Merge pull request #319 from snipsco/develop

Release 0.8.11

README.md

@@ -1,4 +1,4 @@
-# Snips NLU (0.8.10)
+# Snips NLU (0.8.11)
 
 [![Build Status](https://jenkins2.snips.ai/buildStatus/icon?job=SDK/snips-nlu/master)](https://jenkins2.snips.ai/job/SDK/job/snips-nlu/view/Branches/job/master)
 

snips_nlu/__version__

@@ -1 +1 @@
-0.8.10
+0.8.11

snips_nlu/slot_filler/crf_tagger.py

@@ -178,29 +178,27 @@ def from_dict(cls, tagger_config):
         return cls(crf_model=crf, features_signatures=features_signatures,
                    tagging_scheme=tagging_scheme, language=language)
 
+    def __del__(self):
+        if self.crf_model.modelfile.auto \
+                or self.crf_model.modelfile.name is None:
+            return
+        try:
+            os.remove(self.crf_model.modelfile.name)
+        except OSError:
+            pass
+
 
 def serialize_crf_model(crf_model):
-    # Make sure there is an existing filename
-    crf_model.modelfile.ensure_name()
     with io.open(crf_model.modelfile.name, mode='rb') as f:
         crfsuite_data = base64.b64encode(f.read()).decode('ascii')
-    # Clean up temp file afterwards
-    crf_model.modelfile.cleanup()
     return crfsuite_data
 
 
 def deserialize_crf_model(crf_model_data):
     b64_data = base64.b64decode(crf_model_data)
-    with tempfile.NamedTemporaryFile() as f:
+    with tempfile.NamedTemporaryFile(suffix=".crfsuite", prefix="model",
+                                     delete=False) as f:
         f.write(b64_data)
         f.flush()
         crf = CRF(model_filename=f.name)
-        # We need this line in order to initialize the tagger while the
-        # tempfile still exists
-        _ = crf.tagger_
-        # We then need to set a new file resource for the `modelfile` attribute
-        # to make sure the model can still be serialized after
-        crf.modelfile = FileResource(suffix=".crfsuite", prefix="model")
-        crf.modelfile.ensure_name()
-
     return crf

snips_nlu/tests/test_nlu_engine.py

@@ -246,6 +246,7 @@ def test_should_use_fitted_tagger(self):
 
     def test_should_be_serializable_after_fitted_tagger_is_added(self):
         # Given
+        text = "Give me 3 cups of hot tea please"
         trained_engine = SnipsNLUEngine(Language.EN).fit(BEVERAGE_DATASET)
         taggers = trained_engine.probabilistic_parser.crf_taggers
         trained_tagger_coffee = taggers["MakeCoffee"]
@@ -258,8 +259,21 @@ def test_should_be_serializable_after_fitted_tagger_is_added(self):
         engine.add_fitted_tagger("MakeCoffee", trained_tagger_data_coffee)
         engine.add_fitted_tagger("MakeTea", trained_tagger_data_tea)
         engine.fit(BEVERAGE_DATASET, intents=[])
+
+        # Then
         try:
-            engine.to_dict()
+            engine_dict = engine.to_dict()
+            new_engine = SnipsNLUEngine.from_dict(engine_dict)
+            result = new_engine.parse(text)
+            expected_slots = [
+                ParsedSlot((8, 9), '3', 'snips/number',
+                           'number_of_cups').as_dict(),
+                ParsedSlot((18, 21), 'hot', 'Temperature',
+                           'beverage_temperature').as_dict()
+            ]
+            self.assertEqual(result['text'], text)
+            self.assertEqual(result['intent']['intent_name'], 'MakeTea')
+            self.assertListEqual(result['slots'], expected_slots)
         except Exception, e:
             self.fail('Exception raised: %s' % e.message)