Update app.py

app.py

@@ -3,32 +3,24 @@ import pandas as pd
 import xgboost as xgb
 from huggingface_hub import hf_hub_download
 
-# Download and load the model from the Hugging Face repository
-model_path = hf_hub_download(
-    repo_id='caslabs/xgboost-home-price-predictor',
-    filename='xgboost_model.json',
-    repo_type='model'
-)
-
-loaded_model = xgb.XGBRegressor()
-loaded_model.load_model(model_path)
+# Download and load the model
+model_path = hf_hub_download(repo_id="caslabs/xgboost-home-price-predictor", filename="xgboost_model.json")
+model = xgb.XGBRegressor()
+model.load_model(model_path)
 
 # Define the prediction function
-def predict_price(data):
-    # Parse the input data
-    input_data = pd.DataFrame([data])
-
-    # Make a prediction
-    predicted_price = loaded_model.predict(input_data)[0]
-    return {"predicted_price": round(predicted_price, 2)}
+def predict_price(features):
+    df = pd.DataFrame([features])
+    predicted_price = model.predict(df)[0]
+    return {"predicted_price": predicted_price}
 
-# Define the API interface with Gradio
+# Set up Gradio interface as an API endpoint
 iface = gr.Interface(
     fn=predict_price,
-    inputs=gr.JSON(),  # Accept JSON input
-    outputs=gr.JSON(),  # Return JSON output
+    inputs=gr.JSON(),
+    outputs=gr.JSON(),
     title="Home Price Prediction API",
-    description="Provide property details in JSON format, and the model will return the predicted adjusted sale price."
+    description="Predict home price based on features"
 )
 
-iface.launch()
+iface.launch()