def webhook():
try:
ml_prediction
except NameError:
ml_prediction = Prediction(MODEL_DIR)
ml_prediction.load_model()
answer = ''
if request.method == 'POST':
try:
data = json.loads(request.data)
bot_id = data['recipient']['id']
bot_name = data['recipient']['name']
recipient = data['from']
service = data['serviceUrl']
sender = data['conversation']['id']
text = data['text']
bot.send_message(bot_id, bot_name, recipient, service, sender,
ml_prediction.response(text, sender))
except Exception as e:
print(e)
if request.method == 'GET':
question = request.args.get('q')
answer = ml_prediction.response(question if question else 'Hi')
return 'Code: 200. {}'.format(answer)
def train():
if request.method == 'POST':
try:
intents_file = upload_file(request)
except Exception as e:
return "Error: {}".format(e)
if request.method == 'GET':
input_file = request.args.get('file')
intents_file = input_file if input_file else "intents.json"
ml = Train(intents_file, MODEL_DIR)
try:
ml.training()
ml_prediction = Prediction(MODEL_DIR)
ml_prediction.load_model()
return "Train is completed"
except Exception as e:
return "Traing is failed {}".format(str(e))
def webhook():
try:
ml_prediction
except NameError:
ml_prediction = Prediction(MODEL_DIR)
ml_prediction.load_model()
answer = ''
if request.method == 'POST':
try:
data = json.loads(request.data)
bot_id = data['recipient']['id']
bot_name = data['recipient']['name']
recipient = data['from']
service = data['serviceUrl']
sender = data['conversation']['id']
text = data['text']
answer = ml_prediction.response(text, sender)
if answer:
if answer.endswith('.png'):
url = request.url_root + app.static_url_path + '/' + answer
bot.send_media(bot_id, bot_name, recipient, service, sender, 'image/png', url)
else:
bot.send_message(bot_id, bot_name, recipient, service, sender, answer)
else:
bot.send_message(bot_id, bot_name, recipient, service, sender, 'Sorry, I do not understand.')
url = request.url_root + app.static_url_path + '/monkey.gif'
bot.send_media(bot_id, bot_name, recipient, service, sender, 'image/gif', url)
except Exception as e:
print(e)
if request.method == 'GET':
question = request.args.get('q')
answer = ml_prediction.response(question if question else 'Hi')
if answer:
if answer.endswith('.png'): return app.send_static_file(answer)
return answer
return app.send_static_file('monkey.gif')
class MedicalQADataset(Dataset):
"""自定义医疗QA数据集"""
def __init__(self, dataset_ids):
self.dataset_ids = dataset_ids
def __getitem__(self, item):
return self.dataset_ids[item]
def __len__(self):
return len(self.dataset_ids)
if __name__ == "__main__":
# 测试predict是否OK
from prediction import Prediction
model = Prediction()
model.load_model()
result = model.predict(
department='',
title='孕妇经常胃痛会影响到胎儿吗',
ask='"我怀上五个多月了,自从怀上以来就经常胃痛(两个胸之间往下一点儿是胃吧?)有时痛十几分钟,有时痛'
'半个钟,每次都痛得好厉害,不过痛过这一阵之后就不痛了,我怀上初期饮食不规律,经常吃不下东西,会不'
'会是这样引发的呀?我好忧心对胎儿有影响,该怎么办呢?可有食疗的方法可以纾解一下痛呢?"')
print(result)
exit(0)
