Update app.py

app.py

@@ -499,53 +499,58 @@ def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
     st.write("Claude's reply 🧠:")
     st.markdown(response.content[0].text)
 
-    # Save & produce audio
+    # Save & produce audio and text result
     result = response.content[0].text
     create_file(q, result) 
     md_file, audio_file = save_qa_with_audio(q, result)
-    st.subheader("📝 Main Response Audio")
-    play_and_download_audio(audio_file, st.session_state['audio_format'])
-
-    # --- 2) Arxiv RAG
-    st.write("Arxiv's AI this Evening is Mixtral 8x7B...")
-    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
-    refs = client.predict(
-        q, 
-        20, 
-        "Semantic Search", 
-        "mistralai/Mixtral-8x7B-Instruct-v0.1",
-        api_name="/update_with_rag_md"
-    )[0]
-
-    r2 = client.predict(
-        q, 
-        "mistralai/Mixtral-8x7B-Instruct-v0.1", 
-        True, 
-        api_name="/ask_llm"
-    )
 
-    result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
-    md_file, audio_file = save_qa_with_audio(q, result)
-    st.subheader("📝 Main Response Audio")
+    
+    st.subheader("📝 Claude Response Audio")
     play_and_download_audio(audio_file, st.session_state['audio_format'])
 
-    # --- 3) Parse + handle papers
-    papers = parse_arxiv_refs(refs)
-    if papers:
-        # Create minimal links page first
-        paper_links = create_paper_links_md(papers)
-        links_file = create_file(q, paper_links, "md")
-        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)
-    else:
-        st.warning("No papers found in the response.")
-
-    elapsed = time.time() - start
-    st.write(f"**Total Elapsed:** {elapsed:.2f} s")
+    if st.checkbox("Run Arxiv Research AI"):
+        # --- 2) Arxiv RAG
+        st.write("Arxiv's AI this Evening is Mixtral 8x7B...")
+        client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
+        refs = client.predict(
+            q, 
+            #20, 
+            10, 
+            "Semantic Search", 
+            "mistralai/Mixtral-8x7B-Instruct-v0.1",
+            api_name="/update_with_rag_md"
+        )[0]
+    
+        r2 = client.predict(
+            q, 
+            "mistralai/Mixtral-8x7B-Instruct-v0.1", 
+            True, 
+            api_name="/ask_llm"
+        )
+    
+        result = f"### 🔎 {q}\n\n{r2}\n\n{refs}"
+        md_file, audio_file = save_qa_with_audio(q, result)
+        st.subheader("📝 Main Response Audio")
+        play_and_download_audio(audio_file, st.session_state['audio_format'])
+    
+        # --- 3) Parse + handle papers
+        papers = parse_arxiv_refs(refs)
+        if papers:
+            # Create minimal links page first
+            paper_links = create_paper_links_md(papers)
+            links_file = create_file(q, paper_links, "md")
+            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)
+        else:
+            st.warning("No papers found in the response.")
+    
+        elapsed = time.time() - start
+        st.write(f"**Total Elapsed:** {elapsed:.2f} s")
+        
     return result
 
 def process_voice_input(text):