video-accessibility/CLAUDE.md

Accessible Video Processing Platform - Development Guide

@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.