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import gradio as gr
from transformers import BartTokenizer, BartForConditionalGeneration
import torch
MODEL_DIR = './BART model small/model'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = BartTokenizer.from_pretrained(MODEL_DIR)
model = BartForConditionalGeneration.from_pretrained(MODEL_DIR).to(device)
def summarize(text):
try:
inputs = tokenizer(
text,
return_tensors="pt",
max_length=1024,
truncation=True
).to(device)
summary_ids = model.generate(
inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=150,
min_length=30,
num_beams=4,
early_stopping=True
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
except Exception as e:
return str(e)
interface = gr.Interface(
fn=summarize,
inputs="text",
outputs="text",
title="BART Summarization",
live=True,
description="Enter an article to generate a summary using a fine-tuned BART model."
)
interface.launch() |