this is my language translation code /u/Longjumping-Class420 Python Education

import pandas as pd import tensorflow as tf from tensorflow.keras.layers import TextVectorization, Embedding, Dense, Input, LayerNormalization, MultiHeadAttention, Dropout from tensorflow.keras.models import Model import numpy as np # STEP 1: DATA LOADING data = pd.read_csv('eng_-french.csv') # Ensure this file exists with correct columns source_texts = data['English words/sentences'].tolist() target_texts = data['French words/sentences'].tolist() # STEP 2: DATA PARSING start_token = '[start]' end_token = '[end]' target_texts = [f"{start_token} {sentence} {end_token}" for sentence in target_texts] # Text cleaning function def clean_text(text): text = text.lower() text = text.replace('.', '').replace(',', '').replace('?', '').replace('!', '') return text source_texts = [clean_text(sentence) for sentence in source_texts] target_texts = [clean_text(sentence) for sentence in target_texts] # STEP 3: TEXT VECTORIZATION vocab_size = 10000 # Vocabulary size sequence_length = 50 # Max sequence length # Vectorization for source (English) source_vectorizer = TextVectorization(max_tokens=vocab_size, output_sequence_length=sequence_length) source_vectorizer.adapt(source_texts) # Vectorization for target (Spanish) target_vectorizer = TextVectorization(max_tokens=vocab_size, output_sequence_length=sequence_length) target_vectorizer.adapt(target_texts) # STEP 4: BUILD TRANSFORMER MODEL # Encoder Layer class TransformerEncoder(tf.keras.layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1): super().__init__() self.attention = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = tf.keras.Sequential([Dense(ff_dim, activation="relu"), Dense(embed_dim)]) self.layernorm1 = LayerNormalization(epsilon=1e-6) self.layernorm2 = LayerNormalization(epsilon=1e-6) self.dropout1 = Dropout(dropout) self.dropout2 = Dropout(dropout) def call(self, x, training): attn_output = self.attention(x, x) attn_output = self.dropout1(attn_output, training=training) out1 = self.layernorm1(x + attn_output) ffn_output = self.ffn(out1) ffn_output = self.dropout2(ffn_output, training=training) return self.layernorm2(out1 + ffn_output) # Decoder Layer class TransformerDecoder(tf.keras.layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1): super().__init__() self.attention1 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.attention2 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = tf.keras.Sequential([Dense(ff_dim, activation="relu"), Dense(embed_dim)]) self.layernorm1 = LayerNormalization(epsilon=1e-6) self.layernorm2 = LayerNormalization(epsilon=1e-6) self.layernorm3 = LayerNormalization(epsilon=1e-6) self.dropout1 = Dropout(dropout) self.dropout2 = Dropout(dropout) self.dropout3 = Dropout(dropout) def call(self, x, enc_output, training): attn1 = self.attention1(x, x) attn1 = self.dropout1(attn1, training=training) out1 = self.layernorm1(x + attn1) attn2 = self.attention2(out1, enc_output) attn2 = self.dropout2(attn2, training=training) out2 = self.layernorm2(out1 + attn2) ffn_output = self.ffn(out2) ffn_output = self.dropout3(ffn_output, training=training) return self.layernorm3(out2 + ffn_output) # Model Hyperparameters embed_dim = 256 # Embedding dimension num_heads = 4 # Number of attention heads ff_dim = 512 # Feedforward network dimension # Encoder and Decoder inputs encoder_inputs = Input(shape=(sequence_length,)) decoder_inputs = Input(shape=(sequence_length,)) # Embedding layers encoder_embedding = Embedding(input_dim=vocab_size, output_dim=embed_dim)(encoder_inputs) decoder_embedding = Embedding(input_dim=vocab_size, output_dim=embed_dim)(decoder_inputs) # Transformer Encoder and Decoder # Transformer Encoder and Decoder encoder_output = TransformerEncoder(embed_dim, num_heads, ff_dim)(encoder_embedding, training=True) decoder_output = TransformerDecoder(embed_dim, num_heads, ff_dim)(decoder_embedding, encoder_output, training=True) # Output layer output = Dense(vocab_size, activation="softmax")(decoder_output) # Compile the model transformer = Model([encoder_inputs, decoder_inputs], output) transformer.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) transformer.summary() # STEP 5: PREPARE DATA FOR TRAINING # Vectorize the data source_sequences = source_vectorizer(source_texts) target_sequences = target_vectorizer(target_texts) # Shift target sequences for decoder input and output decoder_input_sequences = target_sequences[:, :-1] # Remove last token decoder_input_sequences = tf.pad(decoder_input_sequences, [[0, 0], [0, 1]]) # Pad to match sequence length decoder_output_sequences = target_sequences[:, 1:] # Remove first token decoder_output_sequences = tf.pad(decoder_output_sequences, [[0, 0], [0, 1]]) # Pad to match sequence length # STEP 6: TRAIN THE MODEL transformer.fit( [source_sequences, decoder_input_sequences], np.expand_dims(decoder_output_sequences, -1), batch_size=32, epochs=30, # Change to 30 for full training validation_split=0.2 ) # STEP 7: TRANSLATION FUNCTION def translate(sentence): sentence_vector = source_vectorizer([clean_text(sentence)]) output_sentence = "[start]" for _ in range(sequence_length): # Prepare decoder input target_vector = target_vectorizer([output_sentence]) # Predict next token prediction = transformer.predict([sentence_vector, target_vector], verbose=0) predicted_token = np.argmax(prediction[0, -1, :]) predicted_word = target_vectorizer.get_vocabulary()[predicted_token] # Break if end token is reached if predicted_word == "[end]" or predicted_word == "": break output_sentence += " " + predicted_word # Return cleaned-up sentence return output_sentence.replace("[start]", "").replace("[end]", "").strip() # Test the translation test_sentence = "Hi." print("English:", test_sentence) print("french:", translate(test_sentence)) ######this code just gives me french blank, nothing at all, no error but just a blank 

import pandas as pd import tensorflow as tf from tensorflow.keras.layers import TextVectorization, Embedding, Dense, Input, LayerNormalization, MultiHeadAttention, Dropout from tensorflow.keras.models import Model import numpy as np # STEP 1: DATA LOADING data = pd.read_csv('eng_-french.csv') # Ensure this file exists with correct columns source_texts = data['English words/sentences'].tolist() target_texts = data['French words/sentences'].tolist() # STEP 2: DATA PARSING start_token = '[start]' end_token = '[end]' target_texts = [f"{start_token} {sentence} {end_token}" for sentence in target_texts] # Text cleaning function def clean_text(text): text = text.lower() text = text.replace('.', '').replace(',', '').replace('?', '').replace('!', '') return text source_texts = [clean_text(sentence) for sentence in source_texts] target_texts = [clean_text(sentence) for sentence in target_texts] # STEP 3: TEXT VECTORIZATION vocab_size = 10000 # Vocabulary size sequence_length = 50 # Max sequence length # Vectorization for source (English) source_vectorizer = TextVectorization(max_tokens=vocab_size, output_sequence_length=sequence_length) source_vectorizer.adapt(source_texts) # Vectorization for target (Spanish) target_vectorizer = TextVectorization(max_tokens=vocab_size, output_sequence_length=sequence_length) target_vectorizer.adapt(target_texts) # STEP 4: BUILD TRANSFORMER MODEL # Encoder Layer class TransformerEncoder(tf.keras.layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1): super().__init__() self.attention = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = tf.keras.Sequential([Dense(ff_dim, activation="relu"), Dense(embed_dim)]) self.layernorm1 = LayerNormalization(epsilon=1e-6) self.layernorm2 = LayerNormalization(epsilon=1e-6) self.dropout1 = Dropout(dropout) self.dropout2 = Dropout(dropout) def call(self, x, training): attn_output = self.attention(x, x) attn_output = self.dropout1(attn_output, training=training) out1 = self.layernorm1(x + attn_output) ffn_output = self.ffn(out1) ffn_output = self.dropout2(ffn_output, training=training) return self.layernorm2(out1 + ffn_output) # Decoder Layer class TransformerDecoder(tf.keras.layers.Layer): def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1): super().__init__() self.attention1 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.attention2 = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim) self.ffn = tf.keras.Sequential([Dense(ff_dim, activation="relu"), Dense(embed_dim)]) self.layernorm1 = LayerNormalization(epsilon=1e-6) self.layernorm2 = LayerNormalization(epsilon=1e-6) self.layernorm3 = LayerNormalization(epsilon=1e-6) self.dropout1 = Dropout(dropout) self.dropout2 = Dropout(dropout) self.dropout3 = Dropout(dropout) def call(self, x, enc_output, training): attn1 = self.attention1(x, x) attn1 = self.dropout1(attn1, training=training) out1 = self.layernorm1(x + attn1) attn2 = self.attention2(out1, enc_output) attn2 = self.dropout2(attn2, training=training) out2 = self.layernorm2(out1 + attn2) ffn_output = self.ffn(out2) ffn_output = self.dropout3(ffn_output, training=training) return self.layernorm3(out2 + ffn_output) # Model Hyperparameters embed_dim = 256 # Embedding dimension num_heads = 4 # Number of attention heads ff_dim = 512 # Feedforward network dimension # Encoder and Decoder inputs encoder_inputs = Input(shape=(sequence_length,)) decoder_inputs = Input(shape=(sequence_length,)) # Embedding layers encoder_embedding = Embedding(input_dim=vocab_size, output_dim=embed_dim)(encoder_inputs) decoder_embedding = Embedding(input_dim=vocab_size, output_dim=embed_dim)(decoder_inputs) # Transformer Encoder and Decoder # Transformer Encoder and Decoder encoder_output = TransformerEncoder(embed_dim, num_heads, ff_dim)(encoder_embedding, training=True) decoder_output = TransformerDecoder(embed_dim, num_heads, ff_dim)(decoder_embedding, encoder_output, training=True) # Output layer output = Dense(vocab_size, activation="softmax")(decoder_output) # Compile the model transformer = Model([encoder_inputs, decoder_inputs], output) transformer.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) transformer.summary() # STEP 5: PREPARE DATA FOR TRAINING # Vectorize the data source_sequences = source_vectorizer(source_texts) target_sequences = target_vectorizer(target_texts) # Shift target sequences for decoder input and output decoder_input_sequences = target_sequences[:, :-1] # Remove last token decoder_input_sequences = tf.pad(decoder_input_sequences, [[0, 0], [0, 1]]) # Pad to match sequence length decoder_output_sequences = target_sequences[:, 1:] # Remove first token decoder_output_sequences = tf.pad(decoder_output_sequences, [[0, 0], [0, 1]]) # Pad to match sequence length # STEP 6: TRAIN THE MODEL transformer.fit( [source_sequences, decoder_input_sequences], np.expand_dims(decoder_output_sequences, -1), batch_size=32, epochs=30, # Change to 30 for full training validation_split=0.2 ) # STEP 7: TRANSLATION FUNCTION def translate(sentence): sentence_vector = source_vectorizer([clean_text(sentence)]) output_sentence = "[start]" for _ in range(sequence_length): # Prepare decoder input target_vector = target_vectorizer([output_sentence]) # Predict next token prediction = transformer.predict([sentence_vector, target_vector], verbose=0) predicted_token = np.argmax(prediction[0, -1, :]) predicted_word = target_vectorizer.get_vocabulary()[predicted_token] # Break if end token is reached if predicted_word == "[end]" or predicted_word == "": break output_sentence += " " + predicted_word # Return cleaned-up sentence return output_sentence.replace("[start]", "").replace("[end]", "").strip() # Test the translation test_sentence = "Hi." print("English:", test_sentence) print("french:", translate(test_sentence)) ######this code just gives me french blank, nothing at all, no error but just a blank