Spaces:
Runtime error
Runtime error
File size: 3,523 Bytes
48c823d 8acf519 48c823d 8acf519 48c823d b7f929e 48c823d b7f929e 48c823d 8acf519 fbc5903 8acf519 48c823d b7f929e 48c823d 8acf519 cb15a69 48c823d cb15a69 48c823d 8acf519 cb15a69 48c823d 765f432 da5f9ac 765f432 48c823d 64033b8 48c823d 64033b8 48c823d 8acf519 fbc5903 8acf519 cb15a69 64033b8 cb15a69 48c823d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
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,
''
)
prepare_message_data_for_logging(message_data, nlu_response)
return nlu_response
|