Update app.py

app.py

@@ -353,6 +353,7 @@ def parse_arxiv_refs(ref_text: str):
             break
     return results
 
+
 def perform_ai_lookup(q, vocal_summary=True, extended_refs=False, 
                       titles_summary=True, full_audio=False, selected_voice="en-US-AriaNeural"):
     """Perform Arxiv search and generate audio summaries."""
@@ -364,12 +365,13 @@ def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
     r2 = client.predict(q,"mistralai/Mixtral-8x7B-Instruct-v0.1",True,api_name="/ask_llm")
 
     # 🎯 2) Combine for final text output
-    result = f"### 🔎 {q.replace('\n', ' ')}\n\n{r2}\n\n{refs}"
+    clean_q = q.replace('\n', ' ')
+    result = f"### 🔎 {clean_q}\n\n{r2}\n\n{refs}"
     st.markdown(result)
 
     # 🎯 3) Generate "all at once" audio if requested
     if full_audio:
-        complete_text = f"Complete response for query: {q.replace('\n', ' ')}. {clean_for_speech(r2)} {clean_for_speech(refs)}"
+        complete_text = f"Complete response for query: {clean_q}. {clean_for_speech(r2)} {clean_for_speech(refs)}"
         audio_file_full = speak_with_edge_tts(complete_text, selected_voice)
         st.write("### 📚 Full Audio")
         play_and_download_audio(audio_file_full)
@@ -432,7 +434,7 @@ def perform_ai_lookup(q, vocal_summary=True, extended_refs=False,
     st.write(f"**Total Elapsed:** {elapsed:.2f} s")
 
     # Always create a file with the result
-    create_file(q.replace('\n', ' '), result, "md")
+    create_file(clean_q, result, "md")
 
     return result