README.md

metadata

title: CV to CSV Extraction App
emoji: 📄
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
pinned: false

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

CV to CSV Extraction App

A Gradio application that extracts publications, talks, and other scholarly accomplishments from faculty CVs (PDFs) using Google's Gemini API with Pydantic-AI for robust structured data extraction.

Features

• Extract scholarly accomplishments from faculty CVs in PDF format
• Categorize accomplishments into different types (books, journal articles, conference presentations, etc.)
• Display results in a tabular format
• Download results as CSV
• Password protection using Hugging Face secrets
• Robust JSON parsing with Pydantic-AI

Installation

1. Clone this repository:

   git clone <repository-url>
   cd CV_to_CSV
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Create a .env file in the root directory with your Google API key:

   GOOGLE_API_KEY=your_google_api_key_here
   APP_PASSWORD=your_app_password_here
   

Usage

Running Locally

1. Run the application:

   python cv_extraction_app.py
   

2. Open your browser and navigate to http://localhost:7860

3. Enter the password (if set in the environment variable APP_PASSWORD)

4. Upload one or more faculty CV PDFs and click "Extract Accomplishments"

5. View the extracted accomplishments and download as CSV if desired

Deploying on Hugging Face Spaces

1. Create a new Space on Hugging Face Spaces with the Gradio SDK

2. Upload your code to the Space

3. Set up the following secrets in your Space settings:

   • GOOGLE_API_KEY: Your Google Gemini API key
   • APP_PASSWORD: The password you want to use for app authentication

How It Works

1. Authentication: The app checks if the provided password matches the one stored in the environment variable APP_PASSWORD

2. PDF Processing: The app extracts text from uploaded PDF files using PyPDF2

3. LLM Processing with Pydantic-AI:

   • The extracted text is processed using Pydantic-AI with Google's Gemini model
   • Pydantic models define the structure of the expected data
   • This approach ensures more robust parsing and validation of the extracted data
   • If Pydantic-AI processing fails, the app falls back to the standard Gemini API approach

4. Categorization: Accomplishments are categorized into different types based on a decision tree approach

5. Results Display: The extracted accomplishments are displayed in a tabular format and can be downloaded as CSV

Pydantic-AI Integration

The app uses Pydantic-AI to improve the reliability of structured data extraction:

• Defined Data Models: Clear schema definitions for faculty data and accomplishments
• Type Validation: Ensures fields like years and confidence scores are properly typed
• Default Values: Handles missing fields gracefully with sensible defaults
• Fallback Mechanism: If Pydantic-AI extraction fails, the app falls back to standard extraction

Customization

Changing the Password

To change the password, update the APP_PASSWORD environment variable:

• Locally: Modify the .env file
• On Hugging Face Spaces: Update the secret in the Space settings

Modifying Categories

To modify the categories of scholarly accomplishments, edit the MAIN_CATEGORIES and SCHOLARLY_WORK_TYPES lists in cv_extraction_app.py.

Troubleshooting

• API Key Issues: Ensure your Google API key is correctly set in the environment variables
• PDF Extraction Errors: Some PDFs may be password-protected or have security settings that prevent text extraction
• LLM Processing Errors: If the LLM fails to extract accomplishments, try adjusting the prompt or model parameters

License

This project is licensed under the MIT License.