AI Analytico

AI-powered data analytics dashboard for intelligent data exploration and visualization

Get Started • Features • Local Development • Configuration

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✨ Features

• 🤖 AI-Powered Analytics — Natural language chat interface for data exploration
• 🧹 Intelligent Data Cleaning — AI-generated suggestions for data quality improvement
• 📈 Dynamic Visualizations — Interactive charts with Plotly (bar, line, pie, scatter, etc.)
• 📑 Custom Reports — Build and export customizable dashboards as PDF
• 🔄 Multi-LLM Support — Choose between Google Gemini or OpenAI
• 📊 Data Overview — Automatic statistics, data types, and quality metrics
• 🌓 Dark/Light Mode — Seamless theme switching with next-themes

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🐳 Quick Start (Docker)

The fastest way to get started is with Docker. Our published images are ready to use.

1. Create a docker-compose.yml

version: "3.9"

services:
  backend:
    image: anandavii/aianalytico-backend:1.1.0
    container_name: aianalytico-backend
    ports:
      - "8000:8000"
    environment:
      LLM_PROVIDER: ${LLM_PROVIDER}
      GEMINI_API_KEY: ${GEMINI_API_KEY}
      OPENAI_API_KEY: ${OPENAI_API_KEY}
    restart: unless-stopped

  frontend:
    image: anandavii/aianalytico-frontend:1.1.0
    container_name: aianalytico-frontend
    ports:
      - "3000:3000"
    depends_on:
      - backend
    restart: unless-stopped

2. Create a .env file

# Choose your LLM provider: gemini or openai
LLM_PROVIDER=gemini

# API Keys (add the one you're using)
GEMINI_API_KEY=your_gemini_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

3. Run the application

docker compose up -d

4. Access the dashboard

Service	URL
Frontend	http://localhost:3000
Backend API	http://localhost:8000
API Docs	http://localhost:8000/docs

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🛠 Local Development

For development, you can run the services locally without Docker.

Prerequisites

• Python 3.11+
• Node.js 20+
• npm

Quick Start (Single Command)

./start_app.sh

This script sets up the Python environment, installs dependencies, and starts both services.

Manual Setup

Terminal 1: Backend

cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Create .env and add your API keys
uvicorn app:app --host 0.0.0.0 --port 8000 --reload

Terminal 2: Frontend

cd frontend
npm install
npm run dev

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⚙️ Configuration

Configure the application via environment variables in .env:

Variable	Description	Options
LLM_PROVIDER	AI provider to use	gemini (default), openai
GEMINI_API_KEY	Google Gemini API key	Required if using Gemini
OPENAI_API_KEY	OpenAI API key	Required if using OpenAI

Note: Restart the application after changing the LLM provider.

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📖 Usage

1. Open http://localhost:3000
2. Click Get Started
3. Upload a CSV or Excel file
4. Explore your data with the AI-powered chat
5. Clean data using intelligent suggestions
6. Visualize with dynamic charts
7. Build custom reports and export them

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🛠️ Tech Stack

Layer	Technologies
Frontend	Next.js 16, React 19, TypeScript, Tailwind CSS 4
UI Components	Radix UI, Lucide Icons, Framer Motion
Data Visualization	Plotly.js, React-Plotly
Backend	FastAPI, Python 3.11+, Pydantic
AI/LLM	Google Gemini API, OpenAI API
Data Processing	Pandas, NumPy, OpenPyXL
Deployment	Docker, Docker Compose

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🏗 Architecture

┌─────────────────┐       ┌─────────────────┐
│    Frontend     │──────▶│     Backend     │
│   (Next.js)     │◀──────│    (FastAPI)    │
│   Port 3000     │       │    Port 8000    │
└─────────────────┘       └────────┬────────┘
                                   │
                          ┌────────▼────────┐
                          │   LLM Provider  │
                          │ Gemini / OpenAI │
                          └─────────────────┘

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📜 License

MIT License - See LICENSE for details.

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Made with ❤️ by anandavii