Detoxify — YouTube Comment Toxicity Analyzer

An AI-powered tool that detects offensive and toxic language in YouTube comments, helping build safer online spaces for women.

Built for Tink-Her-Hack hackathon.

Features

• Paste any YouTube video URL and analyze the first 100 comments
• AI-powered toxicity detection using cardiffnlp/twitter-roberta-base-offensive (pretrained RoBERTa model, trained on ~58M tweets)
• Safety Analysis Dashboard — shows safe/flagged counts, severity breakdown (High/Medium/Low), and animated toxicity bar
• Filter tabs — view All, Safe, Flagged, or High Severity comments
• Severity badges on each comment card (color-coded: red for high, orange for medium, purple for low, green for safe)
• PDF Report Generation — download a branded evidence report with video info, summary stats, and a table of all flagged comments (username + comment + severity)
• Video info display (title, channel, views, likes, comment count)
• Glassmorphism UI with soft pinks, lavender & purple palette

Setup

1. Get a YouTube API Key (Free)

1. Go to Google Cloud Console
2. Create a new project (or use an existing one)
3. Enable YouTube Data API v3 from the API Library
4. Go to Credentials → Create Credentials → API Key
5. Copy the API key

2. Configure the API Key

Open the .env file in the project root and replace the placeholder:

YOUTUBE_API_KEY=your_actual_api_key_here

3. Install Dependencies

pip install -r requirements.txt

4. Run the Backend

cd backend
python app.py

The server starts at http://127.0.0.1:5000

Note: The AI model (~500MB) downloads automatically on first run. Subsequent starts are instant.

5. Open the Frontend

Open frontend/index.html in your browser (just double-click it, or use Live Server in VS Code).

Project Structure

Tink-Her-Hack/
├── backend/
│   └── app.py              # Flask API server + AI classifier
├── frontend/
│   ├── index.html           # Main page with dashboard & filters
│   ├── style.css            # Glassmorphism UI (women-friendly palette)
│   └── script.js            # Frontend logic + PDF generation
├── .env                     # Your API key (not committed)
├── .env.example             # Template for API key
├── .gitignore
├── requirements.txt
└── README.md

Tech Stack

• Backend: Python, Flask, YouTube Data API v3
• AI Model: cardiffnlp/twitter-roberta-base-offensive (HuggingFace Transformers, PyTorch)
• Frontend: HTML, CSS, JavaScript (no frameworks)
• PDF Generation: jsPDF + jsPDF-AutoTable (client-side)
• API: Google YouTube Data API (free tier — 10,000 units/day)