Create main.py

main.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+
+# Load the model
+model = joblib.load("house_price_model.pkl")  # or use linear_regression_model.pkl if preferred
+
+# Define input columns (must match training data!)
+input_cols = ['OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', '1stFlrSF', 'FullBath', 'YearBuilt']
+
+def predict_price(OverallQual, GrLivArea, GarageCars, TotalBsmtSF, FirstFlrSF, FullBath, YearBuilt):
+    data = pd.DataFrame([[OverallQual, GrLivArea, GarageCars, TotalBsmtSF, FirstFlrSF, FullBath, YearBuilt]],
+                        columns=input_cols)
+    prediction = model.predict(data)[0]
+    return f"Estimated House Price: ${prediction:,.2f}"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(1, 10, value=5, label="Overall Quality"),
+        gr.Number(label="Above Ground Living Area (GrLivArea)"),
+        gr.Slider(0, 4, step=1, label="Garage Cars"),
+        gr.Number(label="Total Basement Area (TotalBsmtSF)"),
+        gr.Number(label="First Floor Area (1stFlrSF)"),
+        gr.Slider(0, 3, step=1, label="Full Bathrooms"),
+        gr.Number(label="Year Built"),
+    ],
+    outputs="text",
+    title="🏡 House Price Predictor",
+    description="Enter the house details and get an estimated price using a trained ML model."
+)
+
+if __name__ == "__main__":
+    demo.launch()