1 files changed, 70 insertions, 0 deletions
diff --git a/snips_inference_agl/slot_filler/keyword_slot_filler.py b/snips_inference_agl/slot_filler/keyword_slot_filler.py
new file mode 100644
index 0000000..087d997
--- /dev/null
+++ b/snips_inference_agl/slot_filler/keyword_slot_filler.py
@@ -0,0 +1,70 @@
+from __future__ import unicode_literals
+
+import json
+
+from snips_inference_agl.common.utils import json_string
+from snips_inference_agl.preprocessing import tokenize
+from snips_inference_agl.result import unresolved_slot
+from snips_inference_agl.slot_filler import SlotFiller
+
+
+@SlotFiller.register("keyword_slot_filler")
+class KeywordSlotFiller(SlotFiller):
+    def __init__(self, config=None, **shared):
+        super(KeywordSlotFiller, self).__init__(config, **shared)
+        self.slots_keywords = None
+        self.language = None
+
+    @property
+    def fitted(self):
+        return self.slots_keywords is not None
+
+    def fit(self, dataset, intent):
+        self.language = dataset["language"]
+        self.slots_keywords = dict()
+        utterances = dataset["intents"][intent]["utterances"]
+        for utterance in utterances:
+            for chunk in utterance["data"]:
+                if "slot_name" in chunk:
+                    text = chunk["text"]
+                    if self.config.get("lowercase", False):
+                        text = text.lower()
+                    self.slots_keywords[text] = [
+                        chunk["entity"],
+                        chunk["slot_name"]
+                    ]
+        return self
+
+    def get_slots(self, text):
+        tokens = tokenize(text, self.language)
+        slots = []
+        for token in tokens:
+            normalized_value = token.value
+            if self.config.get("lowercase", False):
+                normalized_value = normalized_value.lower()
+            if normalized_value in self.slots_keywords:
+                entity = self.slots_keywords[normalized_value][0]
+                slot_name = self.slots_keywords[normalized_value][1]
+                slot = unresolved_slot((token.start, token.end), token.value,
+                                       entity, slot_name)
+                slots.append(slot)
+        return slots
+
+    def persist(self, path):
+        model = {
+            "language": self.language,
+            "slots_keywords": self.slots_keywords,
+            "config": self.config.to_dict()
+        }
+        with path.open(mode="w", encoding="utf8") as f:
+            f.write(json_string(model))
+
+    @classmethod
+    def from_path(cls, path, **shared):
+        with path.open() as f:
+            model = json.load(f)
+        slot_filler = cls()
+        slot_filler.language = model["language"]
+        slot_filler.slots_keywords = model["slots_keywords"]
+        slot_filler.config = cls.config_type.from_dict(model["config"])
+        return slot_filler