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

   # Visit https://ollama.ai to download and install

2. Model Setup: Pull the Gemma:2b model

   ollama pull gemma:2b

3. Python Dependencies: Install required packages

   pip install -r requirements.txt

🚀 Installation

1. Clone the repository

   git clone <your-repository-url>
   cd cover-letter-generator

2. Install dependencies

   pip install streamlit requests PyMuPDF python-docx

3. Start Ollama service

   ollama serve

4. Run the application

   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:

# 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 for providing local LLM capabilities
• Streamlit for the amazing web framework
• PyMuPDF 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

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