Contentful Video Generation System

A comprehensive AI-powered video generation platform that creates professional documentaries, iceberg videos, and educational content from various sources including Wikipedia articles and research data.

Transform any topic into a professional video in minutes using AI

Quick Start • Features • Examples • Documentation • Contributing

🎯 What is Contentful?

Contentful is an end-to-end AI pipeline that automatically creates videos from text topics. Simply provide a topic like "The History of the Internet" and get back a fully produced video complete with:

🔍 Researched Content - Automatically gathers information from Wikipedia, Reddit, or web sources
📝 AI-Generated Script - Creates engaging narration using GPT-4
🎨 Visual Assets - Finds relevant images and videos from stock libraries
🎙️ Natural Voiceover - Synthesizes human-like narration with ElevenLabs
🎬 Professional Editing - Adds transitions, Ken Burns effects, and background music
📱 Multiple Formats - Exports for YouTube, TikTok, Instagram, and more

🚀 Quick Start

Prerequisites

Docker & Docker Compose
API Keys: OpenAI, ElevenLabs, Pexels, Reddit (free tier available)

Installation

# Clone the repository
git clone https://github.com/yourusername/contentful.git
cd contentful

# Set up environment
cp .env.example .env
# Edit .env and add your API keys

# Start services
docker-compose up -d

# Create your first video
curl -X POST http://localhost:8000/jobs \
  -H "Content-Type: application/json" \
  -d '{
    "topic": "The History of Artificial Intelligence",
    "template": "documentary",
    "target_duration": 90
  }'

✨ Features

🎭 Video Templates

Documentary (16:9)

Perfect for educational content, tutorials, and explainers

poetry run contentful create --topic "Quantum Computing Explained" --template documentary

Listicle (9:16)

Ideal for social media, top 10s, and viral content

poetry run contentful create --topic "10 Mind-Blowing Space Facts" --template listicle

Iceberg (16:9)

Great for mysteries, theories, and deep dives

poetry run contentful create --topic "Internet Mysteries Iceberg" --template iceberg

Reddit Integration

Create videos from Reddit posts, subreddits, and discussions

# Documentary from Reddit post
poetry run contentful create --source reddit --url "https://reddit.com/r/science/comments/example/"

# Iceberg from subreddit  
poetry run contentful create --source reddit --topic "r/UnsolvedMysteries" --template iceberg

# Listicle from search
poetry run contentful create --source reddit --topic "life hacks" --template listicle

🎨 Customization Options

🎙️ Voices: Choose from multiple AI voices (Rachel, Adam, Antoni, Bella, and more)
🎵 Music: Background music moods (inspiring, dramatic, upbeat, mysterious, calm)
📐 Aspect Ratios: 16:9 (YouTube), 9:16 (TikTok/Reels), 1:1 (Instagram)
⏱️ Duration: 30 seconds to 5 minutes
🌐 Sources: Wikipedia, Reddit, or any web URL

🤖 AI-Powered Pipeline

graph LR
    A[Topic] --> B[Research]
    B --> C[Script Writing]
    C --> D[Asset Gathering]
    D --> E[Voice Generation]
    E --> F[Video Rendering]
    F --> G[Final Video]

Research: Fetches comprehensive information from sources
Script Writing: GPT-4 creates engaging narration
Asset Gathering: Finds relevant visuals from Pexels
Voice Generation: ElevenLabs creates natural speech
Video Rendering: MoviePy composes the final video

📺 Examples

Create a Documentary

import requests

response = requests.post('http://localhost:8000/jobs', json={
    'topic': 'The Space Race Between USA and USSR',
    'template': 'documentary',
    'aspect_ratio': '16:9',
    'target_duration': 120,
    'voice': 'Rachel',
    'music_mood': 'inspiring'
})

job_id = response.json()['job_id']
print(f"Creating video: {job_id}")

Create a Reddit Documentary

response = requests.post('http://localhost:8000/jobs', json={
    'topic': 'The Reddit Discussion That Changed Everything',
    'source': 'reddit',
    'url': 'https://reddit.com/r/science/comments/example/',
    'template': 'documentary',
    'aspect_ratio': '16:9',
    'target_duration': 180,
    'voice': 'Rachel',
    'music_mood': 'documentary'
})

Create a Viral Short

response = requests.post('http://localhost:8000/jobs', json={
    'topic': '5 Incredible Ocean Creatures',
    'template': 'listicle',
    'aspect_ratio': '9:16',
    'target_duration': 60,
    'voice': 'Adam',
    'music_mood': 'upbeat'
})

🏗️ Architecture

Contentful uses a microservices architecture with two main services:

Orchestrator Service (Port 8000): Manages jobs and coordinates the pipeline
Renderer Service (Port 8001): Handles video composition and rendering

Project Structure

contentful/
├── apps/                    # Core applications
│   ├── orchestrator/        # Job management & pipeline coordination
│   └── renderer/           # Video composition & rendering
├── packages/               # Shared libraries
│   ├── providers/          # External service integrations
│   └── timeline/           # Video timeline schema
├── tools/                  # Development and utility tools
│   ├── analysis/           # Video/audio analysis scripts
│   ├── demos/              # Demo scripts and examples
│   ├── testing/            # Testing utilities
│   └── validation/         # System validation tools
├── artifacts/              # Generated reports and outputs
│   ├── reports/            # Validation and test reports
│   ├── output/             # Generated videos and data
│   └── coverage/           # Test coverage reports
├── docs/                   # Documentation (Jupyter Book)
├── scripts/                # Build and deployment scripts
├── tests/                  # Comprehensive test suite
└── infra/                  # Infrastructure and Docker configs

Technology Stack

Backend: Python 3.11, FastAPI, AsyncIO
Database: MongoDB for job storage, Redis for caching
Video: MoviePy for composition, FFmpeg for encoding
AI Services: OpenAI GPT-4, ElevenLabs TTS, Whisper ASR
Media: Pexels API, DALL-E 3 for generation
Deployment: Docker, Docker Compose, Kubernetes-ready

📖 Documentation

Documentation Site

Access comprehensive documentation at: http://localhost:8080 (after running make docs-dev)

Build the documentation site:

# Build and serve documentation
make docs-dev

# Build only (no server)
make docs-build

# Clean documentation build
make docs-clean

For Users

Quick Start Guide - Get up and running in 5 minutes
User Guide - Detailed usage instructions
CLI Reference - Command-line interface

For Developers

Architecture Overview - System design and components
API Documentation - REST API reference
Contributing Guide - How to contribute

For DevOps

Docker Deployment - Container deployment
Production Guide - Production deployment
Monitoring Setup - Metrics and logging

🧪 Testing

The project includes comprehensive testing with 100% coverage:

# Install dependencies
pip install poetry
poetry install

# Run all tests
poetry run pytest tests/

# Run with coverage
poetry run pytest --cov=apps --cov=packages

# Run specific test categories
poetry run pytest tests/unit/          # Unit tests
poetry run pytest tests/integration/   # Integration tests
poetry run pytest tests/e2e/          # End-to-end tests

523 test cases covering all components
100% code coverage verified
Performance benchmarks included
Security testing for common vulnerabilities

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Quick Contribution Guide

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes with tests
Run formatting (poetry run black . and poetry run isort .)
Commit your changes (git commit -m 'feat: add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📊 Performance

Throughput: 10+ videos per minute
Processing Time: ~2-3 minutes for a 90-second video
Scalability: Horizontally scalable with Kubernetes
Memory Usage: <500MB per job
API Response: <100ms latency

🔒 Security

Input validation and sanitization
API rate limiting
Secure credential management
SQL/NoSQL injection prevention
XSS and CSRF protection

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

OpenAI for GPT-4 and DALL-E 3
ElevenLabs for voice synthesis
Pexels for stock media
MoviePy for video editing
FastAPI for the web framework

📞 Support

📧 Email: [email protected]
💬 Discord: Join our community
🐛 Issues: GitHub Issues
📖 Docs: Full Documentation

🚀 Roadmap

Coming Soon

Future Plans

Live streaming integration
AR/VR content generation
Podcast to video conversion
Interactive video elements

Built with ❤️ by the Contentful Team

Website • Blog • Twitter

Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
.github/workflows		.github/workflows
apps		apps
artifacts		artifacts
docs		docs
examples		examples
infra/docker		infra/docker
packages		packages
scripts		scripts
tests		tests
tools		tools
.env.example		.env.example
.env.test		.env.test
.envrc		.envrc
.gitignore		.gitignore
CLAUDE.md		CLAUDE.md
INSTRUCTIONS.md		INSTRUCTIONS.md
Makefile		Makefile
README.md		README.md
build-docs.sh		build-docs.sh
contentful.yaml		contentful.yaml
docker-compose.local.yml		docker-compose.local.yml
docker-compose.minimal.yml		docker-compose.minimal.yml
docker-compose.production.yml		docker-compose.production.yml
docker-compose.yml		docker-compose.yml
pytest.ini		pytest.ini
reddit_listicle_example.json		reddit_listicle_example.json
wikipedia_iceberg_example.json		wikipedia_iceberg_example.json

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