Update app.py

app.py

@@ -1,39 +1,27 @@
-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
 
-# Download and load the model from the correct repo
+# 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 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}
 
-# Define the expected input format
-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
+# Set up the Gradio interface
+iface = gr.Interface(
+    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"
+)
 
-# Define the /predict route
-@app.post("/predict")
-async def predict(request: PredictionRequest):
-    # Convert the input data to a DataFrame
-    data = pd.DataFrame([request.dict()])
-    
-    # Make a prediction
-    try:
-        predicted_price = model.predict(data)[0]
-        return {"predicted_price": predicted_price}
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+iface.launch()