prediction_server.py 文件源码

def predict(input_string):
    mask = lambda w, v: 1 if w not in v else v[w] 
    tknzr = TweetTokenizer(reduce_len=True, preserve_case=False)

    words = tknzr.tokenize(input_string)
    vec = [[mask(w, pd.vocab) for w in words]]
    vec = np.array( vec, dtype="int32")
    vec = pad_sequences(vec, maxlen=pd.max_sequence)
    predictions = model.predict(vec)
    sarcasm = round(predictions[0][1], 2) * 100

    return (words, sarcasm)

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