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from transformers import LEDTokenizer, LEDForConditionalGeneration
import torch
import re
tokenizer = LEDTokenizer.from_pretrained("patrickvonplaten/led-large-16384-pubmed")
model = LEDForConditionalGeneration.from_pretrained("patrickvonplaten/led-large-16384-pubmed").to("cuda").half()
import gradio as gr
import os
import docx2txt
tokenizer = LEDTokenizer.from_pretrained("patrickvonplaten/led-large-16384-pubmed")
model = LEDForConditionalGeneration.from_pretrained("patrickvonplaten/led-large-16384-pubmed", return_dict_in_generate=True).to("cuda")
def summarize(text_file):
file_extension = os.path.splitext(text_file.name)[1]
if file_extension == ".txt":
# Load text from a txt file
with open(text_file.name, "r", encoding="utf-8") as f:
text = f.read()
elif file_extension == ".docx":
# Load text from a Word file
text = docx2txt.process(text_file.name)
else:
raise ValueError(f"Unsupported file type: {file_extension}")
input_ids = tokenizer(text, return_tensors="pt").input_ids.to("cuda")
global_attention_mask = torch.zeros_like(input_ids)
# set global_attention_mask on first token
global_attention_mask[:, 0] = 1
sequences = model.generate(input_ids, global_attention_mask=global_attention_mask).sequences
summary = tokenizer.batch_decode(sequences)[0]
return text, summary
iface = gr.Interface(
fn=summarize,
inputs=gr.inputs.File(label="Upload a txt file or a Word file for the input text"),
outputs=[gr.outputs.Textbox(label="Original text"), gr.outputs.Textbox(label="Summary")],
title="Academic Paper Summarization Demo",
description="Upload a txt file or a Word file for the input text. Get a summary generated by a small T5 model from Hugging Face.",
)
iface.launch()
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