Spaces:
Sleeping
Sleeping
File size: 1,836 Bytes
3f36ea6 6a535fc d8e3db6 3f36ea6 d8e3db6 3f36ea6 155f630 670df98 d8e3db6 3f36ea6 6a535fc d8e3db6 3f36ea6 6a535fc 3f36ea6 6a535fc d8e3db6 3f36ea6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 |
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
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):
df = pd.DataFrame([features])
predicted_price = model.predict(df)[0]
return {"predicted_price": predicted_price}
# Set up Gradio interface
iface = gr.Interface(
fn=gradio_predict_price,
inputs=gr.JSON(),
outputs=gr.JSON(),
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)
|