🎬 YouTube Transcript Q&A & Summarizer

An AI-powered application that transforms YouTube videos into searchable, interactive knowledge sources through advanced Large Language Models (LLMs).

🚀 Overview

YouTube Transcript Q&A & Summarizer is a modular, end-to-end pipeline that:

• Retrieves transcripts from YouTube videos automatically
• Dynamically chunks long content for optimal LLM processing
• Offers a Streamlit web interface for seamless user experience
• Transforms videos into searchable, interactive knowledge sources

Project Architecture

Here is a visual overview of the application's pipeline, from user input to final output.

graph TD
    subgraph "User Interface (Streamlit)"
        A[User provides YouTube URL] --> B{Streamlit App};
    end

    subgraph "Backend Processing Pipeline"
        B --> C[Transcript Retriever];
        C -->|Raw Transcript| D[Preprocessor];
        D -->|Cleaned Text| E[Dynamic Chunker];
        E -->|Text Chunks| F[QA Engine];
        F -->|Map: Process each chunk| G((LLM API));
        G -->|Chunk Answers| F;
        F -->|Reduce: Synthesize final answer| G;
    end

    subgraph "Final Output"
        G -->|Unified Answer/Summary| B;
        B --> H[Display to User];
        H --> I[Download Output];
    end

    style F fill:##bbb,stroke:#333,stroke-width:2px
    style G fill:#fff,stroke:#333,stroke-width:2px

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✨ Features

🎯 Instant Transcript Retrieval

Extracts subtitles automatically from YouTube videos with available captions.

🤖 LLM-Powered Summarization

Converts long video transcripts into concise, informative summaries using Google Gemini API.

❓ Ask Any Question

Enter natural language queries and get accurate answers based on video content.

🔄 Map-Reduce Aggregation

Efficiently processes long videos by chunking content and combining answers or summaries.

🌐 Streamlit Web App

Simple, interactive user interface to upload links, ask questions, and view results.

💾 Downloadable Results

Export generated summaries or answers as .txt files for offline use.

🧪 Robust Testing

Unit-tested modular pipeline using pytest with comprehensive coverage.

📊 Logging & Error Handling

Centralized logging system with graceful failure mechanisms and detailed error tracking.

📁 Project Structure

project-root/
├── app.py # Streamlit frontend
├── yt_logo.png # Logo for branding
├── requirements.txt # Dependencies
├── requirements-dev.txt # Dev dependencies (pytest, etc.)
├── pytest.ini # Pytest configuration
├── .env # API keys and configs (not committed)
├── .gitignore # Git ignore rules
├── README.md # Project documentation
├── setup.py # For packaging , still to launch but made it
├── logs/ # Runtime logs
├── src/
│ ├── init.py
│ ├── components/
│ │ ├── init.py
│ │ ├── main.py # Main orchestrator
│ │ ├── preprocessor.py # Text cleaning & chunking
│ │ ├── qa_engine.py # LLM interface
│ │ ├── transcript_retriever.py # YouTube transcript fetching
│ │ └── internalTesting/ # Optional submodules
│ ├── logger.py # Centralized logging
│ ├── exception.py # Custom error handling
│ └── utils.py # Helper utilities
└── tests/
├── test_preprocessor.py
├── test_qa_engine.py
└── test_transcript_retriever.py

🛠️ Prerequisites

• Python 3.8+
• YouTube video with captions
• Google Gemini API key (or any supported LLM)
• Internet connection for API calls

📥 Installation

1. Clone the Repository

git clone https://github.com/siddhamapple/YoutubeSummarizer
cd YoutubeSummarizer

2. Create Virtual Environment

python -m venv venv

Activate (Linux/Mac)

source venv/bin/activate 

Activate WIndows

venv\Scripts\activate 

3. Install Dependencies

For regular usage-

pip install -r requirements.txt

For development (includes testing tools)-

pip install -r requirements-dev.txt

4. Environment Configuration

Create a .env file in the project root:

GEMINI_API_KEY=your_gemini_api_key_here

Usage

Quick Start

streamlit run app.py

Visit: http://localhost:8501

App Workflow

1. Paste YouTube Link → Enter any YouTube video URL
2. Choose Mode → Select "Summarize" or "Ask a Question"
3. Process Transcript → App fetches and processes video transcript
4. LLM Processing → AI runs on each chunk (Map-Reduce approach)
5. View Results → Final answer/summary displayed
6. Download → Optional: Save output as .txt file

🧪 Running Tests

Execute the complete test suite:

pytest

Test Coverage

The testing suite covers:

• ✅ TranscriptRetriever: Verifies transcript fetch accuracy from YouTube
• ✅ Preprocessor: Validates chunking, cleaning, and formatting logic
• ✅ QnAEngine: Checks consistency and quality of LLM-generated answers/summaries

tests/
├── test_preprocessor.py
├── test_qa_engine.py
└── test_transcript_retriever.py

🔧 Core Technologies

Tool/Library	Description
Streamlit	Frontend UI for interacting with the app
Google Gemini API	Large Language Model for Q&A and summarization
python-dotenv	Manages .env configurations securely
Pytest	Testing framework for backend logic verification
Custom Modules	Modular Python architecture under src/components/

Modular Design Components

• transcript_retriever.py: Fetches captions from YouTube videos
• preprocessor.py: Cleans and chunks transcript content
• qa_engine.py: Interfaces with LLM for Q&A/summaries
• main.py: Orchestrates the entire pipeline

🗺️ Roadmap

🔮 Upcoming Features

• 🌍 Multilingual Support: Auto-detect video language and translate
• ⚡ Asynchronous Processing: Faster summarization with async LLM calls
• ☁️ Deployment Options: Push to Streamlit Cloud / Hugging Face Spaces
• ⌨️ CLI Utilities: Command-line support for batch summarization
• 🎨 UI Polish: Improve layout, theme, and mobile responsiveness
• 📈 Analytics: Usage tracking and performance metrics

👨‍💻 Author

Siddham Jain
🎓 B.Tech in Electrical and Computer Engineering | Shiv Nadar IOE
📧 siddhamjainn@gmail.com
📱 +919625208689

🤝 Contributing

We welcome contributions! Follow these steps:

1. Fork this repository
2. Create a new feature branch (git checkout -b feature-name)
3. Commit your changes (git commit -am 'Add feature')
4. Push to the branch (git push origin feature-name)
5. Open a pull request 🚀

Development Guidelines

• Follow PEP 8 style guidelines
• Write comprehensive tests for new features
• Update documentation for any API changes
• Use only meaningful commit messages

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⭐ If you find this project helpful, please give it a star!

For questions, issues, or feature requests, please open an issue on GitHub.