import streamlit as st
import joblib
import numpy as np

# Load the trained model
model = joblib.load('model_wine_binary.pkl')

# Streamlit app
def main():
    st.title('Wine Type Prediction App')
    st.write('This app predicts whether a wine is red or white based on input features.')

    # Feature input names
    feature_names = [
        'Fixed Acidity', 'Volatile Acidity', 'Citric Acid', 'Residual Sugar',
        'Chlorides', 'Free Sulfur Dioxide', 'Total Sulfur Dioxide',
        'Density', 'pH', 'Sulphates', 'Alcohol'
    ]

    # Collect user input
    user_inputs = {}
    for feature in feature_names:
        user_input = st.number_input(feature, min_value=0.0, max_value=200.0, value=0.0, key=feature)
        user_inputs[feature] = user_input
    
    # Add a "Submit" button
    if st.button('Submit'):
        # Check if all inputs are zero (no input)
        if all(v == 0 for v in user_inputs.values()):
            st.warning("No input provided. Please enter values.")
        else:
            # Prepare input for prediction
            input_data = np.array([[user_inputs[feature] for feature in feature_names]])
    
            # Make prediction
            prediction = model.predict(input_data)
            wine_type = "**White Wine**" if prediction[0] == 1 else "**Red Wine**"
    
            # Display prediction
            st.write('Predicted Wine Type:', wine_type)

if __name__ == '__main__':
    main()