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| from fuzzywuzzy import fuzz | |
| from mathtext_fastapi.logging import prepare_message_data_for_logging | |
| from mathtext.sentiment import sentiment | |
| from mathtext.text2int import text2int | |
| from mathtext_fastapi.intent_classification import create_intent_classification_model, retrieve_intent_classification_model, predict_message_intent | |
| import re | |
| def build_nlu_response_object(type, data, confidence): | |
| """ Turns nlu results into an object to send back to Turn.io | |
| Inputs | |
| - type: str - the type of nlu run (integer or sentiment-analysis) | |
| - data: str/int - the student message | |
| - confidence: - the nlu confidence score (sentiment) or '' (integer) | |
| >>> build_nlu_response_object('integer', 8, 0) | |
| {'type': 'integer', 'data': 8, 'confidence': 0} | |
| >>> build_nlu_response_object('sentiment', 'POSITIVE', 0.99) | |
| {'type': 'sentiment', 'data': 'POSITIVE', 'confidence': 0.99} | |
| """ | |
| return {'type': type, 'data': data, 'confidence': confidence} | |
| # def test_for_float_or_int(message_data, message_text): | |
| # nlu_response = {} | |
| # if type(message_text) == int or type(message_text) == float: | |
| # nlu_response = build_nlu_response_object('integer', message_text, '') | |
| # prepare_message_data_for_logging(message_data, nlu_response) | |
| # return nlu_response | |
| def test_for_number_sequence(message_text_arr, message_data, message_text): | |
| """ Determines if the student's message is a sequence of numbers | |
| >>> test_for_number_sequence(['1','2','3'], {"author_id": "57787919091", "author_type": "OWNER", "contact_uuid": "df78gsdf78df", "message_body": "I am tired", "message_direction": "inbound", "message_id": "dfgha789789ag9ga", "message_inserted_at": "2023-01-10T02:37:28.487319Z", "message_updated_at": "2023-01-10T02:37:28.487319Z"}, '1, 2, 3') | |
| {'type': 'integer', 'data': '1,2,3', 'confidence': 0} | |
| >>> test_for_number_sequence(['a','b','c'], {"author_id": "57787919091", "author_type": "OWNER", "contact_uuid": "df78gsdf78df", "message_body": "I am tired", "message_direction": "inbound", "message_id": "dfgha789789ag9ga", "message_inserted_at": "2023-01-10T02:37:28.487319Z", "message_updated_at": "2023-01-10T02:37:28.487319Z"}, 'a, b, c') | |
| {} | |
| """ | |
| nlu_response = {} | |
| if all(ele.isdigit() for ele in message_text_arr): | |
| nlu_response = build_nlu_response_object( | |
| 'integer', | |
| ','.join(message_text_arr), | |
| 0 | |
| ) | |
| prepare_message_data_for_logging(message_data, nlu_response) | |
| return nlu_response | |
| def run_text2int_on_each_list_item(message_text_arr): | |
| """ Attempts to convert each list item to an integer | |
| Input | |
| - message_text_arr: list - a set of text extracted from the student message | |
| Output | |
| - student_response_arr: list - a set of integers (32202 for error code) | |
| >>> run_text2int_on_each_list_item(['1','2','3']) | |
| [1, 2, 3] | |
| """ | |
| student_response_arr = [] | |
| for student_response in message_text_arr: | |
| int_api_resp = text2int(student_response.lower()) | |
| student_response_arr.append(int_api_resp) | |
| return student_response_arr | |
| def run_sentiment_analysis(message_text): | |
| """ Evaluates the sentiment of a student message | |
| >>> run_sentiment_analysis("I am tired") | |
| [{'label': 'NEGATIVE', 'score': 0.9997807145118713}] | |
| >>> run_sentiment_analysis("I am full of joy") | |
| [{'label': 'POSITIVE', 'score': 0.999882698059082}] | |
| """ | |
| # TODO: Add intent labelling here | |
| # TODO: Add logic to determine whether intent labeling or sentiment analysis is more appropriate (probably default to intent labeling) | |
| return sentiment(message_text) | |
| def run_intent_classification(message_text): | |
| """ Process a student's message using basic fuzzy text comparison | |
| >>> run_intent_classification("exit") | |
| {'type': 'intent', 'data': 'exit', 'confidence': 1.0} | |
| >>> run_intent_classification("exi") | |
| {'type': 'intent', 'data': 'exit', 'confidence': 0.86} | |
| >>> run_intent_classification("eas") | |
| {'type': 'intent', 'data': '', 'confidence': 0} | |
| >>> run_intent_classification("hard") | |
| {'type': 'intent', 'data': '', 'confidence': 0} | |
| >>> run_intent_classification("hardier") | |
| {'type': 'intent', 'data': 'harder', 'confidence': 0.92} | |
| """ | |
| label = '' | |
| ratio = 0 | |
| nlu_response = {'type': 'intent', 'data': label, 'confidence': ratio} | |
| commands = [ | |
| 'easier', | |
| 'exit', | |
| 'harder', | |
| 'hint', | |
| 'next', | |
| 'stop', | |
| ] | |
| for command in commands: | |
| try: | |
| ratio = fuzz.ratio(command, message_text.lower()) | |
| except: | |
| ratio = 0 | |
| if ratio > 80: | |
| nlu_response['data'] = command | |
| nlu_response['confidence'] = ratio / 100 | |
| return nlu_response | |
| def evaluate_message_with_nlu(message_data): | |
| """ Process a student's message using NLU functions and send the result | |
| >>> evaluate_message_with_nlu({"author_id": "57787919091", "author_type": "OWNER", "contact_uuid": "df78gsdf78df", "message_body": "8", "message_direction": "inbound", "message_id": "dfgha789789ag9ga", "message_inserted_at": "2023-01-10T02:37:28.487319Z", "message_updated_at": "2023-01-10T02:37:28.487319Z"}) | |
| {'type': 'integer', 'data': 8, 'confidence': 0} | |
| >>> evaluate_message_with_nlu({"author_id": "57787919091", "author_type": "OWNER", "contact_uuid": "df78gsdf78df", "message_body": "I am tired", "message_direction": "inbound", "message_id": "dfgha789789ag9ga", "message_inserted_at": "2023-01-10T02:37:28.487319Z", "message_updated_at": "2023-01-10T02:37:28.487319Z"}) | |
| {'type': 'sentiment', 'data': 'NEGATIVE', 'confidence': 0.9997807145118713} | |
| """ | |
| # Keeps system working with two different inputs - full and filtered @event object | |
| try: | |
| message_text = str(message_data['message_body']) | |
| except KeyError: | |
| message_data = { | |
| 'author_id': message_data['message']['_vnd']['v1']['chat']['owner'], | |
| 'author_type': message_data['message']['_vnd']['v1']['author']['type'], | |
| 'contact_uuid': message_data['message']['_vnd']['v1']['chat']['contact_uuid'], | |
| 'message_body': message_data['message']['text']['body'], | |
| 'message_direction': message_data['message']['_vnd']['v1']['direction'], | |
| 'message_id': message_data['message']['id'], | |
| 'message_inserted_at': message_data['message']['_vnd']['v1']['chat']['inserted_at'], | |
| 'message_updated_at': message_data['message']['_vnd']['v1']['chat']['updated_at'], | |
| } | |
| message_text = str(message_data['message_body']) | |
| # Run intent classification only for keywords | |
| intent_api_response = run_intent_classification(message_text) | |
| if intent_api_response['data']: | |
| prepare_message_data_for_logging(message_data, intent_api_response) | |
| return intent_api_response | |
| number_api_resp = text2int(message_text.lower()) | |
| if number_api_resp == 32202: | |
| # Run intent classification with logistic regression model | |
| predicted_label = predict_message_intent(message_text) | |
| if predicted_label['confidence'] > 0.01: | |
| nlu_response = predicted_label | |
| else: | |
| # Run sentiment analysis | |
| sentiment_api_resp = sentiment(message_text) | |
| nlu_response = build_nlu_response_object( | |
| 'sentiment', | |
| sentiment_api_resp[0]['label'], | |
| sentiment_api_resp[0]['score'] | |
| ) | |
| else: | |
| nlu_response = build_nlu_response_object( | |
| 'integer', | |
| number_api_resp, | |
| 0 | |
| ) | |
| prepare_message_data_for_logging(message_data, nlu_response) | |
| return nlu_response | |