README.md

metadata

title: Document Classification
emoji: 🏷️
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 5.44.1
app_file: app.py
pinned: false
license: mit
short_description: Classify PDF documents into product categories using AI

Document Classification App

A Gradio-based web application that classifies PDF documents into product categories using AI-powered analysis.

Features

• 📄 PDF Upload: Upload any PDF document
• 🔍 Text Extraction: Automatically extract text from PDFs
• 🏷️ AI-Powered Classification: Classify documents into product categories
• 🎯 Multiple Methods: Choose between semantic similarity, keyword matching, or hybrid approach
• 📊 Confidence Scores: Get confidence scores for each classification
• ⚙️ Customizable Products: Define your own product categories and descriptions

How to Use

1. Upload PDF: Click the upload button and select your PDF file
2. Choose Method: Select your preferred classification method (hybrid, semantic, or keyword)
3. Define Products: Use the default product definitions or customize your own in JSON format
4. Classify: Click "Classify Document" to analyze the PDF
5. View Results: See the top 3 product matches with confidence scores

Setup

For Hugging Face Spaces (Production)

1. Set your OPENAI_API_KEY in the Space settings:
   • Go to your Space settings
   • Add OPENAI_API_KEY as a secret
   • Enter your OpenAI API key

For Local Development

1. Clone this repository
2. Install dependencies: pip install -r requirements.txt
3. Optionally create a .env file in the project root with your API key:

   OPENAI_API_KEY=your-api-key-here
   

   Or set it as an environment variable: export OPENAI_API_KEY="your-api-key"
4. Run the app: python app.py

Technical Details

• Framework: Gradio for the web interface
• Embeddings: OpenAI embeddings for semantic similarity
• Vector Store: LangChain InMemoryVectorStore for efficient similarity search
• Classification Methods: Semantic similarity, keyword matching, and hybrid approach
• Text Processing: PyPDF for PDF text extraction
• Architecture: Simple modular design with clean separation of concerns

Project Structure

pdf2product/
├── app.py                 # Main Gradio application
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (create this)
└── pdf_qa/               # Core Q&A package
    ├── __init__.py       # Package initialization
    ├── pdf_processor.py  # PDF text extraction and chunking
    └── qa_engine.py      # Question answering engine

Architecture

Simple and clean architecture:

• Separation of Concerns: UI logic (Gradio) is separate from business logic
• Modularity: Two main components - PDF processing and Q&A
• Simplicity: Minimal, focused modules that do one thing well

Example Product Categories

The app includes several example product configurations:

• Invoice-Focused: Invoice, Receipt, Quote/Estimate
• Travel-Focused: Flight Ticket, Hotel Reservation, Travel Insurance
• Employment-Focused: CV/Resume, Job Offer, Employment Contract

Users can also define their own custom product categories in JSON format.

Limitations

• Currently supports one PDF at a time
• Requires OpenAI API key
• Best results with text-based PDFs (not scanned images)
• Processing time depends on document size
• Classification accuracy depends on document content quality

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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference