Create app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import joblib
+import numpy as np
+
+# Load the saved model and scaler
+model = joblib.load('best_random_forest_model.pkl')
+scaler = joblib.load('scaler.pkl')
+
+def predict_house_price(feature1, feature2, feature3, feature4, feature5):
+    # Combine all features into a single list
+    features = [feature1, feature2, feature3, feature4, feature5]
+    
+    # Scale the input features using the saved scaler
+    scaled_features = scaler.transform([features])
+    
+    # Make predictions using the loaded model
+    prediction = model.predict(scaled_features)
+    
+    return f"Predicted House Price: ${prediction[0]:,.2f}"
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict_house_price,
+    inputs=[
+        gr.Number(label="Feature 1: (e.g., Average number of rooms per dwelling)"),
+        gr.Number(label="Feature 2: (e.g., Total rooms)"),
+        gr.Number(label="Feature 3: (e.g., Total bedrooms)"),
+        gr.Number(label="Feature 4: (e.g., Median age of houses)"),
+        gr.Number(label="Feature 5: (e.g., Population)"),
+    ],
+    outputs=gr.Textbox(label="Predicted House Price"),
+    description="Predict California house prices based on input features.",
+    title="California House Price Predictor"
+)
+
+# Launch the Gradio app
+interface.launch()