def on_get(self, req, resp, name=None):
if name is None:
resp.status = falcon.HTTP_404
raise falcon.HTTPBadRequest('Param :name is required')
else:
try:
int(name)
resp.status = falcon.HTTP_404
raise falcon.HTTPBadRequest(
'Param :name must be a valid string')
except ValueError:
count = 0
for i in name:
count += punctuation.count(i)
if count > 0:
resp.status = falcon.HTTP_404
raise falcon.HTTPBadRequest(
'Param :name must be a valid string')
else:
resp.body = (f'Hola {name}')
def on_post(self, req, resp, userid=None):
# body = json.loads(req.stream.read())
body = req.media # 2 code tuong duong
for k, v in body.items():
if v == '':
resp.status = falcon.HTTP_404
raise falcon.HTTPBadRequest('all values cannot be empty')
for k, v in body.items():
try:
int(v)
resp.status = falcon.HTTP_404
raise falcon.HTTPBadRequest('all values must be valid')
except ValueError:
count = 0
for i in v:
count += punctuation.count(i)
if count > 0:
resp.status = falcon.HTTP_404
raise falcon.HTTPBadRequest('all values must be valid')
name = body['fname']
lastname = body['lname']
content = {'message': f'Hello{name} {lastname}'}
resp.body = json.dumps(content)
def de_punc(self):
return "".join(
[word for word in self.text if punctuation.count(word) == 0])
def ajust_eval(filepath,loop_idx = -1):
#Init reader and load model
reader = Reader()
header_map = words_2_map(reader)
if len(header_map)==0:
return
model = DynamicLSTM(None, is_training=False, reuse=False)
model_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="model")
model_saver = tf.train.Saver(model_variables)
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
sess.run(init_op)
#Model load
ckpt_path = tf.train.latest_checkpoint(config.model_dir)
if loop_idx > 0:
ckpt_path = ckpt_path.split('-')[0] + '-' + str(loop_idx)
if ckpt_path:
model_saver.restore(sess, ckpt_path)
print("Read model parameters from %s" % ckpt_path)
else:
print("model doesn't exists")
#File handle
f = open(filepath)
click_total = {"total":0,1:0,3:0,5:0}
hit_total = {"total":0,1:0,3:0,5:0}
UPPERS=['NUM','TELEPHONE','DIGIT','SCORES','USER','YEARGAP']
#File loop, file must be format context \t(input\t)word
for line in f:
line = line.strip()
line_items = line.split('\t')
if len(line_items)<2:
sys.stderr.write("Error line format, items missing of line: "+line+'\n')
#line info handle
context = line_items[0].strip()
word = line_items[-1].strip()
input_str = word
if len(line_items)==3:
input_str = line_items[1].strip()
if input_str == "" or punctuation.count(word)>0 or word.isdigit() or UPPERS.count(word)>0:
continue
#Data model input output
inputs, inputs_len, outputs = reader.get_batch_from_input(context)
feed_dict={ model.x: inputs, model.x_len: inputs_len}
prob = sess.run([model.output_prob], feed_dict=feed_dict)
last_prob = prob[-1][-1]
#Exp starts
click_total["total"] += len(word)#Click rate's denominator
hit_total["total"] += 1
input_len = len(input_str)
tmp_click_count = {1:input_len,3:input_len,5:input_len}#list to record the click count for top1, top3, top5
tmp_hit_count = {1:0,3:0,5:0}
for i in range(1,(len(input_str)+1)):
input_header = input_str[:i]
top5_ind = get_header_prob(header_map,last_prob,input_header,5)
words = [reader.words[j] for j in top5_ind]
if len(words)==0:
words.append(input_header)
for idx in [1,3,5]:
if words[:idx].count(word)>0:
if tmp_click_count[idx]==input_len:
tmp_click_count[idx] = i
if i == 1:
tmp_hit_count[idx] = 1
print("Context is : "+context)
print("Input is : "+input_header)
print("Expected word is : " + word)
print("Word_predict is : "+"#".join(words))
if words[:1].count(word)>0:
break
for idx in [1,3,5]:
click_total[idx] += tmp_click_count[idx]
hit_total[idx] += tmp_hit_count[idx]
print(filepath + "'s type-rate is >>> top5_type-rate is : %.2f%%, top3_type-rate is : %.2f%%, top1_type-rate is : %.2f%%, top5_type_total is : %f, top3_type_total is : %f, top1_type_total is : %f, total type count is : %f."%(click_total[5]*100/click_total['total'], click_total[3]*100/click_total['total'],click_total[1]*100/click_total['total'],click_total[5], click_total[3],click_total[1],click_total['total']))
print(filepath + "'s uni-input-hit-rate is >>> top5_hit_rate is : %.2f%%, top3_hit_rate is : %.2f%%, top1_hit_rate is : %.2f%%, top5_hit_total is %f,top3_hit_total is %f,top1_hit_total is %f,total count is : %f."%(hit_total[5]*100/hit_total['total'], hit_total[3]*100/hit_total['total'],hit_total[1]*100/hit_total['total'],hit_total[5], hit_total[3],hit_total[1],hit_total['total']))