🍷 VinoPredict: AI-Powered Wine Quality Analysis

A sophisticated full-stack machine learning application designed to predict wine quality using advanced physicochemical analysis. VinoPredict leverages state-of-the-art machine learning to provide instant, professional-grade quality assessments.

📸 Application Preview

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✨ Features

• 🎯 Precision ML Prediction: Instantly analyze wine quality based on 11 physicochemical properties.
• 📚 Chemical Glossary: Interactive guide to understanding the impact of pH, acidity, and alcohol levels.
• 🔬 Science-Driven: Powered by a Random Forest Classifier trained on the renowned UCI Wine Quality dataset.
• 📱 Responsive Design: Seamless experience across desktop and mobile devices.
• ⚡ Fast Inference: High-performance backend providing results in milliseconds.

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🛠️ Tech Stack

Frontend

• Framework: React + Vite
• Styling: TailwindCSS
• State Management: React Hooks
• Deployment: Vercel

Backend

• Framework: FastAPI (Python)
• ML Engine: Scikit-Learn
• Inference: Random Forest Classification
• Deployment: Render

Machine Learning

• Dataset: UCI Wine Quality (Red & White variants)
• Preprocessing: Pandas, NumPy
• Model Storage: Joblib

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📂 Project Structure

Wine-quality-predictor-
├── backend/            # FastAPI Application
│   ├── app/            # Main server logic
│   └── models/         # Serialized ML models
├── frontend/           # Vite + React UI
│   ├── src/            # Components & Styles
│   └── public/         # Static assets
├── ml_pipeline/        # Model training scripts
├── docs/               # Documentation & Screenshots
└── docker-compose.yml  # Container orchestration

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🚀 Local Development Setup

1. Backend Setup

# Navigate to root directory
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Train the model (Required for first run)
python ml_pipeline/train_model.py

# Start the server
uvicorn backend.app.main:app --reload

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

2. Frontend Setup

cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

The application will be available at http://localhost:5173.

3. Docker (Optional)

docker-compose up --build

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• UCI Machine Learning Repository for the Wine Quality Dataset.
• All contributors and open-source packages that made this project possible.