def predict(self):
global ITEM_QUEUE, RETRY_TIME
retry = 0 # retry times
logger.info(f"Start predict thread")
while retry <= RETRY_TIME:
try:
item = ITEM_QUEUE.get(timeout=3)
text = item["clean_text"]
tokens = model.prepross(text)
item["predict"] = model.predict(tokens) if len(tokens) else (1,
0,
0,
0)
# write data
fhandler = FileWriter(self.filename, mode=self.mode)
with fhandler as writer:
writer.write(json.dumps(item, ensure_ascii=False) + "\n")
logger.debug(f"Predict item id is {item['id']}")
# if get item right, re_initial retry
retry = 0
except queue.Empty:
logger.debug(f"Queue is empty, wait 1 seconds.")
retry += 1
time.sleep(1)
# exhausted RETRY_TIME
logger.info(f"Data item is exhausted")
def predict():
data = request.json
image = preprocess.process_image(data['image'])
with graph.as_default():
predictions = model.predict(image)[0]
return jsonify({'predictions': predictions.tolist()})
def get_bot_response_intent():
userText = request.args.get('msg')
results = model.predict([bag_of_words(userText, words)])[0]
results_index = np.argmax(results)
tag = labels[results_index]
if results[results_index] > 0.7:
for tg in data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
return str(random.choice(responses))
else:
return "I didn't get that please try again"
def chat():
# Logic for POST request
if request.method == 'POST':
user_input = request.get_json(force=True)
results = model.predict([bag_of_words(user_input["user_message"], words)])[0]
results_index = np.argmax(results)
tag = labels[results_index]
print(user_input["user_message"])
if results[results_index] > 0.7:
for tg in data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
print(random.choice(responses))
else:
return "I didn't get that please try again"
# print("I didn't get that. PLease type another question")
return jsonify("See ya")
# if you need to create the data:
# test_data = process_test_data()
# if you already have some saved:
test_data = np.load('test_data.npy')
fig = plt.figure()
# plot last 12 of test data and predicted class
for num, data in enumerate(test_data[:12]):
# cat: [1,0]
# dog: [0,1]
img_num = data[1]
img_data = data[0]
y = fig.add_subplot(3, 4, num + 1)
orig = img_data
data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)
model_out = model.predict([data])[0]
if np.argmax(model_out) == 1:
str_label = 'Dog'
else:
str_label = 'Cat'
y.imshow(orig, cmap='gray')
plt.title(str_label)
y.axes.get_xaxis().set_visible(False)
y.axes.get_yaxis().set_visible(False)
plt.show()