Update app.py

app.py

@@ -1,48 +1,159 @@
-import requests
 import streamlit as st
-from dotenv import load_dotenv
+import pandas as pd
 import os
+import logging
+from src.SecondModule.module2 import SimilarQuestionGenerator, generate_similar_question
 
-# .env 파일 로드
-load_dotenv()
+# 로깅 설정
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-# Hugging Face API 정보
-API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
-API_KEY = os.getenv("HUGGINGFACE_API_KEY")
-print(os.getenv("HUGGINGFACE_API_KEY"))
+# Streamlit 페이지 기본 설정
+st.set_page_config(
+    page_title="MisconcepTutor", 
+    layout="wide", 
+    initial_sidebar_state="expanded"
+)
 
+# 경로 설정
+base_path = os.path.dirname(os.path.abspath(__file__))
+data_path = os.path.join(base_path, 'Data')
+misconception_csv_path = os.path.join(data_path, 'misconception_mapping.csv')
 
-# 모델 호출 함수
-def query_model(prompt):
-    headers = {"Authorization": f"Bearer {API_KEY}"}
-    data = {"inputs": prompt}
-    
-    response = requests.post(API_URL, headers=headers, json=data)
-    print(response)
+# 세션 상태 초기화
+if 'initialized' not in st.session_state:
+    st.session_state.initialized = True
+    st.session_state.wrong_questions = []
+    st.session_state.misconceptions = []
+    st.session_state.current_question_index = 0
+    st.session_state.generated_questions = []
+    st.session_state.current_step = 'initial'
+    st.session_state.selected_wrong_answer = None
+    st.session_state.questions = []
+    logger.info("Session state initialized")
+
+# 문제 생성기 초기화
+@st.cache_resource
+def load_question_generator():
+    if not os.path.exists(misconception_csv_path):
+        st.error(f"CSV 파일이 존재하지 않습니다: {misconception_csv_path}")
+        raise FileNotFoundError(f"CSV 파일이 존재하지 않습니다: {misconception_csv_path}")
+    return SimilarQuestionGenerator(misconception_csv_path=misconception_csv_path)
+
+# CSV 데이터 로드
+@st.cache_data
+def load_data(data_file='/train.csv'):
+    try:
+        file_path = os.path.join(data_path, data_file.lstrip('/'))
+        df = pd.read_csv(file_path)
+        logger.info(f"Data loaded successfully from {file_path}")
+        return df
+    except FileNotFoundError:
+        st.error(f"파일을 찾을 수 없습니다: {data_file}")
+        logger.error(f"File not found: {data_file}")
+        return None
+
+# 퀴즈 시작
+def start_quiz():
+    df = load_data()
+    if df is None or df.empty:
+        st.error("데이터를 불러올 수 없습니다. 데이터셋을 확인해주세요.")
+        return
+    st.session_state.questions = df.sample(n=10, random_state=42)
+    st.session_state.current_step = 'quiz'
+    st.session_state.current_question_index = 0
+    st.session_state.wrong_questions = []
+    st.session_state.misconceptions = []
+    st.session_state.generated_questions = []
+    logger.info("Quiz started")
+
+# 답변 처리
+def handle_answer(answer, current_q):
+    if answer != current_q['CorrectAnswer']:
+        wrong_q_dict = current_q.to_dict()
+        st.session_state.wrong_questions.append(wrong_q_dict)
+        st.session_state.selected_wrong_answer = answer
+        
+        misconception_key = f'Misconception{answer}Id'
+        misconception_id = current_q.get(misconception_key)
+        st.session_state.misconceptions.append(misconception_id)
     
-    if response.status_code == 200:
-        result = response.json()
-        print("Response:", result)  # API 응답 전체를 출력
-        if isinstance(result, list) and len(result) > 0:
-            return result[0].get("generated_text", "No output generated")
+    st.session_state.current_question_index += 1
+    if st.session_state.current_question_index >= 10:
+        st.session_state.current_step = 'review'
+
+# 메인 애플리케이션 로직
+def main():
+    st.title("MisconcepTutor")
+    generator = load_question_generator()
+
+    if st.session_state.current_step == 'initial':
+        st.write("#### 학습을 시작하겠습니다. 10개의 문제를 풀어볼까요?")
+        if st.button("학습 시작", key="start_quiz"):
+            start_quiz()
+            st.rerun()
+
+    elif st.session_state.current_step == 'quiz':
+        current_q = st.session_state.questions.iloc[st.session_state.current_question_index]
+        progress = st.session_state.current_question_index / 10
+        st.progress(progress)
+        st.write(f"### 문제 {st.session_state.current_question_index + 1}/10")
+        st.markdown("---")
+        st.write(current_q['QuestionText'])
+
+        col1, col2 = st.columns(2)
+        with col1:
+            if st.button(f"A) {current_q['AnswerAText']}", key="A"):
+                handle_answer('A', current_q)
+                st.rerun()
+            if st.button(f"C) {current_q['AnswerCText']}", key="C"):
+                handle_answer('C', current_q)
+                st.rerun()
+        with col2:
+            if st.button(f"B) {current_q['AnswerBText']}", key="B"):
+                handle_answer('B', current_q)
+                st.rerun()
+            if st.button(f"D) {current_q['AnswerDText']}", key="D"):
+                handle_answer('D', current_q)
+                st.rerun()
+
+    elif st.session_state.current_step == 'review':
+        st.write("### 학습 결과")
+        col1, col2, col3 = st.columns(3)
+        col1.metric("총 문제 수", 10)
+        col2.metric("맞은 문제", 10 - len(st.session_state.wrong_questions))
+        col3.metric("틀린 문제", len(st.session_state.wrong_questions))
+
+        if len(st.session_state.wrong_questions) == 0:
+            st.balloons()
+            st.success("🎉 모든 문제를 맞추셨습니다!")
+        elif len(st.session_state.wrong_questions) <= 3:
+            st.success("잘 하셨어요! 조금만 더 연습하면 완벽해질 거예요!")
         else:
-            return "No valid output from API"
-    else:
-        print(f"Error: {response.status_code}, {response.text}")
-        return f"Error: {response.status_code}, {response.text}"
-
-# Streamlit UI 구성
-st.title("Meta-Llama Text Generator")
-st.write("Enter a prompt to generate text using the Meta-Llama-3B model.")
-
-# 사용자 입력
-prompt = st.text_area("Enter your prompt:", height=200)
-
-if st.button("Generate"):
-    if prompt.strip():
-        st.write("Generating...")
-        output = query_model(prompt)
-        st.write("### Output:")
-        st.write(output)
-    else:
-        st.warning("Please enter a valid prompt!")
+            st.info("천천히 개념을 복습해 보세요. 연습하면 나아질 겁니다.")
+
+        if st.session_state.wrong_questions:
+            st.write("### ✍️ 틀린 문제 분석")
+            for i, (wrong_q, misconception_id) in enumerate(zip(
+                st.session_state.wrong_questions, st.session_state.misconceptions
+            )):
+                with st.expander(f"📝 틀린 문제 #{i + 1}"):
+                    st.write(wrong_q['QuestionText'])
+                    st.write(f"✅ 정답: {wrong_q['CorrectAnswer']}")
+                    if misconception_id:
+                        misconception_text = generator.get_misconception_text(misconception_id)
+                        st.info(f"Misconception: {misconception_text}")
+                    if st.button(f"📚 유사 문제 풀기 #{i + 1}", key=f"retry_{i}"):
+                        new_question = generate_similar_question(wrong_q, misconception_id, generator)
+                        if new_question:
+                            st.write("### 🎯 유사 문제")
+                            st.write(new_question['question'])
+                            for choice, text in new_question['choices'].items():
+                                st.write(f"{choice}) {text}")
+                            st.write(f"✅ 정답: {new_question['correct']}")
+                            st.write(f"📝 해설: {new_question['explanation']}")
+                        else:
+                            st.error("유사 문제를 생성할 수 없습니다.")
+
+if __name__ == "__main__":
+    main()