Create app.py

app.py

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+import gradio as gr
+import pickle
+
+# Load the decision tree model from the pickle file
+with open('best_tree.pkl', 'rb') as file:
+    model = pickle.load(file)
+
+# Define the predict function
+def predict(latitude, longitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income):
+    # Prepare the input features
+    features = [[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income]]
+    
+    # Make predictions using the loaded model
+    prediction = model.predict(features)
+    
+    # Return the predicted output
+    return prediction[0]
+
+# Create the input interface using Gradio
+inputs = [
+    gr.inputs.Number(label="Longitude"),
+    gr.inputs.Number(label="Latitude"),
+    gr.inputs.Number(label="Housing Median Age"),
+    gr.inputs.Number(label="Total Rooms"),
+    gr.inputs.Number(label="Total Bedrooms"),
+    gr.inputs.Number(label="Population"),
+    gr.inputs.Number(label="Households"),
+    gr.inputs.Number(label="Median Income")
+]
+
+# Create the output interface using Gradio
+output = gr.outputs.Label(num_top_classes=1)
+
+# Define example data for demonstration
+examples = [
+    [37.88, -122.23, 41, 880, 129, 322, 126, 8.3252],
+    [37.84, -122.27, 48, 1922, 409, 1026, 335, 1.7969],
+    [37.83, -122.26, 52, 1656, 420, 718, 382, 2.6768]
+]
+
+# Create the Gradio interface
+interface = gr.Interface(fn=predict, inputs=inputs, outputs=output, title="Decision Tree Predictor", examples=examples).launch()