import gradio as gr
import torch
from transformers import T5Tokenizer, MT5ForConditionalGeneration

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
TOKENIZER = T5Tokenizer.from_pretrained('werent4/mt5TranslatorLT')
MODEL = MT5ForConditionalGeneration.from_pretrained("werent4/mt5TranslatorLT")
MODEL.to(DEVICE)

def translate(text, translation_way, max_length, num_beams):
    translations_ways = {
        "en-lt": "<EN2LT>",
        "lt-en": "<LT2EN>"
    }
    if translation_way not in translations_ways:
        raise ValueError(f"Invalid translation way: {translation_way}. Supported ways: {list(translations_ways.keys())}")
    input_text = f"{translations_ways[translation_way]} {text}"
    encoded_input = TOKENIZER(input_text, return_tensors="pt", padding=True, truncation=True, max_length=128).to(DEVICE)
    with torch.no_grad():
        output_tokens = MODEL.generate(
          **encoded_input,
          max_length=max_length,
          num_beams=num_beams,
          no_repeat_ngram_size=2,
          early_stopping=True
      )
    return  TOKENIZER.decode(output_tokens[0], skip_special_tokens=True)



with gr.Blocks() as interface:
  gr.Markdown("<h1>Lt🔄En: Lithuanian to English and vice versa")
  with gr.Row():
      max_length = gr.Slider(1, 512, value=128, label="Max length", interactive=True)
      num_beams = gr.Slider(1, 16, value=5, step=False, label="Num beams", interactive=True)
  with gr.Row():
    input_text = gr.Textbox(label="Text input", placeholder="Enter your text here")
    with gr.Column():
      translation_way = gr.Dropdown(label="Mode", choices=['en-lt', 'lt-en'])
      translate_button = gr.Button("Translate")
  output_text = gr.Textbox(label="Translated text")
  with gr.Accordion("How to run the model locally:", open=False):
    gr.Code("""import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

from transformers import T5Tokenizer, MT5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained('werent4/mt5TranslatorLT')
model = MT5ForConditionalGeneration.from_pretrained("werent4/mt5TranslatorLT")
model.to(device)
def translate(text, model, tokenizer, device, translation_way = "en-lt"):
    translations_ways = {
        "en-lt": "<EN2LT>",
        "lt-en": "<LT2EN>"
    }
    if translation_way not in translations_ways:
        raise ValueError(f"Invalid translation way. Supported ways: {list(translations_ways.keys())}")
    input_text = f"{translations_ways[translation_way]} {text}"
    encoded_input = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=128).to(device)
    with torch.no_grad():
        output_tokens = model.generate(
          **encoded_input,
          max_length=128,
          num_beams=5,
          no_repeat_ngram_size=2,
          early_stopping=True
      )

    translated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
    return translated_text
text = "I live in Kaunas"
translate(text, model, tokenizer, device)
    """, language='python')
    translate_button.click(fn=translate, inputs=[input_text, translation_way, max_length, num_beams], outputs=[output_text])
interface.launch(share=True)