ```bash
 # Check GPU usage
 nvidia-smi
 # Watch in real-time
 watch -n 1 nvidia-smi
 ```
 **Typical GPU memory usage:**
 - Idle (models loaded): ~3GB VRAM
 - During transcription: ~4-5GB VRAM
 ---
 ## Troubleshooting
 ### nvidia-smi fails after driver install
 ```bash
 # Manually load kernel modules
 sudo modprobe nvidia
 nvidia-smi
 ```
 ### Service fails with "Could not download pyannote pipeline"
 1. Verify HF_TOKEN is valid: `echo $HF_TOKEN`
 2. Check model access at https://huggingface.co/pyannote/speaker-diarization-3.1
 3. Regenerate service/compose with correct token
 4. Restart service
 ### cuDNN library loading errors (Systemd only)
 Symptom: `Unable to load libcudnn_cnn.so`
 Regenerate the systemd service file - the `LD_LIBRARY_PATH` must include all NVIDIA package directories.
 ### Cannot connect to HTTPS endpoint
 1. Verify DNS resolves: `dig +short gpu.example.com`
 2. Check firewall: `sudo ufw status` (ports 80, 443 must be open)
 3. Check Caddy: `sudo systemctl status caddy`
 4. View Caddy logs: `sudo journalctl -u caddy -n 50`
 ### SSL certificate not provisioning
 Requirements for Let's Encrypt:
 - Ports 80 and 443 publicly accessible
 - DNS resolves to server's public IP
 - Valid domain (not localhost or private IP)
 ### Docker container won't start
 ```bash
 # Check logs
 sudo docker compose logs
 # Common issues:
 # - Port 8000 already in use
 # - GPU not accessible (nvidia-ctk not configured)
 # - Missing .env file
 ```
 ---
 ## Updating
 ### Docker
 ```bash
 cd ~/reflector/gpu/self_hosted
 git pull
 sudo docker compose build
 sudo docker compose up -d
 ```
 ### Systemd
 ```bash
 cd ~/reflector/gpu/self_hosted
 git pull
 uv sync
 sudo systemctl restart reflector-gpu
 ```
--- a/docs/docs/installation/whereby-setup.md
+++ b/docs/docs/installation/whereby-setup.md
@@ -1,7 +0,0 @@
 ---
 title: whereby setup
 ---
 # whereby setup
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/installation/zulip-setup.md
+++ b/docs/docs/installation/zulip-setup.md
@@ -1,7 +0,0 @@
 ---
 title: zulip setup
 ---
 # zulip setup
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/intro.md
+++ b/docs/docs/intro.md
@@ -1,61 +0,0 @@
 ---
 sidebar_position: 1
 title: Introduction
 ---
 # Welcome to Reflector
 Reflector is a privacy-focused, self-hosted AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation, and summarization for audio content and live meetings. With complete control over your data and infrastructure, you can run models on your own hardware (roadmap - currently supports Modal.com for GPU processing).
 ## What is Reflector?
 Reflector is a web application that utilizes AI to process audio content, providing:
 - **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://github.com/NVIDIA/NeMo) (English) models
 - **Speaker Diarization**: Identify and label different speakers using [Pyannote](https://github.com/pyannote/pyannote-audio) 3.1
 - **Live Translation**: Translate audio content in real-time to 100+ languages with [Facebook Seamless-M4T](https://github.com/facebookresearch/seamless_communication)
 - **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
 - **Meeting Recording**: Create permanent records of meetings with searchable transcripts
 ## Features
 | Feature | Public Mode | Private Mode |
 |---------|------------|--------------|
 | **Authentication** | None required | Required |
 | **Audio Upload** | ✅ | ✅ |
 | **Live Microphone Streaming** | ✅ | ✅ |
 | **Transcription** | ✅ | ✅ |
 | **Speaker Diarization** | ✅ | ✅ |
 | **Translation** | ✅ | ✅ |
 | **Topic Detection** | ✅ | ✅ |
 | **Summarization** | ✅ | ✅ |
 | **Virtual Meeting Rooms (Whereby)** | ❌ | ✅ |
 | **Browse Transcripts Page** | ❌ | ✅ |
 | **Search Functionality** | ❌ | ✅ |
 | **Persistent Storage** | ❌ | ✅ |
 ## Architecture Overview
 Reflector consists of three main components:
 - **Frontend**: React application built with Next.js 14
 - **Backend**: Python server using FastAPI
 - **Processing**: Scalable GPU workers for ML inference (Modal.com or local)
 ## Getting Started
 Ready to deploy Reflector? Head over to our [Installation Guide](./installation/overview) to set up your own instance.
 For a quick overview of how Reflector processes audio, check out our [Pipeline Documentation](./pipelines/overview).
 ## Open Source
 Reflector is open source software developed by [Monadical](https://monadical.com) and licensed under the **MIT License**. We welcome contributions from the community!
 - [GitHub Repository](https://github.com/monadical-sas/reflector)
 - [Issue Tracker](https://github.com/monadical-sas/reflector/issues)
 - [Pull Requests](https://github.com/monadical-sas/reflector/pulls)
 ## Support
 Need help? Reach out to the community through GitHub Discussions.
--- a/docs/docs/pipelines/file-pipeline.md
+++ b/docs/docs/pipelines/file-pipeline.md
@@ -1,343 +0,0 @@
 ---
 sidebar_position: 2
 title: File Processing Pipeline
 ---
 # File Processing Pipeline
 The file processing pipeline handles uploaded audio files, optimizing for accuracy and throughput.
 ## Pipeline Stages
 ### 1. Input Stage
 **Accepted Formats:**
 - MP3 (most common)
 - WAV (uncompressed)
 - M4A (Apple format)
 - WebM (browser recordings)
 - MP4 (video with audio track)
 **File Validation:**
 - Maximum size: 2GB (configurable)
 - Minimum duration: 5 seconds
 - Maximum duration: 6 hours
 - Sample rate: Any (will be resampled)
 ### 2. Pre-processing
 **Audio Normalization:**
 ```yaml
 # Convert to standard format
 - Sample rate: 16kHz (Whisper requirement)
 - Channels: Mono
 - Bit depth: 16-bit
 - Format: WAV
 ```
 **Noise Reduction (Optional):**
 - Background noise removal
 - Echo cancellation
 - High-pass filter for rumble
 ### 3. Chunking Strategy
 **Default Configuration:**
 ```yaml
 chunk_size: 30  # seconds
 overlap: 1      # seconds
 max_parallel: 10
 silence_detection: true
 ```
 **Chunking with Silence Detection:**
 - Detects silence periods
 - Attempts to break at natural pauses
 - Maintains context with overlap
 - Preserves sentence boundaries
 **Chunk Metadata:**
 ```json
 {
  "chunk_id": "chunk_001",
  "start_time": 0.0,
  "end_time": 30.0,
  "duration": 30.0,
  "has_speech": true,
  "audio_hash": "sha256:..."
 }
 ```
 ### 4. Transcription Processing
 **Whisper Models:**
 | Model | Size | Speed | Accuracy | Use Case |
 |-------|------|-------|----------|----------|
 | tiny | 39M | Very Fast | 85% | Quick drafts |
 | base | 74M | Fast | 89% | Good balance |
 | small | 244M | Medium | 91% | Better accuracy |
 | medium | 769M | Slow | 93% | High quality |
 | large-v3 | 1550M | Very Slow | 96% | Best quality |
 **Processing Configuration:**
 ```python
 transcription_config = {
    "model": "whisper-base",
    "language": "auto",  # or specify: "en", "es", etc.
    "task": "transcribe",  # or "translate"
    "temperature": 0,  # deterministic
    "compression_ratio_threshold": 2.4,
    "no_speech_threshold": 0.6,
    "condition_on_previous_text": True,
    "initial_prompt": None,  # optional context
 }
 ```
 **Parallel Processing:**
 - Each chunk processed independently
 - GPU batching for efficiency
 - Automatic load balancing
 - Failure isolation
 ### 5. Diarization (Speaker Identification)
 **Pyannote 3.1 Pipeline:**
 1. **Voice Activity Detection (VAD)**
   - Identifies speech segments
   - Filters out silence and noise
   - Precision: 95%+
 2. **Speaker Embedding**
   - Extracts voice characteristics
   - 256-dimensional vectors
   - Speaker-invariant features
 3. **Clustering**
   - Groups similar voice embeddings
   - Agglomerative clustering
   - Automatic speaker count detection
 4. **Segmentation**
   - Assigns speaker labels to time segments
   - Handles overlapping speech
   - Minimum segment duration: 0.5s
 **Configuration:**
 ```python
 diarization_config = {
    "min_speakers": 1,
    "max_speakers": 10,
    "min_duration": 0.5,
    "clustering": "AgglomerativeClustering",
    "embedding_model": "speechbrain/spkrec-ecapa-voxceleb",
 }
 ```
 ### 6. Alignment & Merging
 **Chunk Assembly:**
 ```python
 # Merge overlapping segments
 for chunk in chunks:
    # Remove overlap duplicates
    if chunk.start < previous.end:
        chunk.text = resolve_overlap(previous, chunk)
    # Maintain timeline
    merged_transcript.append(chunk)
 ```
 **Speaker Alignment:**
 - Map diarization timeline to transcript
 - Resolve speaker changes mid-sentence
 - Handle multiple speakers per segment
 **Quality Checks:**
 - Timeline consistency
 - No gaps in transcript
 - Speaker label continuity
 - Confidence score validation
 ### 7. Post-processing Chain
 **Text Formatting:**
 - Sentence capitalization
 - Punctuation restoration
 - Number formatting
 - Acronym detection
 **Translation (Optional):**
 ```python
 translation_config = {
    "model": "facebook/seamless-m4t-medium",
    "source_lang": "auto",
    "target_langs": ["es", "fr", "de"],
    "preserve_formatting": True
 }
 ```
 **Topic Detection:**
 - LLM-based analysis
 - Extract 3-5 key topics
 - Keyword extraction
 - Entity recognition
 **Summarization:**
 ```python
 summary_config = {
    "model": "openai-compatible",
    "max_length": 500,
    "style": "bullets",  # or "paragraph"
    "include_action_items": True,
    "include_decisions": True
 }
 ```
 ### 8. Storage & Delivery
 **Database Storage:**
 ```sql
 -- Main transcript record
 INSERT INTO transcripts (
    id, title, duration, language,
    transcript_text, transcript_json,
    speakers, topics, summary,
    created_at, processing_time
 ) VALUES (...);
 -- Processing metadata
 INSERT INTO processing_metadata (
    transcript_id, model_versions,
    chunk_count, total_chunks,
    error_count, warnings
 ) VALUES (...);
 ```
 **File Storage:**
 - Original audio: S3 (optional)
 - Processed chunks: Temporary (24h)
 - Transcript exports: JSON, SRT, VTT, TXT
 **Notification:**
 ```json
 {
  "type": "webhook",
  "url": "https://your-app.com/webhook",
  "payload": {
    "transcript_id": "...",
    "status": "completed",
    "duration": 3600,
    "processing_time": 180
  }
 }
 ```
 ## Processing Times
 **Estimated times for 1 hour of audio:**
 | Component | Fast Mode | Balanced | High Quality |
 |-----------|-----------|----------|--------------|
 | Pre-processing | 10s | 10s | 10s |
 | Transcription | 60s | 180s | 600s |
 | Diarization | 30s | 60s | 120s |
 | Post-processing | 20s | 30s | 60s |
 | **Total** | **2 min** | **5 min** | **13 min** |
 ## Error Handling
 ### Retry Strategy
 ```python
@celery.task(
    bind=True,
    max_retries=3,
    default_retry_delay=60,
    retry_backoff=True
 )
 def process_chunk(self, chunk_id):
    try:
        # Process chunk
        result = transcribe(chunk_id)
    except Exception as exc:
        # Exponential backoff
        raise self.retry(exc=exc)
 ```
 ### Partial Recovery
 - Continue with successful chunks
 - Mark failed chunks in output
 - Provide partial transcript
 - Report processing issues
 ### Fallback Options
 1. **Model Fallback:**
   - If large model fails, try medium
   - If GPU fails, try CPU
   - If Modal fails, try local
 2. **Quality Degradation:**
   - Reduce chunk size
   - Disable post-processing
   - Skip diarization if needed
 ## Optimization Tips
 ### For Speed
 1. Use smaller models (tiny/base)
 2. Increase parallel chunks
 3. Disable diarization
 4. Skip post-processing
 5. Use GPU acceleration
 ### For Accuracy
 1. Use larger models (medium/large)
 2. Enable all pre-processing
 3. Reduce chunk size
 4. Enable silence detection
 5. Multiple pass processing
 ### For Cost
 1. Use Modal spot instances
 2. Batch multiple files
 3. Cache common phrases
 4. Optimize chunk size
 5. Selective post-processing
 ## Monitoring
 ### Metrics to Track
 ```python
 metrics = {
    "processing_time": histogram,
    "chunk_success_rate": gauge,
    "model_accuracy": histogram,
    "queue_depth": gauge,
    "gpu_utilization": gauge,
    "cost_per_hour": counter
 }
 ```
 ### Quality Metrics
 - Word Error Rate (WER)
 - Diarization Error Rate (DER)
 - Confidence scores
 - Processing speed
 - User feedback
 ### Alerts
 - Processing time > 30 minutes
 - Error rate > 5%
 - Queue depth > 100
 - GPU memory > 90%
 - Cost spike detected
--- a/docs/docs/pipelines/live-pipeline.md
+++ b/docs/docs/pipelines/live-pipeline.md
@@ -1,7 +0,0 @@
 ---
 title: Live pipeline
 ---
 # Live pipeline
 Documentation coming soon.
--- a/docs/docs/pipelines/overview.md
+++ b/docs/docs/pipelines/overview.md
@@ -1,7 +0,0 @@
 ---
 title: overview
 ---
 # overview
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/api.md
+++ b/docs/docs/reference/api.md
@@ -1,441 +0,0 @@
 ---
 title: API Reference
 ---
 # API Reference
 The Reflector API provides a comprehensive RESTful interface for audio transcription, meeting management, and real-time streaming capabilities.
 ## Base URL
 ```
 http://localhost:8000/v1
 ```
 All API endpoints are prefixed with `/v1/` for versioning.
 ## Authentication
 Reflector supports multiple authentication modes:
 - **No Authentication** (Public Mode): Basic transcription and upload functionality
 - **JWT Authentication** (Private Mode): Full feature access including meeting rooms and persistent storage
 - **OAuth/OIDC via Authentik**: Enterprise single sign-on integration
 ## Core Endpoints
 ### Transcripts
 Manage audio transcriptions and their associated metadata.
 #### List Transcripts
 ```http
 GET /v1/transcripts/
 ```
 Returns a paginated list of transcripts with filtering options.
 #### Create Transcript
 ```http
 POST /v1/transcripts/
 ```
 Create a new transcript from uploaded audio or initialize for streaming.
 #### Get Transcript
 ```http
 GET /v1/transcripts/{transcript_id}
 ```
 Retrieve detailed information about a specific transcript.
 #### Update Transcript
 ```http
 PATCH /v1/transcripts/{transcript_id}
 ```
 Update transcript metadata, summary, or processing status.
 #### Delete Transcript
 ```http
 DELETE /v1/transcripts/{transcript_id}
 ```
 Remove a transcript and its associated data.
 ### Audio Processing
 #### Upload Audio
 ```http
 POST /v1/transcripts_audio/{transcript_id}/upload
 ```
 Upload an audio file for transcription processing.
 **Supported formats:**
 - WAV, MP3, M4A, FLAC, OGG
 - Maximum file size: 500MB
 - Sample rates: 8kHz - 48kHz
 #### Download Audio
 ```http
 GET /v1/transcripts_audio/{transcript_id}/download
 ```
 Download the original or processed audio file.
 #### Stream Audio
 ```http
 GET /v1/transcripts_audio/{transcript_id}/stream
 ```
 Stream audio content with range support for progressive playback.
 ### WebRTC Streaming
 Real-time audio streaming via WebRTC for live transcription.
 #### Initialize WebRTC Session
 ```http
 POST /v1/transcripts_webrtc/{transcript_id}/offer
 ```
 Create a WebRTC offer for establishing a peer connection.
 #### Complete WebRTC Handshake
 ```http
 POST /v1/transcripts_webrtc/{transcript_id}/answer
 ```
 Submit the WebRTC answer to complete connection setup.
 ### WebSocket Streaming
 Real-time updates and live transcription via WebSocket.
 #### WebSocket Endpoint
 ```ws
 ws://localhost:8000/v1/transcripts_websocket/{transcript_id}
 ```
 Receive real-time transcription updates, speaker changes, and processing status.
 **Message Types:**
 - `transcription`: New transcribed text segments
 - `diarization`: Speaker identification updates
 - `status`: Processing status changes
 - `error`: Error notifications
 ### Meetings
 Manage virtual meeting rooms and recordings.
 #### List Meetings
 ```http
 GET /v1/meetings/
 ```
 Get all meetings for the authenticated user.
 #### Create Meeting
 ```http
 POST /v1/meetings/
 ```
 Initialize a new meeting room with Whereby integration.
 #### Join Meeting
 ```http
 POST /v1/meetings/{meeting_id}/join
 ```
 Join an existing meeting and start recording.
 #### End Meeting
 ```http
 POST /v1/meetings/{meeting_id}/end
 ```
 End the meeting and finalize the recording.
 ### Rooms
 Virtual meeting room configuration and management.
 #### List Rooms
 ```http
 GET /v1/rooms/
 ```
 Get available meeting rooms.
 #### Create Room
 ```http
 POST /v1/rooms/
 ```
 Create a new persistent meeting room.
 #### Update Room Settings
 ```http
 PATCH /v1/rooms/{room_id}
 ```
 Modify room configuration and permissions.
 ## Response Formats
 ### Success Response
 ```json
 {
  "id": "uuid",
  "created_at": "2025-01-20T10:00:00Z",
  "updated_at": "2025-01-20T10:30:00Z",
  "data": {...}
 }
 ```
 ### Error Response
 ```json
 {
  "error": {
    "code": "ERROR_CODE",
    "message": "Human-readable error message",
    "details": {...}
  }
 }
 ```
 ### Status Codes
 - `200 OK`: Successful request
 - `201 Created`: Resource created successfully
 - `204 No Content`: Successful deletion
 - `400 Bad Request`: Invalid request parameters
 - `401 Unauthorized`: Authentication required
 - `403 Forbidden`: Insufficient permissions
 - `404 Not Found`: Resource not found
 - `409 Conflict`: Resource conflict
 - `422 Unprocessable Entity`: Validation error
 - `429 Too Many Requests`: Rate limit exceeded
 - `500 Internal Server Error`: Server error
 ## WebSocket Protocol
 The WebSocket connection provides real-time updates during transcription processing. The server sends structured messages to communicate different events and data updates.
 ### Connection
 ```javascript
 const ws = new WebSocket('ws://localhost:8000/v1/transcripts_websocket/{transcript_id}');
 ```
 ### Message Types and Formats
 #### Transcription Update
 Sent when new text is transcribed from the audio stream.
 ```json
 {
  "type": "transcription",
  "data": {
    "text": "The transcribed text segment",
    "speaker": "Speaker 1",
    "timestamp": 1705745623.456,
    "confidence": 0.95,
    "segment_id": "seg_001",
    "is_final": true
  }
 }
 ```
 #### Diarization Update
 Sent when speaker changes are detected or speaker labels are updated.
 ```json
 {
  "type": "diarization",
  "data": {
    "speaker": "Speaker 2",
    "speaker_id": "spk_002",
    "start_time": 1705745620.123,
    "end_time": 1705745625.456,
    "confidence": 0.87
  }
 }
 ```
 #### Processing Status
 Sent to indicate changes in the processing pipeline status.
 ```json
 {
  "type": "status",
  "data": {
    "status": "processing",
    "stage": "transcription",
    "progress": 45.5,
    "message": "Processing audio chunk 12 of 26"
  }
 }
 ```
 Status values:
 - `initializing`: Setting up processing pipeline
 - `processing`: Active transcription/diarization
 - `completed`: Processing finished successfully
 - `failed`: Processing encountered an error
 - `paused`: Processing temporarily suspended
 #### Summary Update
 Sent when AI-generated summaries or topics are available.
 ```json
 {
  "type": "summary",
  "data": {
    "summary": "Brief summary of the conversation",
    "topics": ["topic1", "topic2", "topic3"],
    "action_items": ["action 1", "action 2"],
    "key_points": ["point 1", "point 2"]
  }
 }
 ```
 #### Error Messages
 Sent when errors occur during processing.
 ```json
 {
  "type": "error",
  "data": {
    "code": "AUDIO_FORMAT_ERROR",
    "message": "Unsupported audio format",
    "details": {
      "format": "unknown",
      "sample_rate": 0
    },
    "recoverable": false
  }
 }
 ```
 #### Heartbeat/Keepalive
 Sent periodically to maintain the connection.
 ```json
 {
  "type": "ping",
  "data": {
    "timestamp": 1705745630.000
  }
 }
 ```
 ### Client-to-Server Messages
 Clients can send control messages to the server:
 #### Start/Resume Processing
 ```json
 {
  "action": "start",
  "params": {}
 }
 ```
 #### Pause Processing
 ```json
 {
  "action": "pause",
  "params": {}
 }
 ```
 #### Request Status
 ```json
 {
  "action": "get_status",
  "params": {}
 }
 ```
 ## OpenAPI Specification
 The complete OpenAPI 3.0 specification is available at:
 ```
 http://localhost:8000/v1/openapi.json
 ```
 You can import this specification into tools like:
 - Postman
 - Insomnia
 - Swagger UI
 - OpenAPI Generator (for client SDK generation)
 ## SDK Support
 While Reflector doesn't provide official SDKs, you can generate client libraries using the OpenAPI specification with tools like:
 - **Python**: `openapi-python-client`
 - **TypeScript**: `openapi-typescript-codegen`
 - **Go**: `oapi-codegen`
 - **Java**: `openapi-generator`
 ## Example Usage
 ### Python Example
 ```python
 import requests
 # Upload and transcribe audio
 with open('meeting.mp3', 'rb') as f:
    response = requests.post(
        'http://localhost:8000/v1/transcripts/',
        files={'file': f}
    )
    transcript_id = response.json()['id']
 # Check transcription status
 status = requests.get(
    f'http://localhost:8000/v1/transcripts/{transcript_id}'
 ).json()
 print(f"Transcription status: {status['status']}")
 ```
 ### JavaScript WebSocket Example
 ```javascript
 // Connect to WebSocket for real-time transcription updates
 const ws = new WebSocket(`ws://localhost:8000/v1/transcripts_websocket/${transcriptId}`);
 ws.onopen = () => {
    console.log('Connected to transcription WebSocket');
 };
 ws.onmessage = (event) => {
    const message = JSON.parse(event.data);
    switch(message.type) {
        case 'transcription':
            console.log(`[${message.data.speaker}]: ${message.data.text}`);
            break;
        case 'diarization':
            console.log(`Speaker change: ${message.data.speaker}`);
            break;
        case 'status':
            console.log(`Status: ${message.data.status}`);
            break;
        case 'error':
            console.error(`Error: ${message.data.message}`);
            break;
    }
 };
 ws.onerror = (error) => {
    console.error('WebSocket error:', error);
 };
 ws.onclose = () => {
    console.log('WebSocket connection closed');
 };
 ```
 ## Need Help?
 - Review [example implementations](https://github.com/monadical-sas/reflector/tree/main/examples)
 - Open an issue on [GitHub](https://github.com/monadical-sas/reflector/issues)
--- a/docs/docs/reference/api/overview.md
+++ b/docs/docs/reference/api/overview.md
@@ -1,7 +0,0 @@
 ---
 title: overview
 ---
 # overview
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/backend.md
+++ b/docs/docs/reference/architecture/backend.md
@@ -1,7 +0,0 @@
 ---
 title: backend
 ---
 # backend
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/database.md
+++ b/docs/docs/reference/architecture/database.md
@@ -1,7 +0,0 @@
 ---
 title: database
 ---
 # database
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/frontend.md
+++ b/docs/docs/reference/architecture/frontend.md
@@ -1,7 +0,0 @@
 ---
 title: frontend
 ---
 # frontend
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/overview.md
+++ b/docs/docs/reference/architecture/overview.md
@@ -1,7 +0,0 @@
 ---
 title: overview
 ---
 # overview
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/architecture/workers.md
+++ b/docs/docs/reference/architecture/workers.md
@@ -1,7 +0,0 @@
 ---
 title: workers
 ---
 # workers
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/configuration.md
+++ b/docs/docs/reference/configuration.md
@@ -1,7 +0,0 @@
 ---
 title: configuration
 ---
 # configuration
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/analysis.md
+++ b/docs/docs/reference/processors/analysis.md
@@ -1,7 +0,0 @@
 ---
 title: analysis
 ---
 # analysis
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/diarization.md
+++ b/docs/docs/reference/processors/diarization.md
@@ -1,7 +0,0 @@
 ---
 title: diarization
 ---
 # diarization
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/transcription.md
+++ b/docs/docs/reference/processors/transcription.md
@@ -1,7 +0,0 @@
 ---
 title: transcription
 ---
 # transcription
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/reference/processors/translation.md
+++ b/docs/docs/reference/processors/translation.md
@@ -1,7 +0,0 @@
 ---
 title: translation
 ---
 # translation
 Documentation coming soon. See [TODO.md](/docs/TODO) for required information.
--- a/docs/docs/roadmap.md
+++ b/docs/docs/roadmap.md
@@ -1,139 +0,0 @@
 ---
 sidebar_position: 100
 title: Roadmap
 ---
 # Product Roadmap
 Our development roadmap for Reflector, focusing on expanding capabilities while maintaining privacy and performance.
 ## Planned Features
 ### 🌍 Multi-Language Support Enhancement
 **Current State:**
 - Whisper supports 99+ languages for transcription
 - Parakeet supports English only with high accuracy
 - Translation available to 100+ languages
 **Planned Improvements:**
 - Default language selection per room/user
 - Automatic language detection improvements
 - Multi-language diarization support
 - RTL (Right-to-Left) language UI support
 - Language-specific post-processing rules
 ### 🏠 Self-Hosted Room Providers
 **Jitsi Integration**
 Moving beyond Whereby to support self-hosted video conferencing:
 - No API keys required
 - Complete control over video infrastructure
 - Custom branding and configuration
 - Lower operational costs
 - Enhanced privacy with self-hosted video
 **Implementation Plan:**
 - WebRTC bridge for Jitsi Meet
 - Room management API integration
 - Recording synchronization
 - Participant tracking
 ### 📅 Calendar Integration
 **Planned Capabilities:**
 - Google Calendar synchronization
 - Microsoft Outlook integration
 - Automatic meeting room creation
 - Pre-meeting document preparation
 - Post-meeting transcript delivery
 - Recurring meeting support
 **Features:**
 - Auto-join scheduled meetings
 - Calendar-based access control
 - Meeting agenda import
 - Action item export to calendar
 ### 🖥️ Self-Hosted GPU Service
 **For organizations with dedicated GPU hardware (H100, A100, RTX 4090):**
 **Docker GPU Worker Image:**
 - Self-contained processing service
 - CUDA 11/12 support
 - Pre-loaded models:
  - Whisper (all sizes)
  - Pyannote diarization
  - Seamless-M4T translation
 - Automatic model management
 **Deployment Options:**
 - Kubernetes GPU operators
 - Docker Compose with nvidia-docker
 - Bare metal installation
 - Hybrid cloud/on-premise
 **Benefits:**
 - No Modal.com dependency
 - Complete data isolation
 - Predictable costs
 - Maximum performance
 - Custom model support
 ## Future Considerations
 ### Enhanced Analytics
 - Meeting insights dashboard
 - Speaker participation metrics
 - Topic trends over time
 - Team collaboration patterns
 ### Advanced AI Features
 - Real-time sentiment analysis
 - Emotion detection
 - Meeting quality scores
 - Automated coaching suggestions
 ### Integration Ecosystem
 - Slack/Teams notifications
 - CRM integration (Salesforce, HubSpot)
 - Project management tools (Jira, Asana)
 - Knowledge bases (Notion, Confluence)
 ### Performance Improvements
 - WebAssembly for client-side processing
 - Edge computing support
 - 5G network optimization
 - Blockchain for transcript verification
 ## Contributing
 We welcome community contributions! Areas where you can help:
 1. **Language Support**: Add support for your language
 2. **Integrations**: Connect with your favorite tools
 3. **Models**: Fine-tune models for specific domains
 4. **Documentation**: Improve guides and examples
 See our [Contributing Guide](https://github.com/monadical-sas/reflector/blob/main/CONTRIBUTING.md) for details.
 ## Timeline
 We don't provide specific dates as development depends on community contributions and priorities. Features are generally released when they're ready and properly tested.
 ## Feature Requests
 Have an idea for Reflector? We'd love to hear it!
 - [Open a GitHub Issue](https://github.com/monadical-sas/reflector/issues/new)
 - [Join our Discord](#)
 - [Email us](mailto:reflector@monadical.com)
 ## Stay Updated
 - Watch our [GitHub repository](https://github.com/monadical-sas/reflector)
 - Follow our [blog](#)
 - Subscribe to our [newsletter](#)
--- a/docs/docusaurus.config.ts
+++ b/docs/docusaurus.config.ts
@@ -1,163 +0,0 @@
 import {themes as prismThemes} from 'prism-react-renderer';
 import type {Config} from '@docusaurus/types';
 import type * as Preset from '@docusaurus/preset-classic';
 import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
 const config: Config = {
  title: 'Reflector',
  tagline: 'AI-powered audio transcription and meeting analysis platform',
  favicon: 'img/favicon.ico',
  url: 'https://monadical-sas.github.io',
  baseUrl: '/',
  organizationName: 'monadical-sas',
  projectName: 'reflector',
  onBrokenLinks: 'throw',
  onBrokenMarkdownLinks: 'warn',
  markdown: {
    mermaid: true,
  },
  i18n: {
    defaultLocale: 'en',
    locales: ['en'],
  },
  presets: [
    [
      'classic',
      {
        docs: {
          sidebarPath: './sidebars.ts',
          editUrl: 'https://github.com/monadical-sas/reflector/tree/main/docs/',
        },
        blog: false,
        theme: {
          customCss: './src/css/custom.css',
        },
      } satisfies Preset.Options,
    ],
  ],
  plugins: [
    [
      'docusaurus-plugin-openapi-docs',
      {
        id: 'openapi',
        docsPluginId: 'classic',
        config: {
          reflectorapi: {
            specPath: 'static/openapi.json', // Use local file fetched by script
            outputDir: 'docs/reference/api-generated',
            sidebarOptions: {
              groupPathsBy: 'tag',
              categoryLinkSource: 'tag',
            },
            downloadUrl: '/openapi.json',
            hideSendButton: false,
            showExtensions: true,
          },
        } satisfies OpenApiPlugin.Options,
      },
    ],
  ],
  themes: ['docusaurus-theme-openapi-docs', '@docusaurus/theme-mermaid'],
  themeConfig: {
    image: 'img/reflector-social-card.jpg',
    colorMode: {
      defaultMode: 'light',
      disableSwitch: false,
      respectPrefersColorScheme: true,
    },
    navbar: {
      title: 'Reflector',
      logo: {
        alt: 'Reflector Logo',
        src: 'img/reflector-logo.svg',
      },
      items: [
        {
          type: 'docSidebar',
          sidebarId: 'tutorialSidebar',
          position: 'left',
          label: 'Documentation',
        },
        {
          to: '/docs/reference/api',
          label: 'API',
          position: 'left',
        },
        {
          href: 'https://github.com/monadical-sas/reflector',
          label: 'GitHub',
          position: 'right',
        },
      ],
    },
    footer: {
      style: 'dark',
      links: [
        {
          title: 'Documentation',
          items: [
            {
              label: 'Introduction',
              to: '/docs/intro',
            },
            {
              label: 'Installation',
              to: '/docs/installation/overview',
            },
            {
              label: 'API Reference',
              to: '/docs/reference/api',
            },
          ],
        },
        {
          title: 'Resources',
          items: [
            {
              label: 'Architecture',
              to: '/docs/reference/architecture/overview',
            },
            {
              label: 'Pipelines',
              to: '/docs/pipelines/overview',
            },
            {
              label: 'Roadmap',
              to: '/docs/roadmap',
            },
          ],
        },
        {
          title: 'More',
          items: [
            {
              label: 'GitHub',
              href: 'https://github.com/monadical-sas/reflector',
            },
            {
              label: 'Docker Hub',
              href: 'https://hub.docker.com/r/reflector/backend',
            },
          ],
        },
      ],
      copyright: `Copyright © ${new Date().getFullYear()} <a href="https://monadical.com" target="_blank" rel="noopener noreferrer">Monadical</a>. Licensed under MIT. Built with Docusaurus.`,
    },
    prism: {
      theme: prismThemes.github,
      darkTheme: prismThemes.dracula,
      additionalLanguages: ['python', 'bash', 'docker', 'yaml'],
    },
  } satisfies Preset.ThemeConfig,
 };
 export default config;
--- a/docs/package-lock.json
+++ b/docs/package-lock.json
--- a/docs/package.json
+++ b/docs/package.json
@@ -1,53 +0,0 @@
 {
  "name": "docs",
  "version": "0.0.0",
  "private": true,
  "scripts": {
    "docusaurus": "docusaurus",
    "start": "docusaurus start",
    "build": "docusaurus build",
    "swizzle": "docusaurus swizzle",
    "deploy": "docusaurus deploy",
    "clear": "docusaurus clear",
    "serve": "docusaurus serve",
    "write-translations": "docusaurus write-translations",
    "write-heading-ids": "docusaurus write-heading-ids",
    "typecheck": "tsc",
    "fetch-openapi": "./scripts/fetch-openapi.sh",
    "gen-api-docs": "npm run fetch-openapi && docusaurus gen-api-docs reflector",
    "prebuild": "npm run fetch-openapi"
  },
  "dependencies": {
    "@docusaurus/core": "3.6.3",
    "@docusaurus/preset-classic": "3.6.3",
    "@mdx-js/react": "^3.0.0",
    "clsx": "^2.0.0",
    "docusaurus-plugin-openapi-docs": "^4.5.1",
    "docusaurus-theme-openapi-docs": "^4.5.1",
    "@docusaurus/theme-mermaid": "3.6.3",
    "prism-react-renderer": "^2.3.0",
    "react": "^18.0.0",
    "react-dom": "^18.0.0"
  },
  "devDependencies": {
    "@docusaurus/module-type-aliases": "3.6.3",
    "@docusaurus/tsconfig": "3.6.3",
    "@docusaurus/types": "3.6.3",
    "typescript": "~5.6.2"
  },
  "browserslist": {
    "production": [
      ">0.5%",
      "not dead",
      "not op_mini all"
    ],
    "development": [
      "last 3 chrome version",
      "last 3 firefox version",
      "last 5 safari version"
    ]
  },
  "engines": {
    "node": ">=18.0"
  }
 }
--- a/docs/scripts/fetch-openapi.sh
+++ b/docs/scripts/fetch-openapi.sh
@@ -1,115 +0,0 @@
 #!/bin/bash
 # Script to fetch OpenAPI specification from FastAPI backend
 # Used during documentation build process
 set -e
 echo "📡 Fetching OpenAPI specification from FastAPI backend..."
 # Colors for output
 RED='\033[0;31m'
 GREEN='\033[0;32m'
 YELLOW='\033[1;33m'
 NC='\033[0m' # No Color
 # Configuration
 BACKEND_DIR="../server"
 OPENAPI_OUTPUT="./static/openapi.json"
 SERVER_PORT=1250  # Reflector uses port 1250 by default
 MAX_WAIT=30
 # Check if backend directory exists
 if [ ! -d "$BACKEND_DIR" ]; then
    echo -e "${RED}Error: Backend directory not found at $BACKEND_DIR${NC}"
    exit 1
 fi
 # Function to check if server is running
 check_server() {
    curl -s -o /dev/null -w "%{http_code}" "http://localhost:${SERVER_PORT}/openapi.json" 2>/dev/null
 }
 # Function to cleanup on exit
 cleanup() {
    if [ ! -z "$SERVER_PID" ]; then
        echo -e "\n${YELLOW}Stopping FastAPI server (PID: $SERVER_PID)...${NC}"
        kill $SERVER_PID 2>/dev/null || true
        wait $SERVER_PID 2>/dev/null || true
    fi
 }
 # Set trap to cleanup on exit
 trap cleanup EXIT INT TERM
 # Change to backend directory
 cd "$BACKEND_DIR"
 # Check if uv is installed
 if ! command -v uv &> /dev/null; then
    echo -e "${YELLOW}uv not found, checking for python...${NC}"
    if command -v python3 &> /dev/null; then
        PYTHON_CMD="python3"
    elif command -v python &> /dev/null; then
        PYTHON_CMD="python"
    else
        echo -e "${RED}Error: Neither uv nor python found${NC}"
        exit 1
    fi
    RUN_CMD="$PYTHON_CMD -m"
 else
    RUN_CMD="uv run -m"
 fi
 # Start the FastAPI server in the background (let it use default port 1250)
 echo -e "${YELLOW}Starting FastAPI server...${NC}"
 $RUN_CMD reflector.app > /dev/null 2>&1 &
 SERVER_PID=$!
 # Wait for server to be ready
 echo -n "Waiting for server to be ready"
 WAITED=0
 while [ $WAITED -lt $MAX_WAIT ]; do
    if [ "$(check_server)" = "200" ]; then
        echo -e " ${GREEN}✓${NC}"
        break
    fi
    echo -n "."
    sleep 1
    WAITED=$((WAITED + 1))
 done
 if [ $WAITED -ge $MAX_WAIT ]; then
    echo -e " ${RED}✗${NC}"
    echo -e "${RED}Error: Server failed to start within ${MAX_WAIT} seconds${NC}"
    exit 1
 fi
 # Change back to docs directory
 cd - > /dev/null
 # Create static directory if it doesn't exist
 mkdir -p "$(dirname "$OPENAPI_OUTPUT")"
 # Fetch the OpenAPI specification
 echo -e "${YELLOW}Fetching OpenAPI specification...${NC}"
 if curl -s "http://localhost:${SERVER_PORT}/openapi.json" -o "$OPENAPI_OUTPUT"; then
    echo -e "${GREEN}✓ OpenAPI specification saved to $OPENAPI_OUTPUT${NC}"
    # Validate JSON
    if command -v jq &> /dev/null; then
        if jq empty "$OPENAPI_OUTPUT" 2>/dev/null; then
            echo -e "${GREEN}✓ OpenAPI specification is valid JSON${NC}"
            # Pretty print the JSON
            jq . "$OPENAPI_OUTPUT" > "${OPENAPI_OUTPUT}.tmp" && mv "${OPENAPI_OUTPUT}.tmp" "$OPENAPI_OUTPUT"
        else
            echo -e "${RED}Error: Invalid JSON in OpenAPI specification${NC}"
            exit 1
        fi
    fi
 else
    echo -e "${RED}Error: Failed to fetch OpenAPI specification${NC}"
    exit 1
 fi
 echo -e "${GREEN}✅ OpenAPI specification successfully fetched!${NC}"
--- a/docs/sidebars.ts
+++ b/docs/sidebars.ts
@@ -1,95 +0,0 @@
 import type {SidebarsConfig} from '@docusaurus/plugin-content-docs';
 const sidebars: SidebarsConfig = {
  tutorialSidebar: [
    'intro',
    {
      type: 'category',
      label: 'Concepts',
      collapsed: false,
      items: [
        'concepts/overview',
        'concepts/modes',
        'concepts/pipeline',
      ],
    },
    {
      type: 'category',
      label: 'Installation',
      collapsed: false,
      items: [
        'installation/overview',
        'installation/requirements',
        'installation/docker-setup',
        'installation/modal-setup',
        'installation/self-hosted-gpu-setup',
        'installation/auth-setup',
        'installation/daily-setup',
        {
          type: 'category',
          label: 'Other Integrations',
          collapsed: true,
          items: [
            'installation/whereby-setup',
            'installation/zulip-setup',
          ],
        },
      ],
    },
    {
      type: 'category',
      label: 'Pipelines',
      items: [
        'pipelines/overview',
        'pipelines/file-pipeline',
        'pipelines/live-pipeline',
      ],
    },
    {
      type: 'category',
      label: 'Reference',
      items: [
        {
          type: 'category',
          label: 'Architecture',
          items: [
            'reference/architecture/overview',
            'reference/architecture/backend',
            'reference/architecture/frontend',
            'reference/architecture/workers',
            'reference/architecture/database',
          ],
        },
        {
          type: 'category',
          label: 'Processors',
          items: [
            'reference/processors/transcription',
            'reference/processors/diarization',
            'reference/processors/translation',
            'reference/processors/analysis',
          ],
        },
        {
          type: 'category',
          label: 'API',
          items: [
            {
              type: 'doc',
              id: 'reference/api/overview',
            },
            {
              type: 'link',
              label: 'OpenAPI Reference',
              href: '/docs/reference/api',
            },
          ],
        },
        'reference/configuration',
      ],
    },
    'roadmap',
  ],
 };
 export default sidebars;
--- a/docs/src/components/HomepageFeatures/index.tsx
+++ b/docs/src/components/HomepageFeatures/index.tsx
@@ -1,70 +0,0 @@
 import clsx from 'clsx';
 import Heading from '@theme/Heading';
 import styles from './styles.module.css';
 type FeatureItem = {
  title: string;
  Svg: React.ComponentType<React.ComponentProps<'svg'>>;
  description: JSX.Element;
 };
 const FeatureList: FeatureItem[] = [
  {
    title: 'Easy to Use',
    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
    description: (
      <>
        Docusaurus was designed from the ground up to be easily installed and
        used to get your website up and running quickly.
      </>
    ),
  },
  {
    title: 'Focus on What Matters',
    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
    description: (
      <>
        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go
        ahead and move your docs into the <code>docs</code> directory.
      </>
    ),
  },
  {
    title: 'Powered by React',
    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
    description: (
      <>
        Extend or customize your website layout by reusing React. Docusaurus can
        be extended while reusing the same header and footer.
      </>
    ),
  },
 ];
 function Feature({title, Svg, description}: FeatureItem) {
  return (
    <div className={clsx('col col--4')}>
      <div className="text--center">
        <Svg className={styles.featureSvg} role="img" />
      </div>
      <div className="text--center padding-horiz--md">
        <Heading as="h3">{title}</Heading>
        <p>{description}</p>
      </div>
    </div>
  );
 }
 export default function HomepageFeatures(): JSX.Element {
  return (
    <section className={styles.features}>
      <div className="container">
        <div className="row">
          {FeatureList.map((props, idx) => (
            <Feature key={idx} {...props} />
          ))}
        </div>
      </div>
    </section>
  );
 }
--- a/docs/src/components/HomepageFeatures/styles.module.css
+++ b/docs/src/components/HomepageFeatures/styles.module.css
@@ -1,11 +0,0 @@
 .features {
  display: flex;
  align-items: center;
  padding: 2rem 0;
  width: 100%;
 }
 .featureSvg {
  height: 200px;
  width: 200px;
 }
--- a/docs/src/css/custom.css
+++ b/docs/src/css/custom.css
@@ -1,46 +0,0 @@
 /**
 * Reflector Documentation Theme
 * Based on frontend colors from www/app/styles/theme.ts
 */
@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap');
 :root {
  --ifm-color-primary: #3158E2;
  --ifm-color-primary-dark: #2847C9;
  --ifm-color-primary-darker: #2442BF;
  --ifm-color-primary-darkest: #1D369C;
  --ifm-color-primary-light: #4A6FE5;
  --ifm-color-primary-lighter: #5F81E8;
  --ifm-color-primary-lightest: #8DA6F0;
  --ifm-background-color: #FFFFFF;
  --ifm-background-surface-color: #F4F4F4;
  --ifm-font-color-base: #1A202C;
  --ifm-font-color-secondary: #838383;
  --ifm-code-font-size: 95%;
  --docusaurus-highlighted-code-line-bg: rgba(49, 88, 226, 0.1);
  --ifm-font-family-base: 'Poppins', system-ui, -apple-system, sans-serif;
  --ifm-font-family-monospace: 'Fira Code', 'Monaco', 'Consolas', monospace;
  --ifm-navbar-background-color: #FFFFFF;
  --ifm-heading-font-weight: 600;
 }
 [data-theme='dark'] {
  --ifm-color-primary: #B1CBFF;
  --ifm-color-primary-dark: #91B3FF;
  --ifm-color-primary-darker: #81A7FF;
  --ifm-color-primary-darkest: #5189FF;
  --ifm-color-primary-light: #D1DFFF;
  --ifm-color-primary-lighter: #E1EBFF;
  --ifm-color-primary-lightest: #F0F5FF;
  --ifm-background-color: #0C0D0E;
  --ifm-background-surface-color: #1A202C;
  --ifm-font-color-base: #E2E8F0;
  --ifm-font-color-secondary: #A0AEC0;
  --docusaurus-highlighted-code-line-bg: rgba(177, 203, 255, 0.1);
  --ifm-navbar-background-color: #1A202C;
 }
--- a/docs/src/pages/index.module.css
+++ b/docs/src/pages/index.module.css
@@ -1,23 +0,0 @@
 /**
 * CSS files with the .module.css suffix will be treated as CSS modules
 * and scoped locally.
 */
 .heroBanner {
  padding: 4rem 0;
  text-align: center;
  position: relative;
  overflow: hidden;
 }
@media screen and (max-width: 996px) {
  .heroBanner {
    padding: 2rem;
  }
 }
 .buttons {
  display: flex;
  align-items: center;
  justify-content: center;
 }
--- a/docs/src/pages/index.tsx
+++ b/docs/src/pages/index.tsx
@@ -1,7 +0,0 @@
 import React from 'react';
 import { Redirect } from '@docusaurus/router';
 import useBaseUrl from '@docusaurus/useBaseUrl';
 export default function Home(): JSX.Element {
  return <Redirect to={useBaseUrl('/docs/intro')} />;
 }
--- a/docs/src/pages/markdown-page.md
+++ b/docs/src/pages/markdown-page.md
@@ -1,7 +0,0 @@
 ---
 title: Markdown page example
 ---
 # Markdown page example
 You don't need React to write simple standalone pages.
--- a/docs/static/img/favicon.ico
+++ b/docs/static/img/favicon.ico
--- a/docs/static/img/logo.svg
+++ b/docs/static/img/logo.svg
@@ -1,17 +0,0 @@
 <?xml version="1.0" encoding="utf-8"?>
 <!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
 <svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
 	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
 <style type="text/css">
 	.st0{fill:#B6B6B6;}
 	.st1{fill:#4A4A4A;}
 </style>
 <g>
 	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
 	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
 </g>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
 </image>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
 </image>
 </svg>
--- a/docs/static/img/reflector-logo.svg
+++ b/docs/static/img/reflector-logo.svg
@@ -1,17 +0,0 @@
 <?xml version="1.0" encoding="utf-8"?>
 <!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
 <svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
 	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
 <style type="text/css">
 	.st0{fill:#B6B6B6;}
 	.st1{fill:#4A4A4A;}
 </style>
 <g>
 	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
 	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
 </g>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
 </image>
 <image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
 </image>
 </svg>
--- a/docs/static/img/undraw_docusaurus_mountain.svg
+++ b/docs/static/img/undraw_docusaurus_mountain.svg
@@ -1,171 +0,0 @@
 <svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
  <title>Easy to Use</title>
  <g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
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 </svg>
--- a/docs/static/openapi.json
+++ b/docs/static/openapi.json
--- a/docs/transcript.md
+++ b/docs/transcript.md
@@ -1,241 +0,0 @@
 # Transcript Formats
 The Reflector API provides multiple output formats for transcript data through the `transcript_format` query parameter on the GET `/v1/transcripts/{id}` endpoint.
 ## Overview
 When retrieving a transcript, you can specify the desired format using the `transcript_format` query parameter. The API supports four formats optimized for different use cases:
 - **text** - Plain text with speaker names (default)
 - **text-timestamped** - Timestamped text with speaker names
 - **webvtt-named** - WebVTT subtitle format with participant names
 - **json** - Structured JSON segments with full metadata
 All formats include participant information when available, resolving speaker IDs to actual names.
 ## Query Parameter Usage
 ```
 GET /v1/transcripts/{id}?transcript_format={format}
 ```
 ### Parameters
 - `transcript_format` (optional): The desired output format
  - Type: `"text" | "text-timestamped" | "webvtt-named" | "json"`
  - Default: `"text"`
 ## Format Descriptions
 ### Text Format (`text`)
 **Use case:** Simple, human-readable transcript for display or export.
 **Format:** Speaker names followed by their dialogue, one line per segment.
 **Example:**
 ```
 John Smith: Hello everyone
 Jane Doe: Hi there
 John Smith: How are you today?
 ```
 **Request:**
 ```bash
 GET /v1/transcripts/{id}?transcript_format=text
 ```
 **Response:**
 ```json
 {
  "id": "transcript_123",
  "name": "Meeting Recording",
  "transcript_format": "text",
  "transcript": "John Smith: Hello everyone\nJane Doe: Hi there\nJohn Smith: How are you today?",
  "participants": [
    {"id": "p1", "speaker": 0, "name": "John Smith"},
    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
  ],
  ...
 }
 ```
 ### Text Timestamped Format (`text-timestamped`)
 **Use case:** Transcript with timing information for navigation or reference.
 **Format:** `[MM:SS]` timestamp prefix before each speaker and dialogue.
 **Example:**
 ```
 [00:00] John Smith: Hello everyone
 [00:05] Jane Doe: Hi there
 [00:12] John Smith: How are you today?
 ```
 **Request:**
 ```bash
 GET /v1/transcripts/{id}?transcript_format=text-timestamped
 ```
 **Response:**
 ```json
 {
  "id": "transcript_123",
  "name": "Meeting Recording",
  "transcript_format": "text-timestamped",
  "transcript": "[00:00] John Smith: Hello everyone\n[00:05] Jane Doe: Hi there\n[00:12] John Smith: How are you today?",
  "participants": [
    {"id": "p1", "speaker": 0, "name": "John Smith"},
    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
  ],
  ...
 }
 ```
 ### WebVTT Named Format (`webvtt-named`)
 **Use case:** Subtitle files for video players, accessibility tools, or video editing.
 **Format:** Standard WebVTT subtitle format with voice tags using participant names.
 **Example:**
 ```
 WEBVTT
 00:00:00.000 --> 00:00:05.000
 <v John Smith>Hello everyone
 00:00:05.000 --> 00:00:12.000
 <v Jane Doe>Hi there
 00:00:12.000 --> 00:00:18.000
 <v John Smith>How are you today?
 ```
 **Request:**
 ```bash
 GET /v1/transcripts/{id}?transcript_format=webvtt-named
 ```
 **Response:**
 ```json
 {
  "id": "transcript_123",
  "name": "Meeting Recording",
  "transcript_format": "webvtt-named",
  "transcript": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v John Smith>Hello everyone\n\n...",
  "participants": [
    {"id": "p1", "speaker": 0, "name": "John Smith"},
    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
  ],
  ...
 }
 ```
 ### JSON Format (`json`)
 **Use case:** Programmatic access with full timing and speaker metadata.
 **Format:** Array of segment objects with speaker information, text content, and precise timing.
 **Example:**
 ```json
 [
  {
    "speaker": 0,
    "speaker_name": "John Smith",
    "text": "Hello everyone",
    "start": 0.0,
    "end": 5.0
  },
  {
    "speaker": 1,
    "speaker_name": "Jane Doe",
    "text": "Hi there",
    "start": 5.0,
    "end": 12.0
  },
  {
    "speaker": 0,
    "speaker_name": "John Smith",
    "text": "How are you today?",
    "start": 12.0,
    "end": 18.0
  }
 ]
 ```
 **Request:**
 ```bash
 GET /v1/transcripts/{id}?transcript_format=json
 ```
 **Response:**
 ```json
 {
  "id": "transcript_123",
  "name": "Meeting Recording",
  "transcript_format": "json",
  "transcript": [
    {
      "speaker": 0,
      "speaker_name": "John Smith",
      "text": "Hello everyone",
      "start": 0.0,
      "end": 5.0
    },
    {
      "speaker": 1,
      "speaker_name": "Jane Doe",
      "text": "Hi there",
      "start": 5.0,
      "end": 12.0
    }
  ],
  "participants": [
    {"id": "p1", "speaker": 0, "name": "John Smith"},
    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
  ],
  ...
 }
 ```
 ## Response Structure
 All formats return the same base transcript metadata with an additional `transcript_format` field and format-specific `transcript` field:
 ### Common Fields
 - `id`: Transcript identifier
 - `user_id`: Owner user ID (if authenticated)
 - `name`: Transcript name
 - `status`: Processing status
 - `locked`: Whether transcript is locked for editing
 - `duration`: Total duration in seconds
 - `title`: Auto-generated or custom title
 - `short_summary`: Brief summary
 - `long_summary`: Detailed summary
 - `created_at`: Creation timestamp
 - `share_mode`: Access control setting
 - `source_language`: Original audio language
 - `target_language`: Translation target language
 - `reviewed`: Whether transcript has been reviewed
 - `meeting_id`: Associated meeting ID (if applicable)
 - `source_kind`: Source type (live, file, room)
 - `room_id`: Associated room ID (if applicable)
 - `audio_deleted`: Whether audio has been deleted
 - `participants`: Array of participant objects with speaker mappings
 ### Format-Specific Fields
 - `transcript_format`: The format identifier (discriminator field)
 - `transcript`: The formatted transcript content (string for text/webvtt formats, array for json format)
 ## Speaker Name Resolution
 All formats resolve speaker IDs to participant names when available:
 - If a participant exists for the speaker ID, their name is used
 - If no participant exists, a default name like "Speaker 0" is generated
 - Speaker IDs are integers (0, 1, 2, etc.) assigned during diarization
--- a/docs/tsconfig.json
+++ b/docs/tsconfig.json
@@ -1,8 +0,0 @@
 {
  // This file is not used in compilation. It is here just for a nice editor experience.
  "extends": "@docusaurus/tsconfig",
  "compilerOptions": {
    "baseUrl": "."
  },
  "exclude": [".docusaurus", "build"]
 }
--- a/gpu/modal_deployments/.gitignore
+++ b/gpu/modal_deployments/.gitignore
@@ -1,33 +0,0 @@
 # OS / Editor
 .DS_Store
 .vscode/
 .idea/
 # Python
 __pycache__/
 *.py[cod]
 *$py.class
 # Logs
 *.log
 # Env and secrets
 .env
 .env.*
 *.env
 *.secret
 # Build / dist
 build/
 dist/
 .eggs/
 *.egg-info/
 # Coverage / test
 .pytest_cache/
 .coverage*
 htmlcov/
 # Modal local state (if any)
 modal_mounts/
 .modal_cache/
--- a/gpu/modal_deployments/deploy-all.sh
+++ b/gpu/modal_deployments/deploy-all.sh
@@ -1,150 +0,0 @@
 #!/bin/bash
 set -e
 # --- Usage ---
 usage() {
    echo "Usage: $0 [OPTIONS]"
    echo ""
    echo "Options:"
    echo "  --hf-token TOKEN    HuggingFace token"
    echo "  --help              Show this help message"
    echo ""
    echo "Examples:"
    echo "  $0                              # Interactive mode"
    echo "  $0 --hf-token hf_xxxxx          # Non-interactive mode"
    echo ""
    exit 0
 }
 # --- Parse Arguments ---
 HF_TOKEN=""
 while [[ $# -gt 0 ]]; do
    case $1 in
        --hf-token)
            HF_TOKEN="$2"
            shift 2
            ;;
        --help)
            usage
            ;;
        *)
            echo "Unknown option: $1"
            usage
            ;;
    esac
 done
 echo "=========================================="
 echo "Reflector GPU Functions Deployment"
 echo "=========================================="
 echo ""
 # --- Check Dependencies ---
 if ! command -v modal &> /dev/null; then
    echo "Error: Modal CLI not installed."
    echo "  Install with: pip install modal"
    exit 1
 fi
 if ! command -v openssl &> /dev/null; then
    echo "Error: openssl not found."
    echo "  Mac: brew install openssl"
    echo "  Ubuntu: sudo apt-get install openssl"
    exit 1
 fi
 # Check Modal authentication
 if ! modal profile current &> /dev/null; then
    echo "Error: Not authenticated with Modal."
    echo "  Run: modal setup"
    exit 1
 fi
 # --- HuggingFace Token Setup ---
 if [ -z "$HF_TOKEN" ]; then
    echo "HuggingFace token required for Pyannote diarization model."
    echo "1. Create account at https://huggingface.co"
    echo "2. Accept license at https://huggingface.co/pyannote/speaker-diarization-3.1"
    echo "3. Generate token at https://huggingface.co/settings/tokens"
    echo ""
    read -p "Enter your HuggingFace token: " HF_TOKEN
 fi
 if [ -z "$HF_TOKEN" ]; then
    echo "Error: HuggingFace token is required for diarization"
    exit 1
 fi
 # Basic token format validation
 if [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
    echo "Warning: HuggingFace tokens usually start with 'hf_'"
    if [ -t 0 ]; then
        read -p "Continue anyway? (y/n): " confirm
        if [ "$confirm" != "y" ]; then
            exit 1
        fi
    else
        echo "Non-interactive mode: proceeding anyway"
    fi
 fi
 # --- Auto-generate reflector<->GPU API Key ---
 echo ""
 echo "Generating API key for GPU services..."
 API_KEY=$(openssl rand -hex 32)
 # --- Create Modal Secrets ---
 echo "Creating Modal secrets..."
 # Create or update hf_token secret (delete first if exists)
 if modal secret list 2>/dev/null | grep -q "hf_token"; then
    echo "  -> Recreating secret: hf_token"
    modal secret delete hf_token --yes 2>/dev/null || true
 fi
 echo "  -> Creating secret: hf_token"
 modal secret create hf_token HF_TOKEN="$HF_TOKEN"
 # Create or update reflector-gpu secret (delete first if exists)
 if modal secret list 2>/dev/null | grep -q "reflector-gpu"; then
    echo "  -> Recreating secret: reflector-gpu"
    modal secret delete reflector-gpu --yes 2>/dev/null || true
 fi
 echo "  -> Creating secret: reflector-gpu"
 modal secret create reflector-gpu REFLECTOR_GPU_APIKEY="$API_KEY"
 # --- Deploy Functions ---
 echo ""
 echo "Deploying transcriber (Whisper)..."
 TRANSCRIBER_URL=$(modal deploy reflector_transcriber.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
 if [ -z "$TRANSCRIBER_URL" ]; then
    echo "Error: Failed to deploy transcriber. Check Modal dashboard for details."
    exit 1
 fi
 echo "  -> $TRANSCRIBER_URL"
 echo ""
 echo "Deploying diarizer (Pyannote)..."
 DIARIZER_URL=$(modal deploy reflector_diarizer.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
 if [ -z "$DIARIZER_URL" ]; then
    echo "Error: Failed to deploy diarizer. Check Modal dashboard for details."
    exit 1
 fi
 echo "  -> $DIARIZER_URL"
 # --- Output Configuration ---
 echo ""
 echo "=========================================="
 echo "Deployment complete!"
 echo "=========================================="
 echo ""
 echo "Copy these values to your server's server/.env file:"
 echo ""
 echo "# --- Modal GPU Configuration ---"
 echo "TRANSCRIPT_BACKEND=modal"
 echo "TRANSCRIPT_URL=$TRANSCRIBER_URL"
 echo "TRANSCRIPT_MODAL_API_KEY=$API_KEY"
 echo ""
 echo "DIARIZATION_BACKEND=modal"
 echo "DIARIZATION_URL=$DIARIZER_URL"
 echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
 echo "# --- End Modal Configuration ---"
--- a/gpu/modal_deployments/reflector_transcriber.py
+++ b/gpu/modal_deployments/reflector_transcriber.py
@@ -1,634 +0,0 @@
 import os
 import sys
 import threading
 import uuid
 from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
 from urllib.parse import urlparse
 import modal
 MODEL_NAME = "large-v2"
 MODEL_COMPUTE_TYPE: str = "float16"
 MODEL_NUM_WORKERS: int = 1
 MINUTES = 60  # seconds
 SAMPLERATE = 16000
 UPLOADS_PATH = "/uploads"
 CACHE_PATH = "/models"
 SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
 VAD_CONFIG = {
    "batch_max_duration": 30.0,
    "silence_padding": 0.5,
    "window_size": 512,
 }
 WhisperUniqFilename = NewType("WhisperUniqFilename", str)
 AudioFileExtension = NewType("AudioFileExtension", str)
 app = modal.App("reflector-transcriber")
 model_cache = modal.Volume.from_name("models", create_if_missing=True)
 upload_volume = modal.Volume.from_name("whisper-uploads", create_if_missing=True)
 class TimeSegment(NamedTuple):
    """Represents a time segment with start and end times."""
    start: float
    end: float
 class AudioSegment(NamedTuple):
    """Represents an audio segment with timing and audio data."""
    start: float
    end: float
    audio: any
 class TranscriptResult(NamedTuple):
    """Represents a transcription result with text and word timings."""
    text: str
    words: list["WordTiming"]
 class WordTiming(TypedDict):
    """Represents a word with its timing information."""
    word: str
    start: float
    end: float
 def download_model():
    from faster_whisper import download_model
    model_cache.reload()
    download_model(MODEL_NAME, cache_dir=CACHE_PATH)
    model_cache.commit()
 image = (
    modal.Image.debian_slim(python_version="3.12")
    .env(
        {
            "HF_HUB_ENABLE_HF_TRANSFER": "1",
            "LD_LIBRARY_PATH": (
                "/usr/local/lib/python3.12/site-packages/nvidia/cudnn/lib/:"
                "/opt/conda/lib/python3.12/site-packages/nvidia/cublas/lib/"
            ),
        }
    )
    .apt_install("ffmpeg")
    .pip_install(
        "huggingface_hub==0.27.1",
        "hf-transfer==0.1.9",
        "torch==2.5.1",
        "faster-whisper==1.1.1",
        "fastapi==0.115.12",
        "python-multipart",
        "requests",
        "librosa==0.10.1",
        "numpy<2",
        "silero-vad==5.1.0",
    )
    .run_function(download_model, volumes={CACHE_PATH: model_cache})
 )
 # IMPORTANT: This function is duplicated in multiple files for deployment isolation.
 # If you modify the audio format detection logic, you MUST update all copies:
 #   - gpu/self_hosted/app/utils.py
 #   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
 #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
 #   - gpu/modal_deployments/reflector_diarizer.py
 def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
    parsed_url = urlparse(url)
    url_path = parsed_url.path
    for ext in SUPPORTED_FILE_EXTENSIONS:
        if url_path.lower().endswith(f".{ext}"):
            return AudioFileExtension(ext)
    content_type = headers.get("content-type", "").lower()
    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
        return AudioFileExtension("mp3")
    if "audio/wav" in content_type:
        return AudioFileExtension("wav")
    if "audio/mp4" in content_type:
        return AudioFileExtension("mp4")
    if "audio/webm" in content_type or "video/webm" in content_type:
        return AudioFileExtension("webm")
    raise ValueError(
        f"Unsupported audio format for URL: {url}. "
        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
    )
 def download_audio_to_volume(
    audio_file_url: str,
 ) -> tuple[WhisperUniqFilename, AudioFileExtension]:
    import requests
    from fastapi import HTTPException
    response = requests.head(audio_file_url, allow_redirects=True)
    if response.status_code == 404:
        raise HTTPException(status_code=404, detail="Audio file not found")
    response = requests.get(audio_file_url, allow_redirects=True)
    response.raise_for_status()
    audio_suffix = detect_audio_format(audio_file_url, response.headers)
    unique_filename = WhisperUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
    file_path = f"{UPLOADS_PATH}/{unique_filename}"
    with open(file_path, "wb") as f:
        f.write(response.content)
    upload_volume.commit()
    return unique_filename, audio_suffix
 def pad_audio(audio_array, sample_rate: int = SAMPLERATE):
    """Add 0.5s of silence if audio is shorter than the silence_padding window.
    Whisper does not require this strictly, but aligning behavior with Parakeet
    avoids edge-case crashes on extremely short inputs and makes comparisons easier.
    """
    import numpy as np
    audio_duration = len(audio_array) / sample_rate
    if audio_duration < VAD_CONFIG["silence_padding"]:
        silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
        silence = np.zeros(silence_samples, dtype=np.float32)
        return np.concatenate([audio_array, silence])
    return audio_array
@app.cls(
    gpu="A10G",
    timeout=5 * MINUTES,
    scaledown_window=5 * MINUTES,
    image=image,
    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
 )
@modal.concurrent(max_inputs=10)
 class TranscriberWhisperLive:
    """Live transcriber class for small audio segments (A10G).
    Mirrors the Parakeet live class API but uses Faster-Whisper under the hood.
    """
    @modal.enter()
    def enter(self):
        import faster_whisper
        import torch
        self.lock = threading.Lock()
        self.use_gpu = torch.cuda.is_available()
        self.device = "cuda" if self.use_gpu else "cpu"
        self.model = faster_whisper.WhisperModel(
            MODEL_NAME,
            device=self.device,
            compute_type=MODEL_COMPUTE_TYPE,
            num_workers=MODEL_NUM_WORKERS,
            download_root=CACHE_PATH,
            local_files_only=True,
        )
        print(f"Model is on device: {self.device}")
    @modal.method()
    def transcribe_segment(
        self,
        filename: str,
        language: str = "en",
    ):
        """Transcribe a single uploaded audio file by filename."""
        upload_volume.reload()
        file_path = f"{UPLOADS_PATH}/{filename}"
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"File not found: {file_path}")
        with self.lock:
            with NoStdStreams():
                segments, _ = self.model.transcribe(
                    file_path,
                    language=language,
                    beam_size=5,
                    word_timestamps=True,
                    vad_filter=True,
                    vad_parameters={"min_silence_duration_ms": 500},
                )
        segments = list(segments)
        text = "".join(segment.text for segment in segments).strip()
        words = [
            {
                "word": word.word,
                "start": round(float(word.start), 2),
                "end": round(float(word.end), 2),
            }
            for segment in segments
            for word in segment.words
        ]
        return {"text": text, "words": words}
    @modal.method()
    def transcribe_batch(
        self,
        filenames: list[str],
        language: str = "en",
    ):
        """Transcribe multiple uploaded audio files and return per-file results."""
        upload_volume.reload()
        results = []
        for filename in filenames:
            file_path = f"{UPLOADS_PATH}/{filename}"
            if not os.path.exists(file_path):
                raise FileNotFoundError(f"Batch file not found: {file_path}")
            with self.lock:
                with NoStdStreams():
                    segments, _ = self.model.transcribe(
                        file_path,
                        language=language,
                        beam_size=5,
                        word_timestamps=True,
                        vad_filter=True,
                        vad_parameters={"min_silence_duration_ms": 500},
                    )
            segments = list(segments)
            text = "".join(seg.text for seg in segments).strip()
            words = [
                {
                    "word": w.word,
                    "start": round(float(w.start), 2),
                    "end": round(float(w.end), 2),
                }
                for seg in segments
                for w in seg.words
            ]
            results.append(
                {
                    "filename": filename,
                    "text": text,
                    "words": words,
                }
            )
        return results
@app.cls(
    gpu="L40S",
    timeout=15 * MINUTES,
    image=image,
    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
 )
 class TranscriberWhisperFile:
    """File transcriber for larger/longer audio, using VAD-driven batching (L40S)."""
    @modal.enter()
    def enter(self):
        import faster_whisper
        import torch
        from silero_vad import load_silero_vad
        self.lock = threading.Lock()
        self.use_gpu = torch.cuda.is_available()
        self.device = "cuda" if self.use_gpu else "cpu"
        self.model = faster_whisper.WhisperModel(
            MODEL_NAME,
            device=self.device,
            compute_type=MODEL_COMPUTE_TYPE,
            num_workers=MODEL_NUM_WORKERS,
            download_root=CACHE_PATH,
            local_files_only=True,
        )
        self.vad_model = load_silero_vad(onnx=False)
    @modal.method()
    def transcribe_segment(
        self, filename: str, timestamp_offset: float = 0.0, language: str = "en"
    ):
        import librosa
        import numpy as np
        from silero_vad import VADIterator
        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
        # If you modify this function, you MUST update all copies:
        #   - gpu/modal_deployments/reflector_transcriber.py (this file)
        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
        #   - gpu/self_hosted/app/services/transcriber.py
        def vad_segments(
            audio_array,
            sample_rate: int = SAMPLERATE,
            window_size: int = VAD_CONFIG["window_size"],
        ) -> Generator[TimeSegment, None, None]:
            """Generate speech segments as TimeSegment using Silero VAD."""
            iterator = VADIterator(self.vad_model, sampling_rate=sample_rate)
            audio_duration = len(audio_array) / float(SAMPLERATE)
            start = None
            for i in range(0, len(audio_array), window_size):
                chunk = audio_array[i : i + window_size]
                if len(chunk) < window_size:
                    chunk = np.pad(
                        chunk, (0, window_size - len(chunk)), mode="constant"
                    )
                speech = iterator(chunk)
                if not speech:
                    continue
                if "start" in speech:
                    start = speech["start"]
                    continue
                if "end" in speech and start is not None:
                    end = speech["end"]
                    yield TimeSegment(
                        start / float(SAMPLERATE), end / float(SAMPLERATE)
                    )
                    start = None
            # Handle case where audio ends while speech is still active
            if start is not None:
                yield TimeSegment(start / float(SAMPLERATE), audio_duration)
            iterator.reset_states()
        upload_volume.reload()
        file_path = f"{UPLOADS_PATH}/{filename}"
        if not os.path.exists(file_path):
            raise FileNotFoundError(f"File not found: {file_path}")
        audio_array, _sr = librosa.load(file_path, sr=SAMPLERATE, mono=True)
        # Batch segments up to ~30s windows by merging contiguous VAD segments
        merged_batches: list[TimeSegment] = []
        batch_start = None
        batch_end = None
        max_duration = VAD_CONFIG["batch_max_duration"]
        for segment in vad_segments(audio_array):
            seg_start, seg_end = segment.start, segment.end
            if batch_start is None:
                batch_start, batch_end = seg_start, seg_end
                continue
            if seg_end - batch_start <= max_duration:
                batch_end = seg_end
            else:
                merged_batches.append(TimeSegment(batch_start, batch_end))
                batch_start, batch_end = seg_start, seg_end
        if batch_start is not None and batch_end is not None:
            merged_batches.append(TimeSegment(batch_start, batch_end))
        all_text = []
        all_words = []
        for segment in merged_batches:
            start_time, end_time = segment.start, segment.end
            s_idx = int(start_time * SAMPLERATE)
            e_idx = int(end_time * SAMPLERATE)
            segment = audio_array[s_idx:e_idx]
            segment = pad_audio(segment, SAMPLERATE)
            with self.lock:
                segments, _ = self.model.transcribe(
                    segment,
                    language=language,
                    beam_size=5,
                    word_timestamps=True,
                    vad_filter=True,
                    vad_parameters={"min_silence_duration_ms": 500},
                )
            segments = list(segments)
            text = "".join(seg.text for seg in segments).strip()
            words = [
                {
                    "word": w.word,
                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
                }
                for seg in segments
                for w in seg.words
            ]
            if text:
                all_text.append(text)
            all_words.extend(words)
        return {"text": " ".join(all_text), "words": all_words}
 # IMPORTANT: This function is duplicated in multiple files for deployment isolation.
 # If you modify the audio format detection logic, you MUST update all copies:
 #   - gpu/self_hosted/app/utils.py
 #   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
 #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
 #   - gpu/modal_deployments/reflector_diarizer.py
 def detect_audio_format(url: str, headers: dict) -> str:
    from urllib.parse import urlparse
    from fastapi import HTTPException
    url_path = urlparse(url).path
    for ext in SUPPORTED_FILE_EXTENSIONS:
        if url_path.lower().endswith(f".{ext}"):
            return ext
    content_type = headers.get("content-type", "").lower()
    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
        return "mp3"
    if "audio/wav" in content_type:
        return "wav"
    if "audio/mp4" in content_type:
        return "mp4"
    if "audio/webm" in content_type or "video/webm" in content_type:
        return "webm"
    raise HTTPException(
        status_code=400,
        detail=(
            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
        ),
    )
 def download_audio_to_volume(audio_file_url: str) -> tuple[str, str]:
    import requests
    from fastapi import HTTPException
    response = requests.head(audio_file_url, allow_redirects=True)
    if response.status_code == 404:
        raise HTTPException(status_code=404, detail="Audio file not found")
    response = requests.get(audio_file_url, allow_redirects=True)
    response.raise_for_status()
    audio_suffix = detect_audio_format(audio_file_url, response.headers)
    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
    file_path = f"{UPLOADS_PATH}/{unique_filename}"
    with open(file_path, "wb") as f:
        f.write(response.content)
    upload_volume.commit()
    return unique_filename, audio_suffix
@app.function(
    scaledown_window=60,
    timeout=600,
    secrets=[
        modal.Secret.from_name("reflector-gpu"),
    ],
    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
    image=image,
 )
@modal.concurrent(max_inputs=40)
@modal.asgi_app()
 def web():
    from fastapi import (
        Body,
        Depends,
        FastAPI,
        Form,
        HTTPException,
        UploadFile,
        status,
    )
    from fastapi.security import OAuth2PasswordBearer
    transcriber_live = TranscriberWhisperLive()
    transcriber_file = TranscriberWhisperFile()
    app = FastAPI()
    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
        if apikey == os.environ["REFLECTOR_GPU_APIKEY"]:
            return
        raise HTTPException(
            status_code=status.HTTP_401_UNAUTHORIZED,
            detail="Invalid API key",
            headers={"WWW-Authenticate": "Bearer"},
        )
    class TranscriptResponse(dict):
        pass
    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
    def transcribe(
        file: UploadFile = None,
        files: list[UploadFile] | None = None,
        model: str = Form(MODEL_NAME),
        language: str = Form("en"),
        batch: bool = Form(False),
    ):
        if not file and not files:
            raise HTTPException(
                status_code=400, detail="Either 'file' or 'files' parameter is required"
            )
        if batch and not files:
            raise HTTPException(
                status_code=400, detail="Batch transcription requires 'files'"
            )
        upload_files = [file] if file else files
        uploaded_filenames: list[str] = []
        for upload_file in upload_files:
            audio_suffix = upload_file.filename.split(".")[-1]
            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
                raise HTTPException(
                    status_code=400,
                    detail=(
                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
                    ),
                )
            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
            file_path = f"{UPLOADS_PATH}/{unique_filename}"
            with open(file_path, "wb") as f:
                content = upload_file.file.read()
                f.write(content)
            uploaded_filenames.append(unique_filename)
        upload_volume.commit()
        try:
            if batch and len(upload_files) > 1:
                func = transcriber_live.transcribe_batch.spawn(
                    filenames=uploaded_filenames,
                    language=language,
                )
                results = func.get()
                return {"results": results}
            results = []
            for filename in uploaded_filenames:
                func = transcriber_live.transcribe_segment.spawn(
                    filename=filename,
                    language=language,
                )
                result = func.get()
                result["filename"] = filename
                results.append(result)
            return {"results": results} if len(results) > 1 else results[0]
        finally:
            for filename in uploaded_filenames:
                try:
                    file_path = f"{UPLOADS_PATH}/{filename}"
                    os.remove(file_path)
                except Exception:
                    pass
            upload_volume.commit()
    @app.post("/v1/audio/transcriptions-from-url", dependencies=[Depends(apikey_auth)])
    def transcribe_from_url(
        audio_file_url: str = Body(
            ..., description="URL of the audio file to transcribe"
        ),
        model: str = Body(MODEL_NAME),
        language: str = Body("en"),
        timestamp_offset: float = Body(0.0),
    ):
        unique_filename, _audio_suffix = download_audio_to_volume(audio_file_url)
        try:
            func = transcriber_file.transcribe_segment.spawn(
                filename=unique_filename,
                timestamp_offset=timestamp_offset,
                language=language,
            )
            result = func.get()
            return result
        finally:
            try:
                file_path = f"{UPLOADS_PATH}/{unique_filename}"
                os.remove(file_path)
                upload_volume.commit()
            except Exception:
                pass
    return app
 class NoStdStreams:
    def __init__(self):
        self.devnull = open(os.devnull, "w")
    def __enter__(self):
        self._stdout, self._stderr = sys.stdout, sys.stderr
        self._stdout.flush()
        self._stderr.flush()
        sys.stdout, sys.stderr = self.devnull, self.devnull
    def __exit__(self, exc_type, exc_value, traceback):
        sys.stdout, sys.stderr = self._stdout, self._stderr
        self.devnull.close()
--- a/gpu/self_hosted/.env.example
+++ b/gpu/self_hosted/.env.example
@@ -1,2 +0,0 @@
 REFLECTOR_GPU_APIKEY=
 HF_TOKEN=
--- a/gpu/self_hosted/.gitignore
+++ b/gpu/self_hosted/.gitignore
@@ -1,38 +0,0 @@
 cache/
 # OS / Editor
 .DS_Store
 .vscode/
 .idea/
 # Python
 __pycache__/
 *.py[cod]
 *$py.class
 # Env and secrets
 .env
 *.env
 *.secret
 HF_TOKEN
 REFLECTOR_GPU_APIKEY
 # Virtual env / uv
 .venv/
 venv/
 ENV/
 uv/
 # Build / dist
 build/
 dist/
 .eggs/
 *.egg-info/
 # Coverage / test
 .pytest_cache/
 .coverage*
 htmlcov/
 # Logs
 *.log
--- a/gpu/self_hosted/DEV_SETUP.md
+++ b/gpu/self_hosted/DEV_SETUP.md
@@ -1,137 +0,0 @@
 # Local Development GPU Setup
 Run transcription and diarization locally for development/testing.
 > **For production deployment**, see the [Self-Hosted GPU Setup Guide](../../docs/docs/installation/self-hosted-gpu-setup.md).
 ## Prerequisites
 1. **Python 3.12+** and **uv** package manager
 2. **FFmpeg** installed and on PATH
 3. **HuggingFace account** with access to pyannote models
 ### Accept Pyannote Licenses (Required)
 Before first run, accept licenses for these gated models (logged into HuggingFace):
 - https://hf.co/pyannote/speaker-diarization-3.1
 - https://hf.co/pyannote/segmentation-3.0
 ## Quick Start
 ### 1. Install dependencies
 ```bash
 cd gpu/self_hosted
 uv sync
 ```
 ### 2. Start the GPU service
 ```bash
 cd gpu/self_hosted
 HF_TOKEN=<your-huggingface-token> \
 REFLECTOR_GPU_APIKEY=dev-key-12345 \
 .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
 ```
 Note: The `.env` file is NOT auto-loaded. Pass env vars explicitly or use:
 ```bash
 export HF_TOKEN=<your-token>
 export REFLECTOR_GPU_APIKEY=dev-key-12345
 .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
 ```
 ### 3. Configure Reflector to use local GPU
 Edit `server/.env`:
 ```bash
 # Transcription - local GPU service
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=http://host.docker.internal:8000
 TRANSCRIPT_MODAL_API_KEY=dev-key-12345
 # Diarization - local GPU service
 DIARIZATION_BACKEND=modal
 DIARIZATION_URL=http://host.docker.internal:8000
 DIARIZATION_MODAL_API_KEY=dev-key-12345
 ```
 Note: Use `host.docker.internal` because Reflector server runs in Docker.
 ### 4. Restart Reflector server
 ```bash
 cd server
 docker compose restart server worker
 ```
 ## Testing
 ### Test transcription
 ```bash
 curl -s -X POST http://localhost:8000/v1/audio/transcriptions \
  -H "Authorization: Bearer dev-key-12345" \
  -F "file=@/path/to/audio.wav" \
  -F "language=en"
 ```
 ### Test diarization
 ```bash
 curl -s -X POST "http://localhost:8000/diarize?audio_file_url=<audio-url>" \
  -H "Authorization: Bearer dev-key-12345"
 ```
 ## Platform Notes
 ### macOS (ARM)
 Docker build fails - CUDA packages are x86_64 only. Use local Python instead:
 ```bash
 uv sync
 HF_TOKEN=xxx REFLECTOR_GPU_APIKEY=xxx .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
 ```
 ### Linux with NVIDIA GPU
 Docker works with CUDA acceleration:
 ```bash
 docker compose up -d
 ```
 ### CPU-only
 Works on any platform, just slower. PyTorch auto-detects and falls back to CPU.
 ## Switching Back to Modal.com
 Edit `server/.env`:
 ```bash
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=<modal-api-key>
 DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
 DIARIZATION_MODAL_API_KEY=<modal-api-key>
 ```
 ## Troubleshooting
 ### "Could not download pyannote pipeline"
 - Accept model licenses at HuggingFace (see Prerequisites)
 - Verify HF_TOKEN is set and valid
 ### Service won't start
 - Check port 8000 is free: `lsof -i :8000`
 - Kill orphan processes if needed
 ### Transcription returns empty text
 - Ensure audio contains speech (not just tones/silence)
 - Check audio format is supported (wav, mp3, etc.)
 ### Deprecation warnings from torchaudio/pyannote
 - Safe to ignore - doesn't affect functionality
--- a/gpu/self_hosted/Dockerfile
+++ b/gpu/self_hosted/Dockerfile
@@ -1,46 +0,0 @@
 FROM python:3.12-slim
 ENV PYTHONUNBUFFERED=1 \
    UV_LINK_MODE=copy \
    UV_NO_CACHE=1
 WORKDIR /tmp
 RUN apt-get update \
 && apt-get install -y \
    ffmpeg \
    curl \
    ca-certificates \
    gnupg \
    wget \
 && apt-get clean
 # Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
 ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
 RUN dpkg -i /cuda-keyring.deb \
 && rm /cuda-keyring.deb \
 && apt-get update \
 && apt-get install -y --no-install-recommends \
    cuda-cudart-12-6 \
    libcublas-12-6 \
    libcudnn9-cuda-12 \
    libcudnn9-dev-cuda-12 \
 && apt-get clean \
 && rm -rf /var/lib/apt/lists/*
 ADD https://astral.sh/uv/install.sh /uv-installer.sh
 RUN sh /uv-installer.sh && rm /uv-installer.sh
 ENV PATH="/root/.local/bin/:$PATH"
 ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"
 RUN mkdir -p /app
 WORKDIR /app
 COPY pyproject.toml uv.lock /app/
 COPY ./app /app/app
 COPY ./main.py /app/
 COPY ./runserver.sh /app/
 EXPOSE 8000
 CMD ["sh", "/app/runserver.sh"]
--- a/gpu/self_hosted/README.md
+++ b/gpu/self_hosted/README.md
@@ -1,77 +0,0 @@
 # Self-hosted Model API
 Run transcription, translation, and diarization services compatible with Reflector's GPU Model API. Works on CPU or GPU.
 Environment variables
 - REFLECTOR_GPU_APIKEY: Optional Bearer token. If unset, auth is disabled.
 - HF_TOKEN: Optional. Required for diarization to download pyannote pipelines
 Requirements
 - FFmpeg must be installed and on PATH (used for URL-based and segmented transcription)
 - Python 3.12+
 - NVIDIA GPU optional. If available, it will be used automatically
 Local run
 Set env vars in self_hosted/.env file
 uv sync
 uv run uvicorn main:app --host 0.0.0.0 --port 8000
 Authentication
 - If REFLECTOR_GPU_APIKEY is set, include header: Authorization: Bearer <key>
 Endpoints
 - POST /v1/audio/transcriptions
  - multipart/form-data
  - fields: file (single file) OR files[] (multiple files), language, batch (true/false)
  - response: single { text, words, filename } or { results: [ ... ] }
 - POST /v1/audio/transcriptions-from-url
  - application/json
  - body: { audio_file_url, language, timestamp_offset }
  - response: { text, words }
 - POST /translate
  - text: query parameter
  - body (application/json): { source_language, target_language }
  - response: { text: { <src>: original, <tgt>: translated } }
 - POST /diarize
  - query parameters: audio_file_url, timestamp (optional)
  - requires HF_TOKEN to be set (for pyannote)
  - response: { diarization: [ { start, end, speaker } ] }
 OpenAPI docs
 - Visit /docs when the server is running
 Docker
 - Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above
 # Setup
 [SETUP.md](SETUP.md)
 # Conformance tests
 ## From this directory
 TRANSCRIPT_URL=http://localhost:8000 \
 TRANSCRIPT_API_KEY=dev-key \
 uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_transcript.py
 TRANSLATION_URL=http://localhost:8000 \
 TRANSLATION_API_KEY=dev-key \
 uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_translation.py
 DIARIZATION_URL=http://localhost:8000 \
 DIARIZATION_API_KEY=dev-key \
 uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_diarization.py
--- a/gpu/self_hosted/app/auth.py
+++ b/gpu/self_hosted/app/auth.py
@@ -1,19 +0,0 @@
 import os
 from fastapi import Depends, HTTPException, status
 from fastapi.security import OAuth2PasswordBearer
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
 def apikey_auth(apikey: str = Depends(oauth2_scheme)):
    required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
    if not required_key:
        return
    if apikey == required_key:
        return
    raise HTTPException(
        status_code=status.HTTP_401_UNAUTHORIZED,
        detail="Invalid API key",
        headers={"WWW-Authenticate": "Bearer"},
    )
--- a/gpu/self_hosted/app/config.py
+++ b/gpu/self_hosted/app/config.py
@@ -1,12 +0,0 @@
 from pathlib import Path
 SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
 SAMPLE_RATE = 16000
 VAD_CONFIG = {
    "batch_max_duration": 30.0,
    "silence_padding": 0.5,
    "window_size": 512,
 }
 # App-level paths
 UPLOADS_PATH = Path("/tmp/whisper-uploads")
--- a/gpu/self_hosted/app/factory.py
+++ b/gpu/self_hosted/app/factory.py
@@ -1,30 +0,0 @@
 from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from .routers.diarization import router as diarization_router
 from .routers.transcription import router as transcription_router
 from .routers.translation import router as translation_router
 from .services.transcriber import WhisperService
 from .services.diarizer import PyannoteDiarizationService
 from .utils import ensure_dirs
@asynccontextmanager
 async def lifespan(app: FastAPI):
    ensure_dirs()
    whisper_service = WhisperService()
    whisper_service.load()
    app.state.whisper = whisper_service
    diarization_service = PyannoteDiarizationService()
    diarization_service.load()
    app.state.diarizer = diarization_service
    yield
 def create_app() -> FastAPI:
    app = FastAPI(lifespan=lifespan)
    app.include_router(transcription_router)
    app.include_router(translation_router)
    app.include_router(diarization_router)
    return app
--- a/gpu/self_hosted/app/routers/diarization.py
+++ b/gpu/self_hosted/app/routers/diarization.py
@@ -1,30 +0,0 @@
 from typing import List
 from fastapi import APIRouter, Depends, Request
 from pydantic import BaseModel
 from ..auth import apikey_auth
 from ..services.diarizer import PyannoteDiarizationService
 from ..utils import download_audio_file
 router = APIRouter(tags=["diarization"])
 class DiarizationSegment(BaseModel):
    start: float
    end: float
    speaker: int
 class DiarizationResponse(BaseModel):
    diarization: List[DiarizationSegment]
@router.post(
    "/diarize", dependencies=[Depends(apikey_auth)], response_model=DiarizationResponse
 )
 def diarize(request: Request, audio_file_url: str, timestamp: float = 0.0):
    with download_audio_file(audio_file_url) as (file_path, _ext):
        file_path = str(file_path)
        diarizer: PyannoteDiarizationService = request.app.state.diarizer
        return diarizer.diarize_file(file_path, timestamp=timestamp)
--- a/gpu/self_hosted/app/routers/transcription.py
+++ b/gpu/self_hosted/app/routers/transcription.py
@@ -1,109 +0,0 @@
 import uuid
 from typing import Optional, Union
 from fastapi import APIRouter, Body, Depends, Form, HTTPException, Request, UploadFile
 from pydantic import BaseModel
 from pathlib import Path
 from ..auth import apikey_auth
 from ..config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
 from ..services.transcriber import MODEL_NAME
 from ..utils import cleanup_uploaded_files, download_audio_file
 router = APIRouter(prefix="/v1/audio", tags=["transcription"])
 class WordTiming(BaseModel):
    word: str
    start: float
    end: float
 class TranscriptResult(BaseModel):
    text: str
    words: list[WordTiming]
    filename: Optional[str] = None
 class TranscriptBatchResponse(BaseModel):
    results: list[TranscriptResult]
@router.post(
    "/transcriptions",
    dependencies=[Depends(apikey_auth)],
    response_model=Union[TranscriptResult, TranscriptBatchResponse],
 )
 def transcribe(
    request: Request,
    file: UploadFile = None,
    files: list[UploadFile] | None = None,
    model: str = Form(MODEL_NAME),
    language: str = Form("en"),
    batch: bool = Form(False),
 ):
    service = request.app.state.whisper
    if not file and not files:
        raise HTTPException(
            status_code=400, detail="Either 'file' or 'files' parameter is required"
        )
    if batch and not files:
        raise HTTPException(
            status_code=400, detail="Batch transcription requires 'files'"
        )
    upload_files = [file] if file else files
    uploaded_paths: list[Path] = []
    with cleanup_uploaded_files(uploaded_paths):
        for upload_file in upload_files:
            audio_suffix = upload_file.filename.split(".")[-1].lower()
            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
                raise HTTPException(
                    status_code=400,
                    detail=(
                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
                    ),
                )
            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
            file_path = UPLOADS_PATH / unique_filename
            with open(file_path, "wb") as f:
                content = upload_file.file.read()
                f.write(content)
            uploaded_paths.append(file_path)
        if batch and len(upload_files) > 1:
            results = []
            for path in uploaded_paths:
                result = service.transcribe_file(str(path), language=language)
                result["filename"] = path.name
                results.append(result)
            return {"results": results}
        results = []
        for path in uploaded_paths:
            result = service.transcribe_file(str(path), language=language)
            result["filename"] = path.name
            results.append(result)
        return {"results": results} if len(results) > 1 else results[0]
@router.post(
    "/transcriptions-from-url",
    dependencies=[Depends(apikey_auth)],
    response_model=TranscriptResult,
 )
 def transcribe_from_url(
    request: Request,
    audio_file_url: str = Body(..., description="URL of the audio file to transcribe"),
    model: str = Body(MODEL_NAME),
    language: str = Body("en"),
    timestamp_offset: float = Body(0.0),
 ):
    service = request.app.state.whisper
    with download_audio_file(audio_file_url) as (file_path, _ext):
        file_path = str(file_path)
        result = service.transcribe_vad_url_segment(
            file_path=file_path, timestamp_offset=timestamp_offset, language=language
        )
        return result
--- a/gpu/self_hosted/app/routers/translation.py
+++ b/gpu/self_hosted/app/routers/translation.py
@@ -1,28 +0,0 @@
 from typing import Dict
 from fastapi import APIRouter, Body, Depends
 from pydantic import BaseModel
 from ..auth import apikey_auth
 from ..services.translator import TextTranslatorService
 router = APIRouter(tags=["translation"])
 translator = TextTranslatorService()
 class TranslationResponse(BaseModel):
    text: Dict[str, str]
@router.post(
    "/translate",
    dependencies=[Depends(apikey_auth)],
    response_model=TranslationResponse,
 )
 def translate(
    text: str,
    source_language: str = Body("en"),
    target_language: str = Body("fr"),
 ):
    return translator.translate(text, source_language, target_language)
--- a/gpu/self_hosted/app/services/diarizer.py
+++ b/gpu/self_hosted/app/services/diarizer.py
@@ -1,42 +0,0 @@
 import os
 import threading
 import torch
 import torchaudio
 from pyannote.audio import Pipeline
 class PyannoteDiarizationService:
    def __init__(self):
        self._pipeline = None
        self._device = "cpu"
        self._lock = threading.Lock()
    def load(self):
        self._device = "cuda" if torch.cuda.is_available() else "cpu"
        self._pipeline = Pipeline.from_pretrained(
            "pyannote/speaker-diarization-3.1",
            use_auth_token=os.environ.get("HF_TOKEN"),
        )
        self._pipeline.to(torch.device(self._device))
    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
        if self._pipeline is None:
            self.load()
        waveform, sample_rate = torchaudio.load(file_path)
        with self._lock:
            diarization = self._pipeline(
                {"waveform": waveform, "sample_rate": sample_rate}
            )
        words = []
        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
            words.append(
                {
                    "start": round(timestamp + diarization_segment.start, 3),
                    "end": round(timestamp + diarization_segment.end, 3),
                    "speaker": int(speaker[-2:])
                    if speaker and speaker[-2:].isdigit()
                    else 0,
                }
            )
        return {"diarization": words}
--- a/gpu/self_hosted/app/services/transcriber.py
+++ b/gpu/self_hosted/app/services/transcriber.py
@@ -1,217 +0,0 @@
 import os
 import shutil
 import subprocess
 import threading
 from typing import Generator
 import faster_whisper
 import librosa
 import numpy as np
 import torch
 from fastapi import HTTPException
 from silero_vad import VADIterator, load_silero_vad
 from ..config import SAMPLE_RATE, VAD_CONFIG
 # Whisper configuration (service-local defaults)
 MODEL_NAME = "large-v2"
 # None delegates compute type to runtime: float16 on CUDA, int8 on CPU
 MODEL_COMPUTE_TYPE = None
 MODEL_NUM_WORKERS = 1
 CACHE_PATH = os.path.join(os.path.expanduser("~"), ".cache", "reflector-whisper")
 from ..utils import NoStdStreams
 class WhisperService:
    def __init__(self):
        self.model = None
        self.device = "cpu"
        self.lock = threading.Lock()
    def load(self):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        compute_type = MODEL_COMPUTE_TYPE or (
            "float16" if self.device == "cuda" else "int8"
        )
        self.model = faster_whisper.WhisperModel(
            MODEL_NAME,
            device=self.device,
            compute_type=compute_type,
            num_workers=MODEL_NUM_WORKERS,
            download_root=CACHE_PATH,
        )
    def pad_audio(self, audio_array, sample_rate: int = SAMPLE_RATE):
        audio_duration = len(audio_array) / sample_rate
        if audio_duration < VAD_CONFIG["silence_padding"]:
            silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
            silence = np.zeros(silence_samples, dtype=np.float32)
            return np.concatenate([audio_array, silence])
        return audio_array
    def enforce_word_timing_constraints(self, words: list[dict]) -> list[dict]:
        if len(words) <= 1:
            return words
        enforced: list[dict] = []
        for i, word in enumerate(words):
            current = dict(word)
            if i < len(words) - 1:
                next_start = words[i + 1]["start"]
                if current["end"] > next_start:
                    current["end"] = next_start
            enforced.append(current)
        return enforced
    def transcribe_file(self, file_path: str, language: str = "en") -> dict:
        input_for_model: str | "object" = file_path
        try:
            audio_array, _sample_rate = librosa.load(
                file_path, sr=SAMPLE_RATE, mono=True
            )
            if len(audio_array) / float(SAMPLE_RATE) < VAD_CONFIG["silence_padding"]:
                input_for_model = self.pad_audio(audio_array, SAMPLE_RATE)
        except Exception:
            pass
        with self.lock:
            with NoStdStreams():
                segments, _ = self.model.transcribe(
                    input_for_model,
                    language=language,
                    beam_size=5,
                    word_timestamps=True,
                    vad_filter=True,
                    vad_parameters={"min_silence_duration_ms": 500},
                )
        segments = list(segments)
        text = "".join(segment.text for segment in segments).strip()
        words = [
            {
                "word": word.word,
                "start": round(float(word.start), 2),
                "end": round(float(word.end), 2),
            }
            for segment in segments
            for word in segment.words
        ]
        words = self.enforce_word_timing_constraints(words)
        return {"text": text, "words": words}
    def transcribe_vad_url_segment(
        self, file_path: str, timestamp_offset: float = 0.0, language: str = "en"
    ) -> dict:
        def load_audio_via_ffmpeg(input_path: str, sample_rate: int) -> np.ndarray:
            ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg"
            cmd = [
                ffmpeg_bin,
                "-nostdin",
                "-threads",
                "1",
                "-i",
                input_path,
                "-f",
                "f32le",
                "-acodec",
                "pcm_f32le",
                "-ac",
                "1",
                "-ar",
                str(sample_rate),
                "pipe:1",
            ]
            try:
                proc = subprocess.run(
                    cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
                )
            except Exception as e:
                raise HTTPException(status_code=400, detail=f"ffmpeg failed: {e}")
            audio = np.frombuffer(proc.stdout, dtype=np.float32)
            return audio
        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
        # If you modify this function, you MUST update all copies:
        #   - gpu/modal_deployments/reflector_transcriber.py
        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
        #   - gpu/self_hosted/app/services/transcriber.py (this file)
        def vad_segments(
            audio_array,
            sample_rate: int = SAMPLE_RATE,
            window_size: int = VAD_CONFIG["window_size"],
        ) -> Generator[tuple[float, float], None, None]:
            vad_model = load_silero_vad(onnx=False)
            iterator = VADIterator(vad_model, sampling_rate=sample_rate)
            start = None
            for i in range(0, len(audio_array), window_size):
                chunk = audio_array[i : i + window_size]
                if len(chunk) < window_size:
                    chunk = np.pad(
                        chunk, (0, window_size - len(chunk)), mode="constant"
                    )
                speech = iterator(chunk)
                if not speech:
                    continue
                if "start" in speech:
                    start = speech["start"]
                    continue
                if "end" in speech and start is not None:
                    end = speech["end"]
                    yield (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE))
                    start = None
            # Handle case where audio ends while speech is still active
            if start is not None:
                audio_duration = len(audio_array) / float(sample_rate)
                yield (start / float(SAMPLE_RATE), audio_duration)
            iterator.reset_states()
        audio_array = load_audio_via_ffmpeg(file_path, SAMPLE_RATE)
        merged_batches: list[tuple[float, float]] = []
        batch_start = None
        batch_end = None
        max_duration = VAD_CONFIG["batch_max_duration"]
        for seg_start, seg_end in vad_segments(audio_array):
            if batch_start is None:
                batch_start, batch_end = seg_start, seg_end
                continue
            if seg_end - batch_start <= max_duration:
                batch_end = seg_end
            else:
                merged_batches.append((batch_start, batch_end))
                batch_start, batch_end = seg_start, seg_end
        if batch_start is not None and batch_end is not None:
            merged_batches.append((batch_start, batch_end))
        all_text = []
        all_words = []
        for start_time, end_time in merged_batches:
            s_idx = int(start_time * SAMPLE_RATE)
            e_idx = int(end_time * SAMPLE_RATE)
            segment = audio_array[s_idx:e_idx]
            segment = self.pad_audio(segment, SAMPLE_RATE)
            with self.lock:
                segments, _ = self.model.transcribe(
                    segment,
                    language=language,
                    beam_size=5,
                    word_timestamps=True,
                    vad_filter=True,
                    vad_parameters={"min_silence_duration_ms": 500},
                )
            segments = list(segments)
            text = "".join(seg.text for seg in segments).strip()
            words = [
                {
                    "word": w.word,
                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
                }
                for seg in segments
                for w in seg.words
            ]
            if text:
                all_text.append(text)
            all_words.extend(words)
        all_words = self.enforce_word_timing_constraints(all_words)
        return {"text": " ".join(all_text), "words": all_words}
--- a/gpu/self_hosted/app/services/translator.py
+++ b/gpu/self_hosted/app/services/translator.py
@@ -1,44 +0,0 @@
 import threading
 from transformers import MarianMTModel, MarianTokenizer, pipeline
 class TextTranslatorService:
    """Simple text-to-text translator using HuggingFace MarianMT models.
    This mirrors the modal translator API shape but uses text translation only.
    """
    def __init__(self):
        self._pipeline = None
        self._lock = threading.Lock()
    def load(self, source_language: str = "en", target_language: str = "fr"):
        # Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr
        model_name = self._resolve_model_name(source_language, target_language)
        tokenizer = MarianTokenizer.from_pretrained(model_name)
        model = MarianMTModel.from_pretrained(model_name)
        self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
    def _resolve_model_name(self, src: str, tgt: str) -> str:
        # Minimal mapping; extend as needed
        pair = (src.lower(), tgt.lower())
        mapping = {
            ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
            ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
            ("en", "es"): "Helsinki-NLP/opus-mt-en-es",
            ("es", "en"): "Helsinki-NLP/opus-mt-es-en",
            ("en", "de"): "Helsinki-NLP/opus-mt-en-de",
            ("de", "en"): "Helsinki-NLP/opus-mt-de-en",
        }
        return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
    def translate(self, text: str, source_language: str, target_language: str) -> dict:
        if self._pipeline is None:
            self.load(source_language, target_language)
        with self._lock:
            results = self._pipeline(
                text, src_lang=source_language, tgt_lang=target_language
            )
        translated = results[0]["translation_text"] if results else ""
        return {"text": {source_language: text, target_language: translated}}
--- a/gpu/self_hosted/app/utils.py
+++ b/gpu/self_hosted/app/utils.py
@@ -1,115 +0,0 @@
 import logging
 import os
 import sys
 import uuid
 from contextlib import contextmanager
 from typing import Mapping
 from urllib.parse import urlparse
 from pathlib import Path
 import requests
 from fastapi import HTTPException
 from .config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
 logger = logging.getLogger(__name__)
 class NoStdStreams:
    def __init__(self):
        self.devnull = open(os.devnull, "w")
    def __enter__(self):
        self._stdout, self._stderr = sys.stdout, sys.stderr
        self._stdout.flush()
        self._stderr.flush()
        sys.stdout, sys.stderr = self.devnull, self.devnull
    def __exit__(self, exc_type, exc_value, traceback):
        sys.stdout, sys.stderr = self._stdout, self._stderr
        self.devnull.close()
 def ensure_dirs():
    UPLOADS_PATH.mkdir(parents=True, exist_ok=True)
 # IMPORTANT: This function is duplicated in multiple files for deployment isolation.
 # If you modify the audio format detection logic, you MUST update all copies:
 #   - gpu/self_hosted/app/utils.py (this file)
 #   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
 #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
 #   - gpu/modal_deployments/reflector_diarizer.py
 def detect_audio_format(url: str, headers: Mapping[str, str]) -> str:
    url_path = urlparse(url).path
    for ext in SUPPORTED_FILE_EXTENSIONS:
        if url_path.lower().endswith(f".{ext}"):
            return ext
    content_type = headers.get("content-type", "").lower()
    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
        return "mp3"
    if "audio/wav" in content_type:
        return "wav"
    if "audio/mp4" in content_type:
        return "mp4"
    if "audio/webm" in content_type or "video/webm" in content_type:
        return "webm"
    raise HTTPException(
        status_code=400,
        detail=(
            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
        ),
    )
 def download_audio_to_uploads(audio_file_url: str) -> tuple[Path, str]:
    response = requests.head(audio_file_url, allow_redirects=True)
    if response.status_code == 404:
        raise HTTPException(status_code=404, detail="Audio file not found")
    response = requests.get(audio_file_url, allow_redirects=True)
    response.raise_for_status()
    audio_suffix = detect_audio_format(audio_file_url, response.headers)
    unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
    file_path: Path = UPLOADS_PATH / unique_filename
    with open(file_path, "wb") as f:
        f.write(response.content)
    return file_path, audio_suffix
@contextmanager
 def download_audio_file(audio_file_url: str):
    """Download an audio file to UPLOADS_PATH and remove it after use.
    Yields (file_path: Path, audio_suffix: str).
    """
    file_path, audio_suffix = download_audio_to_uploads(audio_file_url)
    try:
        yield file_path, audio_suffix
    finally:
        try:
            file_path.unlink(missing_ok=True)
        except Exception as e:
            logger.error("Error deleting temporary file %s: %s", file_path, e)
@contextmanager
 def cleanup_uploaded_files(file_paths: list[Path]):
    """Ensure provided file paths are removed after use.
    The provided list can be populated inside the context; all present entries
    at exit will be deleted.
    """
    try:
        yield file_paths
    finally:
        for path in list(file_paths):
            try:
                path.unlink(missing_ok=True)
            except Exception as e:
                logger.error("Error deleting temporary file %s: %s", path, e)
--- a/gpu/self_hosted/compose.yml
+++ b/gpu/self_hosted/compose.yml
@@ -1,10 +0,0 @@
 services:
  reflector_gpu:
    build:
      context: .
    ports:
      - "8000:8000"
    env_file:
      - .env
    volumes:
      - ./cache:/root/.cache
--- a/gpu/self_hosted/main.py
+++ b/gpu/self_hosted/main.py
@@ -1,3 +0,0 @@
 from app.factory import create_app
 app = create_app()
--- a/gpu/self_hosted/pyproject.toml
+++ b/gpu/self_hosted/pyproject.toml
@@ -1,19 +0,0 @@
 [project]
 name = "reflector-gpu"
 version = "0.1.0"
 description = "Self-hosted GPU service for speech transcription, diarization, and translation via FastAPI."
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
    "fastapi[standard]>=0.116.1",
    "uvicorn[standard]>=0.30.0",
    "torch>=2.3.0",
    "faster-whisper>=1.1.0",
    "librosa==0.10.1",
    "numpy<2",
    "silero-vad==5.1.0",
    "transformers>=4.35.0",
    "sentencepiece",
    "pyannote.audio==3.1.0",
    "torchaudio>=2.3.0",
 ]
--- a/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/scripts/setup-authentik-oauth.sh
+++ b/scripts/setup-authentik-oauth.sh
@@ -1,258 +0,0 @@
 #!/bin/bash
 set -e
 # Setup Authentik OAuth provider for Reflector
 #
 # IMPORTANT: Run this script from your Reflector repository directory (cd ~/reflector)
 # The script creates files using relative paths: server/reflector/auth/jwt/keys/
 #
 # Usage: ./setup-authentik-oauth.sh <authentik-url> <admin-password> <frontend-url>
 # Example: ./setup-authentik-oauth.sh https://authentik.example.com MyPassword123 https://app.example.com
 AUTHENTIK_URL="${1:-}"
 ADMIN_PASSWORD="${2:-}"
 FRONTEND_URL="${3:-}"
 if [ -z "$AUTHENTIK_URL" ] || [ -z "$ADMIN_PASSWORD" ] || [ -z "$FRONTEND_URL" ]; then
    echo "Usage: $0 <authentik-url> <admin-password> <frontend-url>"
    echo "Example: $0 https://authentik.example.com MyPassword123 https://app.example.com"
    exit 1
 fi
 # Remove trailing slash from URLs
 AUTHENTIK_URL="${AUTHENTIK_URL%/}"
 FRONTEND_URL="${FRONTEND_URL%/}"
 echo "==========================================="
 echo "Authentik OAuth Setup for Reflector"
 echo "==========================================="
 echo ""
 echo "Authentik URL: $AUTHENTIK_URL"
 echo "Frontend URL: $FRONTEND_URL"
 echo ""
 # Step 1: Create API token via Django shell
 echo "Creating API token..."
 cd ~/authentik || { echo "Error: ~/authentik directory not found"; exit 1; }
 API_TOKEN=$(sudo docker compose exec -T server python -m manage shell 2>&1 << 'PYTHON' | grep "^TOKEN:" | cut -d: -f2
 from authentik.core.models import User, Token, TokenIntents
 user = User.objects.get(username='akadmin')
 token, created = Token.objects.update_or_create(
    identifier='reflector-setup',
    defaults={
        'user': user,
        'intent': TokenIntents.INTENT_API,
        'description': 'Reflector setup token',
        'expiring': False
    }
 )
 print(f"TOKEN:{token.key}")
 PYTHON
 )
 cd - > /dev/null
 if [ -z "$API_TOKEN" ] || [ "$API_TOKEN" = "null" ]; then
    echo "Error: Failed to create API token"
    echo "Make sure Authentik is fully started and akadmin user exists"
    exit 1
 fi
 echo "  -> Got API token"
 # Step 2: Get authorization flow UUID
 echo "Getting authorization flow..."
 FLOW_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/flows/instances/?slug=default-provider-authorization-implicit-consent" \
    -H "Authorization: Bearer $API_TOKEN")
 FLOW_UUID=$(echo "$FLOW_RESPONSE" | jq -r '.results[0].pk')
 if [ -z "$FLOW_UUID" ] || [ "$FLOW_UUID" = "null" ]; then
    echo "Error: Could not find authorization flow"
    echo "Response: $FLOW_RESPONSE"
    exit 1
 fi
 echo "  -> Flow UUID: $FLOW_UUID"
 # Step 3: Get invalidation flow UUID
 echo "Getting invalidation flow..."
 INVALIDATION_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/flows/instances/?slug=default-provider-invalidation-flow" \
    -H "Authorization: Bearer $API_TOKEN")
 INVALIDATION_UUID=$(echo "$INVALIDATION_RESPONSE" | jq -r '.results[0].pk')
 if [ -z "$INVALIDATION_UUID" ] || [ "$INVALIDATION_UUID" = "null" ]; then
    echo "Warning: Could not find invalidation flow, using authorization flow"
    INVALIDATION_UUID="$FLOW_UUID"
 fi
 echo "  -> Invalidation UUID: $INVALIDATION_UUID"
 # Step 4: Get scope mappings (email, openid, profile)
 echo "Getting scope mappings..."
 SCOPE_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/propertymappings/all/" \
    -H "Authorization: Bearer $API_TOKEN")
 EMAIL_SCOPE=$(echo "$SCOPE_RESPONSE" | jq -r '.results[] | select(.name == "authentik default OAuth Mapping: OpenID '\''email'\''") | .pk')
 OPENID_SCOPE=$(echo "$SCOPE_RESPONSE" | jq -r '.results[] | select(.name == "authentik default OAuth Mapping: OpenID '\''openid'\''") | .pk')
 PROFILE_SCOPE=$(echo "$SCOPE_RESPONSE" | jq -r '.results[] | select(.name == "authentik default OAuth Mapping: OpenID '\''profile'\''") | .pk')
 echo "  -> email: $EMAIL_SCOPE"
 echo "  -> openid: $OPENID_SCOPE"
 echo "  -> profile: $PROFILE_SCOPE"
 # Step 5: Get signing key
 echo "Getting signing key..."
 CERT_RESPONSE=$(curl -s "$AUTHENTIK_URL/api/v3/crypto/certificatekeypairs/" \
    -H "Authorization: Bearer $API_TOKEN")
 SIGNING_KEY=$(echo "$CERT_RESPONSE" | jq -r '.results[0].pk')
 echo "  -> Signing key: $SIGNING_KEY"
 # Step 6: Generate client credentials
 CLIENT_ID="reflector"
 CLIENT_SECRET=$(openssl rand -hex 32)
 # Step 7: Create OAuth2 provider
 echo "Creating OAuth2 provider..."
 PROVIDER_RESPONSE=$(curl -s -X POST "$AUTHENTIK_URL/api/v3/providers/oauth2/" \
    -H "Authorization: Bearer $API_TOKEN" \
    -H "Content-Type: application/json" \
    -d "{
        \"name\": \"Reflector\",
        \"authorization_flow\": \"$FLOW_UUID\",
        \"invalidation_flow\": \"$INVALIDATION_UUID\",
        \"client_type\": \"confidential\",
        \"client_id\": \"$CLIENT_ID\",
        \"client_secret\": \"$CLIENT_SECRET\",
        \"redirect_uris\": [{
            \"matching_mode\": \"strict\",
            \"url\": \"$FRONTEND_URL/api/auth/callback/authentik\"
        }],
        \"property_mappings\": [\"$EMAIL_SCOPE\", \"$OPENID_SCOPE\", \"$PROFILE_SCOPE\"],
        \"signing_key\": \"$SIGNING_KEY\",
        \"access_token_validity\": \"hours=1\",
        \"refresh_token_validity\": \"days=30\"
    }")
 PROVIDER_ID=$(echo "$PROVIDER_RESPONSE" | jq -r '.pk')
 if [ -z "$PROVIDER_ID" ] || [ "$PROVIDER_ID" = "null" ]; then
    # Check if provider already exists
    if echo "$PROVIDER_RESPONSE" | grep -q "already exists"; then
        echo "  -> Provider already exists, updating..."
        EXISTING=$(curl -s "$AUTHENTIK_URL/api/v3/providers/oauth2/?name=Reflector" \
            -H "Authorization: Bearer $API_TOKEN")
        PROVIDER_ID=$(echo "$EXISTING" | jq -r '.results[0].pk')
        CLIENT_ID=$(echo "$EXISTING" | jq -r '.results[0].client_id')
        # Update secret and scopes
        curl -s -X PATCH "$AUTHENTIK_URL/api/v3/providers/oauth2/$PROVIDER_ID/" \
            -H "Authorization: Bearer $API_TOKEN" \
            -H "Content-Type: application/json" \
            -d "{
                \"client_secret\": \"$CLIENT_SECRET\",
                \"property_mappings\": [\"$EMAIL_SCOPE\", \"$OPENID_SCOPE\", \"$PROFILE_SCOPE\"],
                \"signing_key\": \"$SIGNING_KEY\"
            }" > /dev/null
    else
        echo "Error: Failed to create provider"
        echo "Response: $PROVIDER_RESPONSE"
        exit 1
    fi
 fi
 echo "  -> Provider ID: $PROVIDER_ID"
 # Step 8: Create application
 echo "Creating application..."
 APP_RESPONSE=$(curl -s -X POST "$AUTHENTIK_URL/api/v3/core/applications/" \
    -H "Authorization: Bearer $API_TOKEN" \
    -H "Content-Type: application/json" \
    -d "{
        \"name\": \"Reflector\",
        \"slug\": \"reflector\",
        \"provider\": $PROVIDER_ID
    }")
 if echo "$APP_RESPONSE" | grep -q "already exists"; then
    echo "  -> Application already exists"
 else
    APP_SLUG=$(echo "$APP_RESPONSE" | jq -r '.slug')
    if [ -z "$APP_SLUG" ] || [ "$APP_SLUG" = "null" ]; then
        echo "Error: Failed to create application"
        echo "Response: $APP_RESPONSE"
        exit 1
    fi
    echo "  -> Application created: $APP_SLUG"
 fi
 # Step 9: Extract public key for JWT verification
 echo "Extracting public key for JWT verification..."
 mkdir -p server/reflector/auth/jwt/keys
 curl -s "$AUTHENTIK_URL/application/o/reflector/jwks/" | \
    jq -r '.keys[0].x5c[0]' | \
    base64 -d | \
    openssl x509 -pubkey -noout > server/reflector/auth/jwt/keys/authentik_public.pem
 if [ ! -s server/reflector/auth/jwt/keys/authentik_public.pem ]; then
    echo "Error: Failed to extract public key"
    exit 1
 fi
 echo "  -> Saved to server/reflector/auth/jwt/keys/authentik_public.pem"
 # Step 10: Update environment files automatically
 echo "Updating environment files..."
 # Update server/.env
 cat >> server/.env << EOF
 # --- Authentik OAuth (added by setup script) ---
 AUTH_BACKEND=jwt
 AUTH_JWT_AUDIENCE=$CLIENT_ID
 AUTH_JWT_PUBLIC_KEY=authentik_public.pem
 # --- End JWT Configuration ---
 EOF
 echo "  -> Updated server/.env"
 # Update www/.env
 cat >> www/.env << EOF
 # --- Authentik OAuth (added by setup script) ---
 FEATURE_REQUIRE_LOGIN=true
 AUTHENTIK_ISSUER=$AUTHENTIK_URL/application/o/reflector
 AUTHENTIK_REFRESH_TOKEN_URL=$AUTHENTIK_URL/application/o/token/
 AUTHENTIK_CLIENT_ID=$CLIENT_ID
 AUTHENTIK_CLIENT_SECRET=$CLIENT_SECRET
 # --- End Authentik Configuration ---
 EOF
 echo "  -> Updated www/.env"
 # Step 11: Restart Reflector services
 echo "Restarting Reflector services..."
 docker compose -f docker-compose.prod.yml up -d server worker web
 echo ""
 echo "==========================================="
 echo "Setup complete!"
 echo "==========================================="
 echo ""
 echo "Authentik admin: $AUTHENTIK_URL"
 echo "  Username: akadmin"
 echo "  Password: (provided as argument)"
 echo ""
 echo "Frontend: $FRONTEND_URL"
 echo "  Authentication is now required"
 echo ""
 echo "Note: Public key saved to server/reflector/auth/jwt/keys/authentik_public.pem"
 echo "      and mounted via docker-compose volume."
 echo ""
 echo "==========================================="
 echo "Configuration values (for reference):"
 echo "==========================================="
 echo ""
 echo "# server/.env"
 echo "AUTH_BACKEND=jwt"
 echo "AUTH_JWT_AUDIENCE=$CLIENT_ID"
 echo "AUTH_JWT_PUBLIC_KEY=authentik_public.pem"
 echo ""
 echo "# www/.env"
 echo "FEATURE_REQUIRE_LOGIN=true"
 echo "AUTHENTIK_ISSUER=$AUTHENTIK_URL/application/o/reflector"
 echo "AUTHENTIK_REFRESH_TOKEN_URL=$AUTHENTIK_URL/application/o/token/"
 echo "AUTHENTIK_CLIENT_ID=$CLIENT_ID"
 echo "AUTHENTIK_CLIENT_SECRET=$CLIENT_SECRET"
 echo ""
--- a/server/.env.example
+++ b/server/.env.example
@@ -1,139 +0,0 @@
 #
 # This file serve as an example of possible configuration
 # All the settings are described here: reflector/settings.py
 #
 ## =======================================================
 ## Core Configuration (Required for Production)
 ## =======================================================
 ## Database (for docker-compose.prod.yml, use postgres hostname)
 #DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
 ## Redis (for docker-compose.prod.yml, use redis hostname)
 #REDIS_HOST=redis
 #REDIS_PORT=6379
 #CELERY_BROKER_URL=redis://redis:6379/1
 #CELERY_RESULT_BACKEND=redis://redis:6379/1
 ## Base URL - your API domain with https
 #BASE_URL=https://api.example.com
 ## CORS - required when frontend and API are on different domains
 #CORS_ORIGIN=https://app.example.com
 #CORS_ALLOW_CREDENTIALS=true
 ## Secret key - generate with: openssl rand -hex 32
 #SECRET_KEY=changeme-generate-a-secure-random-string
 ## =======================================================
 ## User authentication
 ## =======================================================
 ## Using jwt/authentik
 AUTH_BACKEND=jwt
 AUTH_JWT_AUDIENCE=
 ## =======================================================
 ## Transcription backend
 ##
 ## Check reflector/processors/audio_transcript_* for the
 ## full list of available transcription backend
 ## =======================================================
 ## Using local whisper
 #TRANSCRIPT_BACKEND=whisper
 ## Using serverless modal.com (require reflector-gpu-modal deployed)
 #TRANSCRIPT_BACKEND=modal
 #TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-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=
 ## =======================================================
 ## Translation 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 (Required)
 ##
 ## Responsible for generating titles, summaries, and topic detection
 ## Requires OpenAI API key
 ## =======================================================
 ## OpenAI API key - get from https://platform.openai.com/account/api-keys
 LLM_API_KEY=sk-your-openai-api-key
 LLM_MODEL=gpt-4o-mini
 ## Optional: Custom endpoint (defaults to OpenAI)
 # LLM_URL=https://api.openai.com/v1
 ## Context size for summary generation (tokens)
 LLM_CONTEXT_WINDOW=16000
 ## =======================================================
 ## Diarization
 ##
 ## Only available on modal
 ## 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
 ## =======================================================
 ## Transcript Storage
 ##
 ## Where to store audio files and transcripts
 ## AWS S3 is required for production
 ## =======================================================
 TRANSCRIPT_STORAGE_BACKEND=aws
 TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
 TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
 TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
 TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
 ## =======================================================
 ## Sentry
 ## =======================================================
 ## Sentry DSN configuration
 #SENTRY_DSN=
 ## =======================================================
 ## Video Platform Configuration
 ## =======================================================
 ## Whereby
 #WHEREBY_API_KEY=your-whereby-api-key
 #WHEREBY_WEBHOOK_SECRET=your-whereby-webhook-secret
 #WHEREBY_STORAGE_AWS_ACCESS_KEY_ID=your-aws-key
 #WHEREBY_STORAGE_AWS_SECRET_ACCESS_KEY=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
 #DAILY_WEBHOOK_UUID=  # Auto-populated by recreate_daily_webhook.py script
 #DAILYCO_STORAGE_AWS_ROLE_ARN=...  # IAM role ARN for Daily.co S3 access
 #DAILYCO_STORAGE_AWS_BUCKET_NAME=reflector-dailyco
 #DAILYCO_STORAGE_AWS_REGION=us-west-2
 ## Whereby (optional separate bucket)
 #WHEREBY_STORAGE_AWS_BUCKET_NAME=reflector-whereby
 #WHEREBY_STORAGE_AWS_REGION=us-east-1
 ## Platform Configuration
 #DEFAULT_VIDEO_PLATFORM=whereby          # Default platform for new rooms
--- a/server/README.md
+++ b/server/README.md
@@ -1,29 +1,3 @@
 ## API Key Management
 ### Finding Your User ID
 ```bash
 # Get your OAuth sub (user ID) - requires authentication
 curl -H "Authorization: Bearer <your_jwt>" http://localhost:1250/v1/me
 # Returns: {"sub": "your-oauth-sub-here", "email": "...", ...}
 ```
 ### Creating API Keys
 ```bash
 curl -X POST http://localhost:1250/v1/user/api-keys \
  -H "Authorization: Bearer <your_jwt>" \
  -H "Content-Type: application/json" \
  -d '{"name": "My API Key"}'
 ```
 ### Using API Keys
 ```bash
 # Use X-API-Key header instead of Authorization
 curl -H "X-API-Key: <your_api_key>" http://localhost:1250/v1/transcripts
 ```
 ## AWS S3/SQS usage clarification
 Whereby.com uploads recordings directly to our S3 bucket when meetings end.
--- a/server/docs/daily_pipeline.md
+++ b/server/docs/daily_pipeline.md
@@ -1,421 +0,0 @@
 # Daily.co pipeline
 This document details every external call, storage operation, and database write that occurs when a new Daily.co recording is discovered.
 It includes a bunch of common logic that other pipelines use, therefore not everything is Daily-oriented.
 **The doc was generated at 12.12.2025 and things may have changed since.**
 ## Trigger
 Two entry points, both converging to the same handler:
 1. **Webhook**: Daily.co sends `POST /v1/daily/webhook` with `recording.ready-to-download`
 2. **Polling**: `GET /recordings` (paginated, max 100/call) → filter new → convert to same payload format
 Both produce `RecordingReadyPayload` and call `handleRecordingReady(payload)`.
 ```
 ┌─────────────────┐     ┌──────────────────────────┐
 │  Daily Webhook  │────▶│  RecordingReadyPayload   │
 │  (push)         │     │  {room_name, recording_id│
 └─────────────────┘     │   tracks[], ...}         │
                        └────────────┬─────────────┘
 ┌─────────────────┐                  │
 │  GET /recordings│                  ▼
 │  (poll)         │────▶ convert ──▶ handleRecordingReady()
 └─────────────────┘                  │
                                     ▼
                        ┌────────────────────────┐
                        │ process_multitrack_    │
                        │ recording pipeline     │
                        └────────────────────────┘
 ```
 **Polling API**: `GET https://api.daily.co/v1/recordings`
 - Pagination: `limit` (max 100), `starting_after`, `ending_before`
 - Rate limit: ~2 req/sec
 - Response: `{total_count, data: Recording[]}`
 ```mermaid
 flowchart TB
    subgraph Trigger["1. Recording Discovery - Daily.co Webhook"]
        DAILY_WEBHOOK["Daily.co sends POST /v1/daily/webhook<br/>type: recording.ready-to-download"]
        VERIFY["Verify X-Webhook-Signature (HMAC)"]
        PARSE["Parse DailyWebhookEvent<br/>Extract tracks[], room_name, recording_id"]
        FILTER["Filter audio tracks only<br/>track_keys = [t.s3Key for t in tracks if t.type == 'audio']"]
        DISPATCH["process_multitrack_recording.delay()"]
        DAILY_WEBHOOK --> VERIFY --> PARSE --> FILTER --> DISPATCH
    end
    subgraph Init["2. Recording Initialization"]
        FETCH_MEETING[DB READ: meetings_controller.get_by_room_name]
        FETCH_ROOM[DB READ: rooms_controller.get_by_name]
        DAILY_API_REC[Daily API: GET /recordings/recording_id]
        DAILY_API_PART[Daily API: GET /meetings/mtgSessionId/participants]
        CREATE_RECORDING[DB WRITE: recordings_controller.create]
        CREATE_TRANSCRIPT[DB WRITE: transcripts_controller.add]
        MAP_PARTICIPANTS[DB WRITE: transcript.participants upsert]
    end
    subgraph Pipeline["3. Processing Pipeline"]
        direction TB
        PAD[Track Padding & Mixdown]
        TRANSCRIBE[GPU: Transcription per track]
        TOPICS[LLM: Topic Detection]
        TITLE[LLM: Title Generation]
        SUMMARY[LLM: Summary Generation]
    end
    subgraph Storage["4. S3 Operations"]
        S3_PRESIGN[S3: generate_presigned_url for tracks]
        S3_UPLOAD_PADDED[S3 UPLOAD: padded tracks temp]
        S3_UPLOAD_MP3[S3 UPLOAD: audio.mp3]
        S3_DELETE_TEMP[S3 DELETE: cleanup temp files]
    end
    subgraph PostProcess["5. Post-Processing"]
        CONSENT[Consent check & cleanup]
        ZULIP[Zulip: send/update message]
        WEBHOOK_OUT[Webhook: POST to room.webhook_url]
    end
    Trigger --> Init --> Pipeline
    Pipeline --> Storage
    Pipeline --> PostProcess
 ```
 ## Detailed Sequence: Daily.co Multitrack Recording
 ```mermaid
 sequenceDiagram
    participant DailyCo as Daily.co
    participant API as FastAPI /v1/daily/webhook
    participant Worker as Celery Worker
    participant DB as PostgreSQL
    participant DailyAPI as Daily.co REST API
    participant S3 as AWS S3
    participant GPU as Modal.com GPU
    participant LLM as LLM Service
    participant WS as WebSocket
    participant Zulip as Zulip
    participant ExtWH as External Webhook
    Note over DailyCo,API: Phase 0: Webhook Receipt
    DailyCo->>API: POST /v1/daily/webhook
    Note right of DailyCo: X-Webhook-Signature, X-Webhook-Timestamp
    API->>API: verify_webhook_signature()
    API->>API: Extract audio track s3Keys from payload.tracks[]
    API->>Worker: process_multitrack_recording.delay()
    API-->>DailyCo: 200 OK
    Note over Worker,DailyAPI: Phase 1: Recording Initialization
    Worker->>DB: SELECT meeting WHERE room_name=?
    Worker->>DB: SELECT room WHERE name=?
    Worker->>DailyAPI: GET /recordings/{recording_id}
    DailyAPI-->>Worker: {mtgSessionId, ...}
    Worker->>DailyAPI: GET /meetings/{mtgSessionId}/participants
    DailyAPI-->>Worker: [{participant_id, user_name}, ...]
    Worker->>DB: INSERT INTO recording
    Worker->>DB: INSERT INTO transcript (status='idle')
    loop For each track_key (parse participant_id from filename)
        Worker->>DB: UPSERT transcript.participants[speaker=idx, name=X]
    end
    Note over Worker,S3: Phase 2: Track Padding
    Worker->>DB: UPDATE transcript SET status='processing'
    Worker->>WS: broadcast STATUS='processing'
    loop For each track in track_keys (N tracks)
        Worker->>S3: generate_presigned_url(track_key, DAILYCO_BUCKET)
        S3-->>Worker: presigned_url (2hr)
        Note over Worker: PyAV: read WebM, extract start_time
        Note over Worker: PyAV: adelay filter (pad silence)
        Worker->>S3: PUT file_pipeline/{id}/tracks/padded_{idx}.webm
        Worker->>S3: generate_presigned_url(padded_{idx}.webm)
    end
    Note over Worker,S3: Phase 3: Audio Mixdown
    Note over Worker: PyAV: amix filter → stereo MP3
    Worker->>DB: UPDATE transcript SET duration=X
    Worker->>WS: broadcast DURATION
    Worker->>S3: PUT {transcript_id}/audio.mp3
    Worker->>DB: UPDATE transcript SET audio_location='storage'
    Note over Worker: Phase 4: Waveform
    Note over Worker: Generate peaks from MP3
    Worker->>DB: UPDATE events+=WAVEFORM
    Worker->>WS: broadcast WAVEFORM
    Note over Worker,GPU: Phase 5: Transcription (N GPU calls)
    loop For each padded track URL (N tracks)
        Worker->>GPU: POST /v1/audio/transcriptions-from-url
        Note right of GPU: {audio_file_url, language, batch:true}
        GPU-->>Worker: {words: [{word, start, end}, ...]}
        Note over Worker: Assign speaker=track_idx to words
    end
    Note over Worker: Merge all words, sort by start time
    Worker->>DB: UPDATE events+=TRANSCRIPT
    Worker->>WS: broadcast TRANSCRIPT
    Note over Worker,S3: Cleanup temp files
    loop For each padded file
        Worker->>S3: DELETE padded_{idx}.webm
    end
    Note over Worker,LLM: Phase 6: Topic Detection (C LLM calls)
    Note over Worker: C = ceil(total_words / 300)
    loop For each 300-word chunk (C chunks)
        Worker->>LLM: TOPIC_PROMPT + words[i:i+300]
        Note right of LLM: "Extract main topic title + 2-sentence summary"
        LLM-->>Worker: TitleSummary{title, summary}
        Worker->>DB: UPSERT topics[]
        Worker->>DB: UPDATE events+=TOPIC
        Worker->>WS: broadcast TOPIC
    end
    Note over Worker,LLM: Phase 7a: Title Generation (1 LLM call)
    Note over Worker: Input: all TitleSummary[].title joined
    Worker->>LLM: TITLE_PROMPT
    Note right of LLM: "Generate concise title from topic titles"
    LLM-->>Worker: "Meeting Title"
    Worker->>DB: UPDATE transcript SET title=X
    Worker->>DB: UPDATE events+=FINAL_TITLE
    Worker->>WS: broadcast FINAL_TITLE
    Note over Worker,LLM: Phase 7b: Summary Generation (2+2M LLM calls)
    Note over Worker: Reconstruct full transcript from TitleSummary[].transcript
    opt If participants unknown
        Worker->>LLM: PARTICIPANTS_PROMPT
        LLM-->>Worker: ParticipantsResponse
    end
    Worker->>LLM: SUBJECTS_PROMPT (call #1)
    Note right of LLM: "Main high-level topics? Max 6"
    LLM-->>Worker: SubjectsResponse{subjects: ["A", "B", ...]}
    loop For each subject (M subjects, max 6)
        Worker->>LLM: DETAILED_SUBJECT_PROMPT (call #2..#1+M)
        Note right of LLM: "Info about 'A': decisions, actions, deadlines"
        LLM-->>Worker: detailed_response (discarded after next call)
        Worker->>LLM: PARAGRAPH_SUMMARY_PROMPT (call #2+M..#1+2M)
        Note right of LLM: "Summarize in 1 paragraph"
        LLM-->>Worker: paragraph → summaries[]
    end
    Worker->>LLM: RECAP_PROMPT (call #2+2M)
    Note right of LLM: "High-level quick recap, 1 paragraph"
    LLM-->>Worker: recap
    Note over Worker: long_summary = "# Quick recap\n{recap}\n# Summary\n**A**\n{para1}..."
    Note over Worker: short_summary = recap only
    Worker->>DB: UPDATE long_summary, short_summary
    Worker->>DB: UPDATE events+=FINAL_LONG_SUMMARY
    Worker->>WS: broadcast FINAL_LONG_SUMMARY
    Worker->>DB: UPDATE events+=FINAL_SHORT_SUMMARY
    Worker->>WS: broadcast FINAL_SHORT_SUMMARY
    Note over Worker,DB: Phase 8: Finalize
    Worker->>DB: UPDATE transcript SET status='ended'
    Worker->>DB: UPDATE events+=STATUS
    Worker->>WS: broadcast STATUS='ended'
    Note over Worker,ExtWH: Phase 9: Post-Processing Chain
    Worker->>DB: SELECT meeting_consent WHERE meeting_id=?
    alt Any consent denied
        Worker->>S3: DELETE tracks from DAILYCO_BUCKET
        Worker->>S3: DELETE audio.mp3 from TRANSCRIPT_BUCKET
        Worker->>DB: UPDATE transcript SET audio_deleted=true
    end
    opt Room has zulip_auto_post=true
        alt Existing zulip_message_id
            Worker->>Zulip: PATCH /api/v1/messages/{id}
        else New
            Worker->>Zulip: POST /api/v1/messages
            Zulip-->>Worker: {id}
            Worker->>DB: UPDATE transcript SET zulip_message_id=X
        end
    end
    opt Room has webhook_url
        Worker->>ExtWH: POST {webhook_url}
        Note right of ExtWH: X-Webhook-Signature: HMAC-SHA256
        Note right of ExtWH: Body: {transcript_id, room_id, ...}
    end
 ```
 ## Title & Summary Generation Data Flow
 ```mermaid
 flowchart TB
    subgraph Input["Input: TitleSummary[] from Topic Detection"]
        TS1["TitleSummary 1<br/>title: 'Q1 Budget'<br/>transcript: words[0:300]"]
        TS2["TitleSummary 2<br/>title: 'Product Launch'<br/>transcript: words[300:600]"]
        TS3["TitleSummary N..."]
    end
    subgraph TitleGen["Title Generation"]
        T1["Extract .title from each TitleSummary"]
        T2["Concatenate: '- Q1 Budget\n- Product Launch\n...'"]
        T3["LLM: TITLE_PROMPT\n'Generate concise title from topic titles'"]
        T4["Output: FinalTitle"]
        T1 --> T2 --> T3 --> T4
    end
    subgraph SummaryGen["Summary Generation"]
        direction TB
        subgraph Reconstruct["1. Reconstruct Full Transcript"]
            S1["For each TitleSummary.transcript.as_segments()"]
            S2["Map speaker ID → name"]
            S3["Build: 'Alice: hello\nBob: hi\n...'"]
            S1 --> S2 --> S3
        end
        subgraph Subjects["2. Extract Subjects - LLM call #1"]
            S4["LLM: SUBJECTS_PROMPT\n'Main high-level topics? Max 6'"]
            S5["subjects[] = ['Budget Review', ...]"]
            S4 --> S5
        end
        subgraph DetailedSum["3. Per-Subject Summary - LLM calls #2 to #(1+2M)"]
            S6["For each subject:"]
            S7["LLM: DETAILED_SUBJECT_PROMPT\n'Info about subject: decisions, actions...'"]
            S8["detailed_response - NOT STORED"]
            S9["LLM: PARAGRAPH_SUMMARY_PROMPT\n'Summarize in 1 paragraph'"]
            S10["paragraph → summaries[]"]
            S6 --> S7 --> S8 --> S9 --> S10
        end
        subgraph Recap["4. Generate Recap - LLM call #(2+2M)"]
            S11["Concatenate paragraph summaries"]
            S12["LLM: RECAP_PROMPT\n'High-level recap, 1 paragraph'"]
            S13["recap"]
            S11 --> S12 --> S13
        end
        subgraph Output["5. Output"]
            S14["long_summary = markdown:\n# Quick recap\n[recap]\n# Summary\n**Subject 1**\n[para1]..."]
            S15["short_summary = recap only"]
            S14 --> S15
        end
        Reconstruct --> Subjects --> DetailedSum --> Recap --> Output
    end
    Input --> TitleGen
    Input --> SummaryGen
 ```
 ### topics[] vs subjects[]
 | | topics[] | subjects[] |
 |-|----------|------------|
 | **Source** | 300-word chunk splitting | LLM extraction from full text |
 | **Count** | Variable (words / 300) | Max 6 |
 | **Purpose** | Timeline segmentation | Summary structure |
 | **Has timestamp?** | Yes | No |
 ## External API Calls Summary
 ### 1. Daily.co REST API (called during initialization)
 | Endpoint | Method | When | Purpose |
 |----------|--------|------|---------|
 | `GET /recordings/{recording_id}` | GET | After webhook | Get mtgSessionId for participant lookup |
 | `GET /meetings/{mtgSessionId}/participants` | GET | After above | Map participant_id → user_name |
 ### 2. GPU Service (Modal.com or Self-Hosted)
 | Endpoint | Method | Count | Request |
 |----------|--------|-------|---------|
 | `{TRANSCRIPT_URL}/v1/audio/transcriptions-from-url` | POST | **N** (N = num tracks) | `{audio_file_url, language, batch: true}` |
 **Note**: Diarization is NOT called for multitrack - speaker identification comes from separate tracks.
 ### 3. LLM Service (OpenAI-compatible via LlamaIndex)
 | Phase | Operation | Input | LLM Calls | Output |
 |-------|-----------|-------|-----------|--------|
 | Topic Detection | TOPIC_PROMPT per 300-word chunk | words[i:i+300] | **C** = ceil(words/300) | TitleSummary{title, summary, timestamp} |
 | Title Generation | TITLE_PROMPT | All topic titles joined | **1** | FinalTitle |
 | Participant ID | PARTICIPANTS_PROMPT | Full transcript | **0-1** (skipped if known) | ParticipantsResponse |
 | Subject Extraction | SUBJECTS_PROMPT | Full transcript | **1** | SubjectsResponse{subjects[]} |
 | Subject Detail | DETAILED_SUBJECT_PROMPT | Full transcript + subject name | **M** (M = subjects, max 6) | detailed text (discarded) |
 | Subject Paragraph | PARAGRAPH_SUMMARY_PROMPT | Detailed text | **M** | paragraph text → summaries[] |
 | Recap | RECAP_PROMPT | All paragraph summaries | **1** | recap text |
 **Total LLM calls**: C + 2M + 3 (+ 1 if participants unknown)
 - Short meeting (1000 words, 3 subjects): ~4 + 6 + 3 = **13 calls**
 - Long meeting (5000 words, 6 subjects): ~17 + 12 + 3 = **32 calls**
 ## S3 Operations Summary
 ### Source Bucket: `DAILYCO_STORAGE_AWS_BUCKET_NAME`
 Daily.co uploads raw-tracks recordings here.
 | Operation | Key Pattern | When |
 |-----------|-------------|------|
 | **READ** (presign) | `{domain}/{room_name}/{ts}/{participant_id}-cam-audio-{ts}.webm` | Track acquisition |
 | **DELETE** | Same as above | Consent denied cleanup |
 ### Transcript Storage Bucket: `TRANSCRIPT_STORAGE_AWS_BUCKET_NAME`
 Reflector's own storage.
 | Operation | Key Pattern | When |
 |-----------|-------------|------|
 | **PUT** | `file_pipeline/{transcript_id}/tracks/padded_{idx}.webm` | After track padding |
 | **READ** (presign) | Same | For GPU transcription |
 | **DELETE** | Same | After transcription complete |
 | **PUT** | `{transcript_id}/audio.mp3` | After mixdown |
 | **DELETE** | Same | Consent denied cleanup |
 ## Database Operations
 ### Tables Written
 | Table | Operation | When |
 |-------|-----------|------|
 | `recording` | INSERT | Initialization |
 | `transcript` | INSERT | Initialization |
 | `transcript` | UPDATE (participants) | After Daily API participant fetch |
 | `transcript` | UPDATE (status, events, duration, topics, title, summaries, etc.) | Throughout pipeline |
 ### Transcript Update Sequence
 ```
 1.  INSERT: id, name, status='idle', source_kind='room', user_id, recording_id, room_id, meeting_id
 2.  UPDATE: participants[] (speaker index → participant name mapping)
 3.  UPDATE: status='processing', events+=[{event:'STATUS', data:{value:'processing'}}]
 4.  UPDATE: duration=X, events+=[{event:'DURATION', data:{duration:X}}]
 5.  UPDATE: audio_location='storage'
 6.  UPDATE: events+=[{event:'WAVEFORM', data:{waveform:[...]}}]
 7.  UPDATE: events+=[{event:'TRANSCRIPT', data:{text, translation}}]
 8.  UPDATE: topics[]+=topic, events+=[{event:'TOPIC'}]  -- repeated per chunk
 9.  UPDATE: title=X, events+=[{event:'FINAL_TITLE'}]
 10. UPDATE: long_summary=X, events+=[{event:'FINAL_LONG_SUMMARY'}]
 11. UPDATE: short_summary=X, events+=[{event:'FINAL_SHORT_SUMMARY'}]
 12. UPDATE: status='ended', events+=[{event:'STATUS', data:{value:'ended'}}]
 13. UPDATE: zulip_message_id=X  -- if Zulip enabled
 14. UPDATE: audio_deleted=true  -- if consent denied
 ```
 ## WebSocket Events
 All broadcast to room `ts:{transcript_id}`:
 | Event | Payload | Trigger |
 |-------|---------|---------|
 | STATUS | `{value: "processing"\|"ended"\|"error"}` | Status transitions |
 | DURATION | `{duration: float}` | After audio processing |
 | WAVEFORM | `{waveform: float[]}` | After waveform generation |
 | TRANSCRIPT | `{text: string, translation: string\|null}` | After transcription merge |
 | TOPIC | `{id, title, summary, timestamp, duration, transcript, words}` | Per topic detected |
 | FINAL_TITLE | `{title: string}` | After LLM title generation |
 | FINAL_LONG_SUMMARY | `{long_summary: string}` | After LLM summary |
 | FINAL_SHORT_SUMMARY | `{short_summary: string}` | After LLM recap |
 User-room broadcasts to `user:{user_id}`:
 - `TRANSCRIPT_STATUS`
 - `TRANSCRIPT_FINAL_TITLE`
 - `TRANSCRIPT_DURATION`
--- 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/video-platforms/README.md
+++ b/server/docs/video-platforms/README.md
@@ -1,236 +0,0 @@
 # Reflector Architecture: Whereby + Daily.co Recording Storage
 ## System Overview
 ```mermaid
 graph TB
    subgraph "Actors"
        APP[Our App<br/>Reflector]
        WHEREBY[Whereby Service<br/>External]
        DAILY[Daily.co Service<br/>External]
    end
    subgraph "AWS S3 Buckets"
        TRANSCRIPT_BUCKET[Transcript Bucket<br/>reflector-transcripts<br/>Output: Processed MP3s]
        WHEREBY_BUCKET[Whereby Bucket<br/>reflector-whereby-recordings<br/>Input: Raw MP4s]
        DAILY_BUCKET[Daily.co Bucket<br/>reflector-dailyco-recordings<br/>Input: Raw WebM tracks]
    end
    subgraph "AWS Infrastructure"
        SQS[SQS Queue<br/>Whereby notifications]
    end
    subgraph "Database"
        DB[(PostgreSQL<br/>Recordings, Transcripts, Meetings)]
    end
    APP -->|Write processed| TRANSCRIPT_BUCKET
    APP -->|Read/Delete| WHEREBY_BUCKET
    APP -->|Read/Delete| DAILY_BUCKET
    APP -->|Poll| SQS
    APP -->|Store metadata| DB
    WHEREBY -->|Write recordings| WHEREBY_BUCKET
    WHEREBY_BUCKET -->|S3 Event| SQS
    WHEREBY -->|Participant webhooks<br/>room.client.joined/left| APP
    DAILY -->|Write recordings| DAILY_BUCKET
    DAILY -->|Recording webhook<br/>recording.ready-to-download| APP
 ```
 **Note on Webhook vs S3 Event for Recording Processing:**
 - **Whereby**: Uses S3 Events → SQS for recording availability (S3 as source of truth, no race conditions)
 - **Daily.co**: Uses webhooks for recording availability (more immediate, built-in reliability)
 - **Both**: Use webhooks for participant tracking (real-time updates)
 ## Credentials & Permissions
 ```mermaid
 graph LR
    subgraph "Master Credentials"
        MASTER[TRANSCRIPT_STORAGE_AWS_*<br/>Access Key ID + Secret]
    end
    subgraph "Whereby Upload Credentials"
        WHEREBY_CREDS[AWS_WHEREBY_ACCESS_KEY_*<br/>Access Key ID + Secret]
    end
    subgraph "Daily.co Upload Role"
        DAILY_ROLE[DAILY_STORAGE_AWS_ROLE_ARN<br/>IAM Role ARN]
    end
    subgraph "Our App Uses"
        MASTER -->|Read/Write/Delete| TRANSCRIPT_BUCKET[Transcript Bucket]
        MASTER -->|Read/Delete| WHEREBY_BUCKET[Whereby Bucket]
        MASTER -->|Read/Delete| DAILY_BUCKET[Daily.co Bucket]
        MASTER -->|Poll/Delete| SQS[SQS Queue]
    end
    subgraph "We Give To Services"
        WHEREBY_CREDS -->|Passed in API call| WHEREBY_SERVICE[Whereby Service]
        WHEREBY_SERVICE -->|Write Only| WHEREBY_BUCKET
        DAILY_ROLE -->|Passed in API call| DAILY_SERVICE[Daily.co Service]
        DAILY_SERVICE -->|Assume Role| DAILY_ROLE
        DAILY_SERVICE -->|Write Only| DAILY_BUCKET
    end
 ```
 # Video Platform Recording Integration
 This document explains how Reflector receives and identifies multitrack audio recordings from different video platforms.
 ## Platform Comparison
 | Platform | Delivery Method | Track Identification |
 |----------|----------------|---------------------|
 | **Daily.co** | Webhook | Explicit track list in payload |
 | **Whereby** | SQS (S3 notifications) | Single file per notification |
 ---
 ## Daily.co
 **Note:** Primary discovery via polling (`poll_daily_recordings`), webhooks as backup.
 Daily.co uses **webhooks** to notify Reflector when recordings are ready.
 ### How It Works
 1. **Daily.co sends webhook** when recording is ready
   - Event type: `recording.ready-to-download`
   - Endpoint: `/v1/daily/webhook` (`reflector/views/daily.py:46-102`)
 2. **Webhook payload explicitly includes track list**:
 ```json
 {
  "recording_id": "7443ee0a-dab1-40eb-b316-33d6c0d5ff88",
  "room_name": "daily-20251020193458",
  "tracks": [
    {
      "type": "audio",
      "s3Key": "monadical/daily-20251020193458/1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922",
      "size": 831843
    },
    {
      "type": "audio",
      "s3Key": "monadical/daily-20251020193458/1760988935484-a37c35e3-6f8e-4274-a482-e9d0f102a732-cam-audio-1760988943823",
      "size": 408438
    },
    {
      "type": "video",
      "s3Key": "monadical/daily-20251020193458/...-video.webm",
      "size": 30000000
    }
  ]
 }
 ```
 3. **System extracts audio tracks** (`daily.py:211`):
 ```python
 track_keys = [t.s3Key for t in tracks if t.type == "audio"]
 ```
 4. **Triggers multitrack processing** (`daily.py:213-218`):
 ```python
 process_multitrack_recording.delay(
    bucket_name=bucket_name,  # reflector-dailyco-local
    room_name=room_name,      # daily-20251020193458
    recording_id=recording_id, # 7443ee0a-dab1-40eb-b316-33d6c0d5ff88
    track_keys=track_keys      # Only audio s3Keys
 )
 ```
 ### Key Advantage: No Ambiguity
 Even though multiple meetings may share the same S3 bucket/folder (`monadical/`), **there's no ambiguity** because:
 - Each webhook payload contains the exact `s3Key` list for that specific `recording_id`
 - No need to scan folders or guess which files belong together
 - Each track's s3Key includes the room timestamp subfolder (e.g., `daily-20251020193458/`)
 The room name includes timestamp (`daily-20251020193458`) to keep recordings organized, but **the webhook's explicit track list is what prevents mixing files from different meetings**.
 ### Track Timeline Extraction
 Daily.co provides timing information in two places:
 **1. PyAV WebM Metadata (current approach)**:
 ```python
 # Read from WebM container stream metadata
 stream.start_time = 8.130s  # Meeting-relative timing
 ```
 **2. Filename Timestamps (alternative approach, commit 3bae9076)**:
 ```
 Filename format: {recording_start_ts}-{uuid}-cam-audio-{track_start_ts}.webm
 Example: 1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922.webm
 Parse timestamps:
 - recording_start_ts: 1760988935484 (Unix ms)
 - track_start_ts: 1760988935922 (Unix ms)
 - offset: (1760988935922 - 1760988935484) / 1000 = 0.438s
 ```
 **Time Difference (PyAV vs Filename)**:
 ```
 Track 0:
  Filename offset: 438ms
  PyAV metadata:   229ms
  Difference:      209ms
 Track 1:
  Filename offset: 8339ms
  PyAV metadata:   8130ms
  Difference:      209ms
 ```
 **Consistent 209ms delta** suggests network/encoding delay between file upload initiation (filename) and actual audio stream start (metadata).
 **Current implementation uses PyAV metadata** because:
 - More accurate (represents when audio actually started)
 - Padding BEFORE transcription produces correct Whisper timestamps automatically
 - No manual offset adjustment needed during transcript merge
 ### Why Re-encoding During Padding
 Padding coincidentally involves re-encoding, which is important for Daily.co + Whisper:
 **Problem:** Daily.co skips frames in recordings when microphone is muted or paused
 - WebM containers have gaps where audio frames should be
 - Whisper doesn't understand these gaps and produces incorrect timestamps
 - Example: 5s of audio with 2s muted → file has frames only for 3s, Whisper thinks duration is 3s
 **Solution:** Re-encoding via PyAV filter graph (`adelay` + `aresample`)
 - Restores missing frames as silence
 - Produces continuous audio stream without gaps
 - Whisper now sees correct duration and produces accurate timestamps
 **Why combined with padding:**
 - Already re-encoding for padding (adding initial silence)
 - More performant to do both operations in single PyAV pipeline
 - Padded values needed for mixdown anyway (creating final MP3)
 Implementation: `main_multitrack_pipeline.py:_apply_audio_padding_streaming()`
 ---
 ## Whereby (SQS-based)
 Whereby uses **AWS SQS** (via S3 notifications) to notify Reflector when files are uploaded.
 ### How It Works
 1. **Whereby uploads recording** to S3
 2. **S3 sends notification** to SQS queue (one notification per file)
 3. **Reflector polls SQS queue** (`worker/process.py:process_messages()`)
 4. **System processes single file** (`worker/process.py:process_recording()`)
 ### Key Difference from Daily.co
 **Whereby (SQS):** System receives S3 notification "file X was created" - only knows about one file at a time, would need to scan folder to find related files
 **Daily.co (Webhook):** Daily explicitly tells system which files belong together in the webhook payload
 ---
--- a/server/docs/webhook.md
+++ b/server/docs/webhook.md
@@ -14,7 +14,7 @@ Webhooks are configured at the room level with two fields:
 ### `transcript.completed`
-Triggered when a transcript has been fully processed, including transcription, diarization, summarization, topic detection and calendar event integration.
+Triggered when a transcript has been fully processed, including transcription, diarization, summarization, and topic detection.
 ### `test`
@@ -128,27 +128,6 @@ This event includes a convenient URL for accessing the transcript:
  "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"
      }
    ]
  }
 }
 ```
--- a/server/env.example
+++ b/server/env.example
@@ -0,0 +1,73 @@
 #
 # This file serve as an example of possible configuration
 # All the settings are described here: reflector/settings.py
 #
 ## =======================================================
 ## User authentication
 ## =======================================================
 ## Using jwt/authentik
 AUTH_BACKEND=jwt
 AUTH_JWT_AUDIENCE=
 ## =======================================================
 ## Transcription backend
 ##
 ## Check reflector/processors/audio_transcript_* for the
 ## full list of available transcription backend
 ## =======================================================
 ## Using local whisper
 #TRANSCRIPT_BACKEND=whisper
 ## Using serverless modal.com (require reflector-gpu-modal deployed)
 #TRANSCRIPT_BACKEND=modal
 #TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
 #TRANSCRIPT_MODAL_API_KEY=xxxxx
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=
 ## =======================================================
 ## Translation 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
 ##
 ## Responsible for titles and short summary
 ## Check reflector/llm/* for the full list of available
 ## llm backend implementation
 ## =======================================================
 ## Context size for summary generation (tokens)
 # LLM_MODEL=microsoft/phi-4
 LLM_CONTEXT_WINDOW=16000
 LLM_URL=
 LLM_API_KEY=sk-
 ## =======================================================
 ## Diarization
 ##
 ## Only available on modal
 ## 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
 ## =======================================================
 ## Sentry
 ## =======================================================
 ## Sentry DSN configuration
 #SENTRY_DSN=
--- a/server/gpu/modal_deployments/README.md
+++ b/server/gpu/modal_deployments/README.md
--- a/server/gpu/modal_deployments/reflector_diarizer.py
+++ b/server/gpu/modal_deployments/reflector_diarizer.py
@@ -24,12 +24,6 @@ app = modal.App(name="reflector-diarizer")
 upload_volume = modal.Volume.from_name("diarizer-uploads", create_if_missing=True)
 # IMPORTANT: This function is duplicated in multiple files for deployment isolation.
 # If you modify the audio format detection logic, you MUST update all copies:
 #   - gpu/self_hosted/app/utils.py
 #   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
 #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
 #   - gpu/modal_deployments/reflector_diarizer.py (this file)
 def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
    parsed_url = urlparse(url)
    url_path = parsed_url.path
@@ -45,8 +39,6 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtens
        return AudioFileExtension("wav")
    if "audio/mp4" in content_type:
        return AudioFileExtension("mp4")
    if "audio/webm" in content_type or "video/webm" in content_type:
        return AudioFileExtension("webm")
    raise ValueError(
        f"Unsupported audio format for URL: {url}. "
@@ -113,7 +105,7 @@ def download_pyannote_audio():
 diarizer_image = (
-    modal.Image.debian_slim(python_version="3.10")
+    modal.Image.debian_slim(python_version="3.10.8")
    .pip_install(
        "pyannote.audio==3.1.0",
        "requests",
@@ -124,7 +116,7 @@ diarizer_image = (
        "transformers==4.34.0",
        "sentencepiece",
        "protobuf",
-        "numpy<2",
+        "numpy",
        "huggingface_hub",
        "hf-transfer",
    )
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Igor Loskutov	2e94f4ccbe	401 reauth experiments	2025-09-05 14:20:00 -04:00
Igor Loskutov	01c969b8a9	doc	2025-09-05 13:05:00 -04:00
Igor Loskutov	462a897882	nextjs magic	2025-09-05 12:59:18 -04:00
Igor Loskutov	83f3d0bc9d	nextjs magic	2025-09-05 12:44:12 -04:00
Igor Loskutov	2962ba5a7b	CI debug	2025-09-04 22:16:01 -04:00
Igor Loskutov	c08a8d0cc0	CI debug	2025-09-04 22:03:52 -04:00
Igor Loskutov	c4c975eb7b	github debug	2025-09-04 22:00:35 -04:00
Igor Loskutov	988586ee42	github debug	2025-09-04 21:58:06 -04:00
Igor Loskutov	9453ebe356	github debug	2025-09-04 21:43:52 -04:00
Igor Loskutov	50d4bcc0ac	github debug	2025-09-04 21:39:45 -04:00
Igor Loskutov	03f2d2a30b	github debug	2025-09-04 21:35:16 -04:00
Igor Loskutov	24869cb825	github debug	2025-09-04 21:27:41 -04:00
Igor Loskutov	92f5d76d43	github debug	2025-09-04 21:25:01 -04:00
Igor Loskutov	82cc1d26d5	github debug	2025-09-04 21:16:06 -04:00
Igor Loskutov	c62c64362f	github debug	2025-09-04 21:10:23 -04:00
Igor Loskutov	8a94f6d8bb	github debug	2025-09-04 21:02:50 -04:00
Igor Loskutov	1f4ec01e2d	github debug	2025-09-04 20:55:50 -04:00
Igor Loskutov	a5124b599d	node version 20 for tests	2025-09-04 20:49:11 -04:00
Igor Loskutov	cacdcbfba2	INTERVAL_REFRESH_MS	2025-09-04 19:39:55 -04:00
Igor Loskutov	e9318708e1	proper api address from env	2025-09-04 19:22:43 -04:00
Igor Loskutov	89dd05ec84	prettier auth state ternary	2025-09-04 15:13:31 -04:00
Igor Loskutov	6f29d08d1c	prettier auth state ternary	2025-09-04 15:11:16 -04:00
Igor Loskutov	ad780551b7	file upload real-time state management fix	2025-09-04 14:13:49 -04:00
Igor Loskutov	0751d01f13	added vs edited room state cleanup	2025-09-04 13:37:10 -04:00
Igor Loskutov	790a61be0d	less edgy config (ci)	2025-09-04 12:32:15 -04:00
Igor Loskutov	9695cc4bdf	ci randomness	2025-09-04 12:12:46 -04:00
Igor Loskutov	669ebe74d8	ci randomness	2025-09-04 12:08:01 -04:00
Igor Loskutov	41c92b8aeb	ci randomness	2025-09-04 12:02:57 -04:00
Igor Loskutov	3170605d9a	ci randomness	2025-09-04 11:58:22 -04:00
Igor Loskutov	3e629a1ace	less edgy config (ci)	2025-09-04 11:48:26 -04:00
Igor Loskutov	2811540d9a	less edgy config (ci)	2025-09-04 11:45:24 -04:00
Igor Loskutov	8af6bf4998	less edgy config (ci)	2025-09-04 11:38:26 -04:00
Igor Loskutov	c28af33b25	ci randomness	2025-09-04 11:27:43 -04:00
Igor Loskutov	912e009ede	merge	2025-09-04 11:21:34 -04:00
Igor Loskutov	f0eba2b2cd	test ts server	2025-09-04 10:54:09 -04:00
Igor Loskutov	40fe4c1bc7	redis cache	2025-09-04 10:41:31 -04:00
Igor Loskutov	23a119dc3b	invalidate room on room update	2025-09-03 13:11:20 -04:00
Igor Loskutov	2e53eeb5d5	websocket dupe react devmode protection	2025-09-03 13:00:22 -04:00
Igor Loskutov	110d1e53fc	remove react-query tab sharing cache	2025-09-03 12:52:05 -04:00
Igor Loskutov	4f66f14761	remove react-query tab sharing cache	2025-09-03 12:47:35 -04:00
Igor Loskutov	6a793edfb5	clarify access token refresh logic a bit	2025-09-03 12:31:50 -04:00
Igor Loskutov	0cbbd24c65	protect from zombie auth	2025-09-03 10:53:03 -04:00
Igor Loskutov	611e258d96	schema generator error type doc	2025-09-03 09:04:40 -04:00
Igor Loskutov	1b22eabb3f	session auto refresh blink	2025-09-03 08:33:13 -04:00
Igor Loskutov	cff662709d	cover TODOs + cross-tab cache	2025-09-03 07:57:11 -04:00
Igor Loskutov	048ebbd654	room edition state granular management	2025-09-03 07:25:22 -04:00
Igor Loskutov	08b82c76ce	normalize auth provider	2025-09-03 07:10:20 -04:00
Igor Loskutov	97f6db5556	room edit fix	2025-09-02 20:00:35 -04:00
Igor Loskutov	5e4f519c83	compile fix	2025-09-02 19:12:04 -04:00
Igor Loskutov	1d5a22ad1d	room detail page fix	2025-09-02 18:52:31 -04:00
Igor Loskutov	05be6e7f19	fix compose	2025-09-02 18:07:15 -04:00
Igor Loskutov	31c44ac0bb	fix auth	2025-09-02 14:44:10 -04:00
Igor Loskutov	5ffc312d4a	authReady callback simplify	2025-09-02 14:00:13 -04:00
Igor Loskutov	11ed585cea	self-review-fix	2025-09-02 13:04:43 -04:00
Igor Loskutov	bdd899774a	merge	2025-09-02 12:04:30 -04:00
Igor Monadical	ca75a4c95e	Igor/mathieu/frontend openapi react query (#597 ) * small typing * typing fixes --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>	2025-09-02 11:49:00 -04:00
Mathieu Virbel	0df1b224f2	fix: handle undefined access tokens in auth.ts Added fallback to empty string for potentially undefined access_token and refresh_token from NextAuth account object to satisfy JWTWithAccessToken type requirements.	2025-08-29 18:56:08 -06:00
Mathieu Virbel	790b7992bb	fix: remove infinite re-render loop in useSessionAccessToken The hook was maintaining redundant local state that caused re-renders on every update, which triggered NextAuth to continuously refetch the session, resulting in hundreds of POST requests to /api/auth/session. Simplified the hook to directly return session values without unnecessary state duplication.	2025-08-29 18:52:13 -06:00
Mathieu Virbel	bb04407143	fix: add staleTime to prevent cross-tab staled data	2025-08-29 18:33:53 -06:00
Mathieu Virbel	485a263c0d	refactor: remove Redis dependencies from frontend authentication - Replace Redis/Redlock with in-memory cache for token management - Remove @vercel/kv, ioredis, and redlock dependencies from package.json - Implement simple lock mechanism for concurrent token refresh prevention - Use Map-based cache with TTL for token storage - Maintain same authentication flow without external dependencies This simplifies the infrastructure requirements and removes the need for Redis while maintaining the same functionality through in-memory caching.	2025-08-29 17:10:49 -06:00
Mathieu Virbel	449dd23c8f	chore: clean up migration comments from React Query refactoring - Remove temporary "// Use new React Query hooks" comments - Remove "// React Query hooks" comments from browse and rooms pages - Update package.json script name from codegen to openapi for consistency	2025-08-29 17:03:39 -06:00
Mathieu Virbel	c3ea514465	refactor: remove SK helper object and use inline type casting in FilterSidebar Replace the SK (SourceKind) helper object with direct inline type casting to simplify the code and reduce unnecessary abstraction.	2025-08-29 16:53:02 -06:00
Mathieu Virbel	52301d89a7	chore: add .playwright-mcp to .gitignore	2025-08-29 16:47:10 -06:00
Mathieu Virbel	d479d9d4e6	refactor: rename api-hooks.ts to apiHooks.ts for consistency - Renamed api-hooks.ts to apiHooks.ts to follow camelCase convention - Updated all 21 import statements across the codebase - Maintains consistency with other non-component files (apiClient.tsx, useAuthReady.ts, etc.) - Follows established naming pattern: PascalCase for components, camelCase for utilities/hooks	2025-08-29 16:44:21 -06:00
Mathieu Virbel	7ddae5ddd5	refactor: remove api-types.ts compatibility layer - Migrated all 29 files from api-types.ts to use reflector-api.d.ts directly - Removed $SourceKind manual enum in favor of OpenAPI-generated types - Fixed unrelated Spinner component TypeScript error in AuthWrapper.tsx - All imports now use: import type { components } from "path/to/reflector-api" - Deleted api-types.ts file completely	2025-08-29 16:35:33 -06:00
Mathieu Virbel	8c525e09e8	refactor: clean up api-hooks.ts comments and improve search invalidation - Remove redundant function category comments (exports are self-explanatory) - Remove obvious inline comments for query invalidation - Fix search endpoint invalidation to clear all queries regardless of parameters	2025-08-29 16:07:25 -06:00
Mathieu Virbel	a58a49aeb6	fix: resolve authentication race condition with React Query Previously, API calls were being made before the auth token was configured, causing initial 401 errors that would retry with 200 after token setup. Changes: - Add global auth readiness tracking in apiClient - Create useAuthReady hook that checks both session and token state - Update all API hooks to use isAuthReady instead of just session status - Add AuthWrapper component at layout level for consistent loading UX - Show spinner while authentication initializes across all pages This ensures API calls only fire after authentication is fully configured, eliminating the 401/retry pattern and improving user experience.	2025-08-29 15:53:51 -06:00
Mathieu Virbel	59d4c56a48	fix: correct content-type header for FormData uploads Previously, the API client was setting a default Content-Type of application/json for all requests, which broke file uploads that need multipart/form-data. Now the client only sets application/json when the body is not FormData, allowing FormData to automatically set the correct multipart boundary.	2025-08-29 09:49:29 -06:00
Mathieu Virbel	18d656529c	fix: use direct status check for API query authentication Changed all query hooks to use direct `status === "authenticated"` check instead of derived `isAuthenticated && !isLoading` to avoid race conditions where queries might fire before the authentication token is properly set. This prevents the brief 401 errors that occur on page refresh when the session is being restored.	2025-08-29 09:36:55 -06:00
Mathieu Virbel	75fa9ea859	refactor: remove redundant client-side AuthGuard The authentication is already properly handled by Next.js middleware in middleware.ts with LOGIN_REQUIRED_PAGES. The middleware approach is superior as it: - Provides server-side protection before page loads - Prevents flash of unauthorized content - Centralizes auth logic in one place - Better performance (no client-side JS needed) Keep the API hooks conditional to prevent 401 errors before token is ready.	2025-08-29 09:36:55 -06:00
Mathieu Virbel	26154af25c	fix: prevent unauthorized API calls before authentication - Add global AuthGuard component to handle authentication at layout level - Make all API query hooks conditional on authentication status - Define public routes (like /transcripts/new) that don't require auth - Fix login flow to use NextAuth signIn instead of non-existent /login route - Prevent 401 errors by waiting for auth token before making API calls Previously, all routes under (app) were publicly accessible with each page handling auth individually. Now authentication is enforced globally while still allowing specific routes to remain public.	2025-08-29 09:36:55 -06:00
Mathieu Virbel	0eac7501c5	fix: authentication flow with React Query migration - Fix middleware management in apiClient to properly handle auth tokens - Update ApiAuthProvider to correctly configure base URL and auth - Add missing NextAuth API route handler at app/api/auth/[...nextauth]/route.ts - Remove middleware ejection attempts (not supported by openapi-fetch) - Use global variables to store current auth token and API URL - Setup middleware once on initialization instead of repeatedly adding This fixes the login/logout flow that was broken after migrating from the useApi compatibility layer to native React Query hooks.	2025-08-29 09:36:55 -06:00
Mathieu Virbel	fbeeff4c4d	feat: complete migration from @hey-api/openapi-ts to openapi-react-query - Migrated all components from useApi compatibility layer to direct React Query hooks - Added new hooks for participant operations, room meetings, and speaker operations - Updated all imports from old api module to api-types - Fixed TypeScript types and API endpoint signatures - Removed deprecated useApi.ts compatibility layer - Fixed SourceKind enum values to match OpenAPI spec - Added @ts-ignore for Zulip endpoints not in OpenAPI spec yet - Fixed all compilation errors and type issues	2025-08-29 09:36:55 -06:00
Mathieu Virbel	55f83cf5f4	feat: migrate components to React Query hooks - Add comprehensive API hooks for all operations - Migrate rooms page to use React Query mutations - Update transcript title component to use mutation hook - Refactor share/privacy component with proper error handling - Remove direct API client usage in favor of hooks	2025-08-29 09:36:55 -06:00
Mathieu Virbel	68c161ee7e	fix: resolve import errors and add missing api hooks - Create constants.ts for RECORD_A_MEETING_URL - Add api-types.ts for backward compatible type exports - Update all imports from deleted api folder to new locations - Add missing React Query hooks for rooms and zulip operations - Create useApi compatibility layer for unmigrated components	2025-08-29 09:36:55 -06:00
Mathieu Virbel	e8afe82acd	refactor: migrate from @hey-api/openapi-ts to openapi-react-query - Replace @hey-api/openapi-ts with openapi-typescript and openapi-react-query - Generate TypeScript types from OpenAPI spec - Set up React Query infrastructure with QueryClientProvider - Migrate all API hooks to use React Query patterns - Maintain backward compatibility for existing components - Remove old API infrastructure and dependencies	2025-08-29 09:36:55 -06:00
		`@@ -1,3 +0,0 @@`
			`from app.factory import create_app`

			`app = create_app()`