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| 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. | |
| --- | |
| ## 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 | |
| --- | |
| ## Tech Stack | |
| - **Streamlit** – UI | |
| - **Docling** – PDF parsing | |
| - **Pydantic / pydantic-ai** – structured extraction | |
| - **Hugging Face / LLM** – inference | |
| - **Pandas** – data processing | |
| --- | |
| ## Setup | |
| ### 1. Clone repo | |
| ```bash | |
| git clone https://github.com/your-username/cv-analyzer.git | |
| cd cv-analyzer | |
| ```` | |
| ### 2. Create virtual environment | |
| ```bash | |
| python -m venv .venv | |
| source .venv/bin/activate # Linux/macOS | |
| .venv\Scripts\activate # Windows | |
| ``` | |
| ### 3. Install dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### 4. Environment variables | |
| Create a `.env` file: | |
| ``` | |
| HF_TOKEN=your_huggingface_token | |
| ``` | |
| > `.env` is ignored via `.gitignore` | |
| --- | |
| ## Run App | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| --- | |
| ## 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 | |
| --- | |
| ## 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 | |
| --- | |
| ## 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 | |
| --- | |
| ## Future Improvements | |
| * Multi-CV batch processing | |
| * Candidate scoring & ranking | |
| * Semantic search over resumes (FAISS) | |
| * UI improvements (filters, charts) | |
| * Export to JSON / Excel | |
| --- | |
| ## License | |
| MIT License | |
| ``` | |
| ``` |