Update app.py

app.py

@@ -75,58 +75,6 @@ X_test_embeddings = np.array(X_test_embeddings)
 X_train = np.array(X_train_embeddings)
 X_test = np.array(X_test_embeddings)
 
-# Sentiment Prediction with RNN Neural Network and Confusion Matrix
-
-from keras.models import Sequential
-from keras.layers import Dense, SimpleRNN, Reshape, Dropout
-from keras.optimizers import Adam
-from keras.callbacks import LearningRateScheduler
-from sklearn.metrics import confusion_matrix
-import seaborn as sns
-import matplotlib.pyplot as plt
-import numpy as np
-from keras.models import Sequential
-from keras.layers import Embedding, SpatialDropout1D, LSTM, Dense
-
-max_features = 10000  # Adjust this based on your vocabulary size
-embed_dim = 128      # Adjust this based on the desired dimension of the embedding space
-lstm_out = 64        # Adjust this based on the number of LSTM units
-
-model = Sequential()
-model.add(Embedding(max_features, embed_dim, input_length=X_train.shape[1]))
-model.add(SpatialDropout1D(0.4))
-model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
-model.add(Dense(3, activation='softmax'))  # Adjust the number of units based on your task
-model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
-print(model.summary())
-
-
-
-# Use a learning rate scheduler
-def lr_schedule(epoch):
-    return 0.0001 * 0.9 ** epoch
-
-opt = Adam(learning_rate=0.0001)
-lr_scheduler = LearningRateScheduler(lr_schedule)
-
-# Compile the model
-model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-
-# Print model summary to check the architecture
-model.summary()
-
-# Train the model with the learning rate scheduler
-model.fit(X_train_embeddings, y_train, epochs=1, batch_size=32, validation_split=0.1, callbacks=[lr_scheduler])
-
-# Evaluate the model on the test set
-accuracy = model.evaluate(X_test_embeddings, y_test)[1]
-print(f"Accuracy: {accuracy * 100:.2f}%")
-
-# Predictions on the test set
-y_pred_probabilities = model.predict(X_test_embeddings)
-y_pred = np.argmax(y_pred_probabilities, axis=1)
-
-
 # # Sentiment Prediction with RNN Neural Network and Confusion Matrix
 
 # from keras.models import Sequential
@@ -137,15 +85,22 @@ y_pred = np.argmax(y_pred_probabilities, axis=1)
 # import seaborn as sns
 # import matplotlib.pyplot as plt
 # import numpy as np
+# from keras.models import Sequential
+# from keras.layers import Embedding, SpatialDropout1D, LSTM, Dense
+
+# max_features = 10000  # Adjust this based on your vocabulary size
+# embed_dim = 128      # Adjust this based on the desired dimension of the embedding space
+# lstm_out = 64        # Adjust this based on the number of LSTM units
 
-# # Build a neural network model with RNN
 # model = Sequential()
-# model.add(Dense(256, input_shape=(1024,), activation='tanh'))
-# model.add(Reshape((1, 256)))
-# model.add(SimpleRNN(128, activation='relu'))
-# model.add(Dense(64, activation='relu'))
-# model.add(Dropout(0.5))  # Adding dropout for regularization
-# model.add(Dense(3, activation='softmax'))
+# model.add(Embedding(max_features, embed_dim, input_length=X_train.shape[1]))
+# model.add(SpatialDropout1D(0.4))
+# model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
+# model.add(Dense(3, activation='softmax'))  # Adjust the number of units based on your task
+# model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+# print(model.summary())
+
+
 
 # # Use a learning rate scheduler
 # def lr_schedule(epoch):
@@ -153,7 +108,7 @@ y_pred = np.argmax(y_pred_probabilities, axis=1)
 
 # opt = Adam(learning_rate=0.0001)
 # lr_scheduler = LearningRateScheduler(lr_schedule)
-# #
+
 # # Compile the model
 # model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
 
@@ -161,15 +116,60 @@ y_pred = np.argmax(y_pred_probabilities, axis=1)
 # model.summary()
 
 # # Train the model with the learning rate scheduler
-# model.fit(X_train_embeddings, y_train, epochs=30, batch_size=32, validation_split=0.1, callbacks=[lr_scheduler])
-     
+# model.fit(X_train_embeddings, y_train, epochs=1, batch_size=32, validation_split=0.1, callbacks=[lr_scheduler])
+
 # # Evaluate the model on the test set
 # accuracy = model.evaluate(X_test_embeddings, y_test)[1]
+# print(f"Accuracy: {accuracy * 100:.2f}%")
 
 # # Predictions on the test set
 # y_pred_probabilities = model.predict(X_test_embeddings)
 # y_pred = np.argmax(y_pred_probabilities, axis=1)
 
+
+# Sentiment Prediction with RNN Neural Network and Confusion Matrix
+
+from keras.models import Sequential
+from keras.layers import Dense, SimpleRNN, Reshape, Dropout
+from keras.optimizers import Adam
+from keras.callbacks import LearningRateScheduler
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Build a neural network model with RNN
+model = Sequential()
+model.add(Dense(256, input_shape=(1024,), activation='tanh'))
+model.add(Reshape((1, 256)))
+model.add(SimpleRNN(128, activation='relu'))
+model.add(Dense(64, activation='relu'))
+model.add(Dropout(0.5))  # Adding dropout for regularization
+model.add(Dense(3, activation='softmax'))
+
+# Use a learning rate scheduler
+def lr_schedule(epoch):
+    return 0.0001 * 0.9 ** epoch
+
+opt = Adam(learning_rate=0.0001)
+lr_scheduler = LearningRateScheduler(lr_schedule)
+#
+# Compile the model
+model.compile(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+# Print model summary to check the architecture
+model.summary()
+
+# Train the model with the learning rate scheduler
+model.fit(X_train_embeddings, y_train, epochs=30, batch_size=32, validation_split=0.1, callbacks=[lr_scheduler])
+     
+# Evaluate the model on the test set
+accuracy = model.evaluate(X_test_embeddings, y_test)[1]
+
+# Predictions on the test set
+y_pred_probabilities = model.predict(X_test_embeddings)
+y_pred = np.argmax(y_pred_probabilities, axis=1)
+
 language = st.slider('Enter the language:')
 user_text = st.slider('Enter the text:')