app.py

import joblib
import numpy as np
import pandas as pd
from flask import Flask, request, jsonify
super_kart_api=Flask("Superkart_price_prediction")
model=joblib.load('model.joblib')
@super_kart_api.get('/')
def home():
  return "Welcome to SuperKart sales Prediction"
@super_kart_api.post('/v1/spkart_single')
def sale_pred_single():
  sale_data=request.get_json()
  sample={
      'Product_Weight':sale_data['Product_Weight'],
      'Product_Sugar_Content':sale_data['Product_Sugar_Content'],
      'Product_Allocated_Area':sale_data['Product_Allocated_Area'],
      'Product_Type':sale_data['Product_Type'],
      'Product_MRP':sale_data['Product_MRP'],
      'Store_Id':sale_data['Store_Id'],
      'Store_Size':sale_data['Store_Size'],
      'Store_Location_City_Type':sale_data['Store_Location_City_Type'],
      'Store_Type':sale_data['Store_Type'],
      'Store_age':sale_data['Store_age']
  }
  input_data=pd.DataFrame([sample])
  predicted_sale=model.predict(input_data)[0]
  response={'Store_Outlet':sample['Store_Id'],"Sale":round(float(predicted_sale), 2)}
  return jsonify(response)

@super_kart_api.post('/v1/spkart_batch')
def sale_pred_batch():
  file=request.files['file']
  input_data=pd.read_csv(file)
  predicted_sale=model.predict(input_data).tolist()
  predicted_sales=[round(float(i))for i in predicted_sale]
  sale_outlets=input_data['Store_Id'].tolist()
  response=dict(zip(sale_outlets,predicted_sales))
  return jsonify(response)
if __name__=='__main__':
  super_kart_api.run()