Add simple metrics persistence for image/video jobs

Track total jobs, videos, images, detections, per‑label counts, and expose the
metrics via a UI button. This adds persistence to `/tmp/blade_metrics.json`
and helper functions to load, save, and record metrics.

app.py

@@ -56,6 +56,59 @@ def get_model():
         model = YOLO("best2.pt")
     return model
 
+# ────────────────────────────
+# Métricas simples (persistidas en /tmp)
+# ────────────────────────────
+METRICS_PATH = os.path.join(tempfile.gettempdir(), "blade_metrics.json")
+
+def _load_metrics():
+    try:
+        if os.path.exists(METRICS_PATH):
+            with open(METRICS_PATH, "r", encoding="utf-8") as f:
+                return json.load(f)
+    except Exception:
+        pass
+    return {
+        "total_jobs": 0,
+        "videos": 0,
+        "images": 0,
+        "detections_total": 0,
+        "per_label": {},
+        "last_job": None,
+    }
+
+
+def _save_metrics(m):
+    try:
+        with open(METRICS_PATH, "w", encoding="utf-8") as f:
+            json.dump(m, f, ensure_ascii=False, indent=2)
+    except Exception:
+        pass
+
+
+def _record_metrics(job_type, counts):
+    m = _load_metrics()
+    m["total_jobs"] += 1
+    if job_type == "video":
+        m["videos"] += 1
+    elif job_type == "image":
+        m["images"] += 1
+    dets = int(sum(counts.values())) if isinstance(counts, dict) else 0
+    m["detections_total"] += dets
+    # per label aggregate
+    if isinstance(counts, dict):
+        per = m.get("per_label", {})
+        for k, v in counts.items():
+            per[k] = int(per.get(k, 0)) + int(v)
+        m["per_label"] = per
+    m["last_job"] = {"type": job_type, "detections": dets}
+    _save_metrics(m)
+
+
+def get_metrics():
+    """Devuelve el snapshot actual de métricas."""
+    return _load_metrics()
+
 # ────────────────────────────
 # Funciones de Inferencia
 # ────────────────────────────
@@ -129,6 +182,8 @@ def infer_media(media_path, conf=0.25, iou=0.45, out_res="720p", preset="default
         if cap:
             cap.release()
 
+        # registrar métricas
+        _record_metrics("video", counts)
         return {"video": out_vid, "classes": counts}
 
     # ─ Imagen ──────────────────────────────────────────────────────
@@ -154,6 +209,8 @@ def infer_media(media_path, conf=0.25, iou=0.45, out_res="720p", preset="default
 
         out_path = os.path.join(tmpdir, "annotated.png")
         cv2.imwrite(out_path, img)
+        # registrar métricas
+        _record_metrics("image", counts)
         return {"path": out_path, "classes": counts}
 
     else:
@@ -775,5 +832,10 @@ with gr.Blocks(
 demo.queue()
 
 if __name__ == "__main__":
+    # Métricas (UI)
+    btn_metrics = gr.Button("Ver métricas")
+    out_metrics = gr.JSON(label="Métricas", visible=True)
+    btn_metrics.click(fn=get_metrics, outputs=out_metrics, api_name="metrics")
+
     # Permitir acceso de descarga a directorio temporal para evitar 403
     demo.launch(allowed_paths=[tempfile.gettempdir()])