From 218a95e6a0c8881ca1919b3f00d8b15475d03e1f Mon Sep 17 00:00:00 2001
From: kba <unixprog@gmail.com>
Date: Mon, 11 May 2026 12:19:20 +0200
Subject: [PATCH] fix model loading in mb_ro and ocr

---
 src/eynollah/eynollah_ocr.py                    |  12 ++++++------
 src/eynollah/mb_ro_on_layout.py                 |   5 ++---
 .../model_zoo/.nfs00000002feddea7d00000031      | Bin 0 -> 20480 bytes
 src/eynollah/model_zoo/model_zoo.py             |   8 ++++++--
 4 files changed, 14 insertions(+), 11 deletions(-)
 create mode 100644 src/eynollah/model_zoo/.nfs00000002feddea7d00000031

diff --git a/src/eynollah/eynollah_ocr.py b/src/eynollah/eynollah_ocr.py
index 3c918e5..1b49077 100644
--- a/src/eynollah/eynollah_ocr.py
+++ b/src/eynollah/eynollah_ocr.py
@@ -65,14 +65,14 @@ class Eynollah_ocr:
         self.b_s = 2 if batch_size is None and tr_ocr else 8 if batch_size is None else batch_size
 
         if tr_ocr:
-            self.model_zoo.load_model('trocr_processor')
-            self.model_zoo.load_model('ocr', 'tr')
+            self.model_zoo.load_models('trocr_processor')
+            self.model_zoo.load_models(['ocr', 'tr'])
             self.model_zoo.get('ocr').to(self.device)
         else:
-            self.model_zoo.load_model('ocr', '')
-            self.model_zoo.load_model('num_to_char')
-            self.model_zoo.load_model('characters')
-            self.end_character = len(self.model_zoo.get('characters', list)) + 2
+            self.model_zoo.load_models('ocr')
+            self.model_zoo.load_models('num_to_char')
+            self.model_zoo.load_models('characters')
+            self.end_character = len(self.model_zoo.get('characters')) + 2
 
     @property
     def device(self):
diff --git a/src/eynollah/mb_ro_on_layout.py b/src/eynollah/mb_ro_on_layout.py
index 22fe97b..b0b5910 100644
--- a/src/eynollah/mb_ro_on_layout.py
+++ b/src/eynollah/mb_ro_on_layout.py
@@ -19,7 +19,6 @@ import statistics
 
 os.environ['TF_USE_LEGACY_KERAS'] = '1' # avoid Keras 3 after TF 2.15
 import tensorflow as tf
-from tensorflow.keras.models import Model
 
 from .model_zoo import EynollahModelZoo
 from .utils.resize import resize_image
@@ -50,7 +49,7 @@ class machine_based_reading_order_on_layout:
         except:
             self.logger.warning("no GPU device available")
             
-        self.model_zoo.load_model('reading_order')
+        self.model_zoo.load_models('reading_order')
 
     def read_xml(self, xml_file):
         tree1 = ET.parse(xml_file, parser = ET.XMLParser(encoding='utf-8'))
@@ -676,7 +675,7 @@ class machine_based_reading_order_on_layout:
                 tot_counter += 1
                 batch.append(j)
                 if tot_counter % inference_bs == 0 or tot_counter == len(ij_list):
-                    y_pr = self.model_zoo.get('reading_order', Model).predict(input_1 , verbose='0')
+                    y_pr = self.model_zoo.get('reading_order').predict(input_1 , verbose='0')
                     for jb, j in enumerate(batch):
                         if y_pr[jb][0]>=0.5:
                             post_list.append(j)
diff --git a/src/eynollah/model_zoo/.nfs00000002feddea7d00000031 b/src/eynollah/model_zoo/.nfs00000002feddea7d00000031
new file mode 100644
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diff --git a/src/eynollah/model_zoo/model_zoo.py b/src/eynollah/model_zoo/model_zoo.py
index fffd389..9611388 100644
--- a/src/eynollah/model_zoo/model_zoo.py
+++ b/src/eynollah/model_zoo/model_zoo.py
@@ -94,8 +94,12 @@ class EynollahModelZoo:
             elif model_category.endswith('_patched'):
                 load_args[0] = model_category[:-8]
                 load_kwargs["patched"] = True
-            ret[model_category] = Predictor(self.logger, self)
-            ret[model_category].load_model(*load_args, **load_kwargs, device=device)
+            spec = self.specs.get(model_category, load_args[1] if len(load_args) > 1 else '')
+            if spec.type in ['Keras'] and spec.category != 'ocr':
+                ret[model_category] = Predictor(self.logger, self)
+                ret[model_category].load_model(*load_args, **load_kwargs, device=device)
+            else:
+                ret[model_category] = self.load_model(*load_args, **load_kwargs, device=device)
         self._loaded.update(ret)
         return self._loaded