import gradio as gr
import torch 

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

text_summary = pipeline("summarization", model="philschmid/distilbart-cnn-12-6-samsum")

# model_path = "text summarizer/models/models--philschmid--distilbart-cnn-12-6-samsum/snapshots/d8bbb0012bd874c792577caeaab58b6f72f64e2f"
# text_summary = pipeline("summarization", model=model_path,
#                 torch_dtype=torch.bfloat16)

# text = "Elon Reeve Musk (lɒn/ EE-lon; born June 28, 1971) is a businessman and investor. He is the founder, chairman, CEO, and CTO of SpaceX; angel investor, CEO, product architect, and former chairman of Tesla, Inc.; owner, executive chairman, and CTO of X Corp.; founder of the Boring Company and xAI; co-founder of Neuralink and OpenAI; and president of the Musk Foundation. He is one of the wealthiest people in the world; as of April 2024, Forbes estimates his net worth to be $178 billion.[4]"
# result = text_summary(text)
# print(result[0]["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 = "PROJECT 1 : text summarizer",
                     description = "This applicating will be used to summarize text"
                    )

demo.launch()