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app.py

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+from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
+import gradio as gr
+
+model = AutoModelForSeq2SeqLM.from_pretrained('alimboff/nllb-200-kbd').cuda()
+tokenizer = NllbTokenizer.from_pretrained('alimboff/nllb-200-kbd')
+
+def fix_tokenizer(tokenizer, new_lang='kbd_Cyrl'):
+    """
+    Add a new language token to the tokenizer vocabulary
+    (this should be done each time after its initialization)
+    """
+    old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
+    tokenizer.lang_code_to_id[new_lang] = old_len-1
+    tokenizer.id_to_lang_code[old_len-1] = new_lang
+    # always move "mask" to the last position
+    tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset
+
+    tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
+    tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
+    if new_lang not in tokenizer._additional_special_tokens:
+        tokenizer._additional_special_tokens.append(new_lang)
+    # clear the added token encoder; otherwise a new token may end up there by mistake
+    tokenizer.added_tokens_encoder = {}
+    tokenizer.added_tokens_decoder = {}
+
+fix_tokenizer(tokenizer)
+
+language_codes = {
+    "Кабардино-Черкесский": "kbd_Cyrl",
+    "Русский": "rus_Cyrl"
+}
+
+def translate(
+    text, input_language, output_language,
+    a=32, b=3, max_input_length=1024, num_beams=8, **kwargs
+):
+    src_lang = language_codes[input_language]
+    tgt_lang = language_codes[output_language]
+    """Turn a text or a list of texts into a list of translations"""
+    tokenizer.src_lang = src_lang
+    tokenizer.tgt_lang = tgt_lang
+    inputs = tokenizer(
+        text, return_tensors='pt', padding=True, truncation=True,
+        max_length=max_input_length
+    )
+    model.eval() # turn off training mode
+    result = model.generate(
+        **inputs.to(model.device),
+        forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang),
+        max_new_tokens=int(a + b * inputs.input_ids.shape[1]),
+        num_beams=num_beams, **kwargs
+    )
+    return tokenizer.batch_decode(result, skip_special_tokens=True)[0] #без [0]
+
+with gr.Blocks() as demo:
+    gr.Markdown("### Переводчик через ИИ")
+    
+    with gr.Row():
+        input_language = gr.Radio(choices=["Кабардино-Черкесский", "Русский"], label="Выберите язык исходного текста", value="Кабардино-Черкесский")
+        output_language = gr.Radio(choices=["Кабардино-Черкесский", "Русский"], label="Выберите язык для перевода", value="Русский")
+    
+    with gr.Row():
+        text_input = gr.Textbox(label="Введите текст для перевода")
+        text_output = gr.Textbox(label="Перевод", interactive=False)
+    
+    with gr.Row():
+        translate_button = gr.Button("Перевести")
+    
+    translate_button.click(
+        fn=translate,
+        inputs=[text_input, input_language, output_language],
+        outputs=text_output
+    )
+
+demo.launch()
+
+# # Example usage:
+# # Ӏ
+# t = 'пэшым лӀы зыбжанэ щӀэсщ'
+
+# kbdru = translate(t, 'kbd_Cyrl', 'rus_Cyrl')
+# rukbd = translate(kbdru, 'rus_Cyrl', 'kbd_Cyrl')
+
+# print(kbdru)
+# print(rukbd)

requirements.txt

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+transformers
+gradio