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import streamlit as st
st.set_page_config(
page_title="Text Summarizer",
page_icon="📝",
layout="centered"
)
from transformers import BartTokenizer, BartForConditionalGeneration
@st.cache_resource
def load_model():
model_name = "facebook/bart-large-cnn"
tokenizer = BartTokenizer.from_pretrained(model_name)
model = BartForConditionalGeneration.from_pretrained(model_name)
return tokenizer, model
tokenizer, model = load_model()
def summarize(text: str, style: str):
inputs = tokenizer.encode(text, return_tensors="pt", max_length=1024, truncation=True)
input_length = len(inputs[0])
if style == "Normal":
max_length = int(input_length * 0.6)
min_length = int(input_length * 0.5)
length_penalty = 1.5
elif style == "Precise":
max_length = int(input_length * 0.45)
min_length = int(input_length * 0.35)
length_penalty = 1.2
else: # Accurate
max_length = int(input_length * 0.4)
min_length = int(input_length * 0.3)
length_penalty = 1.0
summary_ids = model.generate(
inputs,
max_length=max_length,
min_length=min_length,
length_penalty=length_penalty,
num_beams=4,
no_repeat_ngram_size=3,
early_stopping=True,
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
if not summary.endswith((".", "!", "?")):
summary += "."
return summary
st.title("🧠 Text Summarizer")
st.write("Summarize long text into concise and clear summaries using **BART**.")
user_input = st.text_area("Enter text to summarize:", height=200)
summary_style = st.selectbox("Choose summarization style:", ("Normal", "Precise", "Accurate"))
if st.button("Summarize"):
if user_input.strip():
with st.spinner("Summarizing... ⏳"):
summary = summarize(user_input, summary_style)
st.subheader("Summary:")
st.write(summary)
else:
st.warning("⚠️ Please enter some text to summarize.") |