update app.py

app.py

@@ -18,7 +18,7 @@ model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 recent = deque(maxlen=120)
 
 def render(a):
-    ctx = ", ".join(a["context"])
+    ctx = ", ".join(a["context"]) if isinstance(a.get("context"), list) else (a.get("context") or "")
     return f"**{a['title']}**  \n{a['instruction']}  \nGoal: {a['goal']} • Duration: {a['duration_min']} min • Energy: {a['energy']} • Context: {ctx}"
 
 def pick_unique(idxs, goal, energy, max_minutes, ignore_goal=False, ignore_energy=False, ignore_minutes=False, need=3, used=None):
@@ -54,8 +54,7 @@ def search(query, goal, energy, max_minutes):
         qx = f"{q} Goal:{goal or 'any'} Energy:{energy or 'any'} Max:{int(max_minutes) if max_minutes else ''} minutes"
         v = model.encode([qx], normalize_embeddings=True)
         D, I = index.search(np.asarray(v, dtype="float32"), 800)
-        idxs = list(I[0])
-        random.shuffle(idxs)
+        idxs = list(I[0]); random.shuffle(idxs)
         res, used = [], set()
         step, used = pick_unique(idxs, goal, energy, max_minutes, False, False, False, 3, used); res += step
         if len(res) < 3:
@@ -101,5 +100,5 @@ with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo", neutral_hue="slate"))
         cache_examples=False
     )
 
-demo.queue(concurrency_count=4, max_size=32)
+demo.queue()
 demo.launch()