Spaces:
Sleeping
Sleeping
import pandas as pd | |
import logging | |
from typing import Optional, Tuple | |
from dataclasses import dataclass | |
logger = logging.getLogger(__name__) | |
class GeneratedQuestion: | |
question: str | |
choices: dict | |
correct_answer: str | |
explanation: str | |
class SimilarQuestionGenerator: | |
def __init__(self, misconception_csv_path: str): | |
self._load_data(misconception_csv_path) | |
def _load_data(self, misconception_csv_path: str): | |
self.misconception_df = pd.read_csv(misconception_csv_path) | |
def get_misconception_text(self, misconception_id: float) -> Optional[str]: | |
if pd.isna(misconception_id): | |
return "No misconception provided." | |
row = self.misconception_df[self.misconception_df['MisconceptionId'] == int(misconception_id)] | |
return row.iloc[0]['MisconceptionName'] if not row.empty else "Misconception not found." | |
def generate_similar_question_with_text(self, construct_name, subject_name, question_text, correct_answer_text, wrong_answer_text, misconception_id) -> Tuple[Optional[GeneratedQuestion], Optional[str]]: | |
prompt = f"Generate a similar question for: {question_text}" | |
# Mock API call for demonstration | |
return GeneratedQuestion(question="Sample Question", choices={"A": "Option A", "B": "Option B"}, correct_answer="A", explanation="Sample Explanation"), None | |
def generate_similar_question(wrong_q, misconception_id, generator): | |
if not isinstance(wrong_q, dict): | |
return None | |
misconception_text = generator.get_misconception_text(misconception_id) | |
return {"question": f"Generated Question targeting {misconception_text}"} | |