#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
真实 A 组实验（Two-Step 模式）- 使用真实 OpenAI API
"""

import json
import os
import requests
from datetime import datetime
import re

# 配置
base_dir = os.getcwd()
experiment_dir = os.path.join(base_dir, "tools", "experiments", "wiki-generation-compare")
source_file = os.path.join(base_dir, "raw", "呼吸之间_李谨伯", "第一编 从身体入手.md")
output_dir = os.path.join(experiment_dir, "output", "group-a-real")

# 创建输出目录
os.makedirs(output_dir, exist_ok=True)
os.makedirs(os.path.join(output_dir, "wiki", "concepts"), exist_ok=True)
os.makedirs(os.path.join(output_dir, "wiki", "methods"), exist_ok=True)
os.makedirs(os.path.join(output_dir, "wiki", "entities"), exist_ok=True)

# 记录开始时间
start_time = datetime.now()
start_time_str = start_time.strftime("%Y-%m-%d %H:%M:%S")

print("=== A 组真实实验开始：Two-Step 模式 ===")
print(f"开始时间: {start_time_str}")

# 检查 API Key
api_key = os.environ.get('OPENAI_API_KEY')
if not api_key:
    print("[ERROR] 未找到 OPENAI_API_KEY 环境变量")
    exit(1)

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

# Step 1: Analysis
print("\nStep 1: Analysis 阶段...")

# 读取源文件
with open(source_file, 'r', encoding='utf-8') as f:
    source_content = f.read()

# 读取分析 prompt
analysis_prompt_file = os.path.join(experiment_dir, "prompts", "twostep-analysis.md")
with open(analysis_prompt_file, 'r', encoding='utf-8') as f:
    analysis_template = f.read()

analysis_full_prompt = analysis_template.replace("{SOURCE_CONTENT}", source_content)

print("  调用 OpenAI API 进行分析...")
analysis_body = {
    "model": "gpt-4o-mini",
    "messages": [
        {
            "role": "system",
            "content": "你是一位知识库分析专家。请分析源文件并提取结构化信息，仅输出 JSON 格式。"
        },
        {
            "role": "user",
            "content": analysis_full_prompt
        }
    ],
    "temperature": 0.3,
    "max_tokens": 8000
}

try:
    response = requests.post("https://api.openai.com/v1/chat/completions", 
                            headers=headers, 
                            json=analysis_body, 
                            timeout=180)
    response.raise_for_status()
    analysis_result = response.json()['choices'][0]['message']['content']
    
    # 保存分析结果
    analysis_output = os.path.join(output_dir, "analysis.json")
    with open(analysis_output, 'w', encoding='utf-8') as f:
        f.write(analysis_result)
    
    print(f"  [OK] Analysis 完成")
    
    # 解析 JSON
    try:
        gold_standard = json.loads(analysis_result)
        recommended_count = len(gold_standard.get('recommended_pages', []))
        print(f"  [OK] 识别到 {recommended_count} 个推荐页面")
    except json.JSONDecodeError as e:
        print(f"  [ERROR] JSON 解析失败: {e}")
        print(f"  分析结果预览: {analysis_result[:200]}")
        exit(1)
    
except Exception as e:
    print(f"  [ERROR] LLM API 调用失败: {e}")
    print("  退出实验")
    exit(1)

# Step 2: Generation
print("\nStep 2: Generation 阶段...")

# 读取生成 prompt
generation_prompt_file = os.path.join(experiment_dir, "prompts", "twostep-generation.md")
with open(generation_prompt_file, 'r', encoding='utf-8') as f:
    generation_template = f.read()

generation_full_prompt = generation_template.replace("{ANALYSIS_RESULT}", json.dumps(gold_standard, ensure_ascii=False, indent=2))

print("  调用 OpenAI API 生成 Wiki 页面...")
generation_body = {
    "model": "gpt-4o-mini",
    "messages": [
        {
            "role": "system",
            "content": "你是一位知识库构建专家。请基于分析结果生成 Wiki 页面。"
        },
        {
            "role": "user",
            "content": generation_full_prompt
        }
    ],
    "temperature": 0.5,
    "max_tokens": 16000
}

try:
    response = requests.post("https://api.openai.com/v1/chat/completions", 
                            headers=headers, 
                            json=generation_body, 
                            timeout=300)
    response.raise_for_status()
    generation_result = response.json()['choices'][0]['message']['content']
    
    print("  [OK] LLM 生成完成")
    
except Exception as e:
    print(f"  [ERROR] LLM API 调用失败: {e}")
    exit(1)

# 解析生成结果
sample_pages = []

# 解析 ---FILE: ... ---END FILE--- 块
file_blocks = re.findall(r'---FILE: (.*?)---(.*?)---END FILE---', generation_result, re.DOTALL)

if not file_blocks:
    # 尝试宽松匹配
    file_blocks = re.findall(r'FILE: (.*?)\n(.*?)(?=(FILE:|$))', generation_result, re.DOTALL)

for block in file_blocks:
    file_path = block[0].strip()
    file_content = block[1].strip()
    
    # 创建目录（如果需要）
    full_path = os.path.join(output_dir, file_path)
    file_dir = os.path.dirname(full_path)
    os.makedirs(file_dir, exist_ok=True)
    
    # 写入文件
    with open(full_path, 'w', encoding='utf-8') as f:
        f.write(file_content)
    sample_pages.append(full_path)
    print(f"  [OK] 生成: {file_path}")

# 记录结束时间
end_time = datetime.now()
end_time_str = end_time.strftime("%Y-%m-%d %H:%M:%S")
duration = (end_time - start_time).total_seconds() / 60

print(f"\n=== A 组实验完成 ===")
print(f"结束时间: {end_time_str}")
print(f"总耗时: {round(duration, 2)} 分钟")
print(f"生成文件数: {len(sample_pages)}")

# 保存元数据
metadata = {
    "group": "A-real",
    "mode": "Two-Step (Real API)",
    "sourceFile": "raw/呼吸之间_李谨伯/第一编 从身体入手.md",
    "startTime": start_time_str,
    "endTime": end_time_str,
    "durationMinutes": round(duration, 2),
    "step1Status": "completed",
    "step2Status": "completed",
    "pagesGenerated": len(sample_pages),
    "apiModel": "gpt-4o-mini",
    "apiCost": f"Analysis (8K tokens) + Generation (16K tokens) ≈ ${round(0.15 * 24 / 1000000, 4)}"
}

metadata_file = os.path.join(output_dir, "metadata.json")
with open(metadata_file, 'w', encoding='utf-8') as f:
    json.dump(metadata, f, ensure_ascii=False, indent=2)

print(f"[OK] 元数据已保存到 {metadata_file}")