| import gradio as gr |
| import torch |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| from peft import PeftModel |
|
|
| |
| BASE_MODEL_ID = "facebook/nllb-200-distilled-600M" |
|
|
| EN_TO_KIK = "KnoxDevelopers/English-to-Kikuyu-language-translation-LoRA" |
| EN_TO_LUO = "KnoxDevelopers/English-to-Luo-language-translation-LoRA" |
| EN_TO_KAM = "KnoxDevelopers/nllb-200-English_to_Kikamba-lang-translation-QLoRA" |
| EN_TO_DAV = "KnoxDevelopers/English-to-Taita-language-translation-LoRA" |
| EN_TO_GUZ = "KnoxDevelopers/English-to-Kisii-language-translation-LoRA" |
| EN_TO_MER = "KnoxDevelopers/English-to-Meru-language-translation-LoRA" |
| EN_TO_KLN = "KnoxDevelopers/English-to-Kalenjin-language-translation-LoRA" |
| EN_TO_EBU = "KnoxDevelopers/English-to-Embu-language-translation-LoRA" |
| EN_TO_MAS = "KnoxDevelopers/English-to-Maasai-language-translation-LoRA" |
|
|
| print("[*] Loading base tokenizer and model...") |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID) |
|
|
| base_model = AutoModelForSeq2SeqLM.from_pretrained( |
| BASE_MODEL_ID, |
| tie_word_embeddings=False |
| ) |
|
|
| base_model.resize_token_embeddings(256205) |
|
|
| if hasattr(base_model, "config"): |
| base_model.config.ensure_weight_tying = True |
|
|
| print("[*] adding adapters...") |
| model = PeftModel.from_pretrained(base_model, EN_TO_KIK, adapter_name="en_to_kik") |
| model.load_adapter(EN_TO_LUO, adapter_name="en_to_luo") |
| model.load_adapter(EN_TO_KAM, adapter_name="en_to_kam") |
| model.load_adapter(EN_TO_DAV, adapter_name="en_to_dav") |
| model.load_adapter(EN_TO_GUZ, adapter_name="en_to_guz") |
| model.load_adapter(EN_TO_MER, adapter_name="en_to_mer") |
| model.load_adapter(EN_TO_KLN, adapter_name="en_to_kln") |
| model.load_adapter(EN_TO_EBU, adapter_name="en_to_ebu") |
| model.load_adapter(EN_TO_MAS, adapter_name="en_to_mas") |
|
|
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| model.to(device) |
| model.eval() |
|
|
|
|
| def get_safe_token_id(lang_code, fallback_code="kik_Latn"): |
| token_id = tokenizer.convert_tokens_to_ids(lang_code) |
| max_vocab_limit = base_model.config.vocab_size |
| if token_id is None or token_id == tokenizer.unk_token_id or token_id >= max_vocab_limit: |
| token_id = tokenizer.convert_tokens_to_ids(fallback_code) |
| return token_id |
|
|
|
|
| MAX_CHARACTER_LIMIT = 400 |
| BANNED_WORDS = ["offensive_placeholder"] |
|
|
|
|
| def programmatic_guardrails(text): |
| text_clean = text.strip() |
| if not text_clean: |
| return False, "⚠️ Please enter a valid sentence to translate." |
| if len(text_clean) > MAX_CHARACTER_LIMIT: |
| return False, f"⚠️ Input exceeds the safety limit of {MAX_CHARACTER_LIMIT} characters." |
| for word in BANNED_WORDS: |
| if word in text_clean.lower(): |
| return False, "🛑 Safety Violation: Input violates our translation safety policy." |
| return True, text_clean |
|
|
|
|
| def translate_interface(source_text, direction): |
| is_safe, sanitized_input = programmatic_guardrails(source_text) |
| if not is_safe: |
| return sanitized_input |
|
|
| try: |
| tokenizer.src_lang = "eng_Latn" |
| inputs = tokenizer(sanitized_input, return_tensors="pt").to(device) |
|
|
| if direction == "English to Kikuyu": |
| model.set_adapter("en_to_kik") |
| target_lang_id = get_safe_token_id("kik_Latn") |
| elif direction == "English to Luo": |
| model.set_adapter("en_to_luo") |
| target_lang_id = get_safe_token_id("luo_Latn") |
| elif direction == "English to Kikamba": |
| model.set_adapter("en_to_kam") |
| target_lang_id = get_safe_token_id("kam_Latn") |
| elif direction == "English to Taita": |
| model.set_adapter("en_to_dav") |
| target_lang_id = get_safe_token_id("dav_Latn") |
| elif direction == "English to Kisii": |
| model.set_adapter("en_to_guz") |
| target_lang_id = get_safe_token_id("guz_Latn") |
| elif direction == "English to Meru": |
| model.set_adapter("en_to_mer") |
| target_lang_id = get_safe_token_id("mer_Latn") |
| elif direction == "English to Kalenjin": |
| model.set_adapter("en_to_kln") |
| target_lang_id = get_safe_token_id("kln_Latn") |
| elif direction == "English to Embu": |
| model.set_adapter("en_to_ebu") |
| target_lang_id = get_safe_token_id("ebu_Latn") |
| elif direction == "English to Maasai": |
| model.set_adapter("en_to_mas") |
| target_lang_id = get_safe_token_id("mas_Latn") |
| else: |
| return "Invalid translation direction selected." |
|
|
| with torch.no_grad(): |
| generated_tokens = model.generate( |
| **inputs, |
| forced_bos_token_id=target_lang_id, |
| max_length=512, |
| num_beams=4, |
| early_stopping=True |
| ) |
|
|
| return tokenizer.decode(generated_tokens[0], skip_special_tokens=True) |
| except Exception as e: |
| return f"An internal error occurred during generation: {str(e)}" |
|
|
|
|
| custom_theme = gr.themes.Soft( |
| font=[gr.themes.GoogleFont("Arial"), "sans-serif"] |
| ) |
|
|
| description_html = """ |
| <div style='text-align: center;'> |
| <h1>Multilingual Kenyan Language Translator</h1> |
| <p>Translate English into <b>Kikuyu, Luo, Kikamba, Taita, Kisii, Meru, Kalenjin, Embu and Maasai</b> using fine-tuned Meta NLLB-200 LoRA adapters.</p> |
| <p>Trained by <a href="https://knoxdevelopers.com/" target="_blank">Knox Systems Developers</a></p> |
| </div> |
| """ |
|
|
| with gr.Blocks(theme=custom_theme) as demo: |
| gr.HTML(description_html) |
|
|
| direction_selector = gr.Radio( |
| choices=[ |
| "English to Kikuyu", |
| "English to Luo", |
| "English to Kikamba", |
| "English to Taita", |
| "English to Kisii", |
| "English to Meru", |
| "English to Kalenjin", |
| "English to Embu", |
| "English to Maasai" |
| ], |
| value="English to Kikuyu", |
| label="Translation Direction" |
| ) |
|
|
| with gr.Row(): |
| with gr.Column(): |
| input_box = gr.Textbox( |
| label="Source Input Text", |
| placeholder="Type your English text here...", |
| lines=4 |
| ) |
| submit_btn = gr.Button("Translate Text", variant="primary") |
|
|
| with gr.Column(): |
| output_box = gr.Textbox( |
| label="Translated Output", |
| interactive=False, |
| lines=4 |
| ) |
|
|
| submit_btn.click( |
| fn=translate_interface, |
| inputs=[input_box, direction_selector], |
| outputs=output_box |
| ) |
|
|
| gr.Examples( |
| examples=[ |
| ["The children are playing outside near the tree.", "English to Kikuyu"], |
| ["Where is the market? I need to buy some food.", "English to Luo"], |
| ["The children are playing outside near the tree.", "English to Kikamba"], |
| ["We will meet tomorrow morning.", "English to Taita"], |
| ["Please help me carry this bag.", "English to Kisii"], |
| ["The children are playing outside near the tree.", "English to Meru"], |
| ["Where is the market? I need to buy some food.", "English to Kalenjin"], |
| ["The children are playing outside near the tree.", "English to Embu"], |
| ["Where is the market? I need to buy some food.", "English to Maasai"] |
| ], |
| inputs=[input_box, direction_selector] |
| ) |
|
|
| demo.launch() |
