AI-Powered Resume Analyzer

AI-Powered Resume Analyzer is a web application that helps users analyze and improve their resumes based on a provided job description. The application uses AI models to provide feedback and suggestions for enhancing the resume to better match the job requirements.

Features

• Upload resume in PDF or DOCX format
• Paste job description for analysis
• Analyze resume against job description using AI models
• Generate feedback and suggestions for improving the resume
• Calculate resume match score
• Identify missing keywords and skills
• Download AI feedback as a PDF report
• Support for multiple languages (English and Español)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/ai-powered-resume-analyzer.git
   cd ai-powered-resume-analyzer

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Download NLTK resources:

   import nltk
   nltk.download('punkt')
   nltk.download('stopwords')

Usage

1. Run the Streamlit application:

   streamlit run resume_analyzer_app.py

2. Open your web browser and navigate to http://localhost:8501.

3. Upload your resume (PDF or DOCX) and paste the job description.

4. Select the AI model (Free Public AI or OpenAI API) and enter the OpenAI API key if required.

5. Click the "Analyze Resume" button to get feedback and suggestions.

6. Optionally, click the "Improve Resume" button to generate improved resume bullet points.

7. Download the AI feedback as a PDF report.

Configuration

• OpenAI API Key: If you choose to use the OpenAI API, you need to provide your OpenAI API key. You can enter the key in the application when prompted.
• Hugging Face API Token: If you use the free public AI model, you can set your Hugging Face API token using environment variables.

Setting Up Environment Variables

1. Create a .env file in the root directory of the project.
2. Add your API keys to the .env file:

   HF_TOKEN=your_huggingface_api_token

Translations

The application supports multiple languages. You can add more languages by updating the translations dictionary in the resume_analyzer_app.py file.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Create a new Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgements

• Streamlit
• OpenAI
• Hugging Face
• NLTK
• ReportLab

Support

If you like this app, consider supporting me: