Create app1.py

app1.py

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+import streamlit as st
+from transformers import pipeline
+
+# Initialize summarization pipeline
+summarizer = pipeline("summarization")
+# Streamlit UI setup
+st.title("📝 Text Summarization App")
+
+# User input box (allows large input text)
+user_input = st.text_area("Enter text to summarize:", "", height=300)
+
+# Button to trigger summarization
+if st.button("Summarize"):
+    if user_input.strip():  # Ensure there's input before summarizing
+        try:
+            # Adjust max_length based on input size (e.g., for small inputs, use a lower max_length)
+            max_length = 50 if len(user_input.split()) <= 50 else 150
+
+            # Generate summary using the model
+            summarized_text = summarizer(user_input, max_length=max_length, min_length=5, length_penalty=2.0, num_beams=4, early_stopping=True)[0]['summary_text']
+
+            # Display the generated summary
+            st.subheader("Summarized Text:")
+            st.write(summarized_text)
+
+        except Exception as e:
+            st.error(f"An error occurred while summarizing: {e}")
+    else:
+        st.warning("Please enter some text to summarize.")