from gradio import Textbox
from transformers import pipeline
import gradio as gr

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Summarization Chatbot</h1>
<p>This Space demonstrates the Summarization model <a href="https://huggingface.co/spaces/AyeshaNoreen/chatboot-edit"><b>Text Summarization Chatbot</b></a>. Meta Llama3 is the new open LLM and comes in two sizes: 8b and 70b. Feel free to play with it, or duplicate to run privately!</p>
<p>🦕 Looking for an even more powerful model? Check out the <a href="https://huggingface.co/chat/"><b>Hugging Chat</b></a> integration for facebook/bart-large-cnn</p>
</div>
'''



# Initialize the summarization pipeline
summarization_pipe = pipeline("summarization", model="facebook/bart-large-cnn")

# Function to summarize input text
def summarize_text(text):
    summary = summarization_pipe(text, max_length=100, min_length=50, do_sample=False)[0]['summary_text']
    return summary

  # Create the chat interface
chatbot = gr.Interface(
    summarize_text,
    gr.Textbox(lines=5, label="Input Text"),
    gr.Textbox(label="Summary"),
    #title="Text Summarization Chatbot",
    description=DESCRIPTION,
    #theme="compact"
    
)


# Launch the chat interface
if __name__ == "__main__":
    chatbot.launch(share=True)