Update src/streamlit_app.py

src/streamlit_app.py

@@ -15,35 +15,17 @@ from comet_ml import Experiment
 
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
-
-
-
 st.subheader("DataHarvest", divider="violet")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 st.markdown(':rainbow[**Supported Languages: English**]')
-expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"
-
-Results are presented in easy-to-read tables, visualized in an interactive tree map, pie chart and bar chart, and are available for download along with a Glossary of tags.
-
-**How to Use:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.
 
-**Usage Limits:** You can request results unlimited times for one (1) month.
-
-**Technical issues:** If your connection times out, please refresh the page or reopen the app's URL. 
-
-For any errors or inquiries, please contact us at info@nlpblogs.com""")
+expander = st.expander("**Important notes**")
+expander.write("""**Named Entities:** This DataHarvest web app predicts nine (9) labels: "person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"Results are presented in easy-to-read tables, visualized in an interactive tree map, pie chart and bar chart, and are available for download along with a Glossary of tags.**How to Use:** Type or paste your text into the text area below, then press Ctrl + Enter. Click the 'Results' button to extract and tag entities in your text data.**Usage Limits:** You can request results unlimited times for one (1) month.**Technical issues:** If your connection times out, please refresh the page or reopen the app's URL. For any errors or inquiries, please contact us at info@nlpblogs.com""")
 
 with st.sidebar:
     st.write("Use the following code to embed the DataHarvest web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-    <iframe
-	src="https://aiecosystem-dataharvest.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
-    ></iframe>
-
+    <iframe	src="https://aiecosystem-dataharvest.hf.space"	frameborder="0"	width="850"	height="450"    ></iframe>
     '''
     st.code(code, language="html")
     st.text("")
@@ -79,7 +61,6 @@ def load_ner_model():
     except Exception as e:
         st.error(f"Failed to load NER model. Please check your internet connection or model availability: {e}")
         st.stop()
-
 model = load_ner_model()
 reverse_category_mapping = {label: category for category, label_list in category_mapping.items() for label in label_list}
 
@@ -142,7 +123,8 @@ if st.button("Results"):
                         experiment.end()
             end_time = time.time()
             st.session_state.elapsed_time = end_time - start_time
-            
+            # Place the message here, so it only runs once per button click
+            st.info(f"Results processed in **{st.session_state.elapsed_time:.2f} seconds**.")
         # If the text is the same, do nothing but keep results displayed
         else:
             st.session_state.show_results = True
@@ -164,7 +146,7 @@ if st.session_state.show_results:
                     st.dataframe(df_category_filtered.drop(columns=['category']), use_container_width=True)
                 else:
                     st.info(f"No entities found for the '{category_name}' category.")
-        
+                
         with st.expander("See Glossary of tags"):
             st.write('''
             - **text**: ['entity extracted from your text data']
@@ -175,7 +157,6 @@ if st.session_state.show_results:
             ''')
         
         st.divider()
-
         # Tree map
         st.subheader("Tree map", divider="violet")
         fig_treemap = px.treemap(df, path=[px.Constant("all"), 'category', 'label', 'text'], values='score', color='category')
@@ -184,7 +165,7 @@ if st.session_state.show_results:
         expander.write("""You can easily download the tree map by hovering over it. Look for the download icon that appears in the top right corner.
         """)
         st.plotly_chart(fig_treemap)
-
+        
         # Pie and Bar charts
         grouped_counts = df['category'].value_counts().reset_index()
         grouped_counts.columns = ['category', 'count']
@@ -206,7 +187,7 @@ if st.session_state.show_results:
             expander.write("""You can easily download the bar chart by hovering over it. Look for the download icon that appears in the top right corner.
             """)
             st.plotly_chart(fig_bar)
-
+        
         # Most Frequent Entities
         st.subheader("Most Frequent Entities", divider="violet")
         word_counts = df['text'].value_counts().reset_index()
@@ -237,8 +218,6 @@ if st.session_state.show_results:
         
         with stylable_container(
             key="download_button",
-            
-           
             css_styles="""button { background-color: #8A2BE2; border: 1px solid black; padding: 5px; color: white; }""",
         ):
             st.download_button(
@@ -247,10 +226,7 @@ if st.session_state.show_results:
                 file_name="nlpblogs_results.zip",
                 mime="application/zip"
             )
-
         st.text("")
         st.text("")
-        st.info(f"Results processed in **{st.session_state.elapsed_time:.2f} seconds**.")
-
     else:
         st.warning("No entities were found in the provided text.")