TraningExamples should accept answers with multiple parts.

TNL-708

openassessment/assessment/api/ai_worker.py

@@ -6,6 +6,7 @@ from httplib import HTTPException
 from django.db import DatabaseError
 from dogapi import dog_stats_api
 from openassessment.assessment.models import (
+    essay_text_from_submission,
     AITrainingWorkflow, AIGradingWorkflow,
     ClassifierUploadError, ClassifierSerializeError,
     IncompleteClassifierSet, NoTrainingExamples,
@@ -197,19 +198,13 @@ def get_training_task_params(training_workflow_uuid):
         returned_examples = []
 
         for example in workflow.training_examples.all():
-            answer = example.answer
-            if isinstance(answer, dict):
-                text = answer.get('answer', '')
-            else:
-                text = answer
-
             scores = {
                 option.criterion.name: option.points
                 for option in example.options_selected.all()
             }
 
             returned_examples.append({
-                'text': text,
+                'text': essay_text_from_submission({'answer': example.answer}),
                 'scores': scores
             })
 

openassessment/assessment/models/ai.py

@@ -51,17 +51,21 @@ def essay_text_from_submission(submission):
     directly (convenient for testing).
     """
     if isinstance(submission, dict):
-        # Initially there was one prompt and submission had the structure
-        # {'answer': {'text': 'The text.'}}
-        if 'text' in submission['answer']:
-            essay_text = submission['answer']['text']
-        # When multiple prompts were introduced the structure of submission become:
-        # {'answer': {'parts': [{'text': 'The text part 1.'}, {'text': 'The text part 2.'}]}}
-        # We concatenate these parts and let AI grader evaluate the total text.
-        else:
-            essay_text = u''
-            for part in submission['answer']['parts']:
-                essay_text += '\n' + part['text']
+        if 'answer' in submission:
+            # Format used for answer in examples.
+            if isinstance(submission['answer'], unicode):
+                return submission['answer']
+            # Initially there was one prompt and submission had the structure
+            # {'answer': {'text': 'The text.'}}
+            elif 'text' in submission['answer']:
+                essay_text = submission['answer']['text']
+            # When multiple prompts were introduced the structure of submission become:
+            # {'answer': {'parts': [{'text': 'The text part 1.'}, {'text': 'The text part 2.'}]}}
+            # We concatenate these parts and let AI grader evaluate the total text.
+            else:
+                essay_text = u''
+                for part in submission['answer']['parts']:
+                    essay_text += '\n' + part['text']
     else:
         essay_text = unicode(submission)
     return essay_text

openassessment/assessment/serializers/training.py

@@ -126,7 +126,15 @@ def deserialize_training_examples(examples, rubric_dict):
         >>>
         >>> examples = [
         >>>     {
-        >>>         'answer': u'Lorem ipsum',
+        >>>         'answer': {
+        >>>             'parts': {
+        >>>                 [
+        >>>                     {'text:' 'Answer part 1'},
+        >>>                     {'text:' 'Answer part 2'},
+        >>>                     {'text:' 'Answer part 3'}
+        >>>                 ]
+        >>>             }
+        >>>         },
         >>>         'options_selected': {
         >>>             'vocabulary': 'good',
         >>>             'grammar': 'excellent'