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840f8bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | # 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.
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