debugging

backend/server.py

@@ -39,7 +39,7 @@ async def lifespan(app: FastAPI):
     # Warm up: run a dummy inference so OpenVINO compiles its graph now,
     # not on the first real user request
     try:
-        dummy = np.zeros((1, 3, 640, 640), dtype=np.uint8)
+        dummy = np.zeros((640, 640, 3), dtype=np.uint8)  # HWC — what YOLO predict() expects
         model(dummy, verbose=False)
         print("[BACKEND] Model warm-up complete.")
     except Exception as e:
@@ -148,9 +148,12 @@ async def ws_run(ws: WebSocket):
     report_format = data.get("report_format", "png")
 
     path = videos.get(video_id)
+    if not path:
+        await ws.send_text(json.dumps({"error": f"Unknown video_id: {video_id}"}))
+        await ws.close()
+        return
 
     loop = asyncio.get_running_loop()
-
     queue = asyncio.Queue()
 
     def on_frame(update):
@@ -172,7 +175,7 @@ async def ws_run(ws: WebSocket):
 
             await asyncio.sleep(0.05)
 
-        result = task.result()
+        result = task.result()  # re-raises any exception from the engine
         result["report_format"] = report_format
         run_results[video_id] = result
         await ws.send_text(json.dumps({
@@ -183,8 +186,16 @@ async def ws_run(ws: WebSocket):
             "speed_vs_realtime": result["speed_vs_realtime"],
         }))
         await ws.close()
-    except Exception:
-        pass
+
+    except Exception as e:
+        import traceback
+        err_msg = traceback.format_exc()
+        print(f"[BACKEND] Engine error for {video_id}:\n{err_msg}")
+        try:
+            await ws.send_text(json.dumps({"error": str(e), "detail": err_msg}))
+            await ws.close()
+        except Exception:
+            pass
 
 
 app.mount("/", StaticFiles(directory=str(FRONTEND)), name="frontend")

frontend/vehicles.html

@@ -1352,6 +1352,28 @@
             ws.onmessage = e => {
                 const d = JSON.parse(e.data);
 
+                // Backend engine error — surface it clearly
+                if (d.error) {
+                    processingDone = true;
+                    document.getElementById('proc-label').innerText = 'Engine Error';
+                    const badge = document.getElementById('results-status-badge');
+                    if (badge) {
+                        badge.innerText = 'Failed';
+                        badge.className = 'px-2.5 py-1 bg-red-900/40 text-red-300 text-[10px] font-bold rounded-full uppercase tracking-tighter';
+                    }
+                    console.error('[UrbanFlow] Engine error:', d.detail || d.error);
+                    document.getElementById('run-results-content').innerHTML = `
+                        <div class="flex flex-col items-center justify-center p-8 border-2 border-dashed border-red-900/40 rounded-2xl col-span-3 text-slate-400">
+                            <i class="fa-solid fa-triangle-exclamation text-2xl mb-3 text-red-400"></i>
+                            <span class="text-xs font-semibold mb-1">Inference pipeline failed.</span>
+                            <span class="text-[10px] text-slate-500 mb-4 text-center max-w-xs">${d.error}</span>
+                            <button onclick="startNewAnalysis()" class="text-[10px] font-bold uppercase tracking-widest px-4 py-2 rounded-full" style="background:#111;border:1px solid #2a2a2a;color:#c89a6c">
+                                &larr; Start New Analysis
+                            </button>
+                        </div>`;
+                    return;
+                }
+
                 if (d.done) {
                     processingDone = true;
                     document.getElementById('proc-label').innerText = 'Complete';