# Accessible Video Processing Platform - Development Guide

<!-- Documentation entry point: see @AGENTS.md for full project navigation -->
@AGENTS.md

## Project Overview
This is a comprehensive video accessibility platform that automatically generates closed captions and audio descriptions using AI, with quality control workflows and multi-language support.

**Core Tech Stack:**
- Frontend: React 18 + Vite SPA (TypeScript)
- Backend: FastAPI + Celery workers (Python 3.11+)
- Database: MongoDB Atlas
- Storage: Google Cloud Storage with signed URLs
- AI: Gemini 2.5 Pro, Google Cloud Translate, ElevenLabs TTS
- Queue: Redis + Celery
- Auth: JWT with HttpOnly refresh cookies

## Development Instructions

### CRITICAL: Always Read the Full Development Plan
**Before starting any development work, ALWAYS read the entire `video_accessibility_development_plan.txt` file.** This document contains:
- Complete technical specifications
- API contracts and schemas
- Database models and indexes
- Worker pipeline details
- Frontend component specifications
- Security requirements
- Testing strategies

The development plan is the authoritative source for all implementation details. Refer to it frequently during development to ensure consistency with the overall architecture.

## Key Implementation Phases

### Phase 1: Foundation & Setup
- Monorepo structure (backend/, frontend/, infra/)
- FastAPI backend initialization
- React + Vite frontend setup
- MongoDB and Redis configuration
- JWT authentication with RBAC

### Phase 2: Core Services
- Google Cloud Storage integration
- Gemini 2.5 Pro service
- Job model with state machine
- Celery worker infrastructure

### Phase 3: Ingestion & AI Pipeline
- Video upload system
- Ingestion worker task
- VTT generation
- Gemini prompt system

### Phase 4: Quality Control System
- VTT editor component
- QC dashboard for reviewers
- Approval/rejection workflow
- Video player with captions

### Phase 5: Translation & TTS Pipeline
- Google Cloud Translate integration
- Transcreation system
- Translation worker
- TTS service integration

### Phase 6: Final Review & Delivery
- Final review interface
- Job completion workflow
- Email notifications
- Client download portal

### Phase 7: Production Readiness
- Comprehensive testing
- Security hardening
- Observability setup
- CI/CD configuration

## Job Status State Machine
```
created → ingesting → ai_processing → translating → tts_generating → rendering_video → pending_qc → pending_final_review → completed
                                                                                           ↓
                                                                                       rejected
```

## Key Architecture Decisions

### Security
- Access tokens stored in memory (not localStorage)
- Refresh tokens in HttpOnly cookies
- RBAC enforcement server-side
- Signed URLs for file access (24h expiry)
- Audit logs for all reviewer actions

### Data Flow
1. Client uploads MP4 → GCS + MongoDB record
2. Celery worker processes video with Gemini 2.5 Pro
3. Generates captions.vtt and audio_description.vtt for source language
4. Translation, TTS synthesis, and accessible video rendering run automatically
5. Job enters QC Review for reviewer approval (edits can trigger re-rendering)
6. QC approval moves job directly to Final Review
7. Final review and client notification with download links

### File Structure
```
gs://accessible-video/{jobId}/
  source.mp4
  en/
    captions.vtt
    ad.vtt
    ad.mp3
  {lang}/
    captions.vtt
    ad.vtt
    ad.mp3
```

## Development Guidelines

### Before Each Session
1. Read the complete `video_accessibility_development_plan.txt`
2. Review the current todo list and phase
3. Check existing code patterns and conventions
4. Understand the security and accessibility requirements

### Code Standards
- Follow existing patterns in the codebase
- Implement proper error handling and retries
- Add OpenTelemetry tracing for observability
- Ensure RBAC is enforced on all endpoints
- Validate all VTT outputs for correctness
- Write unit tests for all services and utilities

### Testing Requirements
- Unit tests ≥80% coverage for services/utils
- Integration tests with mocked AI services
- E2E tests for complete workflows
- Performance testing for video processing

### Lint/Type Check Commands
- Backend: `ruff check .` and `mypy .`
- Frontend: `npm run lint` and `npm run type-check`

## Important Files to Reference
- `video_accessibility_development_plan.txt` - Complete specification
- Backend schemas in section 17 of the plan
- API design in section 7 of the plan
- Frontend component specs in section 10 of the plan
- Security requirements in section 11 of the plan

## Risk Mitigations
- Invalid JSON from AI models: Pydantic validation + self-heal prompts
- Timestamp drift: Preserve cue timings in translations
- TTS alignment: Per-cue synthesis with crossfades
- Queue backlog: Autoscaling workers with monitoring
- Security: Secret Manager, least-privilege IAM, no client secrets

## Knowledge Wiki
A cross-project knowledge base is maintained automatically from all Claude Code sessions.
- **Index:** `/Users/aimpress/Library/Mobile Documents/iCloud~md~obsidian/Documents/VadymSamoilenko/wiki/index.md`
- **Query:** `cd ~/.claude/memory-compiler && uv run python scripts/query.py "your question"`
- Every session in this project automatically feeds the knowledge base.
