masteru/mapping

1import gradio as gr2from transformers import AutoTokenizer, AutoModelForSeq2SeqLM3 4# Load model and tokenizer5tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")6model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")7 8# Define summarization function9def summarize(text):10    inputs = tokenizer.encode(text, return_tensors="pt", max_length=1024, truncation=True)11    summary_ids = model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True)12    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)13    return summary14 15# Create Gradio interface16iface = gr.Interface(17    fn=summarize,18    inputs=gr.Textbox(lines=15, placeholder="Enter text to summarize..."),19    outputs="text",20    title="Text Summarizer with BART",21    description="Summarizes long text using Facebook's BART-large-CNN model from Hugging Face Transformers."22)23 24if __name__ == "__main__":25    iface.launch()26