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| import torch | |
| import gradio as gr | |
| import json | |
| from transformers import pipeline | |
| # Load the translation pipeline | |
| text_translator = pipeline( | |
| "translation", | |
| model="facebook/nllb-200-distilled-600M", | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| # Load the JSON data for language codes | |
| with open('language.json', 'r') as file: | |
| language_data = json.load(file) | |
| # Get all available languages (excluding duplicates with different scripts) | |
| available_languages = [] | |
| seen_languages = set() | |
| for entry in language_data: | |
| base_language = entry['Language'].split('(')[0].strip() | |
| if base_language not in seen_languages: | |
| available_languages.append(base_language) | |
| seen_languages.add(base_language) | |
| # Sort languages alphabetically | |
| available_languages.sort() | |
| # Function to retrieve FLORES-200 code for a given language | |
| def get_FLORES_code_from_language(language): | |
| # Try exact match first | |
| for entry in language_data: | |
| if entry['Language'].lower() == language.lower(): | |
| return entry['FLORES-200 code'] | |
| # Fallback to matching base language name | |
| for entry in language_data: | |
| if entry['Language'].lower().startswith(language.lower()): | |
| return entry['FLORES-200 code'] | |
| return None | |
| # Translation function | |
| def translate_text(text, destination_language): | |
| dest_code = get_FLORES_code_from_language(destination_language) | |
| if dest_code is None: | |
| return f"Error: Could not find FLORES code for language {destination_language}" | |
| try: | |
| # Translation call | |
| translation = text_translator(text, src_lang="eng_Latn", tgt_lang=dest_code) | |
| return translation[0]["translation_text"] | |
| except Exception as e: | |
| return f"Error during translation: {str(e)}" | |
| # Initialize the speech-to-text pipeline (Whisper model) | |
| speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small") | |
| # Function to transcribe audio to text | |
| def transcribe_audio(audio_file, destination_language): | |
| try: | |
| transcription_result = speech_to_text(audio_file) | |
| print(f"Transcription result: {transcription_result}") # Debugging output | |
| if "text" in transcription_result: | |
| transcription = transcription_result["text"] | |
| else: | |
| return "Error: Unable to transcribe audio." | |
| # Translate the transcribed text | |
| return translate_text(transcription, destination_language) | |
| except Exception as e: | |
| return f"Error during transcription: {str(e)}" | |
| # Gradio interface | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| gr.Markdown( | |
| """ | |
| # 🌐 AI Translation Assistant | |
| Translate **text or audio** into your desired language with professional accuracy. | |
| """ | |
| ) | |
| with gr.Tabs(): | |
| with gr.Tab("Text Translation"): | |
| text_input = gr.Textbox(label="Input Text", lines=6, placeholder="Enter your text here...") | |
| language_dropdown = gr.Dropdown( | |
| choices=available_languages, label="Select Destination Language" | |
| ) | |
| translated_text_output = gr.Textbox(label="Translated Text", lines=4) | |
| translate_button = gr.Button("Translate") | |
| translate_button.click( | |
| translate_text, inputs=[text_input, language_dropdown], outputs=[translated_text_output] | |
| ) | |
| with gr.Tab("Audio Translation"): | |
| audio_input = gr.Audio(label="Upload Audio File", type="filepath") | |
| audio_language_dropdown = gr.Dropdown( | |
| choices=available_languages, label="Select Destination Language" | |
| ) | |
| audio_translated_text_output = gr.Textbox(label="Transcribed and Translated Text", lines=2) | |
| audio_translate_button = gr.Button("Transcribe and Translate") | |
| audio_translate_button.click( | |
| transcribe_audio, | |
| inputs=[audio_input, audio_language_dropdown], | |
| outputs=[audio_translated_text_output], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |