app.py

import streamlit as st
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np

word_index = imdb.get_word_index()
max_words = 20000

def review_to_sequences(review, word_index, max_words):
  sequences = []
  for word in review.split():
    index = word_index.get(word.lower(), 0)
    if index < max_words:
      sequences.append(index)
  return sequences

model = tf.keras.models.load_model("opiniones.h5")
def predict_sentimiento(review):
    sequences = review_to_sequences(review, word_index, max_words)
    sequences = np.array(sequences)
    sequences = pad_sequences([sequences], maxlen=1000)
    prediction = model.predict(sequences)
    if prediction [0] [0]>=0.5 :
        sentimiento = "Positivo"
    else:
        sentimiento = "Negativo"
    return 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:
        sentimiento = predict_sentimiento(review)
        st.write(f'El sentimiento es: {sentimiento}')
    else:
        st.write(f'Ingrese una review')