🚀 Expert EDA Chatbot

An intelligent multi-agent system built with LangGraph, Streamlit, and Groq for automated Exploratory Data Analysis (EDA). Upload your CSV or Excel files and chat with your data to get insights, statistics, and visualizations instantly.

🌟 Features

• Natural Language Data Analysis: Ask questions about your dataset in plain English.
• Automated Visualizations: Automatically generates charts (histograms, scatter plots, etc.) based on your queries.
• Multi-Agent Orchestration: Uses LangGraph to route queries between a Data Analyst agent and a Chart Generator agent.
• PDF Report Generation: Export your analysis session into a professional PDF report.
• Interactive UI: Built with Streamlit for a smooth and responsive user experience.

🛠️ Tech Stack

• Framework: LangGraph
• LLM: Groq (Llama 3.3 70B)
• Frontend: Streamlit
• Data Handling: Pandas, NumPy
• Visualization: Matplotlib
• Environment: Python 3.8+

🚀 Quick Start

1. Clone the repository

git clone <your-repo-url>
cd linkedin-agent-1

2. Set up a virtual environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Configure environment variables

Create a .env file in the root directory and add your Groq API key:

GROQ_API_KEY=your_api_key_here

5. Run the application

streamlit run app.py

📂 Project Structure

• app.py: The Streamlit frontend and main entry point.
• agents.py: LangGraph workflow definition and agent nodes.
• utils.py: Utility functions (e.g., PDF generation).
• requirements.txt: Python dependencies.
• .env: (Ignored) Environment variables.

🛡️ License

Distributed under the MIT License. See LICENSE for more information.

---

Built with ❤️ by Muhammad Adeel