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import streamlit as st
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, sent_tokenize
nltk.download('stopwords')
# Download the required punkt tokenizer
nltk.download('punkt_tab')
# Define the summarization function
def summarize_text(text, num_sentences=2):
# Tokenize the text into sentences
sentences = sent_tokenize(text)
# Tokenize the text into words and remove stopwords
stop_words = set(stopwords.words('english'))
word_tokens = word_tokenize(text.lower())
filtered_tokens = [word for word in word_tokens if word.isalnum() and word not in stop_words]
# Calculate word frequency
word_freq = nltk.FreqDist(filtered_tokens)
# Assign scores to sentences based on the sum of their word frequencies
sent_scores = {}
for sentence in sentences:
for word in word_tokenize(sentence.lower()):
if word in word_freq.keys():
if len(sentence.split(' ')) < 30: # Consider only sentences with less than 30 words
if sentence not in sent_scores.keys():
sent_scores[sentence] = word_freq[word]
else:
sent_scores[sentence] += word_freq[word]
# Select the top N sentences with highest scores for summarization
summary_sentences = sorted(sent_scores, key=sent_scores.get, reverse=True)[:num_sentences]
# Generate the summary
summary = ' '.join(summary_sentences)
return summary
# Streamlit app
def main():
st.title("Text Summarization App")
st.write("Enter your text below and select the number of sentences for the summary.")
# Text input
text_input = st.text_area("Text", "Paste your text here...")
# Number of sentences for summary
num_sentences = st.slider("Number of sentences in summary", min_value=1, max_value=10, value=2)
if st.button("Generate Summary"):
if text_input:
summary = summarize_text(text_input, num_sentences)
st.write("### Summary:")
st.write(summary)
else:
st.write("Please enter some text to summarize.")
if __name__ == "__main__":
main() |