📌 DataSense-AI

An intelligent CSV dataset analysis platform powered by Gemini AI. The goal is to analyze datasets and provide suggestions, alerts, and observations that help in data-driven decision making. Designed for teams, companies, or users who work with large volumes of data and need quick insights for decision making.

Note: The backend has been significantly improved through advanced prompt engineering techniques to enhance the AI analysis.

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📖 Table of Contents

• About the Project
• Features
• Technologies Used
• Project Structure
• Quick Start
• Installation
• Usage

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📝 About the Project

DataSense-AI allows users to upload CSV files and get comprehensive analysis including metrics, observations, and actionable suggestions through an intuitive web interface powered by Google's Gemini AI.

The platform provides:

• Automated Analysis: AI-powered insights from your CSV data
• Visual Dashboard: Interactive metrics and observations
• Actionable Suggestions: Data-driven recommendations
• Modern UI: Responsive design with smooth animations

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✨ Features

• ✅ Smart CSV Analysis with Gemini AI
• ✅ Interactive Dashboard with key metrics
• ✅ Drag & Drop File Upload interface
• ✅ Real-time Processing with loading states
• ✅ Responsive Design for all devices
• ✅ Type-safe Development with TypeScript
• ✅ Modern Tech Stack (React 19, FastAPI, TailwindCSS)

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🛠️ Technologies Used

Frontend

• React 19 with TypeScript
• Vite for fast development
• TailwindCSS 4 for styling
• TanStack Query for state management
• Framer Motion for animations
• Recharts for data visualization

Backend

• FastAPI for high-performance API
• Google Generative AI (Gemini) for analysis
• Pandas for data processing
• Uvicorn for ASGI server
• Python 3.9+

Tools & Development

• TypeScript for type safety
• ESLint for code quality
• Git for version control
• Zod for data validation

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📁 Project Structure

DataSense-AI-PromptEngineering/
├── frontend/                 # React TypeScript frontend
│   ├── src/
│   │   ├── components/       # Reusable UI components
│   │   ├── views/           # Page components
│   │   ├── api/             # API client
│   │   └── types/           # TypeScript definitions
│   └── README.md            # Frontend documentation
├── backend/                  # FastAPI Python backend
│   ├── analyzer/            # Dataset analysis logic
│   ├── api/                 # API routes
│   ├── config/              # Configuration
│   └── README.md            # Backend documentation
├── _data/                   # Sample data files
└── README.md               # This file

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🚀 Quick Start

Prerequisites

• Node.js 18+ and npm/yarn
• Python 3.9+
• Gemini API Key from Google AI Studio

1. Clone the repository

git clone <your-repo-url>
cd DataSense-AI-PromptEngineering

2. Setup Backend

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: .\venv\Scripts\activate
pip install -r requirements.txt

# Create .env file
echo "GEMINI_API_KEY=your_api_key_here" > .env

# Start backend server
uvicorn service:app --reload

3. Setup Frontend

cd ../frontend
npm install
npm run dev

4. Access the Application

Open your browser and navigate to:

http://localhost:5173

The backend API will be running on:

http://localhost:8000

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⚙️ Installation

For detailed installation instructions, please refer to the individual README files:

• Backend Setup: See backend/README.md
• Frontend Setup: See frontend/README.md

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▶️ Usage

1. Start the backend server (in backend/ directory):

   uvicorn service:app --reload

2. Start the frontend development server (in frontend/ directory):

   npm run dev

3. Upload your CSV file through the web interface at http://localhost:5173

4. View the analysis results including:

   • Dataset health metrics
   • Missing values analysis
   • Data quality observations
   • Actionable improvement suggestions

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📊 API Documentation

Once the backend is running, you can access the interactive API documentation at:

http://localhost:8000/docs