update handler for new output

handler.py

@@ -3,6 +3,9 @@ from scipy.special import softmax
 import numpy as np
 import weakref
 import re
+import nltk
+from nltk.corpus import stopwords
+nltk.download('stopwords')
 
 from utils import clean_str, clean_str_nopunct
 import torch
@@ -10,7 +13,7 @@ from utils import MultiHeadModel, BertInputBuilder, get_num_words, MATH_PREFIXES
 
 import transformers
 from transformers import BertTokenizer, BertForSequenceClassification
-
+from transformers.utils import logging
 
 transformers.logging.set_verbosity_debug()
 
@@ -30,9 +33,15 @@ class Utterance:
         self.endtime = endtime
         self.transcript = weakref.ref(transcript) if transcript else None
         self.props = kwargs
+        self.role = None
+        self.word_count = self.get_num_words()
+        self.timestamp = [starttime, endtime]
+        self.unit_measure = None
+        self.aggregate_unit_measure = endtime
         self.num_math_terms = None
         self.math_terms = None
 
+        # moments
         self.uptake = None
         self.reasoning = None
         self.question = None
@@ -62,6 +71,21 @@ class Utterance:
             **self.props
         }
 
+    def to_talk_timeline_dict(self):
+        return{
+            'speaker': self.speaker,
+            'text': self.text,
+            'uid': self.uid,
+            'role': self.role,
+            'timestamp': self.timestamp,
+            'moments': {'reasoning': True if self.reasoning else False, 'questioning': True if self.question else False, 'uptake': True if self.uptake else False, 'focusingQuestion': True if self.focusing_question else False},
+            'unitMeasure': self.unit_measure,
+            'aggregateUnitMeasure': self.aggregate_unit_measure,
+            'wordCount': self.word_count,
+            'numMathTerms': self.num_math_terms,
+            'mathTerms': self.math_terms
+        }
+
     def __repr__(self):
         return f"Utterance(speaker='{self.speaker}'," \
                f"text='{self.text}', uid={self.uid}," \
@@ -91,6 +115,86 @@ class Transcript:
     def length(self):
         return len(self.utterances)
 
+    def update_utterance_roles(self, uptake_speaker):
+        for utt in self.utterances:
+            if (utt.speaker == uptake_speaker):
+                utt.role = 'teacher'
+            else:
+                utt.role = 'student'
+
+    def get_talk_distribution_and_length(self, uptake_speaker):
+        if ((uptake_speaker is None)):
+            return None
+        teacher_words = 0
+        teacher_utt_count = 0
+        student_words = 0
+        student_utt_count = 0
+        for utt in self.utterances:
+            if (utt.speaker == uptake_speaker):
+                utt.role = 'teacher'
+                teacher_words += utt.get_num_words()
+                teacher_utt_count += 1
+            else:
+                utt.role = 'student'
+                student_words += utt.get_num_words()
+                student_utt_count += 1
+        teacher_percentage = round(
+            (teacher_words / (teacher_words + student_words)) * 100)
+        student_percentage = 100 - teacher_percentage
+        avg_teacher_length = teacher_words / teacher_utt_count
+        avg_student_length = student_words / student_utt_count
+        return {'teacher': teacher_percentage, 'student': student_percentage}, {'teacher': avg_teacher_length, 'student': avg_student_length}
+
+    def get_word_cloud_dicts(self):
+        teacher_dict = {}
+        student_dict = {}
+        uptake_teacher_dict = {}
+        stop_words = stopwords.words('english')
+        # stopwords = nltk.corpus.stopwords.word('english')
+        # print("stopwords: ", stopwords)
+        for utt in self.utterances:
+            words = (utt.get_clean_text(remove_punct=True)).split(' ')
+            for word in words:
+                if word in stop_words: continue
+                if utt.role == 'teacher':
+                    if word not in teacher_dict:
+                        teacher_dict[word] = 0
+                    teacher_dict[word] += 1
+                    if utt.uptake == 1:
+                        if word not in uptake_teacher_dict:
+                            uptake_teacher_dict[word] = 0
+                        uptake_teacher_dict[word] += 1
+                else:
+                    if word not in student_dict:
+                        student_dict[word] = 0
+                    student_dict[word] += 1
+        dict_list = []
+        uptake_dict_list = []
+        for word in uptake_teacher_dict.keys():
+            uptake_dict_list.append({'text': word, 'value': uptake_teacher_dict[word], 'category': 'teacher'})
+        for word in teacher_dict.keys():
+            dict_list.append(
+                {'text': word, 'value': teacher_dict[word], 'category': 'teacher'})
+        for word in student_dict.keys():
+            dict_list.append(
+                {'text': word, 'value': student_dict[word], 'category': 'student'})
+        sorted_dict_list = sorted(dict_list, key=lambda x: x['value'], reverse=True)
+        sorted_uptake_dict_list = sorted(uptake_dict_list, key=lambda x: x['value'], reverse=True)
+        return sorted_dict_list[:50], sorted_uptake_dict_list[:50]
+
+    def get_talk_timeline(self):
+        return [utterance.to_talk_timeline_dict() for utterance in self.utterances]
+    
+    def calculate_aggregate_word_count(self):
+        unit_measures = [utt.unit_measure for utt in self.utterances]
+        if None in unit_measures:
+            aggregate_word_count = 0
+            for utt in self.utterances: 
+                aggregate_word_count += utt.get_num_words()
+                utt.unit_measure = utt.get_num_words()
+                utt.aggregate_unit_measure = aggregate_word_count
+
+
     def to_dict(self):
         return {
             'utterances': [utterance.to_dict() for utterance in self.utterances],
@@ -218,8 +322,6 @@ class UptakeModel:
                             return_pooler_output=False)
         return output
 
-
-
 class FocusingQuestionModel:
     def __init__(self, device, tokenizer, input_builder, max_length=128, path=FOCUSING_QUESTION_MODEL):
         print("Loading models...")
@@ -254,8 +356,7 @@ class FocusingQuestionModel:
         output = self.model(input_ids=instance["input_ids"],
                             attention_mask=instance["attention_mask"],
                             token_type_ids=instance["token_type_ids"])
-        return output        
-
+        return output   
 
 def load_math_terms():
     math_terms = []
@@ -283,7 +384,7 @@ def run_math_density(transcript):
             matches = [match for match in matches if not any(match.start() in range(existing[0], existing[1]) for existing in matched_positions)]
             if len(matches) > 0:
                 match_list.append(math_terms_dict[term])
-            # Update existing match positions
+            # Update matched positions
             matched_positions.update((match.start(), match.end()) for match in matches)
             num_matches += len(matches)
         utt.num_math_terms = num_matches
@@ -319,13 +420,13 @@ class EndpointHandler():
             transcript.add_utterance(Utterance(**utt))
 
         print("Running inference on %d examples..." % transcript.length())
-        uptake_speaker = params.pop("uptake_speaker", None)
+        logging.set_verbosity_info()
         # Uptake
         uptake_model = UptakeModel(
             self.device, self.tokenizer, self.input_builder)
+        uptake_speaker = params.pop("uptake_speaker", None)
         uptake_model.run_inference(transcript, min_prev_words=params['uptake_min_num_words'],
                                    uptake_speaker=uptake_speaker)
-
         # Reasoning
         reasoning_model = ReasoningModel(
             self.device, self.tokenizer, self.input_builder)
@@ -343,4 +444,17 @@ class EndpointHandler():
 
         run_math_density(transcript)
 
-        return transcript.to_dict()
+        transcript.update_utterance_roles(uptake_speaker)
+        transcript.calculate_aggregate_word_count()
+        return_dict = {'talkDistribution': None, 'talkLength': None, 'talkMoments': None, 'commonTopWords': None, 'uptakeTopWords': None}
+        talk_dist, talk_len = transcript.get_talk_distribution_and_length(uptake_speaker)
+        return_dict['talkDistribution'] = talk_dist
+        return_dict['talkLength'] = talk_len
+        talk_moments = transcript.get_talk_timeline()
+        return_dict['talkMoments'] = talk_moments
+        word_cloud, uptake_word_cloud = transcript.get_word_cloud_dicts()
+        return_dict['commonTopWords'] = word_cloud
+        return_dict['uptakeTopWords'] = uptake_word_cloud
+
+
+        return return_dict