Upload 4 files

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load model and scaler
+model = pickle.load(open('model.pkl', 'rb'))
+scaler = pickle.load(open('scaler.pkl', 'rb'))
+
+def predict_price(area, bedrooms, bathrooms, stories, parking, mainroad, guestroom, 
+                  basement, hotwaterheating, airconditioning, prefarea, furnishingstatus):
+    
+    # Encode inputs
+    mainroad = 1.0 if mainroad == "Yes" else 0.0
+    guestroom = 1.0 if guestroom == "Yes" else 0.0
+    basement = 1.0 if basement == "Yes" else 0.0
+    hotwaterheating = 1.0 if hotwaterheating == "Yes" else 0.0
+    airconditioning = 1.0 if airconditioning == "Yes" else 0.0
+    prefarea = 1.0 if prefarea == "Yes" else 0.0
+    
+    furnishing_map = {"furnished": 0.0, "semi-furnished": 1.0, "unfurnished": 2.0}
+    furnishingstatus = furnishing_map[furnishingstatus]
+    
+    # Create input array
+    input_data = np.array([[area, bedrooms, bathrooms, stories, 
+                           mainroad, guestroom, basement,
+                           hotwaterheating, airconditioning,
+                           parking, prefarea, furnishingstatus]])
+    
+    # Scale and predict
+    input_scaled = scaler.transform(input_data)
+    prediction = model.predict(input_scaled)
+    
+    return f" {prediction[0]:,.2f}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Number(label="Area (sq ft)", value=5000),
+        gr.Dropdown(choices=[1, 2, 3, 4, 5, 6], label="Bedrooms", value=3),
+        gr.Dropdown(choices=[1, 2, 3, 4], label="Bathrooms", value=2),
+        gr.Dropdown(choices=[1, 2, 3, 4], label="Stories", value=2),
+        gr.Dropdown(choices=[0, 1, 2, 3], label="Parking", value=1),
+        gr.Dropdown(choices=["Yes", "No"], label="Main Road", value="Yes"),
+        gr.Dropdown(choices=["Yes", "No"], label="Guest Room", value="No"),
+        gr.Dropdown(choices=["Yes", "No"], label="Basement", value="No"),
+        gr.Dropdown(choices=["Yes", "No"], label="Hot Water Heating", value="No"),
+        gr.Dropdown(choices=["Yes", "No"], label="Air Conditioning", value="Yes"),
+        gr.Dropdown(choices=["Yes", "No"], label="Preferred Area", value="Yes"),
+        gr.Dropdown(choices=["furnished", "semi-furnished", "unfurnished"], label="Furnishing", value="furnished")
+    ],
+    outputs=gr.Textbox(label="Predicted Price"),
+    title="🏠 House Price Prediction",
+    description="Enter house features to predict the price"
+)
+
+demo.launch(share=True)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fcec7203319cff3c36f108dd94eb82ed35514feda60d6c0073f00b1d2b8b1c5e
+size 596

requirements.txt

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+gradio
+streamlit
+pandas
+scikit-learn
+numpy

scaler.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1614a15b27dea586aeb6ed1bbb9104c053cafe2aaeb801dfa318843c60bee211
+size 1199