def test_classify():
model = load_model()
r1 = classify("I want the UK to stay EU #remain #remain #remain", model)
assert round(float(r1["pro_brexit"]), 4) == 0.4284
r2 = classify("I want the UK to leave EU #BrexitNow", model)
assert round(float(r2["pro_brexit"]), 4) == 0.8875
def __init__(self):
"""model: keras model with weights loaded, ready to make predictions"""
self.wait = 0
self.auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
self.auth.set_access_token(ACCESS_KEY, ACCESS_SECRET)
self.api = tweepy.API(self.auth)
self.lastTweeted = ""
self.lastTweetedCount = 0
self.model = load_model()
def test_load_model():
assert isinstance(load_model(), Model)
def classify(txt, model):
"""
Params:
txt is the text to classify
model is the model used to perform the classification
"""
x, x_s = main_clean(txt)
results = model.predict([x, x_s]) #> array([[0.57155126, 0.42844874]], dtype=float32)
results = results[0] #> array([0.57155126, 0.42844874], dtype=float32)
response = {"text": txt, "pro_brexit": results[1]}
return response
if __name__ == "__main__":
model = load_model()
if APP_ENV=="production":
example_texts = [
"I want the UK to stay EU #remain #remain #remain",
"I want the UK to leave EU #BrexitNow",
]
for user_text in example_texts:
results = classify(user_text, model)
print(results)
else:
while True:
user_text = input("Your Text (press ENTER at any time to quit): ")
if user_text in ["", "exit", "exit()"]: break
results = classify(user_text, model)
print(results)