refactor: improve snapshot safety and usage tracking

Create atomic snapshots by deep copying usage data structures to prevent race conditions.
Protect concurrent reads of usage counts with explicit locking in endpoint selection.
Replace README screenshot with a video link.

router.py

@@ -868,10 +868,14 @@ class rechunk:
 # SSE Helpser
 # ------------------------------------------------------------------
 async def publish_snapshot():
+    # Take a consistent snapshot while holding the lock
     async with usage_lock:
-        snapshot = orjson.dumps({"usage_counts": usage_counts,
-                                    "token_usage_counts": token_usage_counts,
-                                }, option=orjson.OPT_SORT_KEYS).decode("utf-8")
+        snapshot = orjson.dumps({
+            "usage_counts": dict(usage_counts),  # Create a copy
+            "token_usage_counts": dict(token_usage_counts)
+        }, option=orjson.OPT_SORT_KEYS).decode("utf-8")
+
+    # Distribute the snapshot (no lock needed here since we have a copy)
     async with _subscribers_lock:
         for q in _subscribers:
             # If the queue is full, drop the message to avoid back‑pressure.
@@ -967,18 +971,19 @@ async def choose_endpoint(model: str) -> str:
     #      (concurrently, but only for the filtered list)
     load_tasks = [fetch.loaded_models(ep) for ep in candidate_endpoints]
     loaded_sets = await asyncio.gather(*load_tasks)
-    
+
+    # Protect all reads of usage_counts with the lock
     async with usage_lock:
         # Helper: get current usage count for (endpoint, model)
         def current_usage(ep: str) -> int:
             return usage_counts.get(ep, {}).get(model, 0)
-        
+
         # 3️⃣ Endpoints that have the model loaded *and* a free slot
         loaded_and_free = [
             ep for ep, models in zip(candidate_endpoints, loaded_sets)
             if model in models and usage_counts.get(ep, {}).get(model, 0) < config.max_concurrent_connections
         ]
-        
+
         if loaded_and_free:
             # Sort by per-model usage in DESCENDING order to ensure model affinity
             # Endpoints with higher usage (already handling this model) should be preferred