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import spaces
import gradio as gr
from transformers import MT5ForConditionalGeneration, MT5Tokenizer,T5ForConditionalGeneration, T5Tokenizer
models = {"finetuned mt5-base":"alakxender/mt5-base-dv-en", "madlad400-3b":"google/madlad400-3b-mt"}
def tranlate(text:str,model_name:str):
if (len(text)>2000):
raise gr.Error(f"Try smaller text, yours is {len(text)}. try to fit to 2000 chars.")
if (model_name is None):
raise gr.Error("huh! not sure what to do without a model. select a model.")
if model_name =='finetuned mt5-base':
return mt5_translate(text,model_name)
else:
return t5_tranlaste(text,model_name)
@spaces.GPU(duration=120)
def t5_tranlaste(text:str,model_name:str):
model = T5ForConditionalGeneration.from_pretrained(models[model_name], device_map="auto")
tokenizer = T5Tokenizer.from_pretrained(models[model_name])
text = f"<2en> {text}"
input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device)
outputs = model.generate(input_ids=input_ids, max_new_tokens=1024*2)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return translated_text
def mt5_translate(text:str, model_name:str):
model = MT5ForConditionalGeneration.from_pretrained(models[model_name])
tokenizer = MT5Tokenizer.from_pretrained(models[model_name])
inputs = tokenizer(text, return_tensors="pt")
result = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], max_new_tokens=1024*2)
translated_text = tokenizer.decode(result[0], skip_special_tokens=True)
return translated_text
css = """
.textbox1 textarea {
font-size: 18px !important;
font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
line-height: 1.8 !important;
}
"""
demo = gr.Interface(
fn=tranlate,
inputs= [
gr.Textbox(lines=5, label="Enter Dhivehi Text", rtl=True, elem_classes="textbox1"),
gr.Dropdown(choices=list(models.keys()), label="Select a model", value="finetuned mt5-base"),
],
css=css,
outputs=gr.Textbox(label="English Translation"),
title="Dhivehi to English Translation",
description="Translate Dhivehi text to English",
examples=[["މާލޭގައި ފެންބޮޑުވާ މަގުތައް މަރާމާތު ކުރަން ފަށައިފި"]]
)
demo.launch()
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