import gradio as gr import spaces import torch from transformers import T5Tokenizer, T5ForConditionalGeneration, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, pipeline import languagecodes favourite_langs = {"German": "de", "Romanian": "ro", "English": "en", "-----": "-----"} all_langs = languagecodes.iso_languages # Language options as list, add favourite languages first options = list(favourite_langs.keys()) options.extend(list(all_langs.keys())) models = ["Helsinki-NLP", "t5-base", "t5-small", "t5-large", "facebook/nllb-200-distilled-600M", "facebook/nllb-200-distilled-1.3B", "facebook/mbart-large-50-many-to-many-mmt", "utter-project/EuroLLM-1.7B", "Unbabel/TowerInstruct-7B-v0.2", "Unbabel/TowerInstruct-Mistral-7B-v0.2" ] def model_to_cuda(model): # Move the model to GPU if available if torch.cuda.is_available(): model = model.to('cuda') print("CUDA is available! Using GPU.") else: print("CUDA not available! Using CPU.") return model def eurollm(model_name, sl, tl, input_text): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) prompt = f"{sl}: {input_text} {tl}:" inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=512) output = tokenizer.decode(outputs[0], skip_special_tokens=True) result = output.rsplit(f'{tl}:')[-1].strip() return result def nllb(model_name, sl, tl, input_text): tokenizer = AutoTokenizer.from_pretrained(model_name, src_lang=sl) model = AutoModelForSeq2SeqLM.from_pretrained(model_name, device_map="auto") translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=sl, tgt_lang=tl) translated_text = translator(input_text, max_length=512) return translated_text[0]['translation_text'] @spaces.GPU def translate_text(input_text, sselected_language, tselected_language, model_name): sl = all_langs[sselected_language] tl = all_langs[tselected_language] message_text = f'Translated from {sselected_language} to {tselected_language} with {model_name}' print(message_text) if model_name == "Helsinki-NLP": try: model_name = f"Helsinki-NLP/opus-mt-{sl}-{tl}" tokenizer = AutoTokenizer.from_pretrained(model_name) model = model_to_cuda(AutoModelForSeq2SeqLM.from_pretrained(model_name)) except EnvironmentError: try: model_name = f"Helsinki-NLP/opus-tatoeba-{sl}-{tl}" tokenizer = AutoTokenizer.from_pretrained(model_name) model = model_to_cuda(AutoModelForSeq2SeqLM.from_pretrained(model_name)) except EnvironmentError as error: return f"Error finding model: {model_name}! Try other available language combination.", error if 'eurollm' in model_name.lower(): translated_text = eurollm(model_name, sselected_language, tselected_language, input_text) return translated_text, message_text if 'nllb' in model_name.lower(): nnlbsl, nnlbtl = languagecodes.nllb_language_codes[sselected_language], languagecodes.nllb_language_codes[tselected_language] translated_text = nllb(model_name, nnlbsl, nnlbtl, input_text) return translated_text, message_text if model_name.startswith('facebook/mbart-large'): from transformers import MBartForConditionalGeneration, MBart50TokenizerFast model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt") # translate source to target tokenizer.src_lang = languagecodes.mbart_large_languages[sselected_language] encoded = tokenizer(input_text, return_tensors="pt") generated_tokens = model.generate( **encoded, forced_bos_token_id=tokenizer.lang_code_to_id[languagecodes.mbart_large_languages[tselected_language]] ) return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0], message_text if 'Unbabel' in model_name: pipe = pipeline("text-generation", model=model_name, torch_dtype=torch.bfloat16, device_map="auto") messages = [{"role": "user", "content": f"Translate the following text from {sselected_language} into {tselected_language}.\n{sselected_language}: {input_text}.\n{tselected_language}:"}] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False) outputs = pipe(prompt, max_new_tokens=256, do_sample=False) translated_text = outputs[0]["generated_text"] return translated_text, message_text if model_name.startswith('t5'): tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto") if model_name.startswith("Helsinki-NLP"): prompt = input_text else: prompt = f"translate {sselected_language} to {tselected_language}: {input_text}" input_ids = tokenizer.encode(prompt, return_tensors="pt") output_ids = model.generate(input_ids, max_length=512) translated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) message_text = f'Translated from {sselected_language} to {tselected_language} with {model_name}' print(f'Translating from {sselected_language} to {tselected_language} with {model_name}:', f'{input_text} = {translated_text}', sep='\n') return translated_text, message_text # Define a function to swap dropdown values def swap_languages(src_lang, tgt_lang): return tgt_lang, src_lang def create_interface(): with gr.Blocks() as interface: gr.Markdown("### Machine Text Translation") with gr.Row(): input_text = gr.Textbox(label="Enter text to translate:", placeholder="Type your text here, maximum 512 tokens") with gr.Row(): sselected_language = gr.Dropdown(choices=options, value = options[0], label="Source language", interactive=True) tselected_language = gr.Dropdown(choices=options, value = options[1], label="Target language", interactive=True) swap_button = gr.Button("Swap Languages") swap_button.click(fn=swap_languages, inputs=[sselected_language, tselected_language], outputs=[sselected_language, tselected_language]) model_name = gr.Dropdown(choices=models, label="Select a model", value = models[4], interactive=True) translate_button = gr.Button("Translate") translated_text = gr.Textbox(label="Translated text:", placeholder="Display field for translation", interactive=False, show_copy_button=True) message_text = gr.Textbox(label="Messages:", placeholder="Display field for status and error messages", interactive=False) translate_button.click( translate_text, inputs=[input_text, sselected_language, tselected_language, model_name], outputs=[translated_text, message_text] ) return interface interface = create_interface() interface.launch()