An AI-powered tool that automatically classifies YouTube videos in your "Watch Later" playlist based on their titles and thumbnails using vision-language models through Ollama.

Features ✨

🤖 AI-Powered Classification: Uses Ollama with Qwen2.5-VL and fallback models to analyze video titles and thumbnails
🔄 Robust LLM Integration: Automatic fallback between models with increasing timeouts for reliability
📊 Comprehensive CSV Storage: Saves detailed video information including classifications, metadata, and thumbnails
🌐 Multi-language Detection: Automatically detects video language using AI
🏷️ Smart Tagging: Generates detailed sub-tags for better content organization
🎯 Smart Categories: Uses existing classifications or creates new ones automatically
🖥️ Browser Automation: Selenium-based interaction with YouTube for reliable data extraction
🎨 Beautiful Logging: Rich console output with colors and emojis for better UX
⌨️ Easy Control: Press 'q' at any time to safely quit the process

Quick Start

Prerequisites

Python 3.11.10+
Ollama installed locally
Chrome or Chromium browser

Setup

Install Ollama: Download from https://ollama.ai

Pull Required Models:

ollama pull qwen2.5vl:7b
ollama pull gemma2:2b

Start Ollama Service:
```
ollama serve
```

Clone and Setup Project:

git clone <repository-url>
cd youtube-video-classifier

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

# Install dependencies
pip install -r requirements.txt

Configure Settings (optional): Edit config.ini to customize your setup
Run the Classifier:
```
python script.py
```

How It Works 🔄

Browser Initialization: Opens Chrome/Chromium and navigates to your YouTube "Watch Later" playlist
Video Detection: Finds and extracts information from playlist videos using Selenium
Data Extraction: Captures video title, thumbnail, channel info, duration, and upload date
AI Analysis: Uses Ollama models to:
- Classify the video into categories
- Detect the primary language
- Generate detailed sub-tags
Smart Fallback: If primary model fails/times out, automatically switches to fallback model
Data Storage: Saves all information to CSV with base64-encoded thumbnails
Playlist Management: Removes processed videos from "Watch Later" playlist
Continuous Processing: Continues until all videos are processed or user quits

Configuration

The config.ini file allows you to customize various settings:

[DEFAULT]
# Ollama settings
ollama_host = http://localhost:11434
ollama_model = qwen2.5vl:7b
ollama_fallback_model = gemma2:2b

# File paths
classifications_csv = video_classifications.csv
playlist_url = https://www.youtube.com/playlist?list=WL

# LLM timeout settings (in seconds)
llm_primary_timeout = 60
llm_fallback_timeout = 60

# Processing settings
enable_delete = false
enable_playlist_creation = false

CSV Output Format 📋

The script creates a comprehensive CSV file with the following columns:

video_title: Title of the video
video_url: YouTube URL of the video
thumbnail_url: Path to the saved thumbnail
classification: AI-generated category
language: Detected language of the video
channel_name: Name of the YouTube channel
channel_link: URL to the channel
video_length_seconds: Duration in seconds
video_date: Upload date
detailed_subtags: AI-generated specific tags
playlist_name: Source playlist name
playlist_link: Source playlist URL
image_data: Base64-encoded thumbnail data
timestamp: When the classification was made

File Structure 📁

├── script.py                 # Main classification script
├── config.ini               # Configuration settings
├── requirements.txt         # Python dependencies
├── video_classifications.csv # Generated results (created when first run)
└── README.md               # This file

Features in Detail

AI Classification System

Primary Model: Qwen2.5-VL 7B for high-quality vision-language analysis
Fallback Model: Gemma2 2B for faster processing when primary model is slow
Timeout Management: Automatically increases timeout periods if models are struggling
Continuous Retry: Keeps trying until successful or user cancels

Data Extraction

Video Metadata: Title, URL, duration, upload date
Channel Information: Name and link to channel
Thumbnail Capture: Screenshots saved as base64 in CSV
Playlist Context: Source playlist name and URL

Browser Automation

Multiple Chrome Paths: Automatically finds Chrome/Chromium installation
WebDriver Management: Handles chromedriver setup and fallbacks
Robust Selectors: Multiple CSS selectors for reliable element finding
Error Recovery: Graceful handling of UI changes and loading delays

User Experience

Rich Console Output: Colored logging with emojis and status indicators
Progress Tracking: Clear indication of current processing status
Safe Exit: Press 'q' at any time to cleanly stop processing
Error Reporting: Detailed error messages for troubleshooting

Testing Your Setup

Before running the main script, you can test individual components:

Test Ollama Connection:

import requests
response = requests.get('http://localhost:11434/api/tags')
print(response.json())

Test Browser Automation: Run the script and check if Chrome opens correctly
Test Model Response: The script will verify model availability on startup

Troubleshooting 🔧

Common Issues

Ollama Connection Error:

Ensure Ollama is running: ollama serve
Check the host URL in config.ini
Verify models are installed: ollama list

Browser Issues:

Install Chrome or Chromium
Update chromedriver if needed
Check if browser is in PATH

Model Timeout:

The script automatically handles timeouts with fallback
Consider increasing timeout values in config.ini
Ensure sufficient system resources

Selenium Errors:

YouTube may have changed their HTML structure
Check for browser updates
Verify you're logged into YouTube

Performance Tips

For faster processing: Use smaller models like gemma2:2b as primary
For better accuracy: Use larger models like qwen2.5vl:7b as primary
For stability: Keep both models installed for automatic fallback
For large playlists: Consider running in smaller batches

Contributing

Fork the repository
Create a feature branch
Test your changes thoroughly
Submit a pull request

License

MIT License - see LICENSE file for details

Note: This tool is for personal use and educational purposes. Please respect YouTube's Terms of Service and rate limits.

Languages

Python 86.6%

CSS 5.5%

TypeScript 3.1%

Astro 2.2%

Shell 2.2%

Other 0.4%