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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import time | |
| # Global variables for model and tokenizer | |
| model = None | |
| tokenizer = None | |
| def load_model(): | |
| """Load the model and tokenizer""" | |
| global model, tokenizer | |
| try: | |
| model_name = "UnarineLeo/nllb_eng_ven_terms" | |
| print(f"Loading model: {model_name}") | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| print("Model loaded successfully!") | |
| return True | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| return False | |
| def translate_text(text, max_length=512, num_beams=5): | |
| """ | |
| Translate English text to Venda | |
| Args: | |
| text (str): Input English text | |
| max_length (int): Maximum length of translation | |
| num_beams (int): Number of beams for beam search | |
| Returns: | |
| tuple: (translated_text, status_message) | |
| """ | |
| global model, tokenizer | |
| if not text.strip(): | |
| return "", "Please enter some text to translate." | |
| if model is None or tokenizer is None: | |
| return "", "Model not loaded. Please wait while the model loads." | |
| try: | |
| # Set source language | |
| tokenizer.src_lang = "eng_Latn" | |
| # Tokenize input | |
| inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True) | |
| # Generate translation | |
| start_time = time.time() | |
| with torch.no_grad(): | |
| generated_tokens = model.generate( | |
| **inputs, | |
| forced_bos_token_id=tokenizer.lang_code_to_id["ven_Latn"], | |
| max_length=max_length, | |
| num_beams=num_beams, | |
| early_stopping=True, | |
| do_sample=False | |
| ) | |
| # Decode translation | |
| translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0] | |
| end_time = time.time() | |
| processing_time = round(end_time - start_time, 2) | |
| status = f"✅ Translation completed in {processing_time} seconds" | |
| return translation, status | |
| except Exception as e: | |
| error_msg = f"❌ Translation error: {str(e)}" | |
| return "", error_msg | |
| def translate_batch(text_list): | |
| """ | |
| Translate multiple lines of text | |
| Args: | |
| text_list (str): Multi-line text input | |
| Returns: | |
| tuple: (translated_text, status_message) | |
| """ | |
| if not text_list.strip(): | |
| return "", "Please enter some text to translate." | |
| lines = [line.strip() for line in text_list.split('\n') if line.strip()] | |
| if not lines: | |
| return "", "No valid text lines found." | |
| try: | |
| translations = [] | |
| total_time = 0 | |
| for i, line in enumerate(lines): | |
| translation, status = translate_text(line) | |
| if translation: | |
| translations.append(f"{i+1}. EN: {line}") | |
| translations.append(f" VE: {translation}") | |
| translations.append("") | |
| if translations: | |
| result = "\n".join(translations) | |
| status_msg = f"✅ Successfully translated {len(lines)} lines" | |
| return result, status_msg | |
| else: | |
| return "", "❌ No translations generated" | |
| except Exception as e: | |
| return "", f"❌ Batch translation error: {str(e)}" | |
| # Load model on startup | |
| print("Initializing model...") | |
| model_loaded = load_model() | |
| # Create Gradio interface | |
| with gr.Blocks(title="English to Venda Translator", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(""" | |
| # 🌍 English to Venda Translator | |
| This app translates English text to Venda (Tshivenda) using the NLLB model. | |
| Venda is a Bantu language spoken primarily in South Africa and Zimbabwe. | |
| **Model:** `UnarineLeo/nllb_eng_ven_terms` | |
| """) | |
| with gr.Tab("Single Translation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_text = gr.Textbox( | |
| label="English Text", | |
| placeholder="Enter English text to translate...", | |
| lines=4, | |
| max_lines=10 | |
| ) | |
| with gr.Row(): | |
| max_length_slider = gr.Slider( | |
| minimum=50, | |
| maximum=1000, | |
| value=512, | |
| step=50, | |
| label="Max Translation Length" | |
| ) | |
| num_beams_slider = gr.Slider( | |
| minimum=1, | |
| maximum=10, | |
| value=5, | |
| step=1, | |
| label="Number of Beams (Quality vs Speed)" | |
| ) | |
| translate_btn = gr.Button("🔄 Translate", variant="primary") | |
| with gr.Column(): | |
| output_text = gr.Textbox( | |
| label="Venda Translation", | |
| lines=4, | |
| max_lines=10, | |
| interactive=False | |
| ) | |
| status_text = gr.Textbox( | |
| label="Status", | |
| interactive=False, | |
| lines=1 | |
| ) | |
| # Examples | |
| gr.Examples( | |
| examples=[ | |
| ["Hello, how are you?"], | |
| ["Good morning, everyone."], | |
| ["Thank you for your help."], | |
| ["What is your name?"], | |
| ["I am learning Venda."], | |
| ["Welcome to our school."], | |
| ["The weather is beautiful today."], | |
| ["Can you help me please?"] | |
| ], | |
| inputs=[input_text], | |
| label="Try these examples:" | |
| ) | |
| with gr.Tab("Batch Translation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| batch_input = gr.Textbox( | |
| label="Multiple English Sentences", | |
| placeholder="Enter multiple English sentences, one per line...", | |
| lines=8, | |
| max_lines=15 | |
| ) | |
| batch_translate_btn = gr.Button("🔄 Translate All", variant="primary") | |
| with gr.Column(): | |
| batch_output = gr.Textbox( | |
| label="Batch Translations", | |
| lines=8, | |
| max_lines=15, | |
| interactive=False | |
| ) | |
| batch_status = gr.Textbox( | |
| label="Status", | |
| interactive=False, | |
| lines=1 | |
| ) | |
| with gr.Tab("About"): | |
| gr.Markdown(""" | |
| ## About This Translator | |
| This application uses a fine-tuned NLLB (No Language Left Behind) model specifically trained for English to Venda translation. | |
| ### Features: | |
| - **Single Translation**: Translate individual sentences or paragraphs | |
| - **Batch Translation**: Translate multiple sentences at once | |
| - **Adjustable Parameters**: Control translation quality and length | |
| - **Examples**: Try pre-loaded example sentences | |
| ### About Venda (Tshivenda): | |
| - Spoken by approximately 1.2 million people | |
| - Official language of South Africa | |
| - Also spoken in Zimbabwe | |
| - Part of the Bantu language family | |
| ### Usage Tips: | |
| - Keep sentences reasonably short for best results | |
| - The model works best with common, everyday language | |
| - Higher beam numbers generally produce better quality but slower translations | |
| ### Technical Details: | |
| - **Model**: UnarineLeo/nllb_eng_ven_terms | |
| - **Architecture**: NLLB (No Language Left Behind) | |
| - **Language Codes**: eng_Latn → ven_Latn | |
| """) | |
| # Event handlers | |
| translate_btn.click( | |
| fn=translate_text, | |
| inputs=[input_text, max_length_slider, num_beams_slider], | |
| outputs=[output_text, status_text] | |
| ) | |
| batch_translate_btn.click( | |
| fn=translate_batch, | |
| inputs=[batch_input], | |
| outputs=[batch_output, batch_status] | |
| ) | |
| # Auto-translate on example selection | |
| input_text.submit( | |
| fn=translate_text, | |
| inputs=[input_text, max_length_slider, num_beams_slider], | |
| outputs=[output_text, status_text] | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo.launch( | |
| share=True, | |
| debug=True, | |
| show_error=True | |
| ) |