Text Summarizer

🚀 Live Demo: Try the application on Hugging Face Spaces

Project Overview

A full-stack web application that generates concise summaries from user-provided text. The backend uses a T5-small transformer model served through a FastAPI endpoint, while the frontend provides a minimal interface for text input and summary display.

Features

• Text input via a web-based interface
• Server-side summarization using a T5-small model with beam search decoding
• Automatic device selection (CUDA, MPS, or CPU)
• Input preprocessing: whitespace normalization, HTML tag removal, case folding
• Asynchronous request handling via FastAPI

Tech Stack

Layer	Technology
Backend	Python, FastAPI, Uvicorn
Frontend	HTML, CSS, JavaScript
ML	Hugging Face Transformers, PyTorch
Model	T5-small
Templating	Jinja2

Model Details

• Architecture: T5-small (Text-to-Text Transfer Transformer)
• Input format: Raw text prefixed with summarize:
• Tokenization: T5Tokenizer with padding and truncation at 512 tokens
• Decoding: Beam search (num_beams=4) with max_length=150 and early stopping
• Inference: Runs under torch.no_grad() for reduced memory usage

Project Structure

text-summarizer/
├── app/
│   ├── app.py                 # FastAPI application and inference logic
│   ├── static/
│   │   ├── style.css          # Frontend styles
│   │   └── script.js          # Client-side form handling and API calls
│   └── templates/
│       └── index.html         # Main UI template (Jinja2)
├── requirements.txt           # Python dependencies
├── test-cases.md              # Test scenarios and expected outputs
└── .gitignore

Installation and Setup

1. Clone the repository

git clone https://github.com/unnat-git/Text-Summarizer.git
cd Text-Summarizer

2. Create and activate a virtual environment

python -m venv venv
source venv/bin/activate        # Linux/macOS
venv\Scripts\activate           # Windows

3. Install dependencies

pip install -r requirements.txt

4. Run the application

uvicorn app.app:app --reload

The application will be available at http://127.0.0.1:8000.

Usage

1. Open the application in a browser.
2. Enter or paste text into the input field.
3. Click Summarize.
4. The generated summary appears below the input area.

The /summarize endpoint also accepts direct POST requests:

curl -X POST http://127.0.0.1:8000/summarize \
  -H "Content-Type: application/json" \
  -d '{ "dialogue": "Your text here." }'

Future Improvements

• Support for additional input formats (PDF, URL extraction)
• Adjustable summary length parameter
• Response caching for repeated inputs
• Containerized deployment (Docker)
• Hosting on a cloud platform (Render, Railway)

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Built with ❤️ as a portfolio project showcasing NLP, deep learning, and full-stack Python development.