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| # Resume Parser & Summarizer (Streamlit + Hugging Face) | |
| A Streamlit web app that parses resumes (PDF/DOCX) and generates an AI summary using the Hugging Face Inference API. It extracts contact details and detected skills and produces a concise summary. | |
| ## Features | |
| - Upload resumes in PDF or DOCX | |
| - Extract contact information (email, phone) | |
| - Detect common technical skills via keyword scan | |
| - Summarize resume content using a Hugging Face summarization model | |
| - Streamlit UI with sidebar for HF model and API token | |
| ## Local Installation | |
| 1. Clone the repository: | |
| ```bash | |
| git clone <repository-url> | |
| cd windsurf-project-2 | |
| ``` | |
| 2. Create a virtual environment (recommended): | |
| ```bash | |
| python -m venv venv | |
| source venv/bin/activate # On Windows: venv\Scripts\activate | |
| ``` | |
| 3. Install the required packages: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. (Optional) Set your Hugging Face token for higher rate limits: | |
| ```bash | |
| # Windows (PowerShell) | |
| setx HF_TOKEN "hf_XXXXXXXXXXXXXXXXXXXXXXXXXXXX" | |
| # macOS/Linux | |
| export HF_TOKEN="hf_XXXXXXXXXXXXXXXXXXXXXXXXXXXX" | |
| ``` | |
| ## Usage | |
| 1. Run the Streamlit application: | |
| ```bash | |
| streamlit run app.py | |
| ``` | |
| 2. Open your web browser and navigate to `http://localhost:8501` | |
| 3. Upload a resume file (PDF or DOCX) using the file uploader | |
| 4. In the sidebar, set the summarization model (default: `facebook/bart-large-cnn`) and provide your HF API token if not set as a secret or environment variable. | |
| 5. View extracted info and the AI-generated summary. | |
| ## Hugging Face Inference API | |
| This app uses the Hugging Face Inference API via `huggingface-hub` to run text summarization. You can choose a model such as: | |
| - `facebook/bart-large-cnn` (default) | |
| - `sshleifer/distilbart-cnn-12-6` (faster, smaller) | |
| You may supply an API token via any of the following (priority order): | |
| 1. `st.secrets["HF_TOKEN"]` | |
| 2. Environment variable `HF_TOKEN` | |
| 3. Sidebar password input | |
| Create a token at: https://huggingface.co/settings/tokens | |
| ## Deploy to Hugging Face Spaces | |
| 1. Create a new Space at https://huggingface.co/spaces and choose "Streamlit" as the SDK. | |
| 2. Push your repository files (ensure `app.py` and `requirements.txt` are present). | |
| 3. In the Space, go to Settings → Secrets and add: | |
| - Key: `HF_TOKEN` | |
| - Value: your Hugging Face token | |
| 4. The app will build automatically. Once running, you can change models from the sidebar. | |
| Notes: | |
| - Keep dependencies minimal for faster builds (see `requirements.txt`). | |
| - Uploaded files are kept in the Space runtime; do not rely on persistence across restarts. | |
| ## Dependencies | |
| - Python 3.9+ | |
| - Streamlit | |
| - PyPDF2 | |
| - python-docx | |
| - huggingface-hub | |
| ## License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |
| ## Notes & Next Steps | |
| This is a lightweight implementation designed to run well on Hugging Face Spaces. Ideas to extend: | |
| - Use a custom summarization prompt or instruction-tuned models. | |
| - Add job description matching and scoring. | |
| - Export parsed data to CSV/JSON. | |
| - Improve skill/entity extraction with advanced NLP if needed. | |