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| import gradio as gr | |
| from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM | |
| from tensorflow.keras.models import load_model | |
| import pickle | |
| import json | |
| import keras | |
| custom_objects = { | |
| 'LSTM': keras.layers.LSTM, | |
| } | |
| # Load model | |
| model_lstm = load_model('seq2seq_model.h5', custom_objects=custom_objects, compile=False) | |
| # Recompile the model | |
| model_lstm.compile(optimizer='adam', loss='sparse_categorical_crossentropy') | |
| # Define the model repository and tokenizer checkpoint | |
| model_checkpoint = "himanishprak23/neural_machine_translation" | |
| tokenizer_checkpoint = "Helsinki-NLP/opus-mt-en-hi" | |
| tokenizer_base_nmt = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) | |
| model_base_nmt = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-hi") | |
| # Load the tokenizer from Helsinki-NLP and model from Hugging Face repository | |
| tokenizer_nmt = AutoTokenizer.from_pretrained(tokenizer_checkpoint) | |
| model_nmt = TFAutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) | |
| # Loading models, tokenizer & variables for trained LSTM translation model. | |
| model_lstm = model_lstm | |
| with open('eng_tokenizer.pkl', 'rb') as file: | |
| eng_tokenizer = pickle.load(file) | |
| with open('hin_tokenizer.pkl', 'rb') as file: | |
| hin_tokenizer = pickle.load(file) | |
| max_len_eng = 20 | |
| max_len_hin = 22 | |
| def translate_text_base_nmt(input_text): | |
| tokenized_input = tokenizer_base_nmt(input_text, return_tensors='tf', max_length=128, truncation=True) | |
| generated_tokens = model_base_nmt.generate(**tokenized_input, max_length=128) | |
| predicted_text = tokenizer_nmt.decode(generated_tokens[0], skip_special_tokens=True) | |
| return predicted_text | |
| def translate_text_nmt(input_text): | |
| tokenized_input = tokenizer_nmt(input_text, return_tensors='tf', max_length=128, truncation=True) | |
| generated_tokens = model_nmt.generate(**tokenized_input, max_length=128) | |
| predicted_text = tokenizer_nmt.decode(generated_tokens[0], skip_special_tokens=True) | |
| return predicted_text | |
| def translate_text_lstm(sentence, model, eng_tokenizer, hin_tokenizer, max_len_eng, max_len_hin): | |
| # Tokenize and pad the input sentence | |
| input_seq = eng_tokenizer.texts_to_sequences([sentence]) | |
| input_seq = pad_sequences(input_seq, maxlen=max_len_eng, padding='post') | |
| # Initialize target sequence with start token | |
| target_seq = np.zeros((1, 1)) | |
| target_seq[0, 0] = hin_tokenizer.word_index['start'] | |
| # Create reverse word index for Hindi | |
| reverse_word_index = dict([(idx, word) for word, idx in hin_tokenizer.word_index.items()]) | |
| decoded_sentence = [] | |
| for _ in range(max_len_hin): | |
| output = model.predict([input_seq, target_seq], verbose=0) | |
| sampled_token_index = np.argmax(output[0, -1, :]) | |
| sampled_word = reverse_word_index.get(sampled_token_index, '') | |
| if sampled_word == 'end' or sampled_word == '' or len(decoded_sentence) >= max_len_hin - 1: | |
| break | |
| decoded_sentence.append(sampled_word) | |
| # Update target sequence | |
| target_seq = np.zeros((1, len(decoded_sentence) + 1)) | |
| for t, word in enumerate(decoded_sentence): | |
| target_seq[0, t] = hin_tokenizer.word_index.get(word, 0) # Use 0 for unknown words | |
| target_seq[0, len(decoded_sentence)] = sampled_token_index | |
| return ' '.join(decoded_sentence) | |
| def translate_text(input_text): | |
| translation_lstm = translate_text_lstm(input_text, model_lstm, eng_tokenizer, hin_tokenizer, max_len_eng, max_len_hin) | |
| translation_nmt_base = translate_text_base_nmt(input_text) | |
| translation_nmt_finetuned = translate_text_nmt(input_text) | |
| return translation_lstm, translation_nmt_base, translation_nmt_finetuned | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=translate_text, | |
| inputs=gr.components.Textbox(lines=2, placeholder="Enter text to translate from English to Hindi..."), | |
| outputs=[ | |
| gr.components.Textbox(label="Translation (LSTM Model)"), | |
| gr.components.Textbox(label="Translation (Base Helsinki Model)"), | |
| gr.components.Textbox(label="Translation (Fine-tuned Helsinki Model)") | |
| ], | |
| title="English to Hindi Translator", | |
| description="Enter English text and get the Hindi translation from three different models: LSTM, Base Helsinki-NLP, and Fine-tuned Helsinki-NLP." | |
| ) | |
| # Launch the Gradio app | |
| iface.launch() | |