Update app.py

app.py

@@ -205,18 +205,18 @@ def _align_features(model, features: np.ndarray) -> np.ndarray:
         vec[:limit] = features[:limit]
         return vec.reshape(1, -1)
 
-
 def run_two_stage(features: np.ndarray, crop_rgb: np.ndarray) -> dict:
-    # Stage 1: router_model.keras يحدد حرف (0) أو رقم (1)
-    img_resized = cv2.resize(crop_rgb, (IMG_SIZE, IMG_SIZE))
-    img_array   = np.expand_dims(img_resized, axis=0).astype("float32") / 255.0
+    img_gray    = cv2.cvtColor(crop_rgb, cv2.COLOR_RGB2GRAY)
+    img_resized = cv2.resize(img_gray, (IMG_SIZE, IMG_SIZE))
+    img_array   = np.expand_dims(img_resized, axis=(0, -1)).astype("float32") / 255.0
+    # shape: (1, 64, 64, 1) ✅
 
-    prob    = float(router_model_keras.predict(img_array, verbose=0)[0][0])
-    cls_idx = 1 if prob >= 0.5 else 0
+    prob     = float(router_model_keras.predict(img_array, verbose=0)[0][0])
+    cls_idx  = 1 if prob >= 0.5 else 0
     category = CLASSES[cls_idx]
     cat_conf = prob if cls_idx == 1 else 1.0 - prob
 
-    # Stage 2: اختار الموديل الصح بناءً على النتيجة
+    # Stage 2: اختار الموديل الصح
     model   = mlp_letters if category == "letter" else mlp_numbers
     feat_df = _align_features(model, features)
     label   = str(model.predict(feat_df)[0])
@@ -229,7 +229,6 @@ def run_two_stage(features: np.ndarray, crop_rgb: np.ndarray) -> dict:
         "category_confidence": round(cat_conf, 4),
     }
 
-
 # ─────────────────────────────────────────────────────────────────────────────
 #  Routes
 # ─────────────────────────────────────────────────────────────────────────────