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import pandas as pd
import numpy as np
import joblib
from flask import Flask,jsonify,request
# Initialize flask app
sales_prediction_api=Flask("Forecasted Product Sales Predictor")
# load the model
model=joblib.load('sales_prediction_model_v1_0.joblib')
# create home endpoint
@sales_prediction_api.get('/')
def home():
return "Welcome to the Superkart product sales forecast API"
# create health check endpoint
#@sales_prediction_api.get('/health')
#def health_check():
# return jsonify({"status": "ok"}), 200
# create endpoint for single row data processing
@sales_prediction_api.post('/v1/data')
def predict_data():
data=request.get_json()
user_input={
'Product_Weight':data['Product_Weight'],
'Product_Sugar_Content':data['Product_Sugar_Content'],
'Product_Allocated_Area':data['Product_Allocated_Area'],
'Product_Type':data['Product_Type'],
'Product_MRP':data['Product_MRP'],
'Store_Id':data['Store_Id'],
'Store_Establishment_Year':data['Store_Establishment_Year'],
'Store_Size':data['Store_Size'],
'Store_Location_City_Type':data['Store_Location_City_Type'],
'Store_Type':data['Store_Type']
}
df=pd.DataFrame([user_input])
prediction=model.predict(df).tolist()[0]
return jsonify({'prediction':prediction})
# create endpoint for batch processing
@sales_prediction_api.post('/v1/databatch')
def predict_data_batch():
file1=request.files['file']
df_input=pd.read_csv(file1)
predictionlist=model.predict(df_input.drop(['Product_Id'],axis=1)).tolist()
idlist=df_input.Product_Id.values.tolist()
dictionary1= dict(zip(idlist,predictionlist))
return dictionary1
if __name__=='__main__':
app.run(debug=True)
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