📧 AI-Powered Email Summarizer

A modern productivity tool that transforms lengthy email threads into concise, actionable summaries with AI-powered action item extraction.

✨ Features

🤖 AI-Powered Summarization - Transform long email threads into bullet-point summaries
📋 Action Item Extraction - Automatically identify tasks, deadlines, and assignees
🎯 Smart Parsing - Clean HTML, remove signatures, and extract key information
🔌 Multiple Interfaces - Streamlit web app, Chrome extension, and REST API
⚡ Fast & Lightweight - Built with FastAPI for high performance
🐳 Docker Ready - Easy deployment with Docker Compose

🚀 Quick Start

Option 1: One-Command Setup

git clone https://github.com/arjungop/ai-email-summarizer.git
cd ai-email-summarizer
./setup.sh

Option 2: Manual Setup

# Clone and setup
git clone https://github.com/arjungop/ai-email-summarizer.git
cd ai-email-summarizer

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Start the Application

# Terminal 1: Start Backend API
source venv/bin/activate
uvicorn backend.main:app --reload --port 8080

# Terminal 2: Start Frontend
streamlit run frontend/streamlit_app.py

Access the Application

• Web App: http://localhost:8501
• API Docs: http://localhost:8080/docs
• Health Check: http://localhost:8080/health

🎯 Project Overview

Mission: Reduce time spent reading long email threads by automatically generating intelligent summaries and extracting actionable insights.

Core Capabilities

• Email Thread Analysis - Parse and clean email content from various sources
• Intelligent Summarization - Generate concise bullet-point summaries
• Action Item Detection - Extract tasks, deadlines, and assignments
• Multi-Platform Support - Web app, browser extension, and API access

🛠️ Tech Stack

Backend

• FastAPI - Modern, fast web framework for building APIs
• Python 3.8+ - Core programming language
• Pydantic - Data validation and serialization
• Uvicorn - ASGI server for running FastAPI

AI/ML Components

• Transformers Library - Hugging Face transformers for model integration
• LLaMA 3 or GPT-J - Open-source large language models for summarization
• PyTorch - Deep learning framework
• Sentence-Transformers - For text embeddings and similarity

Frontend Options

Choose one based on your preference:

Option 1: Chrome Extension

• HTML/CSS/JavaScript - Core web technologies
• Chrome Extension APIs - For browser integration
• Webpack - Module bundler
• Manifest V3 - Latest Chrome extension format

Option 2: Streamlit Web App

• Streamlit - Python web app framework
• Streamlit Components - For custom UI elements

Option 3: React Web App

• React.js - Frontend library
• TypeScript - Type-safe JavaScript
• Tailwind CSS - Utility-first CSS framework
• Axios - HTTP client for API calls

Database & Storage

• SQLite - Lightweight database for storing summaries
• Redis (optional) - Caching for improved performance

DevOps & Deployment

• Docker - Containerization
• Docker Compose - Multi-container orchestration
• Nginx - Reverse proxy and load balancer
• GitHub Actions - CI/CD pipeline

📋 Prerequisites

Required Knowledge

• Python Programming - Intermediate level
• Web Development Basics - HTML, CSS, JavaScript
• API Development - REST API concepts
• Machine Learning Basics - Understanding of NLP and transformers

System Requirements

• Python 3.8+
• Node.js 16+ (if building React frontend or Chrome extension)
• Git for version control
• 4GB+ RAM (for running language models)
• GPU (optional but recommended for faster inference)

🚀 Getting Started

1. Environment Setup

# Clone the repository
git clone <your-repo-url>
cd email_summarizer

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

# Install dependencies
pip install -r requirements.txt

2. Model Setup

# Download and cache the language model
python scripts/download_model.py

3. Run the Backend

# Start the FastAPI server
uvicorn main:app --reload --port 8000

4. Frontend Setup (Choose One)

Streamlit Option:

streamlit run frontend/streamlit_app.py

Chrome Extension Option:

cd frontend/chrome_extension
npm install
npm run build
# Load unpacked extension in Chrome

📁 Project Structure

email_summarizer/
├── backend/
│   ├── main.py                 # FastAPI application
│   ├── models/
│   │   ├── summarizer.py       # ML model wrapper
│   │   └── email_parser.py     # Email parsing logic
│   ├── api/
│   │   ├── routes.py           # API endpoints
│   │   └── schemas.py          # Pydantic models
│   ├── core/
│   │   ├── config.py           # Configuration settings
│   │   └── dependencies.py     # Dependency injection
│   └── utils/
│       ├── text_processing.py  # Text preprocessing utilities
│       └── action_extraction.py # Action item extraction
├── frontend/
│   ├── streamlit_app.py        # Streamlit frontend
│   ├── chrome_extension/       # Chrome extension files
│   │   ├── manifest.json
│   │   ├── popup.html
│   │   ├── popup.js
│   │   └── content.js
│   └── react_app/             # React frontend (optional)
├── models/                     # Downloaded model files
├── data/                       # Sample data and datasets
├── tests/                      # Unit and integration tests
├── docker/                     # Docker configuration
├── scripts/                    # Utility scripts
├── requirements.txt            # Python dependencies
├── docker-compose.yml          # Multi-container setup
└── README.md                   # This file

🔧 Key Features Implementation

1. Email Thread Parsing

• Extract email metadata (sender, timestamp, subject)
• Clean HTML and formatting
• Identify email thread structure

2. AI Summarization

• Process email content through LLM
• Generate bullet-point summaries
• Maintain context across thread

3. Action Item Extraction

• Use NER (Named Entity Recognition) for dates and names
• Pattern matching for action verbs
• Extract deadlines and commitments

4. API Endpoints

POST /api/summarize           # Summarize email thread
GET  /api/summary/{id}        # Retrieve summary by ID
POST /api/extract-actions     # Extract action items only
GET  /api/health             # Health check

🧠 Learning Path

Phase 1: Foundation (Week 1-2)

1. Python & FastAPI

   • FastAPI tutorial and documentation
   • RESTful API design principles
   • Pydantic for data validation

2. NLP Basics

   • Transformers library documentation
   • Hugging Face model hub exploration
   • Text preprocessing techniques

Phase 2: Core Development (Week 3-4)

1. Model Integration

   • Loading and using pre-trained models
   • Prompt engineering for summarization
   • Performance optimization

2. Backend Development

   • API endpoint implementation
   • Error handling and validation
   • Database integration

Phase 3: Frontend Development (Week 5-6)

1. Choose Your Frontend

   • Streamlit for rapid prototyping
   • Chrome extension for browser integration
   • React for full web application

2. Integration

   • API consumption
   • User interface design
   • Error handling and feedback

Phase 4: Enhancement (Week 7-8)

1. Advanced Features

   • Caching and performance optimization
   • User authentication
   • Summary customization

2. Deployment

   • Docker containerization
   • Cloud deployment (AWS, GCP, Azure)
   • CI/CD pipeline setup

📚 Resources

Documentation

• FastAPI Documentation
• Hugging Face Transformers
• Streamlit Documentation
• Chrome Extension Guide

Models

• LLaMA 3 on Hugging Face
• GPT-J Model
• Summarization Models

Tutorials

• FastAPI + ML Model Deployment
• Chrome Extension Development
• Streamlit for ML Applications
• Docker for Python Applications

🎯 Success Metrics

• Accuracy: Summary captures key points (>90%)
• Speed: Processing time <5 seconds per thread
• User Experience: Clean, intuitive interface
• Reliability: 99%+ uptime for API

🚀 Future Enhancements

• Multi-language support
• Email client integrations (Gmail, Outlook)
• Custom summary templates
• Team collaboration features
• Analytics dashboard
• Mobile app development

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

MIT License - see LICENSE file for details

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Happy Coding! 🚀

This project combines modern web development, AI/ML, and practical productivity solutions - perfect for building your full-stack development skills while creating something genuinely useful.