README.md

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:

   git clone <repository-url>
   cd windsurf-project-2
   

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. (Optional) Set your Hugging Face token for higher rate limits:

   # Windows (PowerShell)
   setx HF_TOKEN "hf_XXXXXXXXXXXXXXXXXXXXXXXXXXXX"
   # macOS/Linux
   export HF_TOKEN="hf_XXXXXXXXXXXXXXXXXXXXXXXXXXXX"
   

Usage

1. Run the Streamlit application:

   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 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.