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# Imports
import os
import torch
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import transformers
import gradio as gr
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name,use_fast=False)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
def generate_summary(text):
input_ids = tokenizer(
[WHITESPACE_HANDLER(text)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=1024,
no_repeat_ngram_size=2,
num_beams=4
)[0]
#max_output_length=200
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
return summary
demo = gr.Interface(fn=generate_summary,
inputs=gr.Textbox(lines=100, placeholder="Ingrese Texto"),
outputs=gr.Textbox(lines=10)
)
demo.launch() |