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# Conflicts: # docs-site/docs/features/podcast-maker/api-reference.md # docs-site/docs/features/podcast-maker/implementation-overview.md
88 lines
3.2 KiB
Markdown
88 lines
3.2 KiB
Markdown
# Podcast Maker Implementation Overview
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Podcast Maker orchestrates a multi-stage content pipeline: project configuration, research grounding, script composition, media rendering, and publish-state tracking.
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## Architecture & Data Flow
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```mermaid
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flowchart LR
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UI[Podcast Maker UI]
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API[Podcast API Router]
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PROJ[Project Service]
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RESEARCH[Research Handler]
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SCRIPT[Script Handler]
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RENDER[Audio/Video Render Handlers]
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STORE[(Podcast Tables)]
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JOBS[(Render Queue)]
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UI --> API
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API --> PROJ
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API --> RESEARCH
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API --> SCRIPT
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API --> RENDER
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PROJ --> STORE
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RESEARCH --> STORE
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SCRIPT --> STORE
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RENDER --> JOBS
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RENDER --> STORE
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JOBS --> UI
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STORE --> UI
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```
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Podcast Maker is split into:
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- **Frontend orchestration service**: `frontend/src/services/podcastApi.ts`
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- Coordinates step flow (analysis → research → script → audio/video)
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- Runs preflight checks before expensive calls
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- Maps API payloads into UI-friendly objects
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- **Backend podcast handlers**: `backend/api/podcast/handlers/*.py`
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- Route-level APIs for analysis, research, script, media, and projects
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- Authenticated operations with user-scoped media/project data
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## Frontend orchestration responsibilities
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Primary responsibilities in `podcastApi.ts`:
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- Create project analysis payloads and map response into Podcast Analysis UI data.
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- Build/validate research query payloads for Exa research route.
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- Generate script scenes and normalize scene/line structure for editor state.
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- Render per-scene audio and combine scenes into final audio.
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- Trigger scene image and video generation workflows.
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- Persist project state via project CRUD endpoints.
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## Backend handler modules
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- `analysis.py`: idea enhancement, analysis, regenerate-queries.
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- `research.py`: Exa research endpoint.
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- `script.py`: script generation and scene approval.
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- `audio.py`: audio upload, generation, combine, serving audio files.
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- `images.py`: scene image generation and image serving.
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- `video.py`: scene video generation, video listing/serving, combine videos.
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- `avatar.py`: avatar upload, avatar generation, avatar cleanup/presentability.
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- `projects.py`: create, get, update, list, delete, favorite project records.
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- `dubbing.py`: dubbing/voice clone lifecycle endpoints (currently backend-available).
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## Data models (functional view)
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At feature level, the flow revolves around:
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- **Project metadata**: `project_id`, idea, duration, speakers, budget and status fields.
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- **Analysis output**: audience, content type, keywords, outlines, title suggestions.
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- **Research output**: source list, summarized insights, fact cards for script grounding.
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- **Script output**: scenes with IDs, durations, emotions, and speaker lines.
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- **Media output**: audio files, scene images, scene videos, combined episode artifacts.
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## Operational notes
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- Preflight checks are used to fail fast on plan/credit constraints.
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- Some operations are synchronous (analysis/script/audio/image), while video is async task-based.
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- Client-side task polling is used for long-running jobs.
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## Engineering references
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- `docs/Podcast_maker/AI_PODCAST_BACKEND_REFERENCE.md`
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- `docs/Podcast_maker/PODCAST_API_CALL_ANALYSIS.md`
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- `docs/Podcast_maker/PODCAST_PLAN_COMPLETION_STATUS.md`
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