Update app.py

app.py

@@ -3,24 +3,25 @@ import pandas as pd
 import xgboost as xgb
 from huggingface_hub import hf_hub_download
 
-# Download and load the model
-model_path = hf_hub_download(repo_id="caslabs/xgboost-home-price-predictor", filename="xgboost_model.json")
+# Load the model from the Hugging Face Hub
+model_path = hf_hub_download(repo_id="caslabs/home-price-predictor-mockup", filename="xgboost_model.json")
 model = xgb.XGBRegressor()
 model.load_model(model_path)
 
 # Define the prediction function
 def predict_price(features):
+    # Convert the JSON input to a DataFrame
     df = pd.DataFrame([features])
     predicted_price = model.predict(df)[0]
     return {"predicted_price": predicted_price}
 
-# Set up Gradio interface as an API endpoint
+# Set up the Gradio interface
 iface = gr.Interface(
     fn=predict_price,
-    inputs=gr.JSON(),
-    outputs=gr.JSON(),
+    inputs=gr.JSON(),  # Accept JSON input
+    outputs=gr.JSON(),  # Return JSON output
     title="Home Price Prediction API",
-    description="Predict home price based on features"
+    description="Predict home price based on input features"
 )
 
 iface.launch()