Update app.py

app.py

@@ -70,6 +70,7 @@ def rnn_predict_message(input_text):
         else:
             return "Not spam"
 
+            
 # Load the saved LSTM model
 lstm_smsspam_model=tf.keras.models.load_model('lstm_smsspam_model.h5')
 # Load the saved tokenizer
@@ -87,6 +88,23 @@ def lstm_predict_message(message):
         return 'Not spam'
 
 
+# Load the saved model
+gru_movie_model = tf.keras.models.load_model('APP/gru_movie_model.h5')
+with open('APP/tokenizer_movie_gru.pickle', 'rb') as handle:
+    lstm_movie_tokeniser = pickle.load(handle)
+
+# Function to predict sentiment for a given review
+def gru_predict_movie_sentiment(review):
+    maxlen = 100
+    sequence = lstm_movie_tokeniser.texts_to_sequences([review])
+    sequence = tf.keras.preprocessing.sequence.pad_sequences(sequence, padding='post', maxlen=maxlen)
+    prediction = gru_movie_model.predict(sequence)
+    if prediction > 0.5:
+        return "Positive"
+    else:
+        return "Negative"
+
+
 
 # Main function for Streamlit app
 def main():    
@@ -116,26 +134,28 @@ def main():
         
 
     elif task == "Sentiment Classification":
-        model = st.sidebar.radio("Select Model", (["Perceptron", "Backpropagation", "DNN", "RNN", "LSTM"]))
+        model = st.sidebar.radio("Select Model", (["DNN", "RNN", "LSTM", "GRU", "Perceptron", "Backpropagation"]))
         
-        if model == "RNN":
+        
+
+        if model == "DNN":
             st.subheader("SMS Spam Detection")
             user_input = st.text_area("Enter a message to classify as 'Spam' or 'Not spam': ")
                 
             if st.button("Predict"):
                 if user_input:
-                    prediction_result = rnn_predict_message(user_input)
+                    prediction_result = dnn_predict_message(user_input)
                     st.write(f"The message is classified as: {prediction_result}")
                 else:
                     st.write("Please enter some text for prediction")
 
-        elif model == "DNN":
+        elif model == "RNN":
             st.subheader("SMS Spam Detection")
             user_input = st.text_area("Enter a message to classify as 'Spam' or 'Not spam': ")
                 
             if st.button("Predict"):
                 if user_input:
-                    prediction_result = dnn_predict_message(user_input)
+                    prediction_result = rnn_predict_message(user_input)
                     st.write(f"The message is classified as: {prediction_result}")
                 else:
                     st.write("Please enter some text for prediction")
@@ -151,6 +171,19 @@ def main():
                 else:
                     st.write("Please enter some text for prediction")
 
+        elif model == "GRU":
+            st.subheader("Movie Sentiment Analysis")
+            user_review = st.text_area("Enter a movie review: ")
+            
+            if st.button("Analyze Sentiment"):
+                if user_review:
+                    sentiment_result = gru_predict_movie_sentiment(user_review)
+                    st.write(f"The sentiment of the review is: {sentiment_result}")
+                else:
+                    st.write("Please enter a movie review for sentiment analysis")
+
+        
+
                 
 if __name__ == "__main__":
     main()