Update app.py

app.py

@@ -2,6 +2,7 @@
 import nltk
 import spacy
 import tensorflow as tf
+import numpy as np
 from tensorflow.keras.layers import Input, Dense, LSTM, Embedding, Dropout
 from tensorflow.keras.models import Model
 from tensorflow.keras.optimizers import Adam
@@ -12,6 +13,12 @@ from tensorflow.keras.preprocessing.text import Tokenizer
 nlp = spacy.load('en_core_web_sm')
 
 # Define the neural network architecture
+num_words = 10000
+embedding_dim = 128
+lstm_units = 128
+dropout_rate = 0.2
+num_classes = 10
+
 input_text = Input(shape=(None,))
 embedding_layer = Embedding(input_dim=num_words, output_dim=embedding_dim)(input_text)
 lstm_layer = LSTM(units=lstm_units)(embedding_layer)
@@ -19,10 +26,13 @@ dropout_layer = Dropout(rate=dropout_rate)(lstm_layer)
 output_layer = Dense(units=num_classes, activation='softmax')(dropout_layer)
 
 # Compile the model
+learning_rate = 0.001
 model = Model(inputs=input_text, outputs=output_layer)
 model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
 
 # Train the model
+batch_size = 32
+num_epochs = 10
 model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size=batch_size, epochs=num_epochs)
 
 # Define the function for providing feedback and corrections