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import gradio as gr
from transformers import BartTokenizer, BartForConditionalGeneration
model_name = "facebook/bart-large-cnn" # Example BART model for demonstration
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
def generate_text(prompt):
inputs = tokenizer.encode("summarize: " + prompt, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)
return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
interface = gr.Interface(fn=generate_text,
inputs=gr.Textbox(lines=5, placeholder="Enter Text Here..."),
outputs="text",
title="Text Generation with BART",
description="Enter text to generate a summary.")
if __name__ == "__main__":
interface.launch()
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