Update app.py

app.py

@@ -506,56 +506,57 @@ if query or search_button:
                 st.markdown(
                     f"**[Full Text Read]({doi_link})** 🔗", unsafe_allow_html=True
                 )
-
-        plot_df = pd.DataFrame(plot_data)
-
-        # Convert 'Date' to datetime if it's not already in that format
-        plot_df["Date"] = pd.to_datetime(plot_df["Date"])
-
-        # Sort the DataFrame based on the Date to make sure it's ordered
-        plot_df = plot_df.sort_values(by="Date")
-
-       
-        # Create a Plotly figure
-        fig = px.scatter(
-            plot_df,
-            x="Date",
-            y="Score",
-            hover_data=["Title", "DOI"],
-            color='server',
-            title="Publication Times and Scores",
-        )
-        fig.update_traces(marker=dict(size=10))
-        # Customize hover text to display the title and link it to the DOI
-        fig.update_traces(
-            hovertemplate="<b>%{hovertext}</b>",
-            hovertext=plot_df.apply(lambda row: f"{row['Title']}", axis=1),
-        )
-
-        # Show the figure in the Streamlit app
-        st.plotly_chart(fig, use_container_width=True)
-
-        # Generate category counts for the pie chart
-        category_counts = plot_df["category"].value_counts().reset_index()
-        category_counts.columns = ["category", "count"]
-
-        # Create a pie chart with Plotly Express
-        fig = px.pie(
-            category_counts,
-            values="count",
-            names="category",
-            title="Category Distribution",
-        )
-
-        # Show the pie chart in the Streamlit app
-        st.plotly_chart(fig, use_container_width=True)
-
-        prompt = st.text_area("Enter your summary prompt", value=LLM_prompt)
-        summary_button = st.button("AI summary")
-        if summary_button:
-            ai_gen_start = time.time()
-            st.markdown('**AI Summary of 10 abstracts:**')
-            st.markdown(summarize_abstract(abstracts[:9], instructions=prompt))
-            total_ai_time = time.time()-ai_gen_start
-            st.markdown(f'**Time to generate summary:** {total_ai_time:.2f} s')
+        with st.spinner("Under statistics..."):
+            plot_df = pd.DataFrame(plot_data)
+    
+            # Convert 'Date' to datetime if it's not already in that format
+            plot_df["Date"] = pd.to_datetime(plot_df["Date"])
+    
+            # Sort the DataFrame based on the Date to make sure it's ordered
+            plot_df = plot_df.sort_values(by="Date")
+    
+           
+            # Create a Plotly figure
+            fig = px.scatter(
+                plot_df,
+                x="Date",
+                y="Score",
+                hover_data=["Title", "DOI"],
+                color='server',
+                title="Publication Times and Scores",
+            )
+            fig.update_traces(marker=dict(size=10))
+            # Customize hover text to display the title and link it to the DOI
+            fig.update_traces(
+                hovertemplate="<b>%{hovertext}</b>",
+                hovertext=plot_df.apply(lambda row: f"{row['Title']}", axis=1),
+            )
+    
+            # Show the figure in the Streamlit app
+            st.plotly_chart(fig, use_container_width=True)
+    
+            # Generate category counts for the pie chart
+            category_counts = plot_df["category"].value_counts().reset_index()
+            category_counts.columns = ["category", "count"]
+    
+            # Create a pie chart with Plotly Express
+            fig = px.pie(
+                category_counts,
+                values="count",
+                names="category",
+                title="Category Distribution",
+            )
+    
+            # Show the pie chart in the Streamlit app
+            st.plotly_chart(fig, use_container_width=True)
+            
+        with st.spinner("LLM is summarizing..."):
+            prompt = st.text_area("Enter your summary prompt", value=LLM_prompt)
+            summary_button = st.button("AI summary")
+            if summary_button:
+                ai_gen_start = time.time()
+                st.markdown('**AI Summary of 10 abstracts:**')
+                st.markdown(summarize_abstract(abstracts[:9], instructions=prompt))
+                total_ai_time = time.time()-ai_gen_start
+                st.markdown(f'**Time to generate summary:** {total_ai_time:.2f} s')