Update app.py

app.py

@@ -10,6 +10,10 @@ warnings.filterwarnings("ignore", category=DeprecationWarning)
 
 HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
 
+# Configuration: Change this number to control how many examples to show by default
+DEFAULT_EXAMPLES_TO_SHOW = 3
+EXAMPLES_PER_ROW = 3
+
 # Define your example prompts here for easy management
 EXAMPLE_PROMPTS = {
     "Example 1️⃣: Asset Allocation": "Cautious Asset Investment has a total of $150,000 to manage and decides to invest it in money market fund, which yields a 2% return as well as in foreign bonds, which gives and average rate of return of 10.2%. Internal policies require PAI to diversify the asset allocation so that the minimum investment in money market fund is 40% of the total investment. Due to the risk of default of foreign countries, no more than 40% of the total investment should be allocated to foreign bonds. How much should the Cautious Asset Investment allocate in each asset so as to maximize its average return?",
@@ -31,7 +35,6 @@ EXAMPLE_PROMPTS = {
     "Example 9️⃣: Resource Allocation": "A project manager has 4 teams available to work on 3 different projects. Each team has different productivity rates for each project and different hourly costs. Project A requires 100 hours, Project B requires 150 hours, and Project C requires 200 hours. Each team can work a maximum of 160 hours this month. How should the manager assign teams to projects to minimize total cost while completing all projects on time?",
 
     "Example 🔟: Diet Optimization": "A diet plan requires at least 3000 calories, 50 grams of protein, and 70 grams of fat. Food X provides 600 calories, 20g protein, and 10g fat per serving. Food Y provides 500 calories, 10g protein, and 20g fat per serving. How many servings of each food should be consumed to meet the requirements at minimum cost?",
-
 }
 
 @st.cache_resource
@@ -47,6 +50,22 @@ def set_example_text(example_key):
     """Callback to set the text area value based on the selected example."""
     st.session_state.text_input = EXAMPLE_PROMPTS[example_key]
 
+def display_example_buttons(show_all=False):
+    """Display example buttons in organized rows."""
+    example_items = list(EXAMPLE_PROMPTS.items())
+    
+    if not show_all:
+        example_items = example_items[:DEFAULT_EXAMPLES_TO_SHOW]
+    
+    # Display examples in rows
+    for i in range(0, len(example_items), EXAMPLES_PER_ROW):
+        cols = st.columns(EXAMPLES_PER_ROW)
+        
+        for j, (key, value) in enumerate(example_items[i:i+EXAMPLES_PER_ROW]):
+            with cols[j]:
+                if st.button(key, key=f"example_btn_{i+j}", use_container_width=True):
+                    set_example_text(key)
+
 def main():
     st.title("""Ner4Opt: Named Entity Recognition for Optimization""")
     st.markdown("""Given an optimization problem in natural language, Ner4Opt extracts optimization related entities from free-form text. The source code for Ner4Opt is available at https://github.com/skadio/ner4opt""")
@@ -54,15 +73,32 @@ def main():
     # Initialize session state for the text input if it doesn't exist
     if "text_input" not in st.session_state:
         st.session_state.text_input = EXAMPLE_PROMPTS["Example 1️⃣: Asset Allocation"]
+    
+    # Initialize session state for showing all examples
+    if "show_all_examples" not in st.session_state:
+        st.session_state.show_all_examples = False
 
     # --- Add the example prompts section ---
-    st.subheader("Try with an example:")
-    cols = st.columns(len(EXAMPLE_PROMPTS))
-    for i, (key, value) in enumerate(EXAMPLE_PROMPTS.items()):
-        with cols[i]:
-            if st.button(key):
-                set_example_text(key)
+    st.subheader("📝 Try with an example:")
+    
+    # Toggle button for showing more/fewer examples
+    total_examples = len(EXAMPLE_PROMPTS)
+    if total_examples > DEFAULT_EXAMPLES_TO_SHOW:
+        col1, col2 = st.columns([3, 1])
+        with col2:
+            if st.session_state.show_all_examples:
+                if st.button(f"Show fewer ({DEFAULT_EXAMPLES_TO_SHOW})", key="toggle_examples"):
+                    st.session_state.show_all_examples = False
+                    st.rerun()
+            else:
+                if st.button(f"Show all ({total_examples})", key="toggle_examples"):
+                    st.session_state.show_all_examples = True
+                    st.rerun()
     
+    # Display the example buttons
+    display_example_buttons(st.session_state.show_all_examples)
+    
+    st.markdown("---")
     # ----------------------------------------
     
     option = st.sidebar.selectbox(
@@ -70,44 +106,59 @@ def main():
         ('Lexical', 'Lexical Plus', 'Semantic', 'Hybrid'), index=3)
 
     text = st.text_area(
-        "Text",
+        "Text (Tip: Use Ctrl+Enter to process)",
         value=st.session_state.text_input, # Use the session state value here
+        height=120,
+        help="Enter your optimization problem description or select an example above."
     )
+    
     text = text.strip()
     if text == "":
-        st.write("Please write a valid sentence.")
-
-    lexical_ner_model, lexical_plus_ner_model, semantic_ner_model, hybrid_ner_model = load_models()
-
-    # get entities
-    if option == "Lexical":
-        predicted_entities = lexical_ner_model.get_entities(text)
-    elif option == "Lexical Plus":
-        predicted_entities = lexical_plus_ner_model.get_entities(text)
-    elif option == "Semantic":
-        predicted_entities = semantic_ner_model.get_entities(text)
-    elif option == "Hybrid":
-        predicted_entities = hybrid_ner_model.get_entities(text)
-
-    # re-format to match SpaCy, as it uses label instead of entity_group
-    # entity_group follows huggingface design and is compatible with most NER interfaces
-    entities_formatted = []
-    for tag in predicted_entities:
-        entities_formatted.append({
-            "start": tag['start'],
-            "end": tag['end'],
-            "label": tag['entity_group'],
-            "score": tag['score']
-        })
-
-    ner_for_display = [{
-        "text": spacy_tokenize_sentence(preprocess(text)),
-        "ents": entities_formatted,
-        "title": "Named Entities"
-    }]
-    html_ner = displacy.render(ner_for_display, style="ent", manual=True)
-    html_ner = html_ner.replace("\n", " ")
-    st.write(HTML_WRAPPER.format(html_ner), unsafe_allow_html=True)
+        st.warning("Please write a valid sentence or select an example above.")
+        return
+
+    try:
+        with st.spinner("Processing text..."):
+            lexical_ner_model, lexical_plus_ner_model, semantic_ner_model, hybrid_ner_model = load_models()
+
+            # get entities
+            if option == "Lexical":
+                predicted_entities = lexical_ner_model.get_entities(text)
+            elif option == "Lexical Plus":
+                predicted_entities = lexical_plus_ner_model.get_entities(text)
+            elif option == "Semantic":
+                predicted_entities = semantic_ner_model.get_entities(text)
+            elif option == "Hybrid":
+                predicted_entities = hybrid_ner_model.get_entities(text)
+
+            # re-format to match SpaCy, as it uses label instead of entity_group
+            # entity_group follows huggingface design and is compatible with most NER interfaces
+            entities_formatted = []
+            for tag in predicted_entities:
+                entities_formatted.append({
+                    "start": tag['start'],
+                    "end": tag['end'],
+                    "label": tag['entity_group'],
+                    "score": tag['score']
+                })
+
+            ner_for_display = [{
+                "text": spacy_tokenize_sentence(preprocess(text)),
+                "ents": entities_formatted,
+                "title": "Named Entities"
+            }]
+            html_ner = displacy.render(ner_for_display, style="ent", manual=True)
+            html_ner = html_ner.replace("\n", " ")
+            st.write(HTML_WRAPPER.format(html_ner), unsafe_allow_html=True)
+            
+            # Show entity count if any entities found
+            if entities_formatted:
+                st.success(f"Found {len(entities_formatted)} entities using {option} model")
+            else:
+                st.info("No entities detected in the text.")
+                
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
 
 if __name__ == '__main__':
     main()