| # 🧠 Resume-Job Match Application (LLM-Powered) | |
|  | |
| This is a **Streamlit-based web app** that evaluates how well a resume matches a job description using powerful Large Language Models (LLMs) such as: | |
| - OpenAI GPT | |
| - Anthropic Claude | |
| - Google Gemini (Generative AI) | |
| - Groq LLM | |
| - DeepSeek LLM | |
| The app takes a resume and job description as input files, sends them to these LLMs, and returns: | |
| - ✅ Match percentage from each model | |
| - 📊 A ranked table sorted by match % | |
| - 📈 Average match percentage | |
| - 🧠 Simple, responsive UI for instant feedback | |
| ## 📂 Features | |
| - Upload **any file type** for resume and job description (PDF, DOCX, TXT, etc.) | |
| - Automatic extraction and cleaning of text | |
| - Match results across multiple models in real time | |
| - Table view with clean formatting | |
| - Uses `.env` file for secure API key management | |
| ## 🔐 Environment Setup (`.env`) | |
| Create a `.env` file in the project root and add the following API keys: | |
| ```env | |
| OPENAI_API_KEY=your-openai-api-key | |
| ANTHROPIC_API_KEY=your-anthropic-api-key | |
| GOOGLE_API_KEY=your-google-api-key | |
| GROQ_API_KEY=your-groq-api-key | |
| DEEPSEEK_API_KEY=your-deepseek-api-key | |
| ``` | |
| ## ▶️ Running the App | |
| ### Launch the app using Streamlit: | |
| streamlit run resume_agent.py | |
| ### The app will open in your browser at: | |
| 📍 http://localhost:8501 | |