init
This commit is contained in:
Gauri Joshi
parent
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.env.example
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.env.example
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YOUTUBE_API_KEY=your_youtube_api_key_here
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# Optional: For AI-powered search query generation (improves video discovery)
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# OPENAI_API_KEY=sk-your-openai-key-here
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# ANTHROPIC_API_KEY=sk-ant-your-anthropic-key-here
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.gitignore
vendored
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.gitignore
vendored
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# Environment variables and sensitive files
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.env
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.env.local
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.env.*.local
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# Virtual environment
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venv/
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__pycache__/
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*.pyc
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291
README.md
291
README.md
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# Video Inspiration Finder 🎯
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AI-powered YouTube video recommendation system that learns your preferences to suggest coding videos you'll love.
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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.
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## Features
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- 🔍 **Smart Search**: Finds viral coding videos from YouTube
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- 🤖 **Machine Learning**: RandomForest model learns your preferences
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- 📊 **Web Dashboard**: YouTube-like interface showing personalized recommendations
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- 🔒 **Privacy First**: All data stored locally in SQLite
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- ⚡ **Fast Setup**: One command installation and execution
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## 🌟 Features
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## Quick Start
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- 🔍 **Smart YouTube Search**: Automatically finds trending coding videos using configurable search queries
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- 🤖 **Machine Learning Recommendations**: RandomForest model learns your preferences from ratings
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- 📊 **Beautiful Web Dashboard**: YouTube-like interface with AI confidence scores
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- 🔒 **Privacy First**: All data stored locally in SQLite - no external tracking
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- ⚡ **One-Command Setup**: Get started with a single command
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- 📱 **Responsive Design**: Works perfectly on desktop and mobile
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- 🎯 **Real-time Learning**: Model updates instantly as you rate more videos
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### Option 1: Command Line Interface
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## 🚀 Quick Start
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### Option 1: Web Dashboard (Recommended)
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```bash
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git clone https://github.com/yourusername/video-idea-finder-algorithm.git
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cd video-idea-finder-algorithm
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./setup.sh
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# Select option 1 for Dashboard
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```
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### Option 2: Command Line Interface
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```bash
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./setup.sh
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# Select option 2 for CLI mode
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```
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This will:
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1. Create virtual environment
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2. Install dependencies
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3. Search for videos
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4. Start interactive rating session
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### Option 2: Web Dashboard
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The setup script will:
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1. Create a Python virtual environment
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2. Install all dependencies
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3. Help you configure your YouTube API key
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4. Launch your preferred interface
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## 📋 Prerequisites
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- **Python 3.7+**
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- **YouTube Data API v3 Key** (free from [Google Cloud Console](https://console.cloud.google.com/))
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## ⚙️ Configuration
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1. **Get YouTube API Key**:
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- Visit [Google Cloud Console](https://console.cloud.google.com/)
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- Create a project or select existing one
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- Enable YouTube Data API v3
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- Create credentials (API key)
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2. **Set up environment**:
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```bash
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# Copy the example environment file
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cp .env.example .env
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# Edit .env and add your API key
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YOUTUBE_API_KEY=your_actual_api_key_here
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```
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3. **Add your search queries**:
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- Edit `src/youtube/search.py` in the `get_coding_search_queries()` function
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- Add search terms relevant to your interests
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- Example: `"python machine learning"`, `"react tutorial"`, etc.
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## 🏗️ Project Structure
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```
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video-idea-finder-algorithm/
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├── src/
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│ ├── database/ # SQLite database operations
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│ │ ├── manager.py # Database setup and schema
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│ │ ├── video_operations.py # Video data CRUD
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│ │ └── preference_operations.py # User ratings CRUD
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│ ├── youtube/ # YouTube API integration
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│ │ ├── search.py # Video search functionality
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│ │ ├── details.py # Video metadata retrieval
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│ │ └── utils.py # Helper functions
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│ ├── ml/ # Machine learning pipeline
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│ │ ├── feature_extraction.py # Video feature engineering
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│ │ ├── model_training.py # ML model management
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│ │ └── predictions.py # Recommendation engine
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│ └── rating/ # Interactive rating system
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│ ├── display.py # Video information display
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│ ├── session.py # Rating session management
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│ └── user_input.py # User interaction handling
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├── templates/ # Web dashboard frontend
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│ └── dashboard.html # Single-page application
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├── main.py # CLI application entry point
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├── dashboard_api.py # Web API server
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├── run_dashboard.py # Dashboard launcher
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├── search_more_videos.py # Additional video search utility
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├── setup.sh # Automated setup script
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├── .env.example # Environment template
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└── README.md # This file
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```
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## 🧠 How the AI Works
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### Feature Engineering
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The system extracts 11 key features from each video:
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- **Content Features**: Title length, description length, keyword presence
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- **Engagement Metrics**: View count, like ratio, engagement score
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- **Semantic Analysis**: Title sentiment, tutorial/beginner/AI keyword detection
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- **Behavioral Patterns**: Time constraints, challenge keywords
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### Machine Learning Pipeline
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1. **Data Collection**: YouTube API provides video metadata
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2. **Feature Extraction**: Convert raw video data into numerical features
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3. **User Feedback**: Collect like/dislike ratings with optional notes
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4. **Model Training**: RandomForest classifier with 100 trees
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5. **Prediction**: Generate confidence scores for new videos
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### Learning Process
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- **Cold Start**: Shows random videos until you have 10+ ratings
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- **Warm Start**: AI model activates and provides personalized recommendations
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- **Continuous Learning**: Model retrains after each new rating
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## 🖥️ Available Commands
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```bash
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./setup.sh # First time setup
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# Full interactive setup
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./setup.sh
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# CLI-only mode
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python main.py
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# Web dashboard
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python run_dashboard.py
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```
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Opens a YouTube-like dashboard at `http://localhost:5000`
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## How It Works
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# Search for additional videos
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python search_more_videos.py
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1. **Search Phase**: Queries YouTube API for trending coding videos
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2. **Rating Phase**: You rate videos as like/dislike with optional notes
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3. **Learning Phase**: After 10+ ratings, ML model activates
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4. **Recommendation Phase**: AI suggests videos based on your preferences
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## Project Structure
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```
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src/
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├── database/ # SQLite operations
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├── youtube/ # YouTube API integration
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├── ml/ # Machine learning pipeline
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└── rating/ # Interactive rating system
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main.py # CLI application
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dashboard_api.py # Web API server
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templates/ # Dashboard HTML/CSS/JS
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# Start API server directly
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python dashboard_api.py
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```
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## ML Pipeline
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## 🎨 Dashboard Features
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- **Features**: 11 extracted features (keywords, engagement, sentiment)
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- **Model**: RandomForest with 100 trees
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- **Training**: Incremental learning after each rating
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- **Prediction**: Confidence scores for video recommendations
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- **YouTube-like Interface**: Familiar grid layout with thumbnails
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- **AI Confidence Scores**: See how confident the AI is about each recommendation
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- **Real-time Feedback**: Rate videos with instant visual feedback
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- **Model Status**: Track learning progress and training status
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- **Liked Videos**: Review your previously liked videos
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- **Responsive Design**: Perfect on any screen size
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## Requirements
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## 🔧 Customization
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- Python 3.7+
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- YouTube Data API v3 key
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- SQLite (included with Python)
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### Search Queries
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Edit the search queries in `src/youtube/search.py`:
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```python
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def get_coding_search_queries() -> List[str]:
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return [
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"python machine learning tutorial",
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"javascript react project",
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"web development 2024",
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# Add your own search terms
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]
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```
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## Setup
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### ML Model Parameters
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Modify model settings in `src/ml/model_training.py`:
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```python
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model = RandomForestClassifier(
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n_estimators=100, # Number of trees
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max_depth=10, # Tree depth
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min_samples_split=5, # Minimum samples for split
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random_state=42
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)
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```
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1. Get YouTube API key from [Google Cloud Console](https://console.cloud.google.com/)
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2. Create `.env` file:
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```
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YOUTUBE_API_KEY=your_api_key_here
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```
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3. Run `./setup.sh`
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## 🤝 Contributing
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## Dashboard Features
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We welcome contributions! Here's how you can help:
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- 📱 Responsive YouTube-like design
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- 🎯 AI confidence scores for each recommendation
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- 🔄 Real-time model status (learning vs trained)
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- 🖱️ Click videos to open in YouTube
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- 📊 Visual feedback on model training progress
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## Commands
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1. **Fork the repository**
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2. **Create a feature branch**: `git checkout -b feature/amazing-feature`
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3. **Make your changes** and test thoroughly
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4. **Commit your changes**: `git commit -m 'Add amazing feature'`
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5. **Push to the branch**: `git push origin feature/amazing-feature`
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6. **Open a Pull Request**
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### Development Setup
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```bash
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./setup.sh # Full setup and CLI
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python main.py # CLI only
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python run_dashboard.py # Web dashboard
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python dashboard_api.py # API server only
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# Clone your fork
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git clone https://github.com/yourusername/video-idea-finder-algorithm.git
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# Create development environment
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python -m venv venv
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source venv/bin/activate # On Windows: venv\Scripts\activate
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# Install dependencies
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pip install -r requirements.txt
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# Run tests (if available)
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python -m pytest
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```
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Built with Python, Flask, scikit-learn, and YouTube Data API v3.
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## 📝 License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## 🙏 Acknowledgments
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- **YouTube Data API v3** for video data
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- **scikit-learn** for machine learning capabilities
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- **Flask** for the web framework
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- **SQLite** for local data storage
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## 📚 Learn More
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This project demonstrates several key concepts:
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- **API Integration**: YouTube Data API v3 usage
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- **Machine Learning**: Feature engineering and model training
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- **Web Development**: Flask API and responsive frontend
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- **Database Design**: SQLite schema and operations
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- **DevOps**: Environment management and deployment
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Perfect for learning about ML-powered recommendation systems!
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## 🐛 Troubleshooting
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### Common Issues
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**API Key Issues**:
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- Ensure your YouTube API key is valid and has quota remaining
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- Check that YouTube Data API v3 is enabled in Google Cloud Console
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**Database Issues**:
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- Delete `video_inspiration.db` to reset the database
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- Run `./setup.sh` again to reinitialize
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**Import Errors**:
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- Activate the virtual environment: `source venv/bin/activate`
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- Reinstall dependencies: `pip install -r requirements.txt`
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**Port Conflicts**:
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- Dashboard runs on port 5001 by default
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- Change the port in `dashboard_api.py` if needed
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### Need Help?
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- 📧 Open an issue on GitHub
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- 💬 Check existing issues for solutions
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- 🔍 Review the troubleshooting section above
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---
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⭐ **Found this helpful? Give it a star!** ⭐
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Built with ❤️ for the coding community
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# Use different/additional search queries to find new videos
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additional_queries = [
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"python projects for beginners viral",
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"full stack web development course",
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"machine learning crash course",
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"javascript frameworks comparison",
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"coding interview preparation",
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"docker tutorial for developers",
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"git and github tutorial",
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"database design tutorial",
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"API development tutorial"
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# Add your additional search queries here
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# Examples:
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# "python tutorial",
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# "web development course",
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# "coding interview prep",
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# "javascript frameworks",
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# "database tutorial"
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]
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all_videos = []
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def get_coding_search_queries() -> List[str]:
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return [
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# AI focus (shorter terms)
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"AI coding tools 2024",
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"ChatGPT API project",
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"machine learning build",
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"AI developer workflow",
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# Project-based (concise)
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"react project build",
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"python automation script",
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"full stack app build",
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"javascript project code",
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# Implementation focus (brief)
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"coding patterns advanced",
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"developer setup optimization",
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"API design examples",
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"database optimization tips",
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# Recent/specialized (short)
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"modern dev practices",
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"new coding tools 2024",
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"framework comparison"
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# Add your own search queries here
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# Examples:
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# "python tutorial",
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# "javascript project",
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# "machine learning basics",
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# "web development",
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# "coding interview prep"
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]
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