🤖 Nexus: An AI-Powered Data Analyst

🚀 Project Overview

Nexus is an advanced AI-powered data analyst designed to simplify complex data analysis and decision-making. It enables users to interact with structured datasets using natural language and provides insights through intuitive visualizations and automated responses.

🖼️ Screenshots

Landing Page

Dashboard

ChatPage

DataSource

✨ Key Features

📊 Data Compatibility

• Supports multiple data formats:
  • Excel Spreadsheets
  • CSV Files
  • SQL Databases

🤔 AI-Powered Query Responses

• Natural language processing for user queries.
• Context-relevant insights from structured data.

📈 Data Visualizations

• Generate dynamic charts and graphs.
• Identify trends and patterns visually.

🔧 Backend Integration

• Real-time data processing via FastAPI.
• Seamless connection with AI models for analysis.

🎨 Intuitive User Experience

• Modern and responsive design.
• Customizable themes and settings.

🛠 Tech Stack

Backend

Frontend

Deployment

🚦 Quick Start

Backend Setup

# Clone the repository
git clone https://github.com/MITTALBHAVYA/NEXUS

# Navigate to backend directory
cd backend

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env

# Start the server
uvicorn main:app --reload

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

🧪 Testing

Backend Testing

• Pytest for unit and integration tests.
• Postman collection for API testing.

# Run backend tests
pytest

Frontend Testing

• Jest and React Testing Library for components and integration.

# Run frontend tests
npm test

🔮 Roadmap and Future Updates

• Enhanced query understanding using fine-tuned LLMs.
• Support for NoSQL databases.
• Interactive dashboards with drag-and-drop functionality.
• Multi-user collaboration features.
• Advanced analytics with prediction and forecasting.

🤝 Contributing to Nexus

We warmly welcome contributions from the open-source community! Here's how you can help:

1. 🍴 Fork the Repository

   • Create your own copy of the project.

2. 🌿 Create a Feature Branch

   git checkout -b feature/amazing-contribution

3. 🔧 Make Your Changes

   • Implement your feature or bug fix.
   • Ensure code quality and test coverage.

4. 📤 Submit a Pull Request

   • Describe your changes in detail.
   • Link any related issues.

🌟 Star Us on GitHub!

Like what you see? Show your support by starring our repository! ⭐

📄 License

This project is licensed under the MIT License. See LICENSE for details.

👨‍💻 Author

Bhavya Mittal

• GitHub: @MITTALBHAVYA

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Happy Data Analyzing! 🎉