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import streamlit as st
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
word_index = imdb.get_word_index()
maximo_num_palabras = 20000
model = tf.keras.models.load_model("opiniones.h5")
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
def predict_sentimiento(review):
sequence = reviewnueva(review, word_index, maximo_num_palabras)
nuevareviewpad = tf.keras.preprocessing.sequence.pad_sequences([sequence], maxlen= 100)
prediccion = model.predict(nuevareviewpad)
if prediccion[0][0]>=0.5 :
sentimiento = "Positivo"
else:
sentimiento = "Negativo"
return sentimiento
st.title("Ingrese una review para poder calficarla como positiva o negativ")
review = st.text_area("Ingrese la reseña aqui", height= 200)
if st.button("Predecir el sentimiento de la reseña"):
if review:
sentimiento = predict_sentimiento(review)
st.write(f'El sentimiento es :{sentimiento}')
else:
st.write(f'Ingrese una review')
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