📧 SmartSummarize — End-to-End LLM Email Project

A high-performance AI platform that transforms long emails into concise, actionable summaries.

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📖 About the Project

SmartSummarize is a complete, end-to-end Machine Learning ecosystem designed to solve the problem of email overload. Unlike standard generic tools, this project leverages a custom fine-tuned Flan-T5-Base model, trained specifically on over 18,000 real-world emails from the AESLC dataset.

It provides seamless integration into your daily workflow through a Chrome Extension, an elegant Web Dashboard, and a robust FastAPI Backend.

✨ Key Features

• 🧠 Fine-tuned LLM: Custom weights trained on the AESLC email dataset using LoRA for highly accurate summarization.
• 📩 Gmail Integration: Automatically reads and summarizes open emails directly within your Gmail interface.
• 🎨 Web Dashboard: A premium, dark-themed dashboard for direct text summarization.
• 🧩 Chrome Extension: Quick-access tool featuring a right-click "Summarize This" context menu support.
• ⚙️ Dual Mode: Choose between succinct Paragraph or structured Bullet Point summaries.
• ⚡ Fast Inference: Optimized to run efficiently on local CPUs—no expensive GPU required for inference.

🛠️ Architecture & Tech Stack

• Model: google/flan-t5-base fine-tuned via LoRA (Low-Rank Adaptation)
• Backend: Python, FastAPI, Hugging Face Transformers, PyTorch
• Frontend: HTML5, Vanilla CSS (Premium Dark Theme), JavaScript
• Extension: Chrome Extension API (Manifest V3)
• Dataset: AESLC (Annotated Enron Subject Line Corpus) - 18,000 samples

🚀 Getting Started

Follow these instructions to set up the project locally on your machine.

1. Prerequisites

• Python 3.9+
• Google Chrome Browser
• Git

2. Backend Setup (The AI Engine)

1. Clone this repository:

   git clone https://github.com/Husrocks/smart-email-summarizer.git
   cd smart-email-summarizer

2. Install the required dependencies:

   pip install -r backend/requirements.txt

3. Start the backend FastAPI server:

   python backend/main.py

   Note: On the first run, the system will download the base Flan-T5 model (~900MB) and load your custom fine-tuned weights automatically.

3. Web Dashboard Setup

1. Navigate to the frontend/ directory.
2. Open index.html in any modern web browser.
3. Ensure your backend server is running, paste your text, and start summarizing!

4. Chrome Extension Setup

Option A: Pre-packaged (Easiest)

1. Download smart-email-summarizer-extension.zip from the repository root.
2. Extract the ZIP file to a known location.
3. Open Chrome and navigate to chrome://extensions/.
4. Enable Developer mode (top right corner).
5. Click Load unpacked and select the extracted folder.

Option B: From Source

1. Open Chrome and navigate to chrome://extensions/.
2. Enable Developer mode.
3. Click Load unpacked and select the extension/ folder from this project directory.
4. Important: Click the extension icon in your toolbar once to ensure it connects to the running backend.

💡 Usage Guide

• In Gmail: Open any email, click the SmartSummarize extension icon in your toolbar, and it will automatically extract and summarize the email body.
• Right-Click Feature: Highlight text on any webpage, right-click, and select "Summarize This". The extension popup will display the generated summary.
• Web App: Use the standalone web application for custom text inputs and length adjustments (Short, Medium, Long).

📁 Project Structure

smart-email-summarizer/
├── backend/       # FastAPI server and model inference logic
├── extension/     # Chrome Extension (MV3) with Gmail auto-detection
├── frontend/      # Premium web dashboard (HTML/CSS/JS)
├── training/      # Training scripts used to fine-tune the model
├── scripts/       # Data collection and preprocessing pipeline
├── models/        # Fine-tuned LoRA weights and checkpoints
└── README.md      # Project documentation

🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the terms specified in the LICENSE file. See LICENSE for more information.

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Developed with ❤️ for Advanced AI Research.