async def talk(ctx, *, question):
answers, answers_rate = inference_internal(question)
ans_score = {}
answers = inference_internal(question)[0]
conclusion = model.predict([bagOWords(question, words)])[0]
conclusionIndex = numpy.argmax(conclusion)
tag = labels[conclusionIndex]
# execute the main.py prototype
if conclusion[conclusionIndex] > 0.7:
for x in data["Library"]:
if x['tag'] == tag:
responses = x['responses']
print(conclusion[conclusionIndex])
print(random.choice(responses))
await ctx.send(f'{random.choice(responses)}')
# execute the NMT Alanbot
else:
for i, answer in enumerate(answers):
score = scoring.do_scoring(question, answer,
answers_rate[i])
ans_score[answer] = score
scores = [v for k, v in ans_score.items()]
max_score = max(scores)
options = [k for k, v in ans_score.items() if v == max_score]
choice_answer = random.choice(options)
print(choice_answer)
await ctx.send(f'{choice_answer}')
def infer(question):
answers, answers_rate = inference_internal(question)
ans_score = {answer: (answers_rate[i] + scoring.do_scoring(question, answer)) for i, answer in enumerate(answers)}
scores = list(ans_score.values())
max_score = max(scores)
options = [k for k, v in ans_score.items() if v == max_score]
return random.choice(options)
def mainFunction(questionIn):
question = questionIn
answers, answers_rate = inference_internal(question)
ans_score = {}
for i, answer in enumerate(answers):
score = scoring.do_scoring(question, answer, answers_rate[i])
ans_score[answer] = score
scores = [v for k, v in ans_score.items()]
max_score = max(scores)
options = [k for k, v in ans_score.items() if v == max_score]
choice_answer = random.choice(options)
return (choice_answer)
answers = inference_helper(question)
answers = detokenize(answers)
answers = replace_in_answers(answers, 'answers')
answers_rate = score_answers(answers, 'answers')
return (answers, answers_rate)
# interactive mode
if __name__ == "__main__":
print("\n\nStarting interactive mode (first response will take a while):")
colorama.init()
# QAs
while True:
question = input("\n> ")
answers, answers_rate = inference_internal(question)
ans_score = {}
for i, answer in enumerate(answers):
score = scoring.do_scoring(question, answer, answers_rate[i])
ans_score[answer] = score
scores = [v for k, v in ans_score.items()]
max_score = max(scores)
options = [k for k, v in ans_score.items() if v == max_score]
choice_answer = random.choice(options)
print("{}- {}{}".format(colorama.Fore.GREEN, choice_answer,
colorama.Fore.RESET))
# maybe print the others? Anything else with a matching highscore green, yellow mid-range... red lowest?
def infer(question):
answers, answers_rate = inference_internal(question)
ans_score = {answer: (answers_rate[i] + scoring.do_scoring(question, answer)) for i, answer in enumerate(answers)}
scores = list(ans_score.values())
max_score = max(scores)
options = [k for k, v in ans_score.items() if v == max_score]
return random.choice(options)
if __name__ == "__main__":
import colorama
print("\n\nStarting interactive mode (first response will take a while):")
colorama.init()
# QAs
while True:
question = input("\n> ")
answers, answers_rate = inference_internal(question)
ans_score = {answer: (answers_rate[i] + scoring.do_scoring(question, answer)) for i, answer in
enumerate(answers)}
scores = [v for k, v in ans_score.items()]
max_score = max(scores)
options = [k for k, v in ans_score.items() if v == max_score]
choice_answer = random.choice(options)
print("{}- {}{}".format(colorama.Fore.GREEN, choice_answer, colorama.Fore.RESET))
