https://github.com/no0bitah/cover-letter-generator
A Streamlit-powered cover letter generator that uses Ollama AI to create personalized cover letters from resume and job description files. Features real-time customization, multiple file format support (PDF, DOCX, TXT), and an interactive chat interface for further personalization.
https://github.com/no0bitah/cover-letter-generator
Last synced: 10 months ago
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A Streamlit-powered cover letter generator that uses Ollama AI to create personalized cover letters from resume and job description files. Features real-time customization, multiple file format support (PDF, DOCX, TXT), and an interactive chat interface for further personalization.
- Host: GitHub
- URL: https://github.com/no0bitah/cover-letter-generator
- Owner: No0Bitah
- License: mit
- Created: 2025-07-16T01:54:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-16T06:01:48.000Z (about 1 year ago)
- Last Synced: 2025-07-17T03:45:41.919Z (about 1 year ago)
- Language: Python
- Size: 33.2 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Cover Letter Generator 📝
A smart, personalized cover letter generator that leverages the power of Ollama's local LLM models to create tailored cover letters based on your resume and job descriptions.
## 🌟 Features
- **Smart Resume Processing**: Automatically extracts and cleans text from PDF, DOCX, and TXT files
- **Multiple Input Methods**: Upload files or paste text directly
- **Local LLM Integration**: Uses Ollama API for privacy-focused, local processing
- **Real-time Personalization**: Interactive chat interface for further customization
- **Professional Formatting**: Generates email-formatted cover letters with proper structure
- **Typing Animation**: Smooth display of generated content
- **Session Management**: Caches processed files to avoid reprocessing
## 🛠️ Technology Stack
- **Frontend**: Streamlit
- **LLM**: Ollama API with Gemma:2b model
- **PDF Processing**: PyMuPDF (fitz), custom extraction module
- **Document Processing**: python-docx
- **Backend**: Python 3.7+
## 📋 Prerequisites
1. **Ollama Installation**: Install Ollama on your system
```bash
# Visit https://ollama.ai to download and install
```
2. **Model Setup**: Pull the Gemma:2b model
```bash
ollama pull gemma:2b
```
3. **Python Dependencies**: Install required packages
```bash
pip install -r requirements.txt
```
## 🚀 Installation
1. **Clone the repository**
```bash
git clone
cd cover-letter-generator
```
2. **Install dependencies**
```bash
pip install streamlit requests PyMuPDF python-docx
```
3. **Start Ollama service**
```bash
ollama serve
```
4. **Run the application**
```bash
streamlit run letter_creator.py
```
## 📁 Project Structure
```
cover-letter-generator/
├── letter_creator.py # Main Streamlit application
├── extract_resume.py # PDF text extraction utilities
├── resume_cleaner.py # Resume text cleaning and processing
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
## 🎯 Usage
### Step 1: Upload Your Resume
- Upload your resume file (PDF, DOCX, or TXT)
- Or paste your resume text directly in the text area
### Step 2: Add Job Description
- Upload the job description file (PDF, DOCX, or TXT)
- Or paste the job description text directly
### Step 3: Generate Cover Letter
- Click "🚀 Generate Cover Letter"
- Wait for the AI to process and generate your personalized cover letter
### Step 4: Personalize Further (Optional)
- Use the chat interface to request modifications
- Examples: "Make it more formal", "Add emphasis on teamwork", "Make it shorter"
## ⚙️ Configuration
### Ollama Settings
You can modify the Ollama configuration in `letter_creator.py`:
```python
# Default Ollama API endpoint
Ollama_API_URL = "http://localhost:11434/api/generate"
# Model configuration
MODEL_NAME = "gemma:2b" # Change to your preferred model
# Generation parameters
"temperature": 0.7, # Creativity level (0.0-1.0)
"top_p": 0.9, # Diversity control (0.0-1.0)
```
### Supported File Types
- **PDF**: Processed using PyMuPDF with fallback extraction
- **DOCX**: Microsoft Word documents
- **TXT**: Plain text files
## 🔧 Key Functions
### `generate_cover_letter_prompt(resume_text, job_description)`
Creates a comprehensive prompt for the LLM with specific instructions for:
- Professional tone and format
- Technology matching between resume and job description
- Enthusiasm for learning new technologies
- Concise, email-format output
### `read_file(file, file_type)`
Handles multiple file formats with intelligent processing:
- PDF extraction with cleanup
- DOCX paragraph extraction
- TXT direct reading
- Resume cleaning for type 0 (resume files)
### `query_ollama(prompt)`
Communicates with the Ollama API:
- Sends structured requests
- Handles response parsing
- Error handling and reporting
### `display_text(response, typing_speed)`
Creates an engaging user experience:
- Typing animation effect
- Professional styling
- Session state management
## 🎨 UI Features
- **Sidebar Navigation**: Clean file upload and text input areas
- **Real-time Feedback**: Success/error messages and loading spinners
- **Responsive Design**: Professional styling with centered layout
- **Interactive Chat**: Post-generation customization interface
## 🛡️ Privacy & Security
- **Local Processing**: All data processing happens locally using Ollama
- **No Data Storage**: Files are processed in memory, not saved permanently
- **Session-based**: Data exists only during your session
## 🔍 Troubleshooting
### Common Issues
1. **Ollama Connection Error**
- Ensure Ollama is running: `ollama serve`
- Check if the model is installed: `ollama list`
2. **File Processing Issues**
- Ensure files are not corrupted
- Check file size limitations
- Verify file format compatibility
3. **Performance Issues**
- Consider using a more powerful model
- Adjust temperature and top_p parameters
- Ensure sufficient system resources
## 📊 Performance Tips
- **Model Selection**: Gemma:2b is efficient; consider larger models for better quality
- **File Size**: Smaller files process faster
- **Prompt Engineering**: The built-in prompt is optimized for best results
## 🤝 Contributing
1. Fork the repository
2. Create a feature branch: `git checkout -b feature-name`
3. Commit changes: `git commit -m 'Add feature'`
4. Push to branch: `git push origin feature-name`
5. Submit a pull request
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🙏 Acknowledgments
- [Ollama](https://ollama.ai) for providing local LLM capabilities
- [Streamlit](https://streamlit.io) for the amazing web framework
- [PyMuPDF](https://pymupdf.readthedocs.io/) for PDF processing
## 📞 Support
If you encounter any issues or have questions:
1. Check the troubleshooting section
2. Review Ollama documentation
3. Open an issue in the repository
---
**Made with by [No0Bitah](https://no0bitah.github.io/) 👋**