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HousePricePredictorPipeline.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f84bad5df668049fd601b7efef1786230661028df5dfe6a7788c35569fad5b75
+size 144002

gradio_app.py

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+import gradio as gr
+import pandas as pd
+import joblib as jb
+
+# Load the trained pipeline
+# Make sure HousePricePredictorPipeline.pkl is in the same directory as this script
+MODEL_PATH = "HousePricePredictorPipeline.pkl"
+pipe = jb.load(MODEL_PATH)
+
+# Expected feature schema
+NUM_FEATURES = ["area","parking","bedrooms","bathrooms","stories"]
+CAT_FEATURES = ["furnishingstatus","mainroad","guestroom","basement",
+                "hotwaterheating","airconditioning","prefarea"]
+
+ALL_COLUMNS = NUM_FEATURES + CAT_FEATURES
+
+YES_NO = ["yes","no"]
+FURNISHING = ["unfurnished","semi-furnished","furnished"]
+
+def predict_price(area, parking, bedrooms, bathrooms, stories,
+                  furnishingstatus, mainroad, guestroom, basement,
+                  hotwaterheating, airconditioning, prefarea):
+    # Build a single-row DataFrame that matches the training-time schema
+    row = {
+        "area": area,
+        "parking": int(parking),
+        "bedrooms": int(bedrooms),
+        "bathrooms": int(bathrooms),
+        "stories": int(stories),
+        "furnishingstatus": furnishingstatus,
+        "mainroad": mainroad,
+        "guestroom": guestroom,
+        "basement": basement,
+        "hotwaterheating": hotwaterheating,
+        "airconditioning": airconditioning,
+        "prefarea": prefarea
+    }
+    X = pd.DataFrame([row], columns=ALL_COLUMNS)
+    pred = pipe.predict(X)[0]
+    return float(pred)
+
+with gr.Blocks(title="House Price Predictor") as demo:
+    gr.Markdown("# 🏠 House Price Predictor")
+    gr.Markdown(
+        "Provide home features and get an estimated price. "
+        "This app uses your trained scikit-learn pipeline."
+    )
+
+    with gr.Row():
+        with gr.Column():
+            area = gr.Number(label="Area (sq ft)", value=2000, precision=0)
+            parking = gr.Slider(label="Parking Spots", value=1, minimum=0, maximum=5, step=1)
+            bedrooms = gr.Slider(label="Bedrooms", value=3, minimum=0, maximum=10, step=1)
+            bathrooms = gr.Slider(label="Bathrooms", value=2, minimum=0, maximum=10, step=1)
+            stories = gr.Slider(label="Stories", value=2, minimum=0, maximum=10, step=1)
+
+        with gr.Column():
+            furnishingstatus = gr.Dropdown(FURNISHING, value="semi-furnished", label="Furnishing Status")
+            mainroad = gr.Dropdown(YES_NO, value="yes", label="On Main Road?")
+            guestroom = gr.Dropdown(YES_NO, value="no", label="Guest Room?")
+            basement = gr.Dropdown(YES_NO, value="no", label="Basement?")
+            hotwaterheating = gr.Dropdown(YES_NO, value="no", label="Hot Water Heating?")
+            airconditioning = gr.Dropdown(YES_NO, value="yes", label="Air Conditioning?")
+            prefarea = gr.Dropdown(YES_NO, value="no", label="Preferred Area?")
+
+    btn = gr.Button("Predict Price")
+    output = gr.Number(label="Predicted Price (same units as your training data)")
+
+    btn.click(
+        fn=predict_price,
+        inputs=[area, parking, bedrooms, bathrooms, stories,
+                furnishingstatus, mainroad, guestroom, basement,
+                hotwaterheating, airconditioning, prefarea],
+        outputs=output
+    )
+
+    gr.Markdown(
+        "Tip: Ensure **HousePricePredictorPipeline.pkl** is in the same folder.\n"
+        "Run with: `python gradio_app.py` and open the link in your browser."
+    )
+
+if __name__ == "__main__":
+    demo.launch()