app.py

import gradio as gr
import joblib
import pandas as pd

# Load trained model
model = joblib.load("wine_rf_model.pkl")

# Define prediction function
def predict(fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides,
            free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol):
    data = pd.DataFrame([[fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides,
                          free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]],
                        columns=["fixed acidity", "volatile acidity", "citric acid", "residual sugar", 
                                 "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density",
                                 "pH", "sulphates", "alcohol"])
    prediction = model.predict(data)[0]
    return f"Predicted Wine Quality: {prediction}"

# Create Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Fixed Acidity"),
        gr.Number(label="Volatile Acidity"),
        gr.Number(label="Citric Acid"),
        gr.Number(label="Residual Sugar"),
        gr.Number(label="Chlorides"),
        gr.Number(label="Free Sulfur Dioxide"),
        gr.Number(label="Total Sulfur Dioxide"),
        gr.Number(label="Density"),
        gr.Number(label="pH"),
        gr.Number(label="Sulphates"),
        gr.Number(label="Alcohol")
    ],
    outputs=gr.Textbox(label="Prediction"),
    title="Wine Quality Prediction using Random Forest",
    description="Enter chemical properties of wine to predict its quality."
)

iface.launch()