Update app.py

app.py

@@ -54,6 +54,28 @@ transhuman_glossary = {
 }
 
 
+# Function to search glossary and display results
+def search_glossary(query):
+    for category, terms in transhuman_glossary.items():
+        if query.lower() in (term.lower() for term in terms):
+            st.markdown(f"### {category}")
+            st.write(f"- {query}")
+
+    st.write('## Processing query against GPT and Llama:')
+    # ------------------------------------------------------------------------------------------------
+    st.write('Reasoning with your inputs using GPT...')
+    response = chat_with_model(transcript)
+    st.write('Response:')
+    st.write(response)
+    filename = generate_filename(response, "txt")
+    create_file(filename, transcript, response, should_save)
+    
+    st.write('Reasoning with your inputs using Llama...')    
+    response = StreamLLMChatResponse(transcript)
+    filename_txt = generate_filename(transcript, "md")
+    create_file(filename_txt, transcript, response, should_save)
+    # ------------------------------------------------------------------------------------------------
+        
             
 # Display the glossary with Streamlit components, ensuring emojis are used
 def display_glossary(area):
@@ -90,11 +112,9 @@ def display_buttons_with_scores():
             score = load_score(key)
             if st.button(f"{term} {score}🚀", key=key):
                 update_score(key)
+                search_glossary('Create a three level markdown outline with 3 subpoints each where each line defines and writes out the core technology descriptions with appropriate emojis for the glossary term: ' + term)
                 st.experimental_rerun()
 
-
-
-
 def fetch_wikipedia_summary(keyword):
     # Placeholder function for fetching Wikipedia summaries
     # In a real app, you might use requests to fetch from the Wikipedia API
@@ -861,28 +881,6 @@ def add_medical_exam_buttons2():
             StreamLLMChatResponse(descriptions["Ramipril 💊"])
         
 
-# Function to search glossary and display results
-def search_glossary(query):
-    for category, terms in transhuman_glossary.items():
-        if query.lower() in (term.lower() for term in terms):
-            st.markdown(f"### {category}")
-            st.write(f"- {query}")
-
-    st.write('## Processing query against GPT and Llama:')
-    # ------------------------------------------------------------------------------------------------
-    st.write('Reasoning with your inputs using GPT...')
-    response = chat_with_model(transcript)
-    st.write('Response:')
-    st.write(response)
-    filename = generate_filename(response, "txt")
-    create_file(filename, transcript, response, should_save)
-    
-    st.write('Reasoning with your inputs using Llama...')    
-    response = StreamLLMChatResponse(transcript)
-    filename_txt = generate_filename(transcript, "md")
-    create_file(filename_txt, transcript, response, should_save)
-    # ------------------------------------------------------------------------------------------------
-        
 
 # 17. Main
 def main():