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| # 1. Install necessary libraries (if you haven't already) | |
| # pip install gradio-client transformers torch sentencepiece | |
| # 2. Import libraries | |
| import gradio as gr | |
| from transformers import pipeline | |
| import torch # PyTorch is needed as a backend for transformers | |
| # 3. Load the translation pipeline | |
| # Using the NLLB model which supports many languages including English and Telugu | |
| # You might need to adjust device mapping based on your hardware (e.g., device=0 for GPU) | |
| try: | |
| # Try loading the specific model mentioned implicitly by language codes | |
| translator = pipeline('translation', model='facebook/nllb-200-distilled-600M', device=-1) # Use -1 for CPU | |
| print("Translator pipeline loaded successfully.") | |
| except Exception as e: | |
| print(f"Error loading translator pipeline: {e}") | |
| # Define a dummy function if pipeline fails to load, so Gradio interface still runs | |
| def translator(text, src_lang, tgt_lang): | |
| return f"Error: Could not load translation model. {e}" | |
| # 4. Define the translation function for Gradio | |
| def translate_text(text_to_translate, source_language, target_language): | |
| """ | |
| Translates text using the loaded Hugging Face pipeline. | |
| Args: | |
| text_to_translate (str): The text to translate. | |
| source_language (str): The source language code (e.g., 'eng_Latn'). | |
| target_language (str): The target language code (e.g., 'tel_Telu'). | |
| Returns: | |
| str: The translated text or an error message. | |
| """ | |
| if not text_to_translate: | |
| return "Please enter text to translate." | |
| if not source_language or not target_language: | |
| return "Please select both source and target languages." | |
| try: | |
| # Perform the translation using the pipeline | |
| # Note: The pipeline function expects keyword arguments src_lang and tgt_lang | |
| translated_output = translator(text_to_translate, | |
| src_lang=source_language, | |
| tgt_lang=target_language) | |
| # The output is usually a list containing a dictionary | |
| if translated_output and isinstance(translated_output, list): | |
| return translated_output[0]['translation_text'] | |
| else: | |
| # Handle unexpected output format | |
| return f"Translation failed. Unexpected output: {translated_output}" | |
| except Exception as e: | |
| print(f"Translation error: {e}") | |
| # Provide a user-friendly error message | |
| return f"An error occurred during translation: {e}. Make sure the language codes are correct and supported by the model." | |
| # 5. Define language choices for dropdowns (using NLLB codes) | |
| # Add more languages as needed from the NLLB supported list | |
| language_choices = [ | |
| ("English", "eng_Latn"), | |
| ("Telugu", "tel_Telu"), | |
| ("Hindi", "hin_Deva"), | |
| ("Tamil", "tam_Taml"), | |
| ("Spanish", "spa_Latn"), | |
| ("French", "fra_Latn"), | |
| ("German", "deu_Latn"), | |
| ("Chinese (Simplified)", "zho_Hans"), | |
| ] | |
| # 6. Create the Gradio interface | |
| with gr.Blocks(theme=gr.themes.Soft()) as iface: | |
| gr.Markdown("# Text Translator using NLLB Model") | |
| gr.Markdown("Enter text and select the source and target languages.") | |
| with gr.Row(): | |
| # Input text area | |
| input_text = gr.Textbox(label="Text to Translate", placeholder="Enter text here...", lines=5) | |
| # Output text area | |
| output_text = gr.Textbox(label="Translated Text", placeholder="Translation will appear here...", lines=5, interactive=False) | |
| with gr.Row(): | |
| # Source language dropdown | |
| source_lang = gr.Dropdown( | |
| label="Source Language", | |
| choices=language_choices, | |
| value="eng_Latn" # Default to English | |
| ) | |
| # Target language dropdown | |
| target_lang = gr.Dropdown( | |
| label="Target Language", | |
| choices=language_choices, | |
| value="tel_Telu" # Default to Telugu | |
| ) | |
| # Translate button | |
| translate_button = gr.Button("Translate", variant="primary") | |
| # Define the action when the button is clicked | |
| translate_button.click( | |
| fn=translate_text, | |
| inputs=[input_text, source_lang, target_lang], | |
| outputs=output_text, | |
| api_name="translate" # Name for API endpoint if needed | |
| ) | |
| gr.Markdown("Powered by Hugging Face Transformers and Gradio.") | |
| # 7. Launch the Gradio app | |
| # When running locally, this will provide a URL. | |
| # On Hugging Face Spaces, this line makes the app run. | |
| if __name__ == "__main__": | |
| iface.launch() | |