Update app.py

app.py

@@ -1,13 +1,28 @@
 import gradio as gr
 import joblib
-import pandas as pd
+import numpy as np
 
-model = joblib.load("model.joblib")
+# Load trained model
+model = joblib.load("model/house_price_model.pkl")
 
-def predict(rm, lstat, ptratio):
-    df = pd.DataFrame([[rm, lstat, ptratio]], columns=["rm", "lstat", "ptratio"])
-    return model.predict(df)[0]
+# Define prediction function
+def predict_price(rm, lstat, ptratio):
+    input_data = np.array([[rm, lstat, ptratio]])
+    prediction = model.predict(input_data)[0]
+    return round(prediction, 2)
 
-gr.Interface(fn=predict,
-             inputs=["number", "number", "number"],
-             outputs="number").launch()
+# Define Gradio interface
+iface = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(3.0, 9.0, step=0.1, label="Average Rooms (RM)"),
+        gr.Slider(1.0, 40.0, step=0.1, label="Lower Status Population (%) (LSTAT)"),
+        gr.Slider(12.0, 22.0, step=0.1, label="Pupil-Teacher Ratio (PTRATIO)")
+    ],
+    outputs=gr.Number(label="Predicted House Price (in $1000s)"),
+    title="🏠 Boston Housing Price Predictor",
+    description="Enter the house features to predict the price using a linear regression model.",
+)
+
+# Launch app
+iface.launch()