README.md

metadata

title: DocClassify
emoji: 📄
colorFrom: yellow
colorTo: blue
sdk: docker
pinned: false

Document Classifier

A web application that uses BERT-tiny to classify PDF documents by type. Upload a PDF file and get instant classification results.

Features

• 📄 PDF file upload and processing
• 🤖 BERT-tiny model for document classification
• 🎯 Classifies 20+ document types including:
  • Invoice, Receipt, Contract, Resume
  • Letter, Report, Memo, Email
  • Form, Certificate, License, Passport
  • Medical records, Bank statements, Tax documents
  • Legal documents, Academic papers, and more
• 💾 Model is downloaded and cached locally on first use
• 🎨 Modern, user-friendly interface

How It Works

1. The app uses the prajjwal1/bert-tiny model from Hugging Face
2. On first run, the model is automatically downloaded to the models/ directory
3. PDF text is extracted using PyPDF2
4. Document embeddings are computed using BERT-tiny
5. Similarity scores are calculated against pre-computed document type embeddings
6. The document is classified with confidence scores

Setup

Local Development

1. Backend Setup:

   cd backend
   pip install -r requirements.txt
   

2. Frontend Setup:

   cd frontend
   npm install
   

3. Run Backend:

   cd backend
   uvicorn app.main:app --reload --port 8000
   

4. Run Frontend:

   cd frontend
   npm run dev
   

5. Open http://localhost:5173 in your browser

Docker Deployment

docker build -t docclassify .
docker run -p 7860:7860 docclassify

Usage

1. Click "Select PDF File" to choose a PDF document
2. Click "Classify Document" to process the file
3. View the classification result with confidence scores
4. See top 5 document type predictions

Model Information

• Model: prajjwal1/bert-tiny
• Size: ~4.4M parameters
• Architecture: BERT (L=2, H=128)
• Source: Hugging Face Model Card

Technical Stack

• Backend: FastAPI, PyTorch, Transformers, PyPDF2
• Frontend: React, Vite
• Model: BERT-tiny (prajjwal1/bert-tiny)

Notes

• The model will be automatically downloaded on first use (~17MB)
• Classification works best with text-based PDFs
• Image-based PDFs may not work if they don't contain extractable text
• Processing time depends on document size and system resources