app.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import pandas as pd
import xgboost as xgb
from huggingface_hub import hf_hub_download

# Download and load the model from the correct repo
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 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

# 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))