import torch
import gradio as gr

# Use a pipeline as a high-level helper
from transformers import pipeline

text_summary = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6", torch_dtype=torch.bfloat16)

# For Local we can use this only
# text = "Elon Reeve Musk FRS (/ˈiːlɒn/; born June 28, 1971) is a businessman known for his key roles in the space company SpaceX and the automotive company Tesla, Inc. His other involvements include ownership of X Corp., the company that operates the social media platform X (formerly Twitter), and his role in the founding of the Boring Company, xAI, Neuralink, and OpenAI. Musk is the wealthiest individual in the world; as of November 2024, Forbes estimates his net worth to be US$323 billion"
# print(text_summary(text))


def summary(input):
    output = text_summary(input)
    return output[0]['summary_text']

gr.close_all()

demo = gr.Interface(fn=summary, 
                    inputs=[gr.Textbox(label="Input text to summarize", lines=6)], 
                    outputs=[gr.Textbox(label="Summarized text", lines=10)],
                    title = "Gen AI Text Summarization",
                    description= "Enter details text the tool will give you summarized text"
                    )
demo.launch(share=True)