Video Inspiration Finder 🎯

An intelligent YouTube video recommendation system that learns your preferences to suggest coding videos you'll love. Built with machine learning and featuring a beautiful web dashboard.

🌟 Features

• 🔍 Smart YouTube Search: Automatically finds trending coding videos using configurable search queries
• 🤖 Machine Learning Recommendations: RandomForest model learns your preferences from ratings
• 📊 Beautiful Web Dashboard: YouTube-like interface with AI confidence scores
• 🔒 Privacy First: All data stored locally in SQLite - no external tracking
• ⚡ One-Command Setup: Get started with a single command
• 📱 Responsive Design: Works perfectly on desktop and mobile
• 🎯 Real-time Learning: Model updates instantly as you rate more videos

🚀 Quick Start

Option 1: Web Dashboard (Recommended)

git clone https://github.com/yourusername/video-idea-finder-algorithm.git
cd video-idea-finder-algorithm
./setup.sh
# Select option 1 for Dashboard

Option 2: Command Line Interface

./setup.sh
# Select option 2 for CLI mode

The setup script will:

1. Create a Python virtual environment
2. Install all dependencies
3. Help you configure your YouTube API key
4. Launch your preferred interface

📋 Prerequisites

• Python 3.7+
• YouTube Data API v3 Key (free from Google Cloud Console)

⚙️ Configuration

1. Get YouTube API Key:

   • Visit Google Cloud Console
   • Create a project or select existing one
   • Enable YouTube Data API v3
   • Create credentials (API key)

2. Set up environment:

   # Copy the example environment file
   cp .env.example .env
   
   # Edit .env and add your API key
   YOUTUBE_API_KEY=your_actual_api_key_here

3. Add your search queries:

   • Edit src/youtube/search.py in the get_coding_search_queries() function
   • Add search terms relevant to your interests
   • Example: "python machine learning", "react tutorial", etc.

🏗️ Project Structure

video-idea-finder-algorithm/
├── src/
│   ├── database/           # SQLite database operations
│   │   ├── manager.py      # Database setup and schema
│   │   ├── video_operations.py    # Video data CRUD
│   │   └── preference_operations.py # User ratings CRUD
│   ├── youtube/            # YouTube API integration
│   │   ├── search.py       # Video search functionality
│   │   ├── details.py      # Video metadata retrieval
│   │   └── utils.py        # Helper functions
│   ├── ml/                # Machine learning pipeline
│   │   ├── feature_extraction.py  # Video feature engineering
│   │   ├── model_training.py      # ML model management
│   │   └── predictions.py         # Recommendation engine
│   └── rating/            # Interactive rating system
│       ├── display.py      # Video information display
│       ├── session.py      # Rating session management
│       └── user_input.py   # User interaction handling
├── templates/             # Web dashboard frontend
│   └── dashboard.html     # Single-page application
├── main.py               # CLI application entry point
├── dashboard_api.py      # Web API server
├── run_dashboard.py      # Dashboard launcher
├── search_more_videos.py # Additional video search utility
├── setup.sh             # Automated setup script
├── .env.example         # Environment template
└── README.md           # This file

🧠 How the AI Works

Feature Engineering

The system extracts 11 key features from each video:

• Content Features: Title length, description length, keyword presence
• Engagement Metrics: View count, like ratio, engagement score
• Semantic Analysis: Title sentiment, tutorial/beginner/AI keyword detection
• Behavioral Patterns: Time constraints, challenge keywords

Machine Learning Pipeline

1. Data Collection: YouTube API provides video metadata
2. Feature Extraction: Convert raw video data into numerical features
3. User Feedback: Collect like/dislike ratings with optional notes
4. Model Training: RandomForest classifier with 100 trees
5. Prediction: Generate confidence scores for new videos

Learning Process

• Cold Start: Shows random videos until you have 10+ ratings
• Warm Start: AI model activates and provides personalized recommendations
• Continuous Learning: Model retrains after each new rating

🖥️ Available Commands

# Full interactive setup
./setup.sh

# CLI-only mode
python main.py

# Web dashboard
python run_dashboard.py

# Search for additional videos
python search_more_videos.py

# Start API server directly
python dashboard_api.py

🎨 Dashboard Features

• YouTube-like Interface: Familiar grid layout with thumbnails
• AI Confidence Scores: See how confident the AI is about each recommendation
• Real-time Feedback: Rate videos with instant visual feedback
• Model Status: Track learning progress and training status
• Liked Videos: Review your previously liked videos
• Responsive Design: Perfect on any screen size

🔧 Customization

Search Queries

Edit the search queries in src/youtube/search.py:

def get_coding_search_queries() -> List[str]:
    return [
        "python machine learning tutorial",
        "javascript react project",
        "web development 2024",
        # Add your own search terms
    ]

ML Model Parameters

Modify model settings in src/ml/model_training.py:

model = RandomForestClassifier(
    n_estimators=100,        # Number of trees
    max_depth=10,           # Tree depth
    min_samples_split=5,    # Minimum samples for split
    random_state=42
)

🤝 Contributing

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and test thoroughly
4. Commit your changes: git commit -m 'Add amazing feature'
5. Push to the branch: git push origin feature/amazing-feature
6. Open a Pull Request

Development Setup

# Clone your fork
git clone https://github.com/yourusername/video-idea-finder-algorithm.git

# Create development environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run tests (if available)
python -m pytest

📝 License

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

🙏 Acknowledgments

• YouTube Data API v3 for video data
• scikit-learn for machine learning capabilities
• Flask for the web framework
• SQLite for local data storage

📚 Learn More

This project demonstrates several key concepts:

• API Integration: YouTube Data API v3 usage
• Machine Learning: Feature engineering and model training
• Web Development: Flask API and responsive frontend
• Database Design: SQLite schema and operations
• DevOps: Environment management and deployment

Perfect for learning about ML-powered recommendation systems!

🐛 Troubleshooting

Common Issues

API Key Issues:

• Ensure your YouTube API key is valid and has quota remaining
• Check that YouTube Data API v3 is enabled in Google Cloud Console

Database Issues:

• Delete video_inspiration.db to reset the database
• Run ./setup.sh again to reinitialize

Import Errors:

• Activate the virtual environment: source venv/bin/activate
• Reinstall dependencies: pip install -r requirements.txt

Port Conflicts:

• Dashboard runs on port 5001 by default
• Change the port in dashboard_api.py if needed

Need Help?

• 📧 Open an issue on GitHub
• 💬 Check existing issues for solutions
• 🔍 Review the troubleshooting section above

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