#load model
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
import pickle

model = load_model("model.h5")

#load tokenizer
with open("tokenizer.pkl","rb") as handle:
    tokenizer = pickle.load(handle)

#make predictions
# Make predictions
while True:
    text = input("write a review, press e to exit: ")
    if text == 'e':
        break
    TokenText = tokenizer.texts_to_sequences([text])
    PadText = pad_sequences(TokenText, maxlen=100)
    Pred = model.predict(PadText)
    Pred_float = Pred[0][0]  # Extract the single float value
    Pred_float *= 1.3
    binary_pred = (Pred_float > 0.5).astype(int)
    if binary_pred == 0:
        print("bad review")
    else:
        print("good review")
    print(Pred_float)