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import gradio as gr
from transformers import BartForConditionalGeneration
from transformers import BartTokenizer
tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn')
def generate_summary(text):
inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
return summary
def process(text):
return generate_summary(text)
textbox = gr.Textbox(label="Pega el text aca:", placeholder="Texto...", lines=15)
demo = gr.Interface(fn=process, inputs=textbox, outputs="text")
demo.launch()
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