README.md

metadata

title: Pdf Extractor
emoji: 🚀
colorFrom: red
colorTo: red
sdk: docker
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: pdf_extractor

PDF-to-JSON Extractor with AI

Intelligent PDF document parser that extracts structured JSON data using OpenAI's GPT models and computer vision.

📋 Table of Contents

• Overview
• Features
• Technology Stack
• Installation
• Usage
• Configuration
• Author

🎯 Overview

This application converts PDF documents into structured JSON format using:

• OpenAI GPT-4 Vision: For intelligent content extraction
• Template-based extraction: Customizable JSON schemas for different document types
• Streamlit UI: Interactive web interface for easy PDF processing
• Docker support: Containerized deployment for production environments

Perfect for automating data extraction from resumes, invoices, forms, and other structured documents.

✨ Features

• AI-Powered Extraction: Uses GPT-4 Vision to understand document structure
• Template System: Pre-configured JSON templates for common document types
• Batch Processing: Handle multiple PDFs efficiently
• Image Preview: Visual confirmation of PDF pages before extraction
• Format Validation: Ensures extracted JSON matches defined schema
• Hugging Face Spaces: Ready for cloud deployment

🛠 Technology Stack

• Python 3.9+ - Primary programming language
• OpenAI API - GPT-4 Vision for intelligent extraction
• pypdfium2 - PDF rendering and image conversion
• Streamlit - Interactive web UI framework
• Pillow (PIL) - Image processing
• Pandas - Data manipulation

🚀 Installation

Prerequisites

• Python 3.9 or higher
• OpenAI API key (Get one here)

Setup

1. Clone the repository: ```bash git clone https://github.com/pradyten/pdf-extractor.git cd pdf-extractor ```

2. Install dependencies: ```bash pip install -r requirements.txt ```

3. Configure OpenAI API key: ```bash export OPENAI_API_KEY='your-api-key-here' ```

💻 Usage

Command Line

```bash python extractor.py path/to/document.pdf ```

Streamlit Web UI

```bash streamlit run src/streamlit_app.py ```

Docker

```bash docker build -t pdf-extractor . docker run -p 8501:8501 -e OPENAI_API_KEY='your-key' pdf-extractor ```

⚙️ Configuration

Define custom templates in `extractor.py` for different document types (resumes, invoices, forms).

🎓 Use Cases

• HR & Recruitment: Batch process resume PDFs
• Accounting: Extract invoice data
• Data Entry: Automate form digitization
• Document Management: Convert scanned documents to searchable JSON

🔒 Security & Privacy

• Never commit API keys - use environment variables
• PDFs are processed in-memory, not stored
• Review OpenAI's data usage policies for compliance

👨‍💻 Author

Pradyumn Tendulkar

Data Science Graduate Student | ML Engineer

• GitHub: @pradyten
• LinkedIn: Pradyumn Tendulkar
• Email: pktendulkar@wpi.edu

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⭐ If you found this project helpful, please consider giving it a star!

📝 License: MIT