from datasets import load_dataset
from transformers import pipeline

#loading the dataset
xsum_dataset = load_dataset(
    "xsum",
    version="1.2.0",
    cache_dir='/Documents/Huggin_Face/data'
)  # Note: We specify cache_dir to use predownloaded data.
xsum_dataset
# The printed representation of this object shows the `num_rows`
# of each dataset split.

summarizer = pipeline(
    task="summarization",
    model="t5-small",
    min_length=20,
    max_length=40,
    truncation=True,
    model_kwargs={"cache_dir": '/Documents/Huggin_Face/'},
)  # Note: We specify cache_dir to use predownloaded models.

def input_func(input_text):
  input_text = input("Enter the text you want to summarize: ")

# Generate the summary
  summary = summarizer(input_text, max_length=10000, min_length=30, do_sample=False)[0]['summary_text']

  bullet_points = summary.split(". ")

  for point in bullet_points:
    print(f"- {point}")

# Print the generated summary
 return ("Summary:", summary)


iface = gr.Interface(fn = input_func, 
                     inputs = [ 
                               gr.inputs.Textbox(lines=5, placeholder="Enter your text here...", label="input_text")],
                     outputs="text",
                     )

iface.launch()