main.py

import numpy as np
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import load_model
import streamlit as st

word_index=imdb.get_word_index()
reverse_word_index={value: key for key, value in word_index.items()}

## load_model
model=load_model('simple_rnn_imdb.h5')

 # Step 2: Help function
 # Function to decode reviews

def decode_review(encoded_review) :
    return ' '.join([reverse_word_index.get(i-3,'?') for i in encoded_review])

# function to preprocess user input
def preprocess_text(texte):
    words=texte.lower().split()
    encoded_review=[word_index.get(word,2)+3 for word in words]
    padded_review=sequence.pad_sequences([encoded_review],maxlen=500)
    return padded_review

## prediction function
def prediction_sentiment(review):
    preprocessing_input=preprocess_text(review)
    prediction=model.predict(preprocessing_input)
    sentiment='Positive' if prediction[0][0]>0.5 else 'Negative'

    return sentiment,prediction[0][0]

### streamlit app

st.title("IMDB Movie Review Sentiment Analysis")
st.write("Enter a movie review to classify it as as positive or negative.")

# user input
user_input=st.text_area('Movie Review')
if st.button('classify'):
    preprocessed_input=preprocess_text(user_input)
    # prediction
    prediction=model.predict(preprocessed_input)
    sentiment='Positive' if prediction[0][0]>0.5 else 'Negative'
    
    st.write(f'Sentiment: {sentiment}')
    st.write(f'Prediction: {prediction[0][0]}' )
else:
    st.write("Please enter a movie review")