update handler

handler.py

@@ -35,10 +35,10 @@ class EndpointHandler():
                        return_pooler_output=False)
         return output
 
-    def get_uptake_score(self, utterances, speakerA, speakerB):
+    def get_uptake_score(self, textA, textB):
 
-        textA = self.get_clean_text(utterances[speakerA], remove_punct=False)
-        textB = self.get_clean_text(utterances[speakerB], remove_punct=False)
+        textA = self.get_clean_text(textA, remove_punct=False)
+        textB = self.get_clean_text(textB, remove_punct=False)
 
         instance = self.input_builder.build_inputs([textA], textB,
                                               max_length=self.max_length,
@@ -50,34 +50,29 @@ class EndpointHandler():
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
        data args:
-            inputs (:obj: `list`)
+            utterances (:obj: `list`)
             parameters (:obj: `dict`)
       Return:
             A :obj:`list` | `dict`: will be serialized and returned
         """
         # get inputs
-        inputs = data.pop("inputs", data)
+        utterances = data.pop("utterances", data)
         params = data.pop("parameters", None)
 
-        utterances = inputs
         print("EXAMPLES")
-        for utt_pair in utterances[:3]:
-            print("speaker A: %s" % utt_pair[params["speaker_A"]])
-            print("speaker B: %s" % utt_pair[params["speaker_B"]])
-            print("----")
+        for utt in utterances[:3]:
+            print("speaker %s: %s" % (utt["speaker"], utt["text"]))
 
         print("Running inference on %d examples..." % len(utterances))
         self.model.eval()
-        uptake_scores = []
+        prev_num_words = 0
+        prev_text = ""
+        uptake_scores = {}
         with torch.no_grad():
             for i, utt in enumerate(utterances):
-                prev_num_words = get_num_words(utt[params["speaker_A"]])
-                if prev_num_words < params["student_min_words"]:
-                    uptake_scores.append(None)
-                    continue
-                uptake_score = self.get_uptake_score(utterances=utt,
-                                                speakerA=params["speaker_A"],
-                                                speakerB=params["speaker_B"])
-                uptake_scores.append(uptake_score)
+                if utt["speaker"] == params["speaker_2"] and (prev_num_words >= params["speaker_1_min_num_words"]):
+                    uptake_scores[utt["id"]] = self.get_uptake_score(textA=prev_text, textB=utt["text"])
+                prev_num_words = get_num_words(utt["text"])
+                prev_text = utt["text"]
 
         return uptake_scores

test.py

@@ -4,12 +4,17 @@ from handler import EndpointHandler
 my_handler = EndpointHandler(path=".")
 
 # prepare sample payload
-example = {"inputs": [{"speaker_A": "I am quite excited how this will turn out",
-                       "speaker_B": "I'm excited, too"}],
-           "parameters": {"speaker_A": "speaker_A",
-                          "speaker_B": "speaker_B",
-                          "student_min_words": 5
-                          }}
+example = {
+ "utterances": [
+   {"id": 1, "speaker": "Alice", "text": "How much is the fish?" },
+   {"id": 2, "speaker": "Bob", "text": "I have no idea, ask Alice" }
+ ],
+ "parameters": {
+   "speaker_1_min_num_words": 5,
+   "speaker_2": "Bob"
+ }
+}
+
 
 # test the handler
 print(my_handler(example))