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app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+
+# Load the pre-trained pipeline
+pipeline = joblib.load("pipeline.pkl")
+
+def predict(fixed_acidity, volatile_acidity, citric_acid, residual_sugar,
+            chlorides, free_sulfur_dioxide, total_sulfur_dioxide, density,
+            pH, sulphates, alcohol, Id=None):
+    # Create a DataFrame with the input data
+    input_data = {
+        'fixed_acidity': [float(fixed_acidity)],
+        'volatile_acidity': [float(volatile_acidity)],
+        'citric_acid': [float(citric_acid)],
+        'residual_sugar': [float(residual_sugar)],
+        'chlorides': [float(chlorides)],
+        'free_sulfur_dioxide': [float(free_sulfur_dioxide)],
+        'total_sulfur_dioxide': [float(total_sulfur_dioxide)],
+        'density': [float(density)],
+        'pH': [float(pH)],
+        'sulphates': [float(sulphates)],
+        'alcohol': [float(alcohol)],
+        'Id': [Id] if Id is not None else [0]  # Optional ID column
+    }
+    df = pd.DataFrame(input_data)
+    
+    # Make predictions
+    predictions = pipeline.predict(df)
+    return {"Quality Prediction": predictions[0]}
+
+# Define Gradio interface
+inputs = [
+    gr.inputs.Textbox(label='Fixed Acidity'),
+    gr.inputs.Textbox(label='Volatile Acidity'),
+    gr.inputs.Textbox(label='Citric Acid'),
+    gr.inputs.Textbox(label='Residual Sugar'),
+    gr.inputs.Textbox(label='Chlorides'),
+    gr.inputs.Textbox(label='Free Sulfur Dioxide'),
+    gr.inputs.Textbox(label='Total Sulfur Dioxide'),
+    gr.inputs.Textbox(label='Density'),
+    gr.inputs.Textbox(label='pH'),
+    gr.inputs.Textbox(label='Sulphates'),
+    gr.inputs.Textbox(label='Alcohol'),
+    gr.inputs.Textbox(label='Id', optional=True),
+]
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs="json",
+    title="Wine Quality Prediction",
+    description="Enter wine parameters to predict its quality."
+)
+
+interface.launch()

pipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:388f78b8e6955799a702f071db804beb51329f505b2fe51a13310d375c7714e3
+size 309075

requirements.txt

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+pandas
+numpy
+scikit-learn
+catboost
+gradio
+joblib