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app.py

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+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()