import pickle #importing pickle to load the model
import json #import json to load columns
import numpy as np

#creating a global variables
__data_columns = None
__model = None

#creating a function to pass the features
def predict_business(var1, var2, var3,var4, var5, var6, var7):
    a=np.zeros(len(X.columns))
    a[0] = var1
    a[1] = var2
    a[2] = var3 
    a[3] = var4
    a[4] = var5
    a[5] = var6
    a[6] = var7
    return (__model.predict([a]))

def load_saved_artifacts():
    print("loading saved artifacts...start")
    global  __data_columns
    global __locations
with open("columns.json", "r") as f:
       __data_columns = json.load(f)['data_columns']
     
    global __model
    if __model is None:
    with open('your_file.pickle', 'rb') as f:
           __model = pickle.load(f)
    print("loading saved artifacts...done")

def get_data_columns():
    return __data_columns

if __name__ == '__main__':
    load_saved_artifacts()
    print(predict_business(12,12,12,12,21,12,40))

The following code block shows the (app.py) application’s full source code:

from flask import Flask, request, jsonify, render_template
import util
app = Flask(__name__)

@app.route('/')
def home():
    return render_template('demo.html')

@app.route('/predict_business', methods=['GET', 'POST'])
def predict_business():
  
    var1 = int(request.form["var1"])
    var2= int(request.form["var2"])
    var3= int(request.form["var3"])
    var4= int(request.form["var4"])
    var5= int(request.form["var5"])
    var6= int(request.form["var6"])
    var7= int(request.form["var7"])
  
   response = util.predict_business(var1,var2,var3,var4,
               var5, var6, var7)
  
    if response == 1:
        prediction = {'As per the business attributes you should ' : "invest"}      
    elif response == 0:
        prediction =  {"As per the business attributes you should ' : "not invest"}
    else:
       prediction ={'As per the business attributes you should ' : "research"}
      
    
   return render_template("result.html", prediction = prediction)

if __name__ == "__main__":
   print("Starting Python Flask Server For Win Prediction...")
   util.load_saved_artifacts()
   app.run()