README.md

metadata

title: LITVISION Summarization API
emoji: 📚
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
license: mit

LITVISION Book Summarization API

A production-ready FastAPI endpoint for the LITVISION Book Summarization Feature. This service accepts PDF or TXT files, extracts text (using native extraction with OCR fallback for scanned pages), chunks the text smartly, and generates both per-chapter summaries and a final organized summary using facebook/bart-large-cnn.

It is fully configured for deployment on Hugging Face Spaces (Docker).

Features

• Text Extraction: Native PDF text extraction using PyMuPDF.
• OCR Fallback: Scans unextractable PDF pages using pytesseract (supports English and Arabic).
• Smart Chunking: Token-aware sentence grouping to prevent cutting mid-sentence.
• Generative AI: Uses BART-large-CNN on GPU (or CPU fallback) with FP16 optimization.
• FastAPI Backend: Fully async HTTP endpoint for file uploads.
• Hugging Face Ready: Pre-configured Dockerfile with non-root user and correct port mappings.

API Endpoints

GET /

Returns basic API information.

GET /health

Returns health status.

{
  "status": "healthy",
  "model_loaded": true,
  "device": "cuda"
}

POST /summarize

Accepts a PDF or TXT file via multipart/form-data.

Request:

curl -X POST -F "file=@book.pdf" http://localhost:7860/summarize

Response Format:

{
  "success": true,
  "file_name": "book.pdf",
  "num_chapters": 1,
  "chapter_summaries": [
    {
      "chapter": "BOOK",
      "summary": "..."
    }
  ],
  "final_summary": "..."
}

Folder Structure

.
├── app.py                # FastAPI endpoints and startup events
├── summarizer.py         # AI generation logic (BART model)
├── utils.py              # PDF extraction, OCR, and chunking tools
├── requirements.txt      # Python dependencies
├── Dockerfile            # Container configuration
├── .dockerignore
├── .gitignore
└── README.md

Local Development

1. Install System Dependencies (Linux/macOS)

Make sure you have Tesseract and Poppler installed:

• Ubuntu: sudo apt-get install poppler-utils tesseract-ocr tesseract-ocr-eng tesseract-ocr-ara
• Mac: brew install poppler tesseract tesseract-lang

2. Install Python Dependencies

pip install -r requirements.txt

3. Run the Server

uvicorn app:app --host 0.0.0.0 --port 7860 --reload

Docker Build & Run (Local)

docker build -t litvision-summarizer .
docker run -p 7860:7860 --gpus all litvision-summarizer

(Remove --gpus all if running on CPU)

Deployment to Hugging Face Spaces

1. Go to Hugging Face and create a new Space.
2. Select Docker as the Space SDK.
3. Upload all the files in this directory directly to the repository.
4. The space will automatically build the container and start the Uvicorn server on port 7860.

Troubleshooting

• CUDA OOM Errors: Ensure the uploaded book is not excessively long, or adjust the BATCH_SIZE in summarizer.py.
• OCR Not Working: Verify Tesseract language packs (tesseract-ocr-ara and tesseract-ocr-eng) are correctly installed in your environment.