Update src/streamlit_app.py

src/streamlit_app.py

@@ -12,6 +12,7 @@ from streamlit_extras.stylable_container import stylable_container
 from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
+
 st.markdown(
     """
     <style>
@@ -63,13 +64,13 @@ st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
 expander.write("""**Named Entities:** This Business Core web app predicts twenty-six (26) labels: "Person", "Contact", "Company", "Department", "Vendor", "Client", "Office", "Warehouse", "Address", "City", "State", "Country", "Date", "Time", "Time Period", "Revenue", "Cost", "Budget", "Invoice Number", "Product", "Service", "Task", "Project", "Status", "Asset", "Transaction"
 
-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.      
+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.      
+**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.      
+**Usage Limits:** You can request results unlimited times for one (1) month. 
 
-**Supported Languages:** English    
+**Supported Languages:** English
 
 **Technical issues:** If your connection times out, please refresh the page or reopen the app's URL. 
 
@@ -132,12 +133,12 @@ def clear_text():
 st.button("Clear text", on_click=clear_text)
 # --- Results Section ---
 if st.button("Results"):
-    start_time = time.time()
     if not text.strip():
         st.warning("Please enter some text to extract entities.")
     elif word_count > word_limit:
         st.warning(f"Your text exceeds the {word_limit} word limit. Please shorten it to continue.")
     else:
+        start_time = time.time()
         with st.spinner("Extracting entities...", show_time=True):
             entities = model.predict_entities(text, labels)
             df = pd.DataFrame(entities)
@@ -242,10 +243,12 @@ if st.button("Results"):
                 if comet_initialized:
                     experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
                     experiment.end()
+                
+                # These lines are moved to the correct location
+                end_time = time.time()
+                elapsed_time = end_time - start_time
+                st.text("")
+                st.text("")
+                st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
             else: # If df is empty
-                st.warning("No entities were found in the provided text.")
-        end_time = time.time()
-    elapsed_time = end_time - start_time
-    st.text("")
-    st.text("")
-    st.info(f"Results processed in **{elapsed_time:.2f} seconds**.")
+                st.warning("No entities were found in the provided text.")