import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch

MODEL_NAME = "Tamazight-NLP/NLLB-200-600M-Tamazight-All-Data"
LANGS = ["tzm_Tfng", "kab_Latn", "taq_Tfng", "taq_Latn","eng_Latn"]
TASK = "translation"

device = 0 if torch.cuda.is_available() else -1

general_model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME)


tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)


device = 0 if torch.cuda.is_available() else -1



def translate(text, source_lang, target_lang, max_length=450):
    """
    Translate text from source language to target language
    """
    # src_lang = choose_language(source_lang)
    # tgt_lang= choose_language(target_lang)
    # if src_lang==None:
    #     return "Error: the source langage is incorrect"
    # elif tgt_lang==None:
    #     return "Error: the target language is incorrect"

    translation_pipeline = pipeline(TASK,
                                    model=general_model,
                                    tokenizer=tokenizer,
                                    src_lang=source_lang,
                                    tgt_lang=target_lang,
                                    max_length=max_length,
                                    device=device)
    result = translation_pipeline(text)
    return result[0]['translation_text']


gradio_ui= gr.Interface(
    fn=translate,
    title="NLLB Tamazight Translation Demo",
    inputs= [
        gr.components.Textbox(label="Text"),
        gr.components.Dropdown(label="Source Language", choices=LANGS),
        gr.components.Dropdown(label="Target Language", choices=LANGS),
      #  gr.components.Slider(8, 400, value=400, step=8, label="Max Length")
    ],
    outputs=gr.outputs.Textbox(label="Translated text")
)

gradio_ui.launch()