README.md

metadata

title: CV-Extractor
emoji: 📸
sdk: streamlit
sdk_version: 1.37.1
app_file: app.py

CV Analyzer (AI-Powered Resume Parser)

A Streamlit-based app that extracts structured data from CVs (PDF) using Docling + Agentic AI + Pydantic schema, and converts it into a clean, downloadable CSV.

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Features

• Upload CV (PDF)
• Parse document using Docling
• Extract structured data using LLM agent
• Validate with Pydantic schema
• Convert to Pandas DataFrame
• View extracted data in UI
• Download as CSV

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Tech Stack

• Streamlit – UI
• Docling – PDF parsing
• Pydantic / pydantic-ai – structured extraction
• Hugging Face / LLM – inference
• Pandas – data processing

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Setup

1. Clone repo

git clone https://github.com/your-username/cv-analyzer.git
cd cv-analyzer

2. Create virtual environment

python -m venv .venv
source .venv/bin/activate   # Linux/macOS
.venv\Scripts\activate      # Windows

3. Install dependencies

pip install -r requirements.txt

4. Environment variables

Create a .env file:

HF_TOKEN=your_huggingface_token

.env is ignored via .gitignore

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Run App

streamlit run app.py

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How it works

1. User uploads CV (PDF)
2. Docling converts PDF → structured text/markdown
3. LLM agent extracts data using predefined schema
4. Output is validated via Pydantic
5. Data is converted into a DataFrame
6. User can view and download CSV

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Notes

• Schema is designed for AI/ML-focused resumes
• Missing fields are returned as null (no hallucination policy)
• Dates are stored as strings to avoid parsing errors
• Validation is relaxed to improve LLM compatibility

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Limitations

• LLM may still produce inconsistent outputs for poorly formatted CVs
• Complex layouts (tables, multi-column PDFs) may affect parsing quality
• Requires internet access for model inference

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Future Improvements

• Multi-CV batch processing
• Candidate scoring & ranking
• Semantic search over resumes (FAISS)
• UI improvements (filters, charts)
• Export to JSON / Excel

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License

MIT License