Update app.py

app.py

@@ -207,9 +207,10 @@ def get_central_time():
     return datetime.now(central)
 
 def format_timestamp_prefix():
-    """📅 Generate a timestamp prefix: MM_dd_yy_hh_mm_AM/PM."""
+    """📅 Generate a timestamp prefix"""
     ct = get_central_time()
-    return ct.strftime("%m_%d_%y_%I_%M_%p")
+    #return ct.strftime("%m_%d_%y_%I_%M_%p")
+    return ct.strftime("%Y%m%d_%H%M%S")
 
 def initialize_marquee_settings():
     """🌈 Initialize marquee defaults if needed."""
@@ -751,7 +752,7 @@ def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
         # --- 2) Arxiv RAG
         #st.write("Arxiv's AI this Evening is Mixtral 8x7B...")
         st.write('Running Arxiv RAG with Claude inputs.')
-        st.code(q, language="python", line_numbers=True, wrap_lines=True)
+        #st.code(q, language="python", line_numbers=True, wrap_lines=True)
         
         client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
         refs = client.predict(
@@ -769,19 +770,6 @@ def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
         #    api_name="/ask_llm"
         #)
 
-        # --- 3) Claude API with arxiv list of papers to app.py
-        client = anthropic.Anthropic(api_key=anthropic_key)
-        user_input = q + '\n\n' + 'Use the paper list below to answer the question thinking through step by step how to create a streamlit app.py and requirements.txt for the solution that answers the questions with a working app to demonstrate.'+ '\n\n'
-        response = client.messages.create(
-            model="claude-3-sonnet-20240229",
-            max_tokens=1000,
-            messages=[
-                {"role": "user", "content": user_input}
-            ])
-        r2 = response.content[0].text
-        st.write("Claude's reply 🧠:")
-        st.markdown(r2)
-        
         #result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
         result = f"🔎 {r2}\n\n{refs}"
         md_file, audio_file = save_qa_with_audio(q, result)
@@ -797,12 +785,31 @@ def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
             st.markdown(paper_links)
 
             # Then create audio for each paper
-            create_paper_audio_files(papers, input_question=q)
-            display_papers(papers, get_marquee_settings())
-            display_papers_in_sidebar(papers)
+            if st.button("Generate audio file reading aloud paper title and paper summary"):
+                create_paper_audio_files(papers, input_question=q)
+
+            display_papers(papers, get_marquee_settings()) # scrolling marquee per paper and summary
+            
+            display_papers_in_sidebar(papers) # sidebar entry per paper and summary
         else:
             st.warning("No papers found in the response.")
 
+
+        # --- 4) Claude API with arxiv list of papers to app.py
+        client = anthropic.Anthropic(api_key=anthropic_key)
+        user_input = q + '\n\n' + 'Use the reference papers below to answer the question by creating a python streamlit app.py and requirements.txt with python libraries for creating a single app.py application that answers the questions with working code to demonstrate.'+ '\n\n'
+        response = client.messages.create(
+            model="claude-3-sonnet-20240229",
+            max_tokens=1000,
+            messages=[
+                {"role": "user", "content": user_input}
+            ])
+        r2 = response.content[0].text
+        st.write("Claude's reply 🧠:")
+        st.markdown(r2)
+        
+
+    
     elapsed = time.time() - start
     st.write(f"**Total Elapsed:** {elapsed:.2f} s")
     return result