CSV Data Analyzer

An AI-powered Streamlit application for analyzing CSV data using LangChain agents and GROQ's LLaMA-3 model.

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Project Deployed on Render: https://your-csv-analyzer-app.onrender.com

Table of Contents

• Project Overview
• Features
• Tech Stack
• Installation
• Usage
• Folder Structure
• How It Works
• Author

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Project Overview

This project is an intelligent data analysis tool that allows users to:

• Upload CSV files for instant analysis
• Get AI-powered insights through natural language queries
• Visualize data with interactive charts
• Leverage GROQ's high-performance LLaMA-3 model for accurate responses

Key components:

• CSV Processing: Clean loading and preview of uploaded data
• Natural Language Interface: Ask questions in plain English
• Data Visualization: Automatic chart generation for numeric columns
• Custom UI: Professionally styled Streamlit interface

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Features

• CSV Upload & Preview: Quickly view uploaded data structure
• Interactive Visualizations:
  • Bar charts
  • Line charts
  • Area charts
• AI-Powered Analysis:
  • Natural language query processing
  • Intelligent response generation
• User-Friendly Interface:
  • Clean, modern design
  • Responsive layout
  • Custom CSS styling
• Multi-Column Analysis: Select specific columns for visualization

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Tech Stack

• Python 3.10+
• Streamlit - Web application framework
• LangChain - LLM integration framework
• GROQ LLaMA-3 - High-performance LLM API
• Pandas - Data manipulation and analysis
• Python-dotenv - Environment management

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Installation

1. Clone the repository:

git clone https://github.com/thelakshyadubey/csv_data_analysis.git
cd csv_data_analysis

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

echo "GROQ_API_KEY=your_api_key_here" > .env

Usage

1. Run the application:

streamlit run app.py

2. In the browser:

• Upload a CSV file
• View data preview
• Select columns for visualization
• Ask questions about your data
• View generated visualizations and responses

Folder Structure

csv-data-analyzer/
├── app.py                # Main application file
├── llm_utils.py          # LLM integration utilities
├── css_utils.py          # CSS injection utilities
├── style.css             # Custom styles
├── assets/
│   └── background.png    # Background image
├── requirements.txt      # Dependencies
└── README.md             # Project documentation

How It Works

1. Data Upload:

• User uploads a CSV file
• System loads data into Pandas DataFrame
• Displays preview of the data

2. Visualization:

• Automatically detects numeric columns
• Provides multiple chart types
• Allows column selection

3. Natural Language Processing:

• User enters question in text area
• LangChain agent processes query using LLaMA-3
• Returns human-readable response

Author

Lakshya Dubey

Preview