Update app.py

app.py

@@ -1,7 +1,5 @@
 import streamlit as st
 from transformers import pipeline
-
-
 from huggingface_hub import login
 from dotenv import load_dotenv
 import os
@@ -34,15 +32,27 @@ input_text = st.text_area("Enter the long text you want to summarize", height=30
 
 # Button to generate the summary
 if st.button("Generate Summary"):
-    # Load the selected model and summarizer pipeline
-    summarizer = pipeline("summarization", model=models[model_choice])
-    
-    if input_text:
-        # Generate the summary
-        summary = summarizer(input_text, max_length=350, min_length=30, do_sample=False)
+    # Show a spinner while generating the summary
+    with st.spinner("Generating summary, please wait..."):
+        # Load the selected model and summarizer pipeline
+        summarizer = pipeline("summarization", model=models[model_choice])
+        
+        # Log the model choice
+        st.write(f"Using model: **{model_choice}** for summarization.")
         
-        # Display the summary
-        st.subheader("Generated Summary")
-        st.write(summary[0]['summary_text'])
-    else:
-        st.write("Please enter text to summarize!")
+        if input_text:
+            # Generate the summary
+            summary = summarizer(input_text, max_length=350, min_length=30, do_sample=False)
+            
+            # Log the success message
+            st.success("Summary generated successfully!")
+            
+            # Display the summary
+            st.subheader("Generated Summary")
+            st.write(summary[0]['summary_text'])
+        else:
+            st.warning("Please enter text to summarize!")
+
+# Optionally, you can add a footer or additional instructions
+st.markdown("---")
+st.write("Provide a long text and select a model to see the summarization in action!")