InsightAI - AI-Powered Data Analytics SaaS Platform

Production-ready AI SaaS Starter Kit for data science teams, analytics dashboards, and business intelligence platforms.

Features

Core Functionality

• 🔐 JWT Authentication - Secure user management
• 📊 CSV Data Upload - Drag & drop interface with previews
• 🤖 AutoML Pipeline - Classification, Regression, Clustering
• 📈 Interactive Visualizations - Charts, confusion matrices, ROC curves
• 🧠 AI-Generated Insights - GPT-4 powered analysis
• 📄 Report Export - PDF and CSV downloads
• 🌙 Dark Mode - Responsive design with theme toggle

Advanced Features

• 🔍 Data Preprocessing - Auto-detect types, handle missing values
• 📊 Model Performance - Accuracy, RMSE, Silhouette scores
• 🎯 Best Model Selection - Scikit-learn and LazyPredict integration
• 📱 Mobile Responsive - Modern UI with Tailwind CSS

🛠️ Tech Stack

Backend

• FastAPI - High-performance Python web framework
• Pandas - Data manipulation and analysis
• Scikit-learn - Machine learning algorithms
• LazyPredict - Automated model selection
• Pydantic - Data validation
• SQLite - Database (easily switchable to PostgreSQL)
• JWT - Authentication

Frontend

• React - Modern UI framework
• Tailwind CSS - Utility-first styling
• Chart.js - Interactive visualizations
• Axios - HTTP client

AI & Analytics

• OpenAI GPT-4 - Insight generation
• Plotly - Advanced charts
• PyMuPDF - PDF report generation

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• OpenAI API key

Installation

1. Clone the repository

git clone <repository-url>
cd InsightAI

2. Backend Setup

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

3. Frontend Setup

cd frontend
npm install

4. Environment Configuration

# Backend (.env)
OPENAI_API_KEY=your_openai_api_key
SECRET_KEY=your_secret_key
DATABASE_URL=sqlite:///./insightai.db

# Frontend (.env)
VITE_API_URL=http://localhost:8000

5. Run the Application

# Backend (Terminal 1)
cd backend
uvicorn app.main:app --reload

# Frontend (Terminal 2)
cd frontend
npm run dev

Visit http://localhost:5173 to access the application.

📁 Project Structure

InsightAI/
├── backend/
│   ├── app/
│   │   ├── main.py
│   │   ├── routes/
│   │   │   ├── auth.py
│   │   │   ├── upload.py
│   │   │   ├── ml.py
│   │   │   └── reports.py
│   │   ├── services/
│   │   │   ├── ml_service.py
│   │   │   ├── report_service.py
│   │   │   └── llm_service.py
│   │   ├── models/
│   │   │   ├── user.py
│   │   │   └── dataset.py
│   │   └── utils/
│   │       ├── auth.py
│   │       └── data_processing.py
│   ├── requirements.txt
│   └── .env
├── frontend/
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   ├── services/
│   │   └── assets/
│   ├── package.json
│   └── .env
└── README.md

🎯 Use Cases

• No-code AutoML Dashboards - Enable non-technical users to run ML models
• Internal Analytics SaaS - Business intelligence for teams
• Freelancer Toolkit - Client reporting and analysis
• AI-powered BI Platforms - Enterprise analytics solutions

🔧 API Endpoints

Authentication

• POST /auth/register - User registration
• POST /auth/login - User login
• GET /auth/me - Get current user

Data Management

• POST /upload/csv - Upload CSV dataset
• GET /datasets - List user datasets
• GET /datasets/{id} - Get dataset details

Machine Learning

• POST /ml/train - Train ML model
• GET /ml/results/{id} - Get model results
• GET /ml/insights/{id} - Get AI-generated insights

Reports

• GET /reports/{id}/pdf - Download PDF report
• GET /reports/{id}/csv - Download CSV results

🎨 UI Components

• Dashboard - Overview of datasets and models
• Upload Interface - Drag & drop CSV upload with preview
• Model Selection - Interactive task and algorithm selection
• Results Visualization - Charts, metrics, and insights
• Report Generation - Export functionality

🔐 Security Features

• JWT-based authentication
• Input validation and sanitization
• Rate limiting
• CORS configuration
• Secure file upload handling

📊 ML Pipeline

1. Data Upload - CSV parsing and validation
2. Preprocessing - Type detection, missing value handling
3. Task Selection - Classification, Regression, Clustering
4. Model Training - Automated algorithm selection
5. Evaluation - Performance metrics calculation
6. Visualization - Charts and plots generation
7. Insight Generation - AI-powered analysis
8. Report Export - PDF/CSV download

🚀 Deployment

Backend (FastAPI)

# Production with Gunicorn
pip install gunicorn
gunicorn app.main:app -w 4 -k uvicorn.workers.UvicornWorker

Frontend (React)

# Build for production
npm run build

# Serve with nginx or similar

🤝 Contributing

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

📄 License

MIT License - see LICENSE file for details

🆘 Support

For support, email [email protected] or create an issue in the repository.

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