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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']
print("File Received:", file.filename)
# Read input data
input_data = pd.read_csv(file)
print("Input Data Shape:", input_data.shape)
print(input_data.head())
# Make predictions
predicted_sale = model.predict(input_data).tolist()
print("Predicted Sales Length:", len(predicted_sale))
predicted_sales = [round(float(i)) for i in predicted_sale]
sale_outlets = input_data['Store_Id'].tolist()
print("Sale Outlets Length:", len(sale_outlets))
# Create response
response = dict(zip(sale_outlets, predicted_sales))
repshape=dict(zip(input_data.shape,len(predicted_sale)))
print("Response:", response)
return jsonify(response) jsonify(repshape)
if __name__=='__main__':
super_kart_api.run()
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