text_summary / app.py
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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()