Dataverse.ai

A no-login data analysis platform for startup founders and RevOps professionals. Upload CSV files and ask questions in plain English to get instant insights, tables, and charts.

Features

• No Login Required: Start analyzing data immediately
• Natural Language Queries: Ask questions like "Show me top 5 revenue sources" or "What's the average order value?"
• Instant Results: Get tables, charts, and insights in seconds
• File Support: Upload CSV, Excel files up to 50MB
• Modern UI: Clean, responsive interface built with Next.js and shadcn/ui

Tech Stack

Frontend

• Next.js 14 with App Router
• TypeScript for type safety
• Tailwind CSS for styling
• shadcn/ui for UI components
• React Dropzone for file uploads
• Lucide React for icons

Backend

• FastAPI for API development
• Python 3.10+ for data processing
• Pandas for data manipulation
• Uvicorn for ASGI server

Project Structure

dataverse.ai/
├── frontend/                 # Next.js frontend application
│   ├── src/
│   │   ├── app/             # App router pages
│   │   ├── components/      # React components
│   │   │   ├── ui/         # shadcn/ui components
│   │   │   ├── FileUpload.tsx
│   │   │   └── DataAnalysis.tsx
│   │   ├── lib/            # Utility libraries
│   │   ├── types/          # TypeScript type definitions
│   │   └── utils/          # Helper functions
│   └── package.json
├── backend/                 # FastAPI backend application
│   ├── app/
│   │   ├── api/            # API endpoints
│   │   │   ├── upload.py   # File upload endpoints
│   │   │   └── analyze.py  # Data analysis endpoints
│   │   ├── core/           # Core configuration
│   │   ├── models/         # Pydantic models
│   │   └── services/       # Business logic
│   ├── main.py             # FastAPI application entry point
│   └── requirements.txt
└── README.md

Getting Started

Prerequisites

• Node.js 18+ and npm
• Python 3.10+
• Git

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

4. Open http://localhost:3000 in your browser.

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Start the development server:

   python main.py

5. The API will be available at http://localhost:8000

API Endpoints

File Upload

• POST /api/v1/upload - Upload CSV/Excel files
• GET /api/v1/files/{file_id} - Get file information

Data Analysis

• POST /api/v1/analyze - Analyze data with natural language questions

Development

Adding New Components

1. Create new components in frontend/src/components/
2. Use shadcn/ui components for consistency
3. Follow TypeScript best practices

Adding New API Endpoints

1. Create new router files in backend/app/api/
2. Define Pydantic models in backend/app/models/
3. Include routers in backend/main.py

Environment Variables

Create .env.local in the frontend directory:

NEXT_PUBLIC_API_URL=http://localhost:8000

Deployment

Frontend (Vercel)

1. Connect your GitHub repository to Vercel
2. Set environment variables
3. Deploy automatically on push

Backend (Railway/Render)

1. Set up Python environment
2. Install dependencies from requirements.txt
3. Set environment variables
4. Deploy with uvicorn main:app --host 0.0.0.0 --port $PORT

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Roadmap

• Real-time data streaming
• Advanced chart types (scatter plots, heatmaps)
• Data export functionality
• Team collaboration features
• Database integration for persistent storage
• AI-powered insights and recommendations