Remove code (moved to talk move router)

handler.py

@@ -10,18 +10,6 @@ nlp = spacy.load("en_core_web_sm")
 tokenizer = nlp.tokenizer
 token_limit = 200
 
-class Utterance(object):
-
-    def __init__(self, starttime, endtime, speaker, text, 
-                 idx, prev_utterance, prev_prev_utterance):
-        self.starttime = starttime
-        self.endtime = endtime
-        self.speaker = speaker
-        self.text = text
-        self.idx = idx
-        self.prev_utterance = prev_utterance
-        self.prev_prev_utterance = prev_prev_utterance
-
 class EndpointHandler():
     def __init__(self, path="."):
         print("Loading models...")
@@ -30,151 +18,6 @@ class EndpointHandler():
             "roberta", path, use_cuda=cuda_available
         )
 
-    def utterance_to_str(self, utterance: Utterance) -> (List[str], str):
-        #revoicing using prior text and truncates end of the prior text
-        
-        doc = nlp(utterance.text)
-        prior_text = self.truncate_end(self.get_prior_text(utterance))
-
-        if len(doc) > token_limit:
-            utterance_text_list = self.handle_long_utterances(doc)
-            utterance_with_prior_text = []
-            for text in utterance_text_list:
-                utterance_with_prior_text.append([prior_text, text])
-            return utterance_with_prior_text, 'list'
-
-        else:
-            return [prior_text, utterance.text], 'single'
-
-    def truncate_end(self, prior_text: str) -> str:
-        max_seq_length = 512
-        prior_text_max_length = int(max_seq_length / 2) #divide by 2 because 2 columns
-
-        if len(prior_text) > prior_text_max_length:
-            starting_index = len(prior_text) - prior_text_max_length
-            return prior_text[starting_index:]
-        return prior_text
-    
-    def format_speaker(self, speaker: str, source: str) -> str:
-        prior_text = ''
-        if speaker == 'student':
-            prior_text += '***STUDENT '
-        else:
-            prior_text += '***SECTION_LEADER '
-        if source == 'not chat':
-            prior_text += '(audio)*** : '
-        else:
-            prior_text += '(chat)*** : '
-        return prior_text
-    
-    def get_prior_text(self, utterance: Utterance) -> str:
-        prior_text = ''
-        if utterance.prev_utterance != None and utterance.prev_prev_utterance != None:
-            #TODO: add in the source
-            prior_text = '\"' + self.format_speaker(utterance.prev_prev_utterance.speaker, 'not chat') + utterance.prev_prev_utterance.text + ' \n ' 
-            prior_text += self.format_speaker(utterance.prev_utterance.speaker, 'not chat') + utterance.prev_utterance.text + ' \n '
-        else: 
-            prior_text = 'No prior utterance'
-        return prior_text
-    
-    def handle_long_utterances(self, doc: str) -> List[str]:
-        split_count = 1
-        total_sent = len([x for x in doc.sents])
-        sent_count = 0
-        token_count = 0
-        split_utterance = ''
-        utterances = []
-        for sent in doc.sents:
-            # add a sentence to split
-            split_utterance = split_utterance + ' ' + sent.text
-            token_count += len(sent)
-            sent_count +=1
-            if token_count >= token_limit or sent_count == total_sent: 
-                # save utterance segment
-                utterances.append(split_utterance)
-
-                # restart count
-                split_utterance = ''
-                token_count = 0
-                split_count += 1
-
-        return utterances
-    
-    def convert_time(self, time_str):
-        time = datetime.strptime(time_str, "%H:%M:%S.%f")
-        return 1000 * (3600 * time.hour + 60 * time.minute + time.second) + time.microsecond / 1000
-        
-    def process_vtt_transcript(self, vttfile) -> List[Utterance]:
-        """Process raw vtt file."""
-
-        utterances_list = []
-        text = ""
-        prev_start = "00:00:00.000"
-        prev_end = "00:00:00.000"
-        idx = 0
-        prev_speaker = None
-        prev_utterance = None
-        prev_prev_utterance = None
-        for caption in webvtt.read(vttfile):
-
-            # Get speaker
-            check_for_speaker = caption.text.split(":")
-            if len(check_for_speaker) > 1:  # the speaker was changed or restated
-                speaker = check_for_speaker[0]
-            else:
-                speaker = prev_speaker
-
-            # Get utterance
-            new_text = check_for_speaker[1] if len(check_for_speaker) > 1 else check_for_speaker[0]
-
-            # If speaker was changed, start new batch
-            if (prev_speaker is not None) and (speaker != prev_speaker):
-                utterance = Utterance(starttime=self.convert_time(prev_start),
-                              endtime=self.convert_time(prev_end),
-                              speaker=prev_speaker,
-                              text=text.strip(),
-                              idx=idx,
-                              prev_utterance=prev_utterance,
-                              prev_prev_utterance=prev_prev_utterance)
-
-                utterances_list.append(utterance)
-
-                # Start new batch
-                prev_start = caption.start
-                text = ""
-                prev_prev_utterance = prev_utterance
-                prev_utterance = utterance
-                idx+=1
-            text += new_text + " "
-            prev_end = caption.end
-            prev_speaker = speaker
-            
-        # Append last one
-        if prev_speaker is not None:
-            utterance = Utterance(starttime=self.convert_time(prev_start),
-                                  endtime=self.convert_time(prev_end),
-                                  speaker=prev_speaker,
-                                  text=text.strip(),
-                                  idx=idx,
-                                  prev_utterance=prev_utterance,
-                                  prev_prev_utterance=prev_prev_utterance)
-            utterances_list.append(utterance)
-
-        return utterances_list
-
-
     def __call__(self, data_file: str) -> List[Dict[str, Any]]:
         ''' data_file is a str pointing to filename of type .vtt '''
-
-        utterances_list = []
-        for utterance in self.process_vtt_transcript(data_file):
-            #TODO: filter out to only have SL utterances
-            utterance_str, is_list = self.utterance_to_str(utterance)
-            if is_list == 'list':
-                utterances_list.extend(utterance_str)
-            else:
-                utterances_list.append(utterance_str)
-                
-        predictions, raw_outputs = self.model.predict(utterances_list)
-        
-        return predictions
+        return []