def myAvalia(annotation_file, questions_file, corpus_file_path="TestResources/PerguntasPosSistema.txt"):
# process corpus file
questions_answers_reader = AnswerPicker()
questions_answers_reader.process_file(corpus_file_path)
# analyse annotation_file and return the accuracy
annotation_check = AnnotationCheck(annotation_file)
accuracy = annotation_check.evaluate_accuracy(questions_answers_reader, questions_file, 20)
return accuracy
def sss(file_name, question):
# process corpus file
trigger_answer_reader = AnswerPicker()
trigger_answer_reader.process_file(file_name)
# getting the answer
answer = trigger_answer_reader.get_answer(question)
if answer == AnswerPickerAnswerResult.INVALID_USER_INPUT:
return INVALID_USER_INPUT
elif answer == AnswerPickerAnswerResult.TRIGGER_NOT_FOUND:
return TRIGGER_NOT_FOUND
return answer
def sss(file_name, question, trigger_strategy=None, answer_strategy=None):
"""
Returns the most appropriate answer for a question
:param file_name: corpus file
:param question: question
:param trigger_strategy: optional. Strategy for comparing User Input and Trigger
:param answer_strategy: optional. Strategy for comparing answers
:return: The most appropriate answer for the question
"""
# process corpus file
file_reader = UserInputTriggerAnswerReader()
answer_picker = AnswerPicker(file_reader, trigger_strategy, answer_strategy)
file_reader.process_file(file_name, answer_picker.process_user_input_answer)
# getting the answer
answer = answer_picker.get_answer(question)
if answer == AnswerPickerAnswerResult.INVALID_USER_INPUT:
return INVALID_USER_INPUT
elif answer == AnswerPickerAnswerResult.TRIGGER_NOT_FOUND:
return TRIGGER_NOT_FOUND
return answer
