Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import MarianMTModel, MarianTokenizer | |
| import torch | |
| # 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 text after detecting complete sentences. | |
| """ | |
| if not model_name or not text.strip(): | |
| return "Select a model and provide text." | |
| try: | |
| # Load the selected model | |
| model, tokenizer = load_model(model_name) | |
| # Tokenize the input text | |
| tokens = tokenizer(text, return_tensors="pt", padding=True) | |
| if torch.cuda.is_available(): | |
| tokens = {k: v.to('cuda') for k, v in tokens.items()} | |
| # Generate translated tokens | |
| translated = model.generate(**tokens) | |
| # Decode translation | |
| return tokenizer.decode(translated[0], skip_special_tokens=True) | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| def process_input(model_name, text): | |
| """ | |
| Process user input to detect completed sentences and translate in real-time. | |
| """ | |
| sentences = text.strip().split('. ') | |
| translations = [] | |
| for sentence in sentences: | |
| if sentence.endswith('.') or sentence.endswith('!') or sentence.endswith('?'): | |
| translations.append(translate_text(model_name, sentence)) | |
| else: | |
| # If the sentence is incomplete, skip translation | |
| translations.append(f"Waiting for completion: {sentence}") | |
| return '\n'.join(translations) | |
| # Define Gradio Interface | |
| with gr.Blocks() as app: | |
| gr.Markdown("## 🌍 Real-Time Sentence Translation") | |
| gr.Markdown("### Enter text in the textbox, and it will be translated after each sentence ends!") | |
| model_dropdown = gr.Dropdown( | |
| label="Select Translation Model", | |
| choices=[ | |
| "Helsinki-NLP/opus-mt-tc-big-en-tr", # English to Turkish | |
| "Helsinki-NLP/opus-mt-tc-big-tr-en", # Turkish to English | |
| "Helsinki-NLP/opus-mt-tc-big-en-fr", # English to French | |
| "Helsinki-NLP/opus-mt-tc-big-fr-en", # French to English | |
| "Helsinki-NLP/opus-mt-en-de", # English to German | |
| "Helsinki-NLP/opus-mt-de-en", # German to English | |
| "Helsinki-NLP/opus-mt-tc-big-en-es", # English to Spanish | |
| "Helsinki-NLP/opus-mt-es-en", # Spanish to English | |
| "Helsinki-NLP/opus-mt-tc-big-en-ar", # English to Arabic | |
| "Helsinki-NLP/opus-mt-tc-big-ar-en", # Arabic to English | |
| "Helsinki-NLP/opus-mt-en-ur", # English to Urdu | |
| "Helsinki-NLP/opus-mt-ur-en", # Urdu to English | |
| "Helsinki-NLP/opus-mt-en-hi", # English to Hindi | |
| "Helsinki-NLP/opus-mt-hi-en", # Hindi to English | |
| "Helsinki-NLP/opus-mt-en-zh", # English to Chinese | |
| "Helsinki-NLP/opus-mt-zh-en", # Chinese to English", | |
| ], | |
| value="Helsinki-NLP/opus-mt-tc-big-en-tr", | |
| interactive=True | |
| ) | |
| input_textbox = gr.Textbox( | |
| label="Enter Text:", | |
| placeholder="Type here...", | |
| lines=5, | |
| interactive=True | |
| ) | |
| output_textbox = gr.Textbox( | |
| label="Translated Text:", | |
| lines=5, | |
| interactive=False | |
| ) | |
| input_textbox.change( | |
| fn=process_input, | |
| inputs=[model_dropdown, input_textbox], | |
| outputs=[output_textbox], | |
| ) | |
| # Launch Gradio App | |
| app.launch() | |