Update src/streamlit_app.py

src/streamlit_app.py

@@ -13,6 +13,7 @@ from streamlit_extras.stylable_container import stylable_container
 from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
+
 st.markdown(
     """
     <style>
@@ -21,37 +22,31 @@ 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;
@@ -59,6 +54,7 @@ st.markdown(
     </style>
     """,
     unsafe_allow_html=True)
+
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
 st.subheader("DataHarvest", divider="violet")
@@ -94,6 +90,7 @@ with st.sidebar:
     st.divider()
     st.subheader("🚀 Ready to build your own AI Web App?", divider="violet")
     st.link_button("AI Web App Builder", "https://nlpblogs.com/build-your-named-entity-recognition-app/", type="primary")
+
 # --- Comet ML Setup ---
 COMET_API_KEY = os.environ.get("COMET_API_KEY")
 COMET_WORKSPACE = os.environ.get("COMET_WORKSPACE")
@@ -101,6 +98,7 @@ COMET_PROJECT_NAME = os.environ.get("COMET_PROJECT_NAME")
 comet_initialized = bool(COMET_API_KEY and COMET_WORKSPACE and COMET_PROJECT_NAME)
 if not comet_initialized:
     st.warning("Comet ML not initialized. Check environment variables.")
+
 # --- Label Definitions ---
 labels = ["person", "country", "city", "organization", "date", "time", "cardinal", "money", "position"]
 # Corrected mapping dictionary
@@ -110,6 +108,7 @@ category_mapping = {
     "Locations": ["country", "city"],
     "Time": ["date", "time"],
     "Numbers": ["money", "cardinal"]}
+
 # --- Model Loading ---
 @st.cache_resource
 def load_ner_model():
@@ -119,30 +118,36 @@ 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()
 # Flatten the mapping to a single dictionary
 reverse_category_mapping = {label: category for category, label_list in category_mapping.items() for label in label_list}
+
 # --- Text Input and Clear Button ---
 word_limit = 200
 text = st.text_area(f"Type or paste your text below (max {word_limit} words), and then press Ctrl + Enter", height=250, key='my_text_area')
 word_count = len(text.split())
 st.markdown(f"**Word count:** {word_count}/{word_limit}")
+
 def clear_text():
     """Clears the text area."""
     st.session_state['my_text_area'] = ""
+
 def remove_punctuation(text):
     """Removes punctuation from a string."""
     translator = str.maketrans('', '', string.punctuation)
     return text.translate(translator)
+
 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()
         # Call the new function to remove punctuation from the input text
         cleaned_text = remove_punctuation(text)
         with st.spinner("Extracting entities...", show_time=True):
@@ -245,10 +250,12 @@ 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.")