📝 AI Text Summarizer

An AI-powered abstractive text summarization web application built using the T5 Transformer model. The application generates concise and meaningful summaries from long-form text through a FastAPI backend and a simple HTML frontend.

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🚀 Features

• Generate abstractive summaries using Google's T5 Transformer
• FastAPI backend for efficient inference
• Clean and responsive HTML interface
• Automatic text preprocessing
• Supports long-form text summarization
• Uses GPU (CUDA/MPS) when available for faster inference

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🛠️ Tech Stack

Languages

• Python
• HTML

Frameworks & Libraries

• FastAPI
• Hugging Face Transformers
• PyTorch
• Pydantic

Model

• T5 (Text-To-Text Transfer Transformer)

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📂 Project Structure

AI-Text-Summarizer/
│
├── app.py                     # FastAPI backend
├── index.html                 # Frontend UI
├── requirements.txt
├── README.md
├── .gitignore
├── .gitattributes
│
└── saved_summary_model/
    ├── config.json
    ├── generation_config.json
    ├── model.safetensors
    ├── tokenizer.json
    └── tokenizer_config.json

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⚙️ Installation

1. Clone the repository

git clone https://github.com/yourusername/ai-text-summarizer.git
cd ai-text-summarizer

2. Create a virtual environment (Optional)

python -m venv venv

Activate it:

Windows

venv\Scripts\activate

macOS/Linux

source venv/bin/activate

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3. Install dependencies

pip install -r requirements.txt

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4. Run the application

uvicorn app:app --reload

Open your browser and visit

http://127.0.0.1:8000

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📌 API Endpoint

Summarize Text

POST

/summarize/

Request Body

{
  "dialogue": "Enter your long text here..."
}

Response

{
  "summary": "Generated summary..."
}

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🧠 Model Details

• Transformer: T5
• Framework: Hugging Face Transformers
• Backend: PyTorch
• Inference Device:
  • Apple Silicon (MPS)
  • CUDA GPU
  • CPU

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📊 Workflow

Input Text
      │
      ▼
Text Preprocessing
      │
      ▼
Tokenization
      │
      ▼
T5 Transformer
      │
      ▼
Generated Summary
      │
      ▼
Display Output

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📷 Screenshots

🔮 Future Improvements

• Upload PDF/DOCX files
• Multi-language summarization
• Adjustable summary length
• Deploy using Hugging Face Spaces or Render
• User authentication
• Batch summarization

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