Resume & Cover Letter Generator with LLM

This application allows users to generate professional resumes and cover letters using a locally-running Large Language Model (LLM) through Ollama. Simply enter your personal information, and the app creates well-formatted PDF documents ready for job applications.

Features

• AI-powered document generation using your local LLMs through Ollama
• Professionally formatted PDF output with proper styling and structure
• User-friendly interface built with Streamlit
• Works offline once set up, as it runs entirely on your machine
• Supports multiple LLM models that are available in Ollama

Prerequisites

• Python 3.8+
• Ollama installed and running with at least one model
• Recommended models: Gemma, Llama3, or any model that performs well with text generation

Installation

1. Clone the repository

   git clone https://github.com/hari7261/Resume-CoverLetterGenerator-LLM.git
   cd Resume-CoverLetterGenerator-LLM
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Install and run Ollama

   • Download from ollama.ai
   • Follow installation instructions
   • Run ollama serve to start the service
   • Pull a model, e.g., ollama pull gemma3

Usage

1. Start the application

   streamlit run app.py
   

2. Fill in your information

   • Enter your full name
   • Select or type your target job role
   • Add your work experience
   • List your key skills (comma-separated)

3. Select a model

   • Choose from available Ollama models
   • Or enter a model name manually

4. Generate and download

   • Click "Generate Documents"
   • Download your resume and cover letter as PDFs

Application Structure

• app.py - Main Streamlit application file
• DejaVuSansCondensed.ttf - Font file for PDF generation
• requirements.txt - Python dependencies
• docs/ - Documentation assets

How It Works

1. Data Collection: User inputs their information through a Streamlit interface
2. LLM Generation: Data is sent to a locally running LLM via Ollama
3. Document Processing: The application splits and formats the LLM response
4. PDF Creation: Professional PDFs are created with FPDF
5. Download: Documents are made available for download

Customization

You can customize the document generation by:

• Modifying the prompt in the app.py file
• Changing the PDF styling in the create_pdf function
• Adding additional input fields to collect more user information

Troubleshooting

• "Could not connect to Ollama": Ensure Ollama is running with ollama serve
• No models available: Make sure you've pulled at least one model with ollama pull <model-name>
• PDF generation errors: Check that the font file is accessible

License

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

Acknowledgments

• Streamlit for the web interface framework
• FPDF for PDF generation
• Ollama for local LLM integration

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Created by Hariom Kumar