[Python] Sentiment analysis model return identical output for any input

So I made this sentiment analysis model, and it works just fine in the training-testing script. I built a simple interface using streamlit for my saved model, but it always returned identical scores for any input text. Plus, it somehow returns so many scores when it should only return a single score for a single input.

here is my code:

import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import pickle

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

@st.cache(allow_output_mutation=True)
def load_model():
model = tf.keras.models.load_model('C:/Users/k/Downloads/test/model_final.h5')
return model

if __name__ == '__main__':

model = load_model()
st.title('Analisis Sentimen')

txt = st.text_input('masukkan teks')

if not txt:
st.warning("masukkan teks sebelum lanjut")
st.stop()
else:
text = txt
text = tokenizer.texts_to_sequences(text)
text = pad_sequences(text)
prediction = model.predict(text)
st.title('sentimen: ')
if (prediction > 0.5).any():
st.write(prediction)
st.write('positif')
else:
st.write(prediction)
st.write('negatif')


here are some snaps when i try it with streamlit



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