Update app.py

app.py

@@ -1,62 +1,28 @@
-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 Hugging Face Hub
+# Load the model from the 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)
 
-# 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):
+# 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
+# Set up the Gradio interface
 iface = gr.Interface(
-    fn=gradio_predict_price,
-    inputs=gr.JSON(),
-    outputs=gr.JSON(),
+    fn=predict_price,
+    inputs=gr.JSON(),  # Accept JSON input
+    outputs=gr.JSON(),  # Return JSON output
     title="Home Price Prediction API",
     description="Predict home price based on input features"
 )
 
-# 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)
+# Launch the interface without 'enable_api'
+iface.launch()