server_app.py

import anvil.server
import time
from urllib.request import urlopen
import pandas as pd
import pickle
import warnings

warnings.filterwarnings('ignore')

def app_func():

  anvil.server.connect("server_BT6EO7O6OJWWUG4GZ3ZLJAMY-RGKODPUNK74BJTQ6")

  @anvil.server.callable
  def pred(age, education, hours_per_week, marital_status, sex, gain_or_loss, workclass, race, occupation, country):

      cols = ['age_logarithmic', 'education_rank', 'hours_per_week', 'marital_status',\
          'sex', 'gain/loss', 'Local_gov', 'Private', 'Self_emp_inc',\
          'Self_emp_not_in_inc', 'State_gov', 'others', 'Asian_Pac_Islander',\
          'Black', 'Other', 'White', 'Armed_Forces', 'Craft_repair',\
          'Exec_managerial', 'Farming_fishing', 'Handlers_cleaners',\
          'Machine_op_inspct', 'Other_service', 'Priv_house_serv',\
          'Prof_specialty', 'Protective_serv', 'Sales', 'Tech_support',\
          'Transport_moving', 'El Salvador', 'Germany', 'India', 'Mexico',\
          'Philippines', 'Puerto Rico', 'United States', 'others']
      
      
      dct = {'10th': 6.0,'11th': 7.0,'12th': 8.0,'1st-4th': 2.0,'5th-6th': 3.0,'7th-8th': 4.0,\
            '9th': 5.0,'Assoc-acdm': 12.0, 'Assoc-voc': 11.0,'Bachelors': 13.0, 'Doctorate': 16.0,'HS-grad': 9.0, 'Masters': 14.0,'Preschool': 1.0,\
            'Prof-school': 15.0,'Some-college': 10.0}
  
      path = urlopen(r'https://github.com/RauhanAhmed/Ineuron_internship/raw/main/pipeline.pkl')
      data_pipeline = pickle.load(path)
      
      x = [0 for x in range(1, 38)]
      x[0] = age
      x[1] = dct[education]
      x[2] = hours_per_week
      x[3] = marital_status
      if sex == 'Male':
        x[4] = 1
      else:
        x[4] = 0
      x[5] = gain_or_loss
      if workclass in cols[:12]:
        x[cols[:12].index(workclass)] = 1
      if race == 'others':
        x[14] = 1
      elif race == 'Asian_Pac_Islander':
        x[cols.index('Asian_Pac_Islander')] = 1
      elif race == 'Black':
        x[cols.index('Black')] = 1
      elif race == 'White':
        x[cols.index('White')] = 1
      if occupation in cols:
        x[cols.index(occupation)] = 1
      if country == 'others':
        x[36] = 1
      elif country in cols:
        x[cols.index(country)] = 1


      test_row = pd.DataFrame(x).transpose()
      test_row.columns = cols
      result = data_pipeline.predict(test_row)
      
      time.sleep(3)

      return result[0]


  anvil.server.wait_forever()