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

# model_path = ("Models\models--sshleifer--distilbart-cnn-12-6\snapshots\a4f8f3ea906ed274767e9906dbaede7531d660ff")

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

# text="Once upon a time, while flying through the air, a stork noticed the sparkle of a ring. It belonged to a rabbit who was getting married that day. The rabbit went inside its burrow leaving the ring outside, and the stork decided to try it on quickly without asking."
# 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="@GenAILearniverse Project 1: Text Summarizer",
                    description="THIS APPLICATION WILL BE USED TO SUMMARIZE THE TEXT")
demo.launch()