Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,76 @@
+"""
+Gradio app for House Price Prediction Model
+Deploy this to Hugging Face Spaces for interactive inference
+"""
+import gradio as gr
+import joblib
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+# Download model files
+model_path = hf_hub_download(
+    repo_id="niru-nny/house-price-prediction",
+    filename="house_price_model.joblib"
+)
+pipeline_path = hf_hub_download(
+    repo_id="niru-nny/house-price-prediction",
+    filename="preprocessing_pipeline.joblib"
+)
+
+# Load model and pipeline
+model = joblib.load(model_path)
+pipeline = joblib.load(pipeline_path)
+
+def predict_price(longitude, latitude, housing_median_age, total_rooms,
+                  total_bedrooms, population, households, median_income,
+                  ocean_proximity):
+    """Predict house price based on input features"""
+    
+    # Create input dataframe
+    input_data = pd.DataFrame({
+        'longitude': [longitude],
+        'latitude': [latitude],
+        'housing_median_age': [housing_median_age],
+        'total_rooms': [total_rooms],
+        'total_bedrooms': [total_bedrooms],
+        'population': [population],
+        'households': [households],
+        'median_income': [median_income],
+        'ocean_proximity': [ocean_proximity]
+    })
+    
+    # Preprocess and predict
+    processed_data = pipeline.transform(input_data)
+    prediction = model.predict(processed_data)[0]
+    
+    return f"${prediction:,.2f}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(-124.5, -114.0, value=-122.23, label="Longitude"),
+        gr.Slider(32.5, 42.0, value=37.88, label="Latitude"),
+        gr.Slider(0, 52, value=41, step=1, label="Housing Median Age"),
+        gr.Slider(0, 40000, value=880, step=10, label="Total Rooms"),
+        gr.Slider(0, 6500, value=129, step=1, label="Total Bedrooms"),
+        gr.Slider(0, 35000, value=322, step=1, label="Population"),
+        gr.Slider(0, 6000, value=126, step=1, label="Households"),
+        gr.Slider(0, 15, value=8.3252, step=0.1, label="Median Income (in $10,000s)"),
+        gr.Dropdown(
+            choices=["NEAR BAY", "INLAND", "<1H OCEAN", "NEAR OCEAN", "ISLAND"],
+            value="NEAR BAY",
+            label="Ocean Proximity"
+        )
+    ],
+    outputs=gr.Textbox(label="Predicted House Price"),
+    title="🏠 California House Price Prediction",
+    description="Predict California house prices based on location and features",
+    examples=[
+        [-122.23, 37.88, 41, 880, 129, 322, 126, 8.3252, "NEAR BAY"],
+        [-121.22, 39.43, 7, 1430, 244, 515, 226, 3.8462, "INLAND"],
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()