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>
@@ -20,37 +21,31 @@ st.markdown(
         background-color: #E8F5E9; /* A very light green */
         color: #1B5E20; /* Dark green for the text */
     }
-    
-    /* Sidebar background color */
+        /* Sidebar background color */
     .css-1d36184 {
         background-color: #A5D6A7; /* A medium light green */
         secondary-background-color: #A5D6A7;
     }
-    
-    /* Expander background color and header */
+        /* Expander background color and header */
     .streamlit-expanderContent, .streamlit-expanderHeader {
         background-color: #E8F5E9;
     }
-    
-    /* Text Area background and text color */
+        /* Text Area background and text color */
     .stTextArea textarea {
         background-color: #81C784; /* A slightly darker medium green */
         color: #1B5E20; /* Dark green for text */
     }
-    
-    /* Button background and text color */
+        /* Button background and text color */
     .stButton > button {
         background-color: #81C784;
         color: #1B5E20;
     }
-    
-    /* Warning box background and text color */
+        /* Warning box background and text color */
     .stAlert.st-warning {
         background-color: #66BB6A; /* A medium-dark green for the warning box */
         color: #1B5E20;
     }
-    
-    /* Success box background and text color */
+        /* Success box background and text color */
     .stAlert.st-success {
         background-color: #66BB6A; /* A medium-dark green for the success box */
         color: #1B5E20;
@@ -77,12 +72,10 @@ Results are presented in easy-to-read tables, visualized in an interactive tree
 
 For any errors or inquiries, please contact us at info@nlpblogs.com""")
 
-
-
 with st.sidebar:
     st.write("Use the following code to embed the ChainSense web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-   <iframe
+    <iframe
 	src="https://aiecosystem-chainsense.hf.space"
 	frameborder="0"
 	width="850"
@@ -121,8 +114,7 @@ category_mapping = {
     "Temporal & Events": ["Date", "Transportation_Mode"],
     "Locations": ["Location"]}
 # --- Model Loading ---
-@st.cache_resource
-def load_ner_model():
+@st.cache_resourcedef load_ner_model():
     """Loads the GLiNER model and caches it."""
     try:
         return GLiNER.from_pretrained("gliner-community/gliner_large-v2.5", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
@@ -146,13 +138,13 @@ def clear_text():
 st.button("Clear text", on_click=clear_text)
 # --- Results Section ---
 if st.button("Results"):
-    start_time = time.time()
     # Check for word limit and empty text first
     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)
@@ -257,10 +249,12 @@ if st.button("Results"):
                 if comet_initialized:
                     experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
                     experiment.end()
+                
+                # Corrected placement for time calculation and display
+                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.")