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app.py

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+import streamlit as st
+import numpy as np
+import pickle
+
+# Function to load the trained model # Cache the model so it's loaded only once per session
+def load_model(model_file):
+    with open(model_file, 'rb') as f:
+        model = pickle.load(f)
+    return model
+
+# Function to predict wine quality
+def predict_quality(model, features):
+    input_features = np.array(features).reshape(1, -1)
+    prediction = model.predict(input_features)
+    predicted_quality = prediction[0]
+    return predicted_quality
+
+# Load the model
+model_file = 'model.pkl'  # Replace with your model file path
+model = load_model(model_file)
+
+# Streamlit UI
+st.title('Wine Quality Prediction')
+
+# Input fields for each feature
+st.header('Input Features')
+fixed_acidity = st.slider('Fixed Acidity', min_value=4.0, max_value=16.0, value=8.0, step=0.1)
+volatile_acidity = st.slider('Volatile Acidity', min_value=0.1, max_value=2.0, value=0.5, step=0.01)
+citric_acid = st.slider('Citric Acid', min_value=0.0, max_value=1.0, value=0.5, step=0.01)
+residual_sugar = st.slider('Residual Sugar', min_value=0.0, max_value=20.0, value=10.0, step=0.1)
+chlorides = st.slider('Chlorides', min_value=0.0, max_value=1.0, value=0.08, step=0.01)
+free_sulfur_dioxide = st.slider('Free Sulfur Dioxide', min_value=1, max_value=100, value=30, step=1)
+total_sulfur_dioxide = st.slider('Total Sulfur Dioxide', min_value=1, max_value=300, value=100, step=1)
+density = st.slider('Density', min_value=0.8, max_value=1.0, value=0.996, step=0.001)
+pH = st.slider('pH', min_value=2.0, max_value=4.0, value=3.0, step=0.01)
+sulphates = st.slider('Sulphates', min_value=0.2, max_value=2.0, value=0.5, step=0.01)
+alcohol = st.slider('Alcohol', min_value=8.0, max_value=15.0, value=10.0, step=0.1)
+
+# Predict button
+if st.button('Predict'):
+    features = [fixed_acidity, volatile_acidity, citric_acid, residual_sugar, chlorides,
+                free_sulfur_dioxide, total_sulfur_dioxide, density, pH, sulphates, alcohol]
+    predicted_quality = predict_quality(model, features)
+    st.success(f'Predicted wine quality: {predicted_quality}')

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:87939868f417c7f27248f5d1aba3a49ffeec8d1aa2819a352fdb907db2c23c2b
+size 826

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn