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| import gradio as gr | |
| from transformers import MarianMTModel, MarianTokenizer | |
| import torch | |
| import nltk | |
| # Download punkt for sentence tokenization | |
| nltk.download('punkt') | |
| nltk.download('punkt_tab') | |
| from nltk.tokenize import sent_tokenize | |
| # Cache for storing models and tokenizers | |
| models_cache = {} | |
| def load_model(model_name): | |
| """ | |
| Load and cache the MarianMT model and tokenizer. | |
| """ | |
| if model_name not in models_cache: | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| if torch.cuda.is_available(): | |
| model = model.to('cuda') | |
| models_cache[model_name] = (model, tokenizer) | |
| return models_cache[model_name] | |
| def translate_text(model_name, text): | |
| """ | |
| Translate input text sentence by sentence using the specified model. | |
| """ | |
| if not model_name or not text: | |
| return "Please select a model and provide text for translation." | |
| try: | |
| # Load the model and tokenizer | |
| model, tokenizer = load_model(model_name) | |
| # Split text into sentences | |
| sentences = sent_tokenize(text) | |
| translated_sentences = [] | |
| for sentence in sentences: | |
| # Tokenize the sentence | |
| print(f"Sentence: {sentence}\n") | |
| tokens = tokenizer(sentence, return_tensors="pt", padding=True) | |
| if torch.cuda.is_available(): | |
| tokens = {k: v.to('cuda') for k, v in tokens.items()} | |
| # Generate translation for the sentence | |
| translated = model.generate(**tokens) | |
| translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) | |
| translated_sentences.append(translated_text) | |
| # Join translated sentences back into a single string | |
| return " ".join(translated_sentences) | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Model options | |
| model_options = [ | |
| ("English to Turkish", "Helsinki-NLP/opus-mt-tc-big-en-tr"), | |
| ("Turkish to English", "Helsinki-NLP/opus-mt-tc-big-tr-en"), | |
| ("English to French", "Helsinki-NLP/opus-mt-tc-big-en-fr"), | |
| ("French to English", "Helsinki-NLP/opus-mt-tc-big-fr-en"), | |
| ("English to German", "Helsinki-NLP/opus-mt-en-de"), | |
| ("German to English", "Helsinki-NLP/opus-mt-de-en"), | |
| ("English to Spanish", "Helsinki-NLP/opus-mt-tc-big-en-es"), | |
| ("Spanish to English", "Helsinki-NLP/opus-mt-es-en"), | |
| ("English to Arabic", "Helsinki-NLP/opus-mt-tc-big-en-ar"), | |
| ("Arabic to English", "Helsinki-NLP/opus-mt-tc-big-ar-en"), | |
| ("English to Urdu", "Helsinki-NLP/opus-mt-en-ur"), | |
| ("Urdu to English", "Helsinki-NLP/opus-mt-ur-en"), | |
| ("English to Hindi", "Helsinki-NLP/opus-mt-en-hi"), | |
| ("Hindi to English", "Helsinki-NLP/opus-mt-hi-en"), | |
| ("English to Chinese", "Helsinki-NLP/opus-mt-en-zh"), | |
| ("Chinese to English", "Helsinki-NLP/opus-mt-zh-en") | |
| ] | |
| # Create Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🌍 Real-Time Sentence Translation") | |
| with gr.Row(): | |
| model_dropdown = gr.Dropdown( | |
| label="Select Translation Model", | |
| choices=[option[1] for option in model_options], | |
| type="value", | |
| ) | |
| with gr.Row(): | |
| input_text = gr.Textbox( | |
| label="Enter text (complete sentences)", | |
| lines=5, | |
| placeholder="Type here...", | |
| ) | |
| with gr.Row(): | |
| translate_button = gr.Button("Translate") | |
| clear_button = gr.Button("Clear") | |
| output_text = gr.Textbox(label="Translated Text", interactive=False) | |
| def clear_inputs(): | |
| return "", "" | |
| translate_button.click( | |
| fn=translate_text, | |
| inputs=[model_dropdown, input_text], | |
| outputs=output_text, | |
| ) | |
| clear_button.click( | |
| fn=clear_inputs, | |
| inputs=[], | |
| outputs=[input_text, output_text], | |
| ) | |
| # Run the Gradio app | |
| demo.launch() | |