Fix deepaas-cli to handle models missing get_train_args() or get_predict_args()

deepaas/cmd/cli.py

@@ -203,8 +203,8 @@ def _get_model_name():
 model_name, model_obj = _get_model_name()
 
 # use deepaas.model.v2.wrapper.ModelWrapper(). deepaas>1.2.1dev4
-# model_obj = v2_wrapper.ModelWrapper(name=model_name,
-#                                    model_obj=model_obj)
+model_obj = v2_wrapper.ModelWrapper(name=model_name,
+                                   model_obj=model_obj)
 
 # Once we know the model name,
 # we get arguments for predict and train as dictionaries

deepaas/tests/test_cli_fix.py

@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2024 Spanish National Research Council (CSIC)
+#
+# Licensed under the Apache License, Version 2.0 (the "License"); you may
+# not use this file except in compliance with the License. You may obtain
+# a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+"""Tests for CLI fix handling models with missing get_predict_args or get_train_args"""
+
+import unittest
+
+from webargs import fields
+from deepaas.model.v2 import wrapper as v2_wrapper
+
+
+class TestCLIModelHandling(unittest.TestCase):
+    """Test CLI functionality with models that have missing methods"""
+
+    def test_model_wrapper_handles_missing_predict_args(self):
+        """Test that ModelWrapper handles models without get_predict_args"""
+
+        class TrainOnlyModel:
+            def get_metadata(self):
+                return {"name": "train-only", "version": "1.0"}
+
+            def train(self, **kwargs):
+                return {"status": "trained"}
+
+            def get_train_args(self):
+                return {"epochs": fields.Int(required=True)}
+
+            # NOTE: Missing get_predict_args method
+
+        model = TrainOnlyModel()
+        wrapper = v2_wrapper.ModelWrapper("train-only", model, None)
+
+        # Should not raise AttributeError
+        predict_args = wrapper.get_predict_args()
+        train_args = wrapper.get_train_args()
+
+        self.assertEqual(predict_args, {})
+        self.assertIn("epochs", train_args)
+
+    def test_model_wrapper_handles_missing_train_args(self):
+        """Test that ModelWrapper handles models without get_train_args"""
+
+        class PredictOnlyModel:
+            def get_metadata(self):
+                return {"name": "predict-only", "version": "1.0"}
+
+            def predict(self, **kwargs):
+                return {"prediction": "result"}
+
+            def get_predict_args(self):
+                return {"data": fields.Str(required=True)}
+
+            # NOTE: Missing get_train_args method
+
+        model = PredictOnlyModel()
+        wrapper = v2_wrapper.ModelWrapper("predict-only", model, None)
+
+        # Should not raise AttributeError
+        predict_args = wrapper.get_predict_args()
+        train_args = wrapper.get_train_args()
+
+        self.assertIn("data", predict_args)
+        self.assertEqual(train_args, {})
+
+    def test_model_wrapper_handles_missing_both_args(self):
+        """Test that ModelWrapper handles models without both args methods"""
+
+        class MinimalModel:
+            def get_metadata(self):
+                return {"name": "minimal", "version": "1.0"}
+
+            # NOTE: Missing both get_predict_args and get_train_args methods
+
+        model = MinimalModel()
+        wrapper = v2_wrapper.ModelWrapper("minimal", model, None)
+
+        # Should not raise AttributeError for either method
+        predict_args = wrapper.get_predict_args()
+        train_args = wrapper.get_train_args()
+
+        self.assertEqual(predict_args, {})
+        self.assertEqual(train_args, {})
+
+    def test_direct_model_fails_as_expected(self):
+        """Test that direct model access fails as expected (validating the problem)"""
+
+        class PredictOnlyModel:
+            def get_predict_args(self):
+                return {"data": fields.Str(required=True)}
+            # NOTE: Missing get_train_args method
+
+        model = PredictOnlyModel()
+
+        # Direct access should work for existing method
+        predict_args = model.get_predict_args()
+        self.assertIn("data", predict_args)
+
+        # Direct access should fail for missing method
+        with self.assertRaises(AttributeError):
+            model.get_train_args()
+
+
+if __name__ == '__main__':
+    unittest.main()

deepaas/tests/test_cmd.py

@@ -24,6 +24,8 @@
 from deepaas.cmd import execute
 from deepaas.cmd import run
 from deepaas.tests import base
+from deepaas.model.v2 import wrapper as v2_wrapper
+from webargs import fields
 
 
 class TestRun(base.TestCase):
@@ -112,3 +114,76 @@ def test_execute_ct(self, m_out_pred):
             execute.main()
         shutil.rmtree(output_dir)
         os.remove(output_dir + ".zip")
+
+
+class TestCLIModels(base.TestCase):
+    """Test CLI functionality with models that have missing methods"""
+
+    def test_model_wrapper_handles_missing_predict_args(self):
+        """Test that ModelWrapper handles models without get_predict_args"""
+
+        class TrainOnlyModel:
+            def get_metadata(self):
+                return {"name": "train-only", "version": "1.0"}
+
+            def train(self, **kwargs):
+                return {"status": "trained"}
+
+            def get_train_args(self):
+                return {"epochs": fields.Int(required=True)}
+
+            # NOTE: Missing get_predict_args method
+
+        model = TrainOnlyModel()
+        wrapper = v2_wrapper.ModelWrapper("train-only", model, None)
+
+        # Should not raise AttributeError
+        predict_args = wrapper.get_predict_args()
+        train_args = wrapper.get_train_args()
+
+        self.assertEqual(predict_args, {})
+        self.assertIn("epochs", train_args)
+
+    def test_model_wrapper_handles_missing_train_args(self):
+        """Test that ModelWrapper handles models without get_train_args"""
+
+        class PredictOnlyModel:
+            def get_metadata(self):
+                return {"name": "predict-only", "version": "1.0"}
+
+            def predict(self, **kwargs):
+                return {"prediction": "result"}
+
+            def get_predict_args(self):
+                return {"data": fields.Str(required=True)}
+
+            # NOTE: Missing get_train_args method
+
+        model = PredictOnlyModel()
+        wrapper = v2_wrapper.ModelWrapper("predict-only", model, None)
+
+        # Should not raise AttributeError
+        predict_args = wrapper.get_predict_args()
+        train_args = wrapper.get_train_args()
+
+        self.assertIn("data", predict_args)
+        self.assertEqual(train_args, {})
+
+    def test_model_wrapper_handles_missing_both_args(self):
+        """Test that ModelWrapper handles models without both args methods"""
+
+        class MinimalModel:
+            def get_metadata(self):
+                return {"name": "minimal", "version": "1.0"}
+
+            # NOTE: Missing both get_predict_args and get_train_args methods
+
+        model = MinimalModel()
+        wrapper = v2_wrapper.ModelWrapper("minimal", model, None)
+
+        # Should not raise AttributeError for either method
+        predict_args = wrapper.get_predict_args()
+        train_args = wrapper.get_train_args()
+
+        self.assertEqual(predict_args, {})
+        self.assertEqual(train_args, {})