Update src/streamlit_app.py

src/streamlit_app.py

@@ -12,9 +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>
@@ -23,36 +20,30 @@ st.markdown(
         background-color: #F3E5F5; /* A very light purple */
         color: #1A0A26; /* Dark purple for the text */
     }
-
     /* Sidebar background color */
     .css-1d36184 {
         background-color: #D1C4E9; /* A medium light purple */
         secondary-background-color: #D1C4E9;
     }
-
     /* Expander background color and header */
     .streamlit-expanderContent, .streamlit-expanderHeader {
         background-color: #F3E5F5;
     }
-
     /* Text Area background and text color */
     .stTextArea textarea {
         background-color: #B39DDB; /* A slightly darker medium purple */
         color: #1A0A26; /* Dark purple for text */
     }
-
     /* Button background and text color */
     .stButton > button {
         background-color: #B39DDB;
         color: #1A0A26;
     }
-
     /* Warning box background and text color */
     .stAlert.st-warning {
         background-color: #9575CD; /* A medium-dark purple for the warning box */
         color: #1A0A26;
     }
-
     /* Success box background and text color */
     .stAlert.st-success {
         background-color: #9575CD; /* A medium-dark purple for the success box */
@@ -60,19 +51,13 @@ 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("MediaTagger", divider="violet")
 st.link_button("by nlpblogs", "https://nlpblogs.com", type="tertiary")
 expander = st.expander("**Important notes**")
-expander.write("""**Named Entities:** This MediaTagger web app predicts eighteen (18) labels: 'person', 'organization', 'location', 'date', 'time', 'event', 'title', 'product', 'law', 'policy', 'work of art', 'geopolitical entity', 'number', 'cause of death',
-'weapon', 'vehicle', 'facility', 'temporal expression'
+expander.write("""**Named Entities:** This MediaTagger web app predicts eighteen (18) labels: 'person', 'organization', 'location', 'date', 'time', 'event', 'title', 'product', 'law', 'policy', 'work of art', 'geopolitical entity', 'number', 'cause of death','weapon', 'vehicle', 'facility', 'temporal expression'
 
 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.      
 
@@ -89,14 +74,8 @@ For any errors or inquiries, please contact us at info@nlpblogs.com""")
 with st.sidebar:
     st.write("Use the following code to embed the MediaTagger web app on your website. Feel free to adjust the width and height values to fit your page.")
     code = '''
-    <iframe
-	src="https://aiecosystem-mediatagger.hf.space"
-	frameborder="0"
-	width="850"
-	height="450"
+    <iframe	src="https://aiecosystem-mediatagger.hf.space"	frameborder="0"	width="850"	height="450"
     ></iframe>
-
-   
     '''
     st.code(code, language="html")
     st.text("")
@@ -104,16 +83,13 @@ 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")
 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',
@@ -135,26 +111,16 @@ labels = [
     'facility',
     'temporal expression',
 ]
-
-
 # Corrected mapping dictionary
-
 # Create a mapping dictionary for labels to categories
 category_mapping = {
     "People & Groups": ["person", "organization", "title"],
     "Topics & Objects": ["event", "product", "law", "policy", "work of art", "weapon", "vehicle"],
     "Temporal": ["date", "time", "temporal expression"],
     "Locations": ["location", "geopolitical entity", "facility"],
-    "Quantitative & Contextual": ["number", "cause of death"]
-}
-
-
-
-
-
+    "Quantitative & Contextual": ["number", "cause of death"]}
 # --- 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("EmergentMethods/gliner_large_news-v2.1", nested_ner=True, num_gen_sequences=2, gen_constraints= labels)
@@ -162,30 +128,28 @@ 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:
@@ -196,13 +160,10 @@ if st.button("Results"):
                     )
                     experiment.log_parameter("input_text", text)
                     experiment.log_table("predicted_entities", df)
-                
                 st.subheader("Grouped Entities by Category", divider = "violet")
-                
                 # 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]
@@ -210,9 +171,6 @@ 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']
@@ -222,18 +180,15 @@ if st.button("Results"):
                     - **end**: ['index of the end of the corresponding entity']
                     ''')
                 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')
                 fig_treemap.update_layout(margin=dict(t=50, l=25, r=25, b=25), paper_bgcolor='#F3E5F5', plot_bgcolor='#F3E5F5')
                 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 = "violet")
                     fig_pie = px.pie(grouped_counts, values='count', names='category', hover_data=['count'], labels={'count': 'count'}, title='Percentage of predicted categories')
@@ -243,10 +198,6 @@ if st.button("Results"):
                         plot_bgcolor='#F3E5F5'
                     )
                     st.plotly_chart(fig_pie)
-                
-                
-
-
                 with col2:
                     st.subheader("Bar chart", divider = "violet")
                     fig_bar = px.bar(grouped_counts, x="count", y="category", color="category", text_auto=True, title='Occurrences of predicted categories')
@@ -255,7 +206,6 @@ if st.button("Results"):
                         plot_bgcolor='#F3E5F5'
                     )
                     st.plotly_chart(fig_bar)
-                
                 # Most Frequent Entities
                 st.subheader("Most Frequent Entities", divider="violet")
                 word_counts = df['text'].value_counts().reset_index()
@@ -270,10 +220,8 @@ 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'],
@@ -283,7 +231,6 @@ if st.button("Results"):
                             'accuracy score; how accurately a tag has been assigned to a given entity',
                             'index of the start of the corresponding entity',
                             'index of the end of the corresponding entity',
-                            
                         ]
                     }
                 )
@@ -291,7 +238,6 @@ 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; }""",
@@ -302,15 +248,13 @@ 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**.")