model_path

app.py

@@ -25,11 +25,8 @@ def predict_with_catboost(features_df, model_path):
     try:
         model = CatBoostClassifier()
         # Try loading without format specification first
-        try:
-            model.load_model(model_path)
-        except:
-            # If that fails, try with explicit format
-            model.load_model(model_path, format='binary')
+        print(model_path)
+        model.load_model(model_path)
         predictions = model.predict(features_df)
         return predictions
     except Exception as e:

test_model.py

@@ -8,16 +8,22 @@ def test_model_loading():
         "catboost_model.bin"
     ]
     
+    formats_to_try = [None, 'binnar', 'json', 'coreml']
+    
     for path in possible_paths:
         if os.path.exists(path):
             print(f"Found model at: {path}")
-            model = CatBoostClassifier()
-            try:
-                model.load_model(path)
-                print(f"Successfully loaded model from {path}")
-                return True
-            except Exception as e:
-                print(f"Failed to load from {path}: {str(e)}")
+            for format in formats_to_try:
+                model = CatBoostClassifier()
+                try:
+                    if format is None:
+                        model.load_model(path)
+                    else:
+                        model.load_model(path, format=format)
+                    print(f"Successfully loaded model from {path} with format {format}")
+                    return True
+                except Exception as e:
+                    print(f"Failed to load from {path} with format {format}: {str(e)}")
     
     print("Model not found in any location")
     return False