Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+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)
+
+# 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)}
+
+# Define the API interface with Gradio
+iface = gr.Interface(
+    fn=predict_price,
+    inputs=gr.JSON(),  # Accept JSON input
+    outputs=gr.JSON(),  # Return JSON output
+    title="Home Price Prediction API",
+    description="Provide property details in JSON format, and the model will return the predicted adjusted sale price."
+)
+
+iface.launch()