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import gradio as gr
import random
import spacy
import torch
from transformers import MT5Tokenizer, MT5ForConditionalGeneration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = MT5Tokenizer.from_pretrained("potsawee/mt5-english-thai-large-translation")
translator = MT5ForConditionalGeneration.from_pretrained("potsawee/mt5-english-thai-large-translation")
summarizer = MT5ForConditionalGeneration.from_pretrained("potsawee/mt5-english-thai-large-summarization")
translator.eval()
summarizer.eval()
translator.to(device)
summarizer.to(device)
nlp = spacy.load("en_core_web_sm")
def generate_output(
task,
text,
):
if task == 'Translation':
sentences = [sent.text.strip() for sent in nlp(text).sents] # List[spacy.tokens.span.Span]
gen_texts = []
for sentence in sentences:
inputs = tokenizer(
[sentence],
padding="longest",
max_length=1024,
truncation=True,
return_tensors="pt",
).to(device)
outputs = translator.generate(
**inputs,
max_new_tokens=256,
)
gen_text_ = tokenizer.decode(outputs[0], skip_special_tokens=True)
gen_texts.append(gen_text_)
return " ".join(gen_texts)
elif task == 'Summarization':
inputs = tokenizer(
[text],
padding="longest",
max_length=1024,
truncation=True,
return_tensors="pt",
).to(device)
outputs = summarizer.generate(
**inputs,
max_new_tokens=256,
)
gen_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
else:
raise ValueError("task undefined!")
return gen_text
TASKS = ["Translation", "Summarization"]
demo = gr.Interface(
fn=generate_output,
inputs=[
gr.components.Radio(label="Task", choices=TASKS, value="Translation"),
gr.components.Textbox(label="Text (in English)", lines=10),
],
outputs=gr.Textbox(label="Text (in Thai)", lines=4),
# examples=[["Building a translation demo with Gradio is so easy!", "eng_Latn", "spa_Latn"]],
cache_examples=False,
title="English🇬🇧 to Thai🇹🇭 | Translation or Summarization",
description="Provide some text (in English) & select one of the tasks (Translation or Summarization). Note that currently the model only supports text up to 1024 tokens. The base architecture is mt5-large with the embeddings filtered to only English and Thai tokens and fine-tuned to XSum (Eng2Thai) Dataset (https://huggingface.co/datasets/potsawee/xsum_eng2thai). This is only after training for 1 epoch of xsum (the quality is not production-ready), just a quick proof-of-concept about fine-tuning on translated texts.",
allow_flagging='never'
)
demo.launch()
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