app.py

import streamlit as st
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.processing.secuence import pad_sequences
import numpy as np

word_index = imdb.get_word_index()
maximo_num_palabras = 20000

def reviewnueva(review, word_index, maximo_num_palabras):
  sequence = []
  for word in review.split():
    index = word_index.get(word.lower(), 0)
    if index < maximo_num_palabras:
      sequence.append(index)
  return sequence

model = tf.keras.models.load_model("opiniones.h5")
def predict_sentimiento(review)
    sequence = reviewnueva(review, word_index)
    if prediction [0] [0]>=0.5 :
        sentimiento = "Positivo"
    else:
        sentimiento = "Negativo"
    return predict_sentimiento

st.title("Ingrese una review para poder calificarla como positiva o negativa")
review = st.text_area("Ingrese reseña aqui", height=200)

if st.button("Predicir sentimiento"):
    if review:
        sentiemiento = predict_sentimiento(review)
        st.write(f'El sentimiento es: {sentimiento}')
    else:
        st.write(f'Ingrese una review')