Resume-Analyzer-Agent

# README.md # Resume Analyzer + LinkedIn/Naukri Job Fetcher + MCP Server This project allows you to: - Upload a Resume (PDF) - Analyze Resume Summary, Skill Gaps, and Future Roadmap using EURI AI - Auto-fetch matching jobs from LinkedIn and Naukri using Apify - Wrap the job fetch functions into a FastMCP Server for integration with external tools like Claude Desktop, MCP Inspector, etc. # 💪 Full Setup Instructions ## 1. Clone the Repository ```bash git clone <your-repo-link> cd resume_job_fetcher_project ``` ## 2. Create Conda Virtual Environment (Recommended) ```bash conda create -n resume_fetcher_env python=3.10 conda activate resume_fetcher_env ``` ## 3. Install Required Packages Using pip: ```bash pip install -r requirements.txt ``` requirements.txt contains: - streamlit - pymupdf - euriai - python-dotenv - apify-client - fastmcp Alternatively, using UV: Install UV on Windows Powershell: ```bash powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` Install requirements: ```bash uv pip install -r requirements.txt ``` ## 4. Configure Environment Variables Create a `.env` file: ```plaintext EURI_API_KEY=your_real_euri_api_key_here APIFY_API_TOKEN=your_real_apify_api_token_here ``` ## 5. Run the Streamlit App ```bash streamlit run app.py ``` ## 6. Run MCP Server (FastMCP) ```bash python mcp_server.py ``` ## 7. Test MCP Server (Optional) Install MCP Inspector: ```bash npm install -g @modelcontextprotocol/inspector ``` Run the Inspector: ```bash npx @modelcontextprotocol/inspector python mcp_server.py ``` # 🚀 UV-Based Project Setup (Optional) ```bash uv init mcp-server-demo cd mcp-server-demo uv add "mcp[cli]" ``` To manually install MCP CLI: ```bash pip install "mcp[cli]" ``` Running standalone MCP locally: ```bash uv run mcp ``` # 📆 Project Structure ```plaintext resume_job_fetcher_project/ ├── app.py # Streamlit frontend app ├── mcp_server.py # FastMCP server exposing job fetchers ├── requirements.txt ├── README.md └── .env # API keys configuration ``` # 🌟 Technology Stack | Component | Technology | | :--------------------- | :------------------------------- | | Resume Analysis | EURI AI (GPT-4.1-nano) | | Job Fetching | Apify (LinkedIn + Naukri Actors) | | Frontend | Streamlit | | MCP Server | FastMCP | | Environment Management | Conda / UV | | Inspector Tool | MCP Inspector | # 📃 Important Links - [EURI API Documentation](https://api.euron.one/docs) - [Apify API Documentation](https://docs.apify.com/) - [FastMCP GitHub](https://github.com/modelcontextprotocol/fastmcp) - [Streamlit Documentation](https://docs.streamlit.io/) - [UV Installation Guide](https://docs.astral.sh/uv/getting-started/installation/#__tabbed_1_2) - [MCP Inspector GitHub](https://github.com/modelcontextprotocol/inspector) # 🔫 Quick Commands Summary | Task | Command | | :------------------- | :---------------------------------------------------------------------------- | | Create Conda Env | `conda create -n resume_fetcher_env python=3.10` | | Activate Env | `conda activate resume_fetcher_env` | | Install Packages | `pip install -r requirements.txt` or `uv pip install -r requirements.txt` | | Run App | `streamlit run app.py` | | Run MCP Server | `python mcp_server.py` | | Launch MCP Inspector | `npx @modelcontextprotocol/inspector python mcp_server.py` | # 💪 Conclusion - Resume Analyzer + Live Job Fetcher using AI 🚀 - Fully ready MCP tool server integration - Modular, scalable, and professional setup # 🚀 Let's Build Smarter Applications Faster with AI, Apify, MCP & Streamlit!