DataWise AI is an automated machine learning and data analysis platform that converts CSV data into insights, models, and visualizations.
The platform is fully containerized using Docker.
Create a .env file in the root directory:
GOOGLE_API_KEY=your_gemini_api_key_hereLaunch the ecosystem using Docker Compose:
docker-compose up -d --buildAccess the dashboard at http://localhost:3000.
If you prefer to run the backend without Docker:
- Ensure Python 3.12+ is installed.
- Install dependencies:
pip install -r requirements.txt - Set your environment variable:
GOOGLE_API_KEY=your_key - Run:
python app/main.py
The platform accepts any standard CSV file up to 50MB.
Several pre-verified datasets are available in the TestDatasets/ directory for immediate testing:
- Car price prediction (Regression)
- Amazon sales trends (Time-series/Sales)
- Supermarket analytics (Retail)
- Credit card churn (Classification)
Upload your own CSV directly through the dashboard interface once the system is running.
- Frontend: React-based dashboard served via Nginx.
- Backend: Flask API orchestrating ML and Data Analysis logic.
- Output: All runtime artifacts (scripts, logs, and plots) are stored in the
output/directory.
To verify the pipeline independently of the UI, run the automated test suite. It will automatically upload the sample car price dataset to the backend and execute the full ML flow:
# Basic test against Docker (localhost:3000) or local (localhost:5000)
python test_pipeline.py --host http://localhost:5000To pass a dataset manually to the backend API, send a POST request to /api/home with:
- Header:
multipart/form-data - Body: A file field named
file-uploadcontaining your CSV. - Body: A text field named
queryInputdescribing your analysis goal.
- Backend: Python 3.12 (Flask, Pandas, Scikit-Learn).
- Frontend: React 18 (MUI).
- AI: Google Gemini (2.0 Flash).