import gradio as gr from transformers import MarianMTModel, MarianTokenizer # Function to dynamically load the model and tokenizer based on selected languages def translate_text(text, source_language, target_language): # Construct model name based on selected languages model_name = f"Helsinki-NLP/opus-mt-{source_language}-{target_language}" # Load tokenizer and model try: tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) except Exception as e: return f"Failed to load model for {source_language} to {target_language}: {str(e)}" # Translate text translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)) translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) return translated_text # Define language options (ISO 639-1 codes and names) # Note: This is a simplified subset for demonstration. Expand based on available models. language_options = [ ('en', 'English'), ('es', 'Spanish'), ('fr', 'French'), ('de', 'German'), ('zh', 'Chinese'), ('ru', 'Russian'), ('ar', 'Arabic'), ('it', 'Italian'), ('pt', 'Portuguese'), ('nl', 'Dutch'), # Add more languages as needed ] # Convert language options to the format expected by the dropdown language_dropdown_options = [(code, f"{name} ({code})") for code, name in language_options] # Create dropdowns for source and target languages source_language_dropdown = gr.inputs.Dropdown(choices=language_dropdown_options, label="Source Language") target_language_dropdown = gr.inputs.Dropdown(choices=language_dropdown_options, label="Target Language") # Define the interface iface = gr.Interface( fn=translate_text, inputs=[gr.inputs.Textbox(lines=2, placeholder="Enter text to translate..."), source_language_dropdown, target_language_dropdown], outputs=gr.outputs.Textbox(), title="Text Translator with Dynamic Helsinki NLP Models", description="Select source and target languages to translate text using Helsinki NLP models." ) # Launch the app iface.launch()