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import os |
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import typing |
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from openai import OpenAI |
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client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
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from django.contrib.postgres import fields |
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from django.db import models |
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class Quiz(models.Model): |
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name = models.CharField(max_length=100) |
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def __str__(self): |
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return self.name |
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class Question(models.Model): |
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quiz = models.ForeignKey(Quiz, on_delete=models.CASCADE) |
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prompt = models.CharField(max_length=200) |
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rubrics = models.TextField( |
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blank=True, null=True, verbose_name="Grading Rubrics - For LLM-graded questions only. You can leave this empty." |
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) |
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def __str__(self): |
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return self.prompt |
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def get_answer(self) -> typing.Union["Answer", None]: |
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return ( |
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getattr(self, "multiplechoiceanswer", None) |
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or getattr(self, "freetextanswer", None) |
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or self.llmgradedanswer |
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) |
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class Answer(models.Model): |
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question = models.OneToOneField(Question, on_delete=models.CASCADE) |
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class Meta: |
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abstract = True |
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def __str__(self) -> str: |
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return ( |
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getattr(self, "correct_answer", None) or getattr(self, "rubrics", None) or "No answer or rubrics provided" |
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) |
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def is_correct(self, user_answer) -> bool: |
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return user_answer == getattr(self, "correct_answer", None) |
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class FreeTextAnswer(Answer): |
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correct_answer = models.CharField(max_length=200, default="") |
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case_sensitive = models.BooleanField(default=False) |
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def is_correct(self, user_answer) -> bool: |
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if not self.case_sensitive: |
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return user_answer.lower() == self.correct_answer.lower() |
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return user_answer == self.correct_answer |
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class LLMGradedAnswer(Answer): |
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def grade(self, user_answer) -> dict: |
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""" |
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Grades the user's answer by calling the grading API. |
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Args: |
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user_answer (str): The answer provided by the user. |
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Returns: |
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dict: The result of the grading. |
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""" |
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try: |
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from dotenv import load_dotenv |
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load_dotenv() |
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prompt = f"Grade the following answer based on the rubric:\nRubric: {self.question.rubrics}\nAnswer: {user_answer}" |
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response = client.chat.completions.create( |
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model="gpt-4o", |
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messages=[ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": prompt}, |
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], |
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) |
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return {"result": "success", "message": response.choices[0].message.content} |
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except Exception as e: |
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print(f"An error occurred: {e}") |
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return {"result": "error", "message": str(e)} |
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class MultipleChoiceAnswer(Answer): |
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correct_answer = models.CharField(max_length=200, default="") |
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choices = fields.ArrayField(models.CharField(max_length=200, blank=True)) |
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def __str__(self) -> str: |
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return f"{self.correct_answer} from {self.choices}" |
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class LLMGradedAnswer(Answer): |
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correct_answer = models.CharField(max_length=200, default="") |
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def grade(self, user_answer) -> dict: |
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""" |
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Grades the user's answer by calling the grading API. |
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Args: |
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user_answer (str): The answer provided by the user. |
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Returns: |
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dict: The result of the grading. |
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""" |
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try: |
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from dotenv import load_dotenv |
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load_dotenv() |
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prompt = f"Grade the following answer based on the rubric:\nRubric: {self.question.rubrics}\nAnswer: {user_answer}" |
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response = client.chat.completions.create( |
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model="gpt-4o", |
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messages=[ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": prompt}, |
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], |
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) |
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return {"result": "success", "message": response.choices[0].message.content} |
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except Exception as e: |
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print(f"An error occurred: {e}") |
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return {"result": "error", "message": str(e)} |
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