import gradio as gr
import openai
import PyPDF2
import os
from datetime import datetime

# ✅ openai 0.28：API 金鑰設定
openai.api_key = os.getenv("OPENAI_API_KEY")

topics = ["教育哲學", "教育社會學", "教育心理學", "課程與教學", "教學原理", "班級經營", "教育測驗與評量", "青少年問題與輔導"]
difficulties = ["簡單", "中等", "困難"]

user_errors = {}
error_history = {}
reference_answers = {}

DEFAULT_PDF_PATH = "教材.pdf"

def extract_text_from_pdf():
    if not os.path.exists(DEFAULT_PDF_PATH):
        print(f"[錯誤] 教材未找到：{DEFAULT_PDF_PATH}")
        return ""
    try:
        with open(DEFAULT_PDF_PATH, "rb") as f:
            reader = PyPDF2.PdfReader(f)
            return "\n".join([page.extract_text() or "" for page in reader.pages])
    except Exception as e:
        print(f"[錯誤] PDF 載入失敗：{e}")
        return ""

pdf_text = extract_text_from_pdf()

def generate_question(topic, difficulty):
    if not pdf_text.strip():
        return "⚠️ 無法載入教材內容，請確認 PDF 是否存在。"
    
    # 修改提示以生成選擇題或填空題
    prompt = f"請根據以下教育學教材內容，設計一個屬於「{topic}」主題、「{difficulty}」難度的選擇題或填空題擇一：\n\n{pdf_text}"
    
    try:
        response = openai.ChatCompletion.create(
            model="gpt-4o-mini-2024-07-18",
            messages=[
                {"role": "system", "content": "你是一位教育專家，請根據教材內容設計題目。(不需要包含解析)"},
                {"role": "user", "content": prompt}
            ]
        )
        question = response["choices"][0]["message"]["content"]
        return question.strip()  # 返回生成的問題
    except Exception as e:
        return f"⚠️ 發生錯誤：{e}"

def save_answer(question):
    if not pdf_text.strip():
        return "⚠️ 教材內容未載入，請確認 PDF。"
    
    # 獲取參考答案
    answer_prompt = f"請根據以下問題提供正確答案：\n問題：{question}\n\n教材內容：\n{pdf_text}"
    try:
        answer_response = openai.ChatCompletion.create(
            model="gpt-4o-mini-2024-07-18",
            messages=[
                {"role": "system", "content": "你是一位教育專家，請根據教材內容提供問題的正確答案。"},
                {"role": "user", "content": answer_prompt}
            ]
        )
        correct_answer = answer_response["choices"][0]["message"]["content"]
        reference_answers[question] = correct_answer.strip()  # 儲存正確答案
    except Exception as e:
        return f"⚠️ 發生錯誤：{e}"

def save_error(question, user_input, correct_answer, feedback):
    current_date = datetime.today().strftime("%Y-%m-%d")
    error_history.setdefault(current_date, []).append({
        "題目": question,
        "回答": user_input,
        "正確答案": correct_answer,
        "AI 分析": feedback
    })

def analyze_answer(user_input, question):
    global user_errors
    if not user_input.strip():
        return "⚠️ 請輸入回答。"
    
    correct_answer = reference_answers.get(question, "無法獲取正確答案")
    
    # 獲取參考答案
    save_answer(question)  # 在分析之前儲存答案
    
    prompt = f"請根據以下教材內容，檢查學生的回答是否正確，並提供正確答案與講解：\n{pdf_text}\n\n問題：{question}\n學生回答：'{user_input}'"

    try:
        response = openai.ChatCompletion.create(
            model="gpt-4o-mini-2024-07-18",
            messages=[
                {"role": "system", "content": "你是一位教育專家，請根據教材內容分析學生回答。"},
                {"role": "user", "content": prompt}
            ]
        )
        feedback = response["choices"][0]["message"]["content"]
    except Exception as e:
        return f"⚠️ 發生錯誤：{e}"

    if "❌" in feedback or "錯" in feedback:
        user_errors[question] = user_errors.get(question, 0) + 1
        save_error(question, user_input, correct_answer, feedback)  # 儲存錯題
        # 自動輸出錯題記錄
        error_output = f"🔹 題目: {question}\n📝 回答: {user_input}\n📖 正確答案: {correct_answer}\n📖 AI 分析: {feedback}"
        return feedback, error_output
    return feedback, ""

def clear_fields():
    return "", "", ""  # 清空問題、回答和分析結果，但不清空錯題紀錄

with gr.Blocks() as demo:
    gr.Markdown("# 👨‍🏫 教師檢定智慧陪讀家教 ")

    with gr.Row():
        topic_input = gr.Dropdown(choices=topics, label="選擇複習主題")
        difficulty_input = gr.Dropdown(choices=difficulties, label="選擇難度等級")
    ask_btn = gr.Button("🎯 生成問題")
    clear_btn = gr.Button("🧹 清空")
    question_output = gr.Textbox(label="題目", lines=4)
    ask_btn.click(fn=lambda t, d: generate_question(t, d),
                  inputs=[topic_input, difficulty_input],
                  outputs=question_output)
    
    user_answer = gr.Textbox(label="你的回答", lines=3)
    analyze_btn = gr.Button("分析回答")
    analysis_result = gr.Textbox(label="分析與講解", lines=5)
    error_history_output = gr.Textbox(label="錯題紀錄", lines=5)
    
    analyze_btn.click(fn=lambda ans, q: analyze_answer(ans, q),
                      inputs=[user_answer, question_output],
                      outputs=[analysis_result, error_history_output])

    clear_btn.click(fn=clear_fields, outputs=[question_output, user_answer, analysis_result])

demo.launch()