predict.py

from joblib import dump, load
from keras.models import load_model
import time
from keras.utils import pad_sequences

TOKENIZER_MODEL = "tokenizer.pkl"
POSITIVE = "POSITIVE"
NEGATIVE = "NEGATIVE"
NEUTRAL = "NEUTRAL"
SENTIMENT_THRESHOLDS = (0.4, 0.7)
SEQUENCE_LENGTH = 300



model = load_model('ex_model.h5')
tokenizer = load(TOKENIZER_MODEL)



def decode_sentiment(score, include_neutral=True):
    if include_neutral:        
        label = NEUTRAL
        if score <= SENTIMENT_THRESHOLDS[0]:
            label = NEGATIVE
        elif score >= SENTIMENT_THRESHOLDS[1]:
            label = POSITIVE

        return label
    else:
        return NEGATIVE if score < 0.5 else POSITIVE
    
def predict(text, include_neutral=True):
    model = load_model('ex_model.h5')
    tokenizer = load("tokenizer.pkl")
    start_at = time.time()
    # Tokenize text
    x_test = pad_sequences(tokenizer.texts_to_sequences([text]), maxlen=SEQUENCE_LENGTH)
    # Predict
    score = model.predict([x_test])[0]
    # Decode sentiment
    label = decode_sentiment(score, include_neutral=include_neutral)

    return {"label": label, "score": float(score),
       "elapsed_time": time.time()-start_at}

while True:
    inp = input("Enter Text/Sentence : ")
    print(predict(inp))
    print()