Update app.py

app.py

@@ -1,7 +1,32 @@
 import gradio as gr
+import pandas as pd
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load your trained model (replace with the actual path)
+model = LinearRegression()  # Assuming you saved the model
+model.load("rmaitest/housepricepridiction.pkl")  # Replace with the path
 
-demo = gr.Interface(fn=greet, inputs="number",inputs="number",inputs="number", outputs="number")
-demo.launch()
+def predict(size, bedrooms, age):
+  """Predicts house price based on input features."""
+  # Create a DataFrame from user input
+  input_data = pd.DataFrame({
+      'Size (sq ft)': [size],
+      'Number of Bedrooms': [bedrooms],
+      'Age of House (years)': [age]
+  })
+
+  # Predict the price using the trained model
+  predicted_price = model.predict(input_data)[0]
+
+  # Format the output with dollar sign and two decimal places
+  return f"Predicted Price: ${predicted_price:,.2f}"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=["number", "number", "number"],  # Three numeric input fields
+    outputs="text",  # Output is the predicted price (text)
+    title="House Price Prediction",
+    description="Enter house details to predict the price."
+)
+
+iface.launch(share=True)