Update app.py

app.py

@@ -1,19 +1,23 @@
 import streamlit as st
 from transformers import pipeline
 
-# Load the T5 summarization pipeline
-summarizer = pipeline("summarization", model="t5-small", tokenizer="t5-small")
+# Load the summarization pipeline with a different model
+summarizer = pipeline("summarization", model="t5-small")
 
 def summarize_text(text):
-    """Summarize the input text using the T5 model."""
+    """Summarize the input text using Hugging Face's pipeline."""
     summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
     return summary[0]['summary_text']
 
 # Streamlit UI
-st.title("Text Summarization with T5")
+st.title("Text Summarization with Hugging Face")
 
 st.write("Enter the text you want to summarize:")
 
+# Initialize history in session state if not already done
+if 'history' not in st.session_state:
+    st.session_state.history = []
+
 # Text input from the user
 user_input = st.text_area("Input Text", height=200)
 
@@ -21,7 +25,21 @@ if st.button("Summarize"):
     if user_input:
         # Generate summary
         summary = summarize_text(user_input)
+        
+        # Save to history
+        st.session_state.history.append({"input": user_input, "summary": summary})
+        
         st.subheader("Summary:")
         st.write(summary)
     else:
         st.error("Please enter some text to summarize.")
+
+# Display history
+st.subheader("Summary History:")
+if st.session_state.history:
+    for entry in st.session_state.history:
+        st.write(f"**Input Text:** {entry['input']}")
+        st.write(f"**Summary:** {entry['summary']}")
+        st.write("---")
+else:
+    st.write("No summaries available yet.")