Create app.py

app.py

@@ -0,0 +1,25 @@
+import joblib
+import gradio as gr
+import numpy as np
+
+# Load the pre-trained model
+model = joblib.load("house_price_model.pkl")
+
+# Define a prediction function
+def predict_price(area):
+    # Convert input to the correct format
+    area_array = np.array([[float(area)]])  # Reshape to 2D array
+    predicted_price = model.predict(area_array)
+    return f"The predicted price for {area} sq ft house is ${predicted_price[0]:,.2f}"
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=predict_price,  # Function to call for predictions
+    inputs=gr.Number(label="Enter Area (sq ft)"),  # Input field
+    outputs=gr.Textbox(label="Predicted Price"),  # Output display
+    title="House Price Prediction",
+    description="Enter the area of a house (in square feet), and this application will predict its price."
+)
+
+# Launch the app
+interface.launch()