📄 Document Summarizer

AI-powered document summarization tool that extracts key insights from PDFs, DOCX, and plain text files.

---

📑 Table of Contents

• 🌟 Features
• 🏗️ Tech Stack
• 📂 Project Structure
• 🚀 Deployment Status
• 🚀 Installation & Setup
• 🎨 Figma Design
• 🎥 How to Run the Application
• 🎥 Demo Video
• 👥 Contributors
• 📜 License
• 🏆 Acknowledgment

---

🌟 Features

🔹 Key Highlights

✅ Extract text from PDFs & DOCX files effortlessly.
✅ AI-generated summaries using state-of-the-art transformer models.
✅ Intelligent keyword extraction for enhanced insights.
✅ Multi-model support (T5, BART, Pegasus, etc.).
✅ Vector search for precise retrieval.
✅ Sleek React.js frontend for a seamless experience.
✅ Question Answering support.

---

🏗️ Tech Stack

🔹 Backend

• Flask - Lightweight web framework
• Transformers (Hugging Face) - Pretrained NLP models (T5, BART, Pegasus)
• PDFPlumber - Extracts text from PDFs
• docx - Parses DOCX files
• spaCy & YAKE - NLP processing & keyword extraction
• LangChain & FAISS - AI-powered document retrieval & vector similarity search
• Sentence Transformers - Embedding generation
• Gemini 1.5 Flash API - Used for Question Answering

🔹 Frontend

• React.js + Vite - Blazing fast UI development
• Tailwind CSS - Modern styling framework
• Lucide Icons - Minimalist UI enhancements

---

📂 Project Structure

Document-Summarizer/
│
├───.gitattributes
├───.gitignore
├───eslint.config.js
├───index.html
├───LICENSE
├───package-lock.json
├───package.json
├───postcss.config.js
├───tailwind.config.js
├───tsconfig.app.json
├───tsconfig.json
├───tsconfig.node.json
├───vite.config.ts
│
├───.github/
│    └───README.md
│
├───Archive/
│   ├───Document_Summarizer_first_prototype.ipynb
│   ├───Document_Summarizer_second_prototype.ipynb
│   ├───summarizer_web.ipynb
│   └───README.md
│
├───Assets/
│   ├───Contributors/
│   │   ├───Akshit Github Photo.png
│   │   ├───Ansh Github Photo.png
│   │   ├───Bindupautra Github Photo.png
│   │   └───Rana Github Photo.png
│   │
│   ├───Videos/
│   │   ├───Demo.mp4
│   │   └───To Run in Local Machine.mp4
│   │
│   └───README.md
│
├───backend/
│   ├───.env
│   ├───app.py
│   ├───Readme.md
│   ├───requirements.txt
│   └───README.md
│
├───NoteBooks/
│   ├───Summarizer.ipynb
│   └───README.md
│
└───src/
    ├───App.tsx
    ├───index.css
    ├───main.tsx
    ├───vite-env.d.ts
    └───README.md

---

🚀 Deployment Status

🔹 Frontend Deployment

The frontend of the application is deployed and accessible for preview. You can explore the user interface and interact with the design at the following link:

👉 Live Frontend Preview

However, please note that the backend has not been deployed yet. As a result, the full functionality of the Document Summarizer, including AI-powered summarization and document processing, is not available in the live version.

To experience the complete features, you will need to set up the backend locally by following the Installation & Setup instructions.

---

🚀 Installation & Setup

🔧 1️⃣ Clone the Repository

 git clone https://github.com/Jyotibrat/Document-Summarizer.git
 cd document-summarizer

🔑 2️⃣ Obtain a Gemini API Key

To use the Gemini 1.5 Flash API for question answering, you need to obtain an API key. Follow these steps:

1. Sign Up for Gemini API:

   • Go to the Gemini API website and sign up for an account.
   • Follow the instructions to create a new API key.

2. Set Up the API Key:

   • Navigate to the backend directory:

     cd backend

   • Open the .env file in a text editor:
   • Add your Gemini API key to the .env file:

     GEMINI_API_KEY='your_api_key_here'

   • Save and close the file.

🖥️ 2️⃣ Backend Setup

Navigate to the backend directory.

cd backend

Create a virtual environment and install dependencies:

 python -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt

Run the Flask server:

 python app.py

🎨 3️⃣ Frontend Setup

Navigate to the root directory and install dependencies:

 npm install

Run the development server:

 npm run dev

🎨 Figma Design

The UI/UX design for the Document Summarizer was created using Figma. You can view the design and interact with the prototype using the link below:

👉 Figma Design

---

🎥 How to Run the Application

📌 Watch the video tutorial on setting up and running the application locally:

To.Run.in.Local.Machine.compressed.mp4

🎥 Demo Video

📌 Watch the live demo of the Document Summarizer in action:

Demo.compressed.mp4

---

👥 Contributors

This project was made possible by the contributions of these amazing individuals:

---

📜 License

🔹 Licensing Details

This project is licensed under the MIT License - see the LICENSE file for details.

---

🏆 Acknowledgment

This project is part of Neurathon '25 conducted by NIT Silchar.