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Author: aloncohen1

Updating cases catepories via Salesforce API.py

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+# This code will automatically update cases' categories field in your Salesforce system.
+# The code is using the model that was built previously and it's inserting the model's classification to the Salesforce relevant field via API.
+
+# To review the model development, please visit: https://github.com/aloncohen1/My-Projects/blob/master/NLP%20Project.ipynb
+
+# Made by: Alon Cohen
+# [email protected]
+
+from simple_salesforce import Salesforce
+import requests
+import base64
+import json
+import salesforce_reporting
+import pandas as pd
+import pickle
+import re
+
+# Connect to Salesforce API
+sf_production = Salesforce(username="[email protected]"
+                           , password="xxxxxxxx"
+                           , security_token="xxxxxxxxx", )
+
+# Connect to Salesforce API - REPORTS
+reports_sf = salesforce_reporting.Connection(username="[email protected]"
+                                             , password="xxxxxxxx"
+                                             , security_token="xxxxxxxxx", )
+report = reports_sf.get_report(
+    'Ener the report ID', )  ### Create a report of all the uncataloged cases and enter his ID here
+
+parser = salesforce_reporting.ReportParser(report)
+
+# Extract the report of the uncataloged cases and transform it into a Pandas data frame
+cases_to_catalog = pd.DataFrame(
+    columns=['Case Number', 'Case ID', 'Subject', 'Description', 'Case Comments', 'Topic - for PC'],
+    index=range(len(parser.records())))
+for i in range(len(parser.records())):
+    cases_to_catalog['Case Number'][i] = parser.records()[i][0]
+    cases_to_catalog['Case ID'][i] = parser.records()[i][1]
+    cases_to_catalog['Subject'][i] = parser.records()[i][2]
+    cases_to_catalog['Description'][i] = parser.records()[i][3]
+    cases_to_catalog['Case Comments'][i] = parser.records()[i][4]
+
+cases_to_catalog = cases_to_catalog.rename(index=str, columns={"Subject": "Title"})
+
+
+# Load the trained models
+def load_obj(name):
+    with open(name + '.pkl', 'rb') as f:
+        return pickle.load(f)
+
+
+pipeline_lr_linking = load_obj('pipeline_lr_linking')
+pipeline_lr_others = load_obj('pipeline_lr_others')
+
+# Create dictionary of cases
+cases_to_catalog_dict = dict()
+count = 0
+for i in cases_to_catalog['Case ID']:
+    if i not in cases_to_catalog_dict.keys():
+        cases_to_catalog_dict[(cases_to_catalog['Case ID'][count])] = cases_to_catalog['Topic - for PC'][count]
+    count += 1
+len(cases_to_catalog_dict)
+
+
+# Create functions that clean the text
+def my_function(raw):
+    raw = raw.lower()
+    raw = raw.replace(']', '')
+    raw = raw.replace('[', '')
+    raw = raw.replace(')', '')
+    raw = raw.replace('(', '')
+    raw = raw.replace(':', '')
+    raw = raw.replace('.', '')
+    raw = raw.replace(',', '')
+    raw = raw.replace('  ', ' ')
+    raw = raw.replace('"', '')
+    raw = raw.replace('\n', ' ')
+    raw = raw.replace('\t', ' ')
+    raw = raw.replace('?', '')
+    raw = re.sub(r"http\S+", "", raw)
+    raw = re.sub('https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+', "", raw)
+    raw = re.sub(" \d{13} ", " isbn ", raw)
+    raw = re.sub(" \d{10} ", " isbn ", raw)
+    raw = re.sub(" \d{7}\d{1}[\dx] ", " issn ", raw)
+    raw = re.sub(" \d{4}[-]\d{3}[\dx] ", " issn ", raw)
+    raw = re.sub(" 10\.\S+ ", " doi ", raw)
+    raw = re.sub('<.*>', "", raw)
+    raw = re.sub('\S+@\S+', "email", raw)
+    raw = re.sub('[0-9]+', "", raw)
+    raw = re.sub(r'(\d+/\d+/\d+)', "date", raw)
+    for i in raw.split():
+        if len(i) > 22:
+            raw = raw.replace(i, '')
+    raw = raw.replace('$', '')
+    raw = raw.replace('!', '')
+    raw = raw.replace("'", '')
+    raw = raw.replace("->", '')
+    raw = raw.replace('&', '')
+    raw = raw.replace('/', '')
+    raw = raw.replace('%', ' ')
+    raw = raw.replace(' - ', ' ')
+    raw = raw.replace('+', '')
+    raw = raw.replace('_', '')
+    raw = raw.replace('@', '')
+    raw = raw.replace('--', '')
+    raw = raw.replace('#', '')
+    raw = raw.replace('=', '')
+    raw = raw.replace('â', '')
+    raw = raw.replace('*', '')
+    raw = raw.replace('-', '')
+    raw = raw.replace(';', '')
+    raw = raw.replace('<', '')
+    raw = raw.replace('>', '')
+    raw = raw.replace('ß', '')
+    ' '.join(raw.split())
+    raw = raw.replace('  ', ' ')
+    raw = raw.replace('  ', ' ')
+
+    return raw
+
+
+# Create data frame that flattens the information (from several comments to one long string)
+merged_cases_to_catalog = pd.DataFrame(columns=['Case ID', 'Mixed_Comments', 'Categorie'], )
+cases_by_id = cases_to_catalog.groupby('Case ID')
+
+counter = 0
+for i in cases_to_catalog_dict.keys():
+    case = cases_by_id.get_group(i)
+
+    mix_comment = ''
+
+    for x in case['Title']:
+        title = x
+    for y in case['Description']:
+        description = y
+
+    mix_title = my_function(str(title)) + ' ' + my_function(str(description))
+    mix_comment += mix_title
+    for comment in case['Case Comments']:
+        if len(str(comment).split()) > 4:
+            mix_comment += (' ' + my_function(str(comment)))
+
+    for t in case['Topic - for PC']:
+        topic = t
+    merged_cases_to_catalog.loc[counter] = [i, mix_comment, topic]
+    counter += 1
+
+# Condition - the update will occur only if there are cases to update
+if len(merged_cases_to_catalog) > 0:
+
+    # Predict the cases topics using the trained models
+    first_prediction_for_catalog = pipeline_lr_linking.predict(merged_cases_to_catalog['Mixed_Comments'])
+    second_prediction_for_catalog = pipeline_lr_others.predict(merged_cases_to_catalog['Mixed_Comments'])
+
+    # Create a file that aggregate the data and the prediction
+    final_prediction = pd.DataFrame(columns=['Case ID', 'Mixed_Comments', 'Linking/Not Linking', 'General Prediction',
+                                             'Final Predicted Categorie'], )
+    final_prediction['Case ID'] = merged_cases_to_catalog['Case ID']
+    final_prediction['Mixed_Comments'] = merged_cases_to_catalog['Mixed_Comments']
+    final_prediction['Linking/Not Linking'] = first_prediction_for_catalog
+    final_prediction['General Prediction'] = second_prediction_for_catalog
+    final_prediction['Final Predicted Categorie'] = second_prediction_for_catalog
+
+    # Cataloging to "Data linking" or "Other" - if "Other", then the second prediction will be counted
+    for i in range(len(final_prediction)):
+        if final_prediction['Linking/Not Linking'][i] == 'Data linking':
+            final_prediction['Final Predicted Categorie'][i] = 'Data linking'
+
+    # Create a finale data frame for the update
+    update_file = pd.DataFrame(columns=['Topic - for PC'], index=final_prediction['Case ID'])
+    update_file['Topic - for PC'] = list(final_prediction['Final Predicted Categorie'])
+
+    # Update the cases topics using Salesforce API
+    counter = 0
+    for i in update_file.index:
+        if sf_production.Case.get(i)['bl_New_Category__c'] == None:
+            sf_production.Case.update(i, {'bl_New_Category__c': 'General'})
+        sf_production.Case.update(i, {'Topic_for_PC__c': update_file['Topic - for PC'][i]})
+        counter += 1
+print(str(counter) + ' ' + 'Cases has been updated')