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| from mathtext_fastapi.logging import prepare_message_data_for_logging | |
| from mathtext.sentiment import sentiment | |
| from mathtext.text2int import text2int | |
| 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 - the student message | |
| - confidence: - the nlu confidence score (sentiment) or '' (integer) | |
| """ | |
| 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): | |
| nlu_response = {} | |
| if all(ele.isdigit() for ele in message_text_arr): | |
| nlu_response = build_nlu_response_object( | |
| 'integer', | |
| ','.join(message_text_arr), | |
| '' | |
| ) | |
| 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) | |
| """ | |
| 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): | |
| # 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 evaluate_message_with_nlu(message_data): | |
| # Keeps system working with two different inputs - full and filtered @event object | |
| try: | |
| message_text = 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 = message_data['message_body'] | |
| number_api_resp = text2int(message_text.lower()) | |
| if number_api_resp == 32202: | |
| 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 | |