Update src/SecondModule/module2.py

src/SecondModule/module2.py

@@ -34,169 +34,6 @@ class GeneratedQuestion:
     correct_answer: str
     explanation: str
 
-# class SimilarQuestionGenerator:
-#     def __init__(self, misconception_csv_path: str = 'misconception_mapping.csv'):
-#         """
-#         Initialize the generator by loading the misconception mapping and the language model.
-#         """
-#         self._load_data(misconception_csv_path)
-
-#     def _load_data(self, misconception_csv_path: str):
-#         logger.info("Loading misconception mapping...")
-#         self.misconception_df = pd.read_csv(misconception_csv_path)
-
-#     def get_misconception_text(self, misconception_id: float) -> Optional[str]:
-#         # MisconceptionId를 받아 해당 ID에 매칭되는 오개념 설명 텍스트를 반환합니다
-#         """Retrieve the misconception text based on the misconception ID."""
-#         if pd.isna(misconception_id):  # NaN 체크
-#             logger.warning("Received NaN for misconception_id.")
-#             return "No misconception provided."
-        
-#         try:
-#             row = self.misconception_df[self.misconception_df['MisconceptionId'] == int(misconception_id)]
-#             if not row.empty:
-#                 return row.iloc[0]['MisconceptionName']
-#         except ValueError as e:
-#             logger.error(f"Error processing misconception_id: {e}")
-        
-#         logger.warning(f"No misconception found for ID: {misconception_id}")
-#         return "Misconception not found."
-
-#     def generate_prompt(self, construct_name: str, subject_name: str, question_text: str, correct_answer_text: str, wrong_answer_text: str, misconception_text: str) -> str:
-#         """Create a prompt for the language model."""
-#         #문제 생성을 위한 프롬프트 텍스트를 생성
-#         logger.info("Generating prompt...")
-#         misconception_clause = (f"that targets the following misconception: \"{misconception_text}\"." if misconception_text != "There is no misconception" else "")
-#         prompt = f"""
-#             <|begin_of_text|>
-#             <|start_header_id|>system<|end_header_id|>
-#             You are an educational assistant designed to generate multiple-choice questions {misconception_clause}
-#             <|eot_id|>
-#             <|start_header_id|>user<|end_header_id|>
-#             You need to create a similar multiple-choice question based on the following details:
-
-#             Construct Name: {construct_name}
-#             Subject Name: {subject_name}
-#             Question Text: {question_text}
-#             Correct Answer: {correct_answer_text}
-#             Wrong Answer: {wrong_answer_text}
-
-#             Please follow this output format:
-#             ---
-#             Question: <Your Question Text>
-#             A) <Choice A>
-#             B) <Choice B>
-#             C) <Choice C>
-#             D) <Choice D>
-#             Correct Answer: <Correct Choice (e.g., A)>
-#             Explanation: <Brief explanation for the correct answer>
-#             ---
-#             Ensure that the question is conceptually similar but not identical to the original. Ensure clarity and educational value.
-#             <|eot_id|>
-#             <|start_header_id|>assistant<|end_header_id|>
-#         """.strip()
-#         logger.debug(f"Generated prompt: {prompt}")
-#         return prompt
-
-#     def call_model_api(self, prompt: str) -> str:
-#         """Hugging Face API 호출"""
-#         logger.info("Calling Hugging Face API...")
-#         headers = {"Authorization": f"Bearer {API_KEY}"}
-        
-#         try:
-#             response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
-#             response.raise_for_status()
-            
-#             response_data = response.json()
-#             logger.debug(f"Raw API response: {response_data}")
-            
-#             # API 응답이 리스트인 경우 처리
-#             if isinstance(response_data, list):
-#                 if response_data and isinstance(response_data[0], dict):
-#                     generated_text = response_data[0].get('generated_text', '')
-#                 else:
-#                     generated_text = response_data[0] if response_data else ''
-#             # API 응답이 딕셔너리인 경우 처리
-#             elif isinstance(response_data, dict):
-#                 generated_text = response_data.get('generated_text', '')
-#             else:
-#                 generated_text = str(response_data)
-                
-#             logger.info(f"Generated text: {generated_text}")
-#             return generated_text
-            
-#         except requests.exceptions.RequestException as e:
-#             logger.error(f"API request failed: {e}")
-#             raise
-#         except Exception as e:
-#             logger.error(f"Unexpected error in call_model_api: {e}")
-#             raise
-#     def parse_model_output(self, output: str) -> GeneratedQuestion:
-#         if not isinstance(output, str):
-#             logger.error(f"Invalid output format: {type(output)}. Expected string.")
-#             raise ValueError("Model output is not a string.")
-
-#         logger.info(f"Parsing output: {output}")
-#         output_lines = output.strip().splitlines()
-#         logger.debug(f"Split output into lines: {output_lines}")
-
-#         question, choices, correct_answer, explanation = "", {}, "", ""
-
-#         for line in output_lines:
-#             if line.lower().startswith("question:"):
-#                 question = line.split(":", 1)[1].strip()
-#             elif line.startswith("A)"):
-#                 choices["A"] = line[2:].strip()
-#             elif line.startswith("B)"):
-#                 choices["B"] = line[2:].strip()
-#             elif line.startswith("C)"):
-#                 choices["C"] = line[2:].strip()
-#             elif line.startswith("D)"):
-#                 choices["D"] = line[2:].strip()
-#             elif line.lower().startswith("correct answer:"):
-#                 correct_answer = line.split(":", 1)[1].strip()
-#             elif line.lower().startswith("explanation:"):
-#                 explanation = line.split(":", 1)[1].strip()
-
-#         if not question or len(choices) < 4 or not correct_answer or not explanation:
-#             logger.warning("Incomplete generated question.")
-#         return GeneratedQuestion(question, choices, correct_answer, explanation)
-
-#     def generate_similar_question_with_text(self, construct_name: str, subject_name: str, question_text: str, correct_answer_text: str, wrong_answer_text: str, misconception_id: float) -> Tuple[Optional[GeneratedQuestion], Optional[str]]:
-#         logger.info("generate_similar_question_with_text initiated")
-
-#         # 예외 처리 추가
-#         try:
-#             misconception_text = self.get_misconception_text(misconception_id)
-#             logger.info(f"Misconception text retrieved: {misconception_text}")
-#         except Exception as e:
-#             logger.error(f"Error retrieving misconception text: {e}")
-#             return None, None
-
-#         if not misconception_text:
-#             logger.info("Skipping question generation due to lack of misconception.")
-#             return None, None
-
-#         prompt = self.generate_prompt(construct_name, subject_name, question_text, correct_answer_text, wrong_answer_text, misconception_text)
-#         logger.info(f"Generated prompt: {prompt}")
-
-#         generated_text = None  # 기본값으로 초기화
-#         try:
-#             logger.info("Calling call_model_api...")
-#             generated_text = self.call_model_api(prompt)
-#             logger.info(f"Generated text from API: {generated_text}")
-
-#             # 파싱
-#             generated_question = self.parse_model_output(generated_text)
-#             logger.info(f"Generated question object: {generated_question}")
-#             return generated_question, generated_text
-
-#         except Exception as e:
-#             logger.error(f"Failed to generate question: {e}")
-#             logger.debug(f"API output for debugging: {generated_text}")
-#             return None, generated_text
-
-
 class SimilarQuestionGenerator:
     def __init__(self, misconception_csv_path: str = 'misconception_mapping.csv'):
         """