Update src/streamlit_app.py

src/streamlit_app.py

@@ -12,8 +12,6 @@ from streamlit_extras.stylable_container import stylable_container
 from typing import Optional
 from gliner import GLiNER
 from comet_ml import Experiment
-
-
 st.markdown(
     """
     <style>
@@ -57,18 +55,13 @@ st.markdown(
     }
     </style>
     """,
-    unsafe_allow_html=True
-)
-
-
+    unsafe_allow_html=True)
 # --- Page Configuration and UI Elements ---
 st.set_page_config(layout="wide", page_title="Named Entity Recognition App")
 st.subheader("Multilingual", divider="green")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
-
-expander.write("""**Named Entities:** This Multilingual web app predicts fourteen (14) labels: 
-"Person", "First_name", "Last_name", "Title", "Job_title", "Affiliation", "Gender", "Age", "Date", "Nationality", "Location", "Country", "Role", "Relationship"
+expander.write("""**Named Entities:** This Multilingual web app predicts fourteen (14) labels: "Person", "First_name", "Last_name", "Title", "Job_title", "Affiliation", "Gender", "Age", "Date", "Nationality", "Location", "Country", "Role", "Relationship"
 
 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.      
 
@@ -83,17 +76,11 @@ Results are presented in easy-to-read tables, visualized in an interactive tree
 **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 Multilingual web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-   <iframe
-	src="https://aiecosystem-multilingual.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
+   <iframe	src="https://aiecosystem-multilingual.hf.space"	frameborder="0"	width="850"	height="450"
     ></iframe>
-
     '''
     st.code(code, language="html")
     st.text("")
@@ -101,44 +88,28 @@ with st.sidebar:
     st.divider()
     st.subheader("🚀 Ready to build your own AI Web App?", divider="orange")
     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")
 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",
+labels = [ "PERSON",
     "FIRST_NAME",
-    
     "LAST_NAME",
-    
     "TITLE", "JOB_TITLE",
     "AFFILIATION",  "GENDER",
     "AGE",
     "DATE",
-   
     "NATIONALITY", "LOCATION","COUNTRY", "ROLE",
-    "RELATIONSHIP"
-
-]
-
-
+    "RELATIONSHIP"]
 # Create a mapping dictionary for labels to categories
-
-category_mapping = {
-  "Identity": [
+category_mapping = {  "Identity": [
     "PERSON",
     "FIRST_NAME",
-   
     "LAST_NAME",
-    
     "TITLE"
   ],
   "Professional": [
@@ -149,23 +120,13 @@ category_mapping = {
     "GENDER",
     "AGE",
     "DATE",
-    
     "NATIONALITY",
     "LOCATION","COUNTRY"
   ],
   "Relational": [
     "ROLE",
     "RELATIONSHIP"
-  ]
-}
-
-   
-    
-
-
-
-
-
+  ]}
 # --- Model Loading ---
 @st.cache_resource
 def load_ner_model():
@@ -176,30 +137,28 @@ def load_ner_model():
         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 ---
-text = st.text_area("Type or paste your text below, and then press Ctrl + Enter", height=250, key='my_text_area')
-
+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'] = ""
-
 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:
         with st.spinner("Extracting entities...", show_time=True):
             entities = model.predict_entities(text, labels)
             df = pd.DataFrame(entities)
-
             if not df.empty:
                 df['category'] = df['label'].map(reverse_category_mapping)
                 if comet_initialized:
@@ -210,13 +169,10 @@ if st.button("Results"):
                     )
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
-                
                 st.subheader("Grouped Entities by Category", divider = "green")
-                
                 # Create tabs for each category
                 category_names = sorted(list(category_mapping.keys()))
                 category_tabs = st.tabs(category_names)
-                
                 for i, category_name in enumerate(category_names):
                     with category_tabs[i]:
                         df_category_filtered = df[df['category'] == category_name]
@@ -224,9 +180,6 @@ if st.button("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']
@@ -237,18 +190,15 @@ if st.button("Results"):
                     - **end**: ['index of the end of the corresponding entity']
                     ''')
                 st.divider()
-                                
                 # Tree map
                 st.subheader("Tree map", divider = "green")
                 fig_treemap = px.treemap(df, path=[px.Constant("all"), 'category', 'label', 'text'], values='score', color='category')
                 fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F0F2F5', plot_bgcolor='#F0F2F5')
                 st.plotly_chart(fig_treemap)
-                
                 # Pie and Bar charts
                 grouped_counts = df['category'].value_counts().reset_index()
                 grouped_counts.columns = ['category', 'count']
                 col1, col2 = st.columns(2)
-                
                 with col1:
                     st.subheader("Pie chart", divider = "green")
                     fig_pie = px.pie(grouped_counts, values='count', names='category', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted categories')
@@ -258,7 +208,6 @@ if st.button("Results"):
                         plot_bgcolor='#F0F2F5'
                     )
                     st.plotly_chart(fig_pie)
-                
                 with col2:
                     st.subheader("Bar chart", divider = "green")
                     fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
@@ -267,7 +216,6 @@ if st.button("Results"):
                         plot_bgcolor='#F0F2F5'
                     )
                     st.plotly_chart(fig_bar)
-                
                 # Most Frequent Entities
                 st.subheader("Most Frequent Entities", divider="green")
                 word_counts = df['text'].value_counts().reset_index()
@@ -282,10 +230,8 @@ if st.button("Results"):
                     st.plotly_chart(fig_repeating_bar)
                 else:
                     st.warning("No entities were found that occur more than once.")
-                
                 # Download Section
                 st.divider()
-                                
                 dfa = pd.DataFrame(
                     data={
                         'Column Name': ['text', 'label', 'score', 'start', 'end', 'category'],
@@ -303,7 +249,6 @@ if st.button("Results"):
                 with zipfile.ZipFile(buf, "w") as myzip:
                     myzip.writestr("Summary of the results.csv", df.to_csv(index=False))
                     myzip.writestr("Glossary of tags.csv", dfa.to_csv(index=False))
-                
                 with stylable_container(
                     key="download_button",
                     css_styles="""button { background-color: red; border: 1px solid black; padding: 5px; color: white; }""",
@@ -314,15 +259,13 @@ if st.button("Results"):
                         file_name="nlpblogs_results.zip",
                         mime="application/zip",
                     )
-                                
                 if comet_initialized:
                     experiment.log_figure(figure=fig_treemap, figure_name="entity_treemap_categories")
                     experiment.end()
             else: # If df is empty
                 st.warning("No entities were found in the provided text.")
-    
-    end_time = time.time()
+        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.info(f"Results processed in **{elapsed_time:.2f} seconds**.")