import requests import pandas as pd import gradio as gr from transformers import MarianMTModel, MarianTokenizer # Fetch and parse language options from the provided URL url = "https://huggingface.co/Lenylvt/LanguageISO/resolve/main/iso.md" response = requests.get(url) df = pd.read_csv(response.url, delimiter="|", skiprows=2, header=None).dropna(axis=1, how='all') df.columns = ['ISO 639-1', 'ISO 639-2', 'Language Name', 'Native Name'] df['ISO 639-1'] = df['ISO 639-1'].str.strip() # Prepare language options for the dropdown language_options = [(row['ISO 639-1'], f"{row['ISO 639-1']}") for index, row in df.iterrows()] def translate_text(text, source_language_code, target_language_code): # Construct model name using ISO 639-1 codes model_name = f"Helsinki-NLP/opus-mt-{source_language_code}-{target_language_code}" # Check if source and target languages are the same, which is not supported for translation if source_language_code == target_language_code: return "Translation between the same languages is not supported." # 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_code} to {target_language_code}: {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 source_language_dropdown = gr.Dropdown(choices=language_options, label="Source Language") target_language_dropdown = gr.Dropdown(choices=language_options, label="Target Language") iface = gr.Interface( fn=translate_text, inputs=[gr.Textbox(lines=2, placeholder="Enter text to translate..."), source_language_dropdown, target_language_dropdown], outputs=gr.Textbox(), title="Text Translator with Dynamic Language Options", description="Select source and target languages to translate text." ) iface.launch()