reformat data returned

handler.py

@@ -132,7 +132,7 @@ class Transcript:
         student_percentage = 100 - teacher_percentage
         avg_teacher_length = teacher_words / teacher_utt_count
         avg_student_length = student_words / student_utt_count
-        return {'talk_distribution': {'teacher': teacher_percentage, 'student': student_percentage}}, {'talk_length': {'teacher': avg_teacher_length, 'student': avg_student_length}}
+        return {'teacher': teacher_percentage, 'student': student_percentage}, {'teacher': avg_teacher_length, 'student': avg_student_length}
 
     def get_word_cloud_dicts(self):
         teacher_dict = {}
@@ -360,9 +360,16 @@ class EndpointHandler():
         question_model.run_inference(transcript)
         transcript.update_utterance_roles(uptake_speaker)
         transcript.calculate_aggregate_word_count()
+        return_dict = {'talk_distribution': None, 'talk_length': None, 'talk_moments': None, 'common_top_words': None, 'uptake_top_words': None}
         talk_dist, talk_len = transcript.get_talk_distribution_and_length(uptake_speaker)
+        return_dict['talk_distribution'] = talk_dist
+        return_dict['talk_length'] = talk_len
         talk_timeline = transcript.get_talk_timeline()
-        talk_moments = {"talk_moments": talk_timeline}
+        talk_moments = talk_timeline
+        return_dict['talk_moments'] = talk_moments
         word_cloud, uptake_word_cloud = transcript.get_word_cloud_dicts()
+        return_dict['common_top_words'] = word_cloud
+        return_dict['uptake_top_words'] = uptake_word_cloud
 
-        return talk_dist, talk_len, talk_moments, word_cloud, uptake_word_cloud
+
+        return return_dict