import torch
import gradio as gr

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

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

# model_path = ("../models/models--sshleifer--distilbart-cnn-12-6/snapshots/a4f8f3ea906ed274767e9906dbaede7531d660ff")
# text_summary = pipeline("summarization", model=model_path, torch_dtype=torch.bfloat16)


# text = '''Elon Reeve Musk (/ˈiːlɒn/ EE-lon; born June 28, 1971) is a businessman known for his key roles in Tesla, Inc., SpaceX, and Twitter (which he rebranded as X). Since 2025, he has been a senior advisor to United States president Donald Trump and de facto head of the Department of Government Efficiency (DOGE). Musk is the wealthiest person in the world; as of February 2025, Forbes estimates his net worth to be US$353 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="text", outputs="text")

demo = gr.Interface(fn=summary,
                    inputs=[gr.Textbox(label="Input text to Summarize", lines=6)],
                    outputs=[gr.Textbox(label="Summarized text", lines=4)],
                    title="TEXT SUMMARIZER",
                    description="THIS APPLICATION WILL BE USED TO SUMMARIZE TEXT")
demo.launch()