Spaces:

gruhit-patel
/

deployment_app

Sleeping

Gruhit Patel

Update server.py

bbf029a unverified about 2 years ago

734 Bytes

from fastapi import FastAPI
from models import PredictReq
import pickle
import json
import numpy as np

app = FastAPI()

ml_model = None
# Load the XGBoost model
with open('xgb_model.pkl', 'rb') as model_file:
	ml_model = pickle.load(model_file)

# This is just the normal getter api to check if working of the entire backend
@app.get("/")
def foo():
	return {
		"status": "House Price Prediction"
	}

@app.post("/predict_house_price")
def predice_house_price(req: PredictReq):
	inp = np.array([[
				req.medInc,
				req.houseAge,
				req.avgRooms,
				req.avgBdrms,
				req.population,
				req.avgOccup,
				req.latitude,
				req.longitude,
			]])

	prediction = ml_model.predict(inp)[0]

	return {
		'price': str(prediction*100)
	}

	from fastapi import FastAPI
	from models import PredictReq
	import pickle
	import json
	import numpy as np

	app = FastAPI()

	ml_model = None
	# Load the XGBoost model
	with open('xgb_model.pkl', 'rb') as model_file:
	ml_model = pickle.load(model_file)

	# This is just the normal getter api to check if working of the entire backend
	@app.get("/")
	def foo():
	return {
	"status": "House Price Prediction"
	}

	@app.post("/predict_house_price")
	def predice_house_price(req: PredictReq):
	inp = np.array([[
	req.medInc,
	req.houseAge,
	req.avgRooms,
	req.avgBdrms,
	req.population,
	req.avgOccup,
	req.latitude,
	req.longitude,
	]])

	prediction = ml_model.predict(inp)[0]

	return {
	'price': str(prediction*100)
	}