Update app.py

app.py

@@ -1,28 +1,62 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 import gradio as gr
 import pandas as pd
 import xgboost as xgb
 from huggingface_hub import hf_hub_download
+import uvicorn
 
-# Load the model from the Hugging Face Hub
+# Load the model from Hugging Face Hub
 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(features):
-    # Convert the JSON input to a DataFrame
+# Initialize FastAPI app
+app = FastAPI()
+
+# Define the input data model for FastAPI
+class PredictionRequest(BaseModel):
+    Site_Area_sqft: float
+    Actual_Age_Years: int
+    Total_Rooms: int
+    Bedrooms: int
+    Bathrooms: float
+    Gross_Living_Area_sqft: float
+    Design_Style_Code: int
+    Condition_Code: int
+    Energy_Efficient_Code: int
+    Garage_Carport_Code: int
+
+# Define a prediction endpoint in FastAPI
+@app.post("/predict")
+async def predict(request: PredictionRequest):
+    data = pd.DataFrame([request.dict()])
+    try:
+        predicted_price = model.predict(data)[0]
+        return {"predicted_price": predicted_price}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Define the Gradio prediction function
+def gradio_predict_price(features):
     df = pd.DataFrame([features])
     predicted_price = model.predict(df)[0]
     return {"predicted_price": predicted_price}
 
-# Set up the Gradio interface
+# Set up Gradio interface
 iface = gr.Interface(
-    fn=predict_price,
-    inputs=gr.JSON(),  # Accept JSON input
-    outputs=gr.JSON(),  # Return JSON output
+    fn=gradio_predict_price,
+    inputs=gr.JSON(),
+    outputs=gr.JSON(),
     title="Home Price Prediction API",
     description="Predict home price based on input features"
 )
 
-# Launch the interface without 'enable_api'
-iface.launch()
+# Launch Gradio on a separate route
+@app.on_event("startup")
+async def startup_event():
+    iface.launch(server_name="0.0.0.0", server_port=7860, share=False)
+
+# Run FastAPI app if this script is executed
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)