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README.md

@@ -1,5 +1,5 @@
 ---
-title: Chan Compass · US Stocks
+title: Chan Compass · US
 emoji: 🧭
 colorFrom: blue
 colorTo: purple
@@ -10,7 +10,7 @@ license: mit
 short_description: Local Chan-theory US stock signals, fine-tuned 1.7B
 ---
 
-# 🧭 Chan Compass — US Stocks
+# 🧭 Chan Compass — US Edition
 
 Multi-timeframe **缠论 (Chan theory)** signal engine for US stocks — monthly → weekly →
 daily → 60m → 30m → 15m → 5m → 1m **nested-interval (区间套) confirmation** — plus a
@@ -143,7 +143,7 @@ while still being a Gradio app (`gradio.Server`) running through llama.cpp.
 
 ## Design system
 
-UI follows the **Adobe Spectrum 2** design system. The live frontend is
+UI follows the **Chan Compass · Spectrum 2** design system. The live frontend is
 served from `ui_kits/chan-compass/` by `gradio.Server`; the source
 tokens/components are under `design_system/` for reference.
 
@@ -156,4 +156,4 @@ analyzer cache · `data_us.py` — yfinance loader · `signal_runner.py` · `rot
 `llm_local.py` · `emailer.py` · `finetune_data.py` · `trace_publish.py` · `server.py` (the gr.Server entry) · `ui_kits/chan-compass/` (the React frontend) ·
 `finetune/` — the fine-tuning notebook + guide.
 
-*Analysis Tool for My Family.*
+*Educational tool — not investment advice.*

ui_kits/chan-compass/views.jsx

@@ -407,8 +407,13 @@ function ModelView() {
     const tick = ()=> API.modelStatus().then(r=>{setStatus(r.status); setWorkers(r.workers||{});}).catch(()=>{});
     tick(); const id=setInterval(tick, 3000); return ()=>clearInterval(id);
   },[]);
-  useEffect(()=>{ API.modelList().then(r=>{ setModels(r.models||[]); setAnalyst(r.analyst||"");
-    setPick(p=>p||r.analyst||""); }).catch(()=>{}); },[]);
+  useEffect(()=>{
+    const tick = ()=> API.modelList().then(r=>{
+      setModels(r.models||[]); setAnalyst(r.analyst||"");
+      setPick(p=>p||r.analyst||"");
+    }).catch(()=>{});
+    tick(); const id=setInterval(tick, 3000); return ()=>clearInterval(id);
+  },[]);
   useEffect(()=>{ API.finetuneStatus().then(r=>setFt(r.status)).catch(()=>{}); },[]);
   const runTest = async () => {
     setTest("⏳ Testing sub-agents… (this can take a moment on CPU)");
@@ -422,9 +427,25 @@ function ModelView() {
   };
   const loadModel = async () => {
     if (!pick) return;
-    setLoadMsg("⏳ Loading…");
-    try { const r = await API.modelLoad(pick); setLoadMsg(r.status); }
-    catch(e){ setLoadMsg("❌ " + e.message); }
+    const target = pick;
+    setLoadMsg("⏳ Loading " + target + "… (first load downloads the GGUF; can take a while on CPU)");
+    try {
+      await API.modelLoad(target);
+      // poll until the Analyst worker reports the new model loaded, then clear
+      const poll = setInterval(async () => {
+        try {
+          const r = await API.modelList();
+          setAnalyst(r.analyst || "");
+          if (r.analyst === target && r.analyst_ready) {
+            setLoadMsg("✅ " + target + " is now the Analyst model.");
+            clearInterval(poll);
+            setTimeout(()=>setLoadMsg(""), 4000);
+          }
+        } catch (_) {}
+      }, 2000);
+      // safety stop after 5 min
+      setTimeout(()=>clearInterval(poll), 300000);
+    } catch(e){ setLoadMsg("❌ " + e.message); }
   };
   const [dlUrl, setDlUrl] = useState("");
   const exportDs = async () => {