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app.py

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+import streamlit as st
+import torch
+from summarizer.bert import Summarizer
+
+# Summarization function using BERT-based summarizer
+
+
+def summarize_text(text, num_sentences=5):
+    model = Summarizer()
+    summarized_text = model(text, num_sentences=num_sentences)
+    return summarized_text
+
+
+# Streamlit app layout
+st.title("Content Summarizer")
+
+# Textbox for content to be summarized
+user_input_text = st.text_area("Enter the content you want to summarize:")
+
+# Slider to select the number of sentences in the summary
+num_sentences = st.slider("Number of summary sentences:", min_value=1, max_value=10)
+
+# Button to trigger summarization
+if st.button('Summarize'):
+    # Check if there is text input
+    if user_input_text:
+        # Use num_sentences as the max_length parameter
+        summarized_content = summarize_text(user_input_text, num_sentences)
+        st.subheader("Summary")
+        st.write(summarized_content)
+    else:
+        st.warning("Please enter some content to summarize.")

requirements.txt

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+bert-extractive-summarizer==0.10.1
+torch==2.1.1
+streamlit==1.29.0
+
+