Update app.py

app.py

@@ -1,50 +1,44 @@
-# Step 1: Import Libraries and Load the Model
-import numpy as np
-import tensorflow as tf
-from tensorflow.keras.datasets import imdb
-from tensorflow.keras.preprocessing import sequence
-from tensorflow.keras.models import load_model
-
-# Load the IMDB dataset word index
-word_index = imdb.get_word_index()
-reverse_word_index = {value: key for key, value in word_index.items()}
-
-# Load the pre-trained model with ReLU activation
-model = load_model('simple_rnn_imdb.h5')
-
-# Step 2: Helper Functions
-# 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(text):
-    words = text.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
-
-
-import streamlit as st
-## streamlit app
-# Streamlit app
-st.title('IMDB Movie Review Sentiment Analysis')
-st.write('Enter a movie review to classify it as positive or negative.')
-
-# User input
-user_input = st.text_area('Movie Review')
-
-if st.button('Classify'):
-
-    preprocessed_input=preprocess_text(user_input)
-
-    ## MAke prediction
-    prediction=model.predict(preprocessed_input)
-    sentiment='Positive' if prediction[0][0] > 0.5 else 'Negative'
-
-    # Display the result
-    st.write(f'Sentiment: {sentiment}')
-    st.write(f'Prediction Score: {prediction[0][0]}')
-else:
-    st.write('Please enter a movie review.')
-
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.datasets import imdb
+from tensorflow.keras.preprocessing import sequence
+from tensorflow.keras.models import load_model
+import streamlit as st
+
+# Load the IMDB dataset word index
+word_index = imdb.get_word_index()
+reverse_word_index = {value: key for key, value in word_index.items()}
+
+# Load the pre-trained model with ReLU activation
+model = load_model('simple_rnn_imdb.h5')
+
+# Helper 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(text, max_word_index=10000, maxlen=500):
+    words = text.lower().split()
+    encoded_review = [min(word_index.get(word, 2) + 3, max_word_index - 1) for word in words]
+    padded_review = sequence.pad_sequences([encoded_review], maxlen=maxlen)
+    return padded_review
+
+# Streamlit app
+st.title('IMDB Movie Review Sentiment Analysis')
+st.write('Enter a movie review to classify it as positive or negative.')
+
+# User input
+user_input = st.text_area('Movie Review')
+
+if st.button('Classify'):
+    preprocessed_input = preprocess_text(user_input)
+
+    # Make prediction
+    prediction = model.predict(preprocessed_input)
+    sentiment = 'Positive' if prediction[0][0] > 0.5 else 'Negative'
+
+    # Display the result
+    st.write(f'Sentiment: {sentiment}')
+    st.write(f'Prediction Score: {prediction[0][0]}')
+else:
+    st.write('Please enter a movie review.')