Calculate Grading Summary Logic

app.py

@@ -11,6 +11,7 @@ from helper.heygen_api import create_videos_and_save_to_db
 from helper.speech_to_text_helper import *
 from helper.token_counter import count_tokens
 from helper.openai_interface import make_openai_call, make_openai_instruct_call
+from grading_summary.grading_summary import calculate_grading_summary
 import os
 import re
 import logging
@@ -37,6 +38,7 @@ thread_event = threading.Event()
 logging.basicConfig(level=logging.DEBUG,  # Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
                     format='%(asctime)s - %(levelname)s - %(message)s')
 
+
 @app.route('/healthcheck', methods=['GET'])
 def healthcheck():
     return {"healthy": True}
@@ -407,10 +409,10 @@ def grade_speaking_task_2():
                                       "Speaking Part 2 question: '" + question + "'")
             token_count = count_tokens(perfect_answer_message)["n_tokens"]
             response['perfect_answer'] = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT,
-                                                                  perfect_answer_message,
-                                                                  token_count,
-                                                                  None,
-                                                                  GEN_QUESTION_TEMPERATURE)
+                                                                   perfect_answer_message,
+                                                                   token_count,
+                                                                   None,
+                                                                   GEN_QUESTION_TEMPERATURE)
             return response
         else:
             return {
@@ -495,15 +497,15 @@ def grade_speaking_task_3():
                                       "Speaking Part 3 question: '" + item["question"] + "'")
             token_count = count_tokens(perfect_answer_message)["n_tokens"]
             perfect_answers.append(make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT,
-                                                                   perfect_answer_message,
-                                                                   token_count,
-                                                                   None,
-                                                                   GEN_QUESTION_TEMPERATURE))
+                                                             perfect_answer_message,
+                                                             token_count,
+                                                             None,
+                                                             GEN_QUESTION_TEMPERATURE))
         message = (
-                "Grade this Speaking Part 3 answer according to ielts grading system and provide "
-                "an elaborated comment where you deep dive into what is wrong and right about the answers."
-                "Please assign a grade of 0 if the answer provided does not address the question."
-                "\n\n The questions and answers are: \n\n'")
+            "Grade this Speaking Part 3 answer according to ielts grading system and provide "
+            "an elaborated comment where you deep dive into what is wrong and right about the answers."
+            "Please assign a grade of 0 if the answer provided does not address the question."
+            "\n\n The questions and answers are: \n\n'")
 
         formatted_text = ""
         for i, entry in enumerate(answers, start=1):
@@ -511,9 +513,10 @@ def grade_speaking_task_3():
             formatted_text += f"**Answer {i}:**\n{entry['answer']}\n\n"
 
         message += formatted_text
-        message += ("'\n\nProvide your answer on the following json format: {'comment': 'comment about answer quality', "
-                    "'overall': 0.0, 'task_response': {'Fluency and Coherence': 0.0, 'Lexical Resource': 0.0, "
-                    "'Grammatical Range and Accuracy': 0.0, 'Pronunciation': 0.0}}")
+        message += (
+            "'\n\nProvide your answer on the following json format: {'comment': 'comment about answer quality', "
+            "'overall': 0.0, 'task_response': {'Fluency and Coherence': 0.0, 'Lexical Resource': 0.0, "
+            "'Grammatical Range and Accuracy': 0.0, 'Pronunciation': 0.0}}")
 
         token_count = count_tokens(message)["n_tokens"]
         response = make_openai_instruct_call(GPT_3_5_TURBO_INSTRUCT, message, token_count,
@@ -690,5 +693,14 @@ def fetch_answer_tips():
         return str(e)
 
 
+@app.route('/grading_summary', methods=['POST'])
+@jwt_required()
+def grading_summary():
+    try:
+        return calculate_grading_summary(request.get_json())
+    except Exception as e:
+        return str(e)
+
+
 if __name__ == '__main__':
     app.run()

grading_summary/grading_summary.py

@@ -3,7 +3,6 @@ import json
 import openai
 import os
 from dotenv import load_dotenv
-from functools import reduce
 
 load_dotenv()
 openai.api_key = os.getenv("OPENAI_API_KEY")
@@ -35,12 +34,7 @@ tools = [{
 }]
 
 
-# Input Format
-# {'sections': Array of {'code': key, 'name': name, 'grade': grade}}
-# Output
-# {'sections': Array of {'code': key, 'name': name, 'grade': grade, 'evaluation': evaluation, 'suggestions': suggestions}}
-
-def grading_summary(body):
+def calculate_grading_summary(body):
     extracted_sections = extract_existing_sections_from_body(body, section_keys)
 
     ret = []