🍷 Wine Analysis Interface

This branch contains an interactive dashboard designed to run experiments on multiple GC-MS wine datasets.
The dashboard provides all the functionality needed to execute the complete wine analysis workflow — including model training, evaluation, and visualization.
It allows users to train different machine-learning models, compute classification accuracies, generate confusion matrices, and visualize results across all wine families considered in the project.

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🧭 Getting Started

1. Clone the Repository

Open a terminal (or PowerShell on Windows) and run:

git clone https://github.com/pougetlab/wine_analysis.git
cd wine_analysis
git checkout main

This will create a local folder named wine_analysis containing the web interface branch.

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2. Install Prerequisites

Make sure the following tools are installed on your system:

• Python 3.9+
• pip (Python package manager)
• Node.js and npm (for the frontend)

Check if they’re installed:

python --version        # or python3 --version
pip --version
node --version
npm --version

If any of these commands fail, install the missing tools as follows:

🐍 Python and pip

• Download and install from https://www.python.org/downloads/
• When installing on Windows, make sure to check “Add Python to PATH.”

⚙️ Node.js and npm

• Download from https://nodejs.org/.
  npm (Node Package Manager) comes bundled with Node.js automatically.
  Choose the LTS (Long-Term Support) version for stability.

Alternatively, on Linux you can install it from the terminal:

# Ubuntu / Debian
sudo apt update
sudo apt install nodejs npm

# Fedora
sudo dnf install nodejs npm

# macOS (with Homebrew)
brew install node

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3. Set Up the Python Environment

It is strongly recommended to use a virtual environment for the backend to isolate dependencies.

On Linux or macOS:

python3 -m venv .venv
source .venv/bin/activate

On Windows (PowerShell):

python -m venv .venv
.venv\Scripts\activate

Once the environment is active (you should see (.venv) in your terminal prompt),
install the required Python dependencies — including Uvicorn to run the FastAPI server:

pip install -r requirements.txt

If you encounter missing dependencies (e.g., during model training or data visualization),
you can install these additional commonly used packages:

pip install torch torchvision pynndescent netCDF4 seaborn umap-learn tqdm scikit-optimize pycairo fastapi uvicorn[standard] 

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🚀 Running the Wine Analysis Dashboard

This project includes two main components:

• Backend API — built with FastAPI
• Frontend Dashboard — built with React

Both must be running simultaneously for the dashboard to function.

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🧩 1. Start the Backend (FastAPI)

Open a terminal and run:

cd api_web
uvicorn main:app --reload --host 0.0.0.0 --port 8000

The backend will start on http://localhost:8000.

API documentation:

• Swagger UI: http://localhost:8000/docs

⚠️ Note: These documentation endpoints are provided by FastAPI and are only available on port 8000, not on the frontend (port 3000).

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💻 2. Start the Frontend (React)

In another terminal window (or Command Prompt on Windows), run:

cd frontend
npm install
npm start

The frontend will start on http://localhost:3000 and will automatically connect to the backend running on port 8000.

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🔄 Summary

Component	Command	URL
Backend (FastAPI)	uvicorn main:app --reload --host 0.0.0.0 --port 8000	http://localhost:8000
Frontend (React)	npm start	http://localhost:3000
Swagger UI (API Docs)	—	http://localhost:8000/docs

Press Ctrl + C in each terminal to stop the servers.

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📄 Documentation

The dashboard includes its own documentation panel.
For detailed explanations of the GC-MS pipelines, models, and visualization logic, refer to:
👉 https://pougetlab.github.io/wine_analysis/