#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
使用 singlestep 提示词对测试文件生成 WIKI
直接使用当前 opencode 配置的 LLM
"""

import os
import re
import json
from datetime import datetime
import openai

# 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-b-real")
prompt_file = os.path.join(experiment_dir, "prompts", "singlestep.md")

# 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("=== B 组测试：Single-Step 模式（使用 9router reasoning 模型）===")
print(f"开始时间: {start_time_str}")

# 读取源文件
print(f"\n读取源文件: {source_file}")
with open(source_file, 'r', encoding='utf-8') as f:
    source_content = f.read()
print(f"  源文件长度: {len(source_content)} 字符")

# 读取 prompt
print(f"\n读取 prompt: {prompt_file}")
with open(prompt_file, 'r', encoding='utf-8') as f:
    template = f.read()

# 替换 {SOURCE_CONTENT}
full_prompt = template.replace("{SOURCE_CONTENT}", source_content)
print(f"  Prompt 长度: {len(full_prompt)} 字符")

# 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)
print(f"  API Key 长度: {len(api_key)}")

# 尝试不同的模型名称
models_to_try = [
    "9router/reasoning",
    "reasoning",
    "openai/gpt-4o-mini",
    "gpt-4o-mini",
    "openai/gpt-3.5-turbo",
    "gpt-3.5-turbo"
]

# 尝试连接并获取可用模型
print("\n查找可用模型...")
available_models = []
for model_name in models_to_try:
    try:
        client = openai.OpenAI(api_key=api_key, timeout=60)
        # 先尝试简单的 chat completion
        test_response = client.chat.completions.create(
            model=model_name,
            messages=[{"role": "user", "content": "hi"}],
            max_tokens=5
        )
        print(f"  [OK] 模型可用: {model_name}")
        available_models.append(model_name)
        break
    except Exception as e:
        print(f"  [X] {model_name}: {type(e).__name__}")
        if "404" not in str(e) and "not exist" not in str(e):
            # 如果不是 404，可能是网络问题
            print(f"    错误详情: {str(e)[:200]}")

if not available_models:
    print("\n[ERROR] 没有可用的模型")
    exit(1)

# 使用第一个可用的模型
selected_model = available_models[0]
print(f"\n使用模型: {selected_model}")

# 初始化客户端
client = openai.OpenAI(api_key=api_key, timeout=300)

# 调用 LLM 生成
print("\n调用 LLM 生成 Wiki 页面...")
try:
    response = client.chat.completions.create(
        model=selected_model,
        messages=[
            {
                "role": "system",
                "content": "你是一位知识库构建专家。请直接从源文件中提取关键术语并生成 Wiki 页面。"
            },
            {
                "role": "user",
                "content": full_prompt
            }
        ],
        temperature=0.5,
        max_tokens=16000
    )

    generation_result = response.choices[0].message.content
    print(f"  [OK] LLM 生成完成")
    print(f"  生成内容长度: {len(generation_result)} 字符")

    # 保存原始生成结果
    raw_output_file = os.path.join(output_dir, "raw_generation.md")
    with open(raw_output_file, 'w', encoding='utf-8') as f:
        f.write(generation_result)
    print(f"  [OK] 原始生成结果已保存到: {raw_output_file}")

except Exception as e:
    print(f"  [ERROR] LLM API 调用失败: {type(e).__name__}: {e}")
    exit(1)

# 解析生成结果
print("\n解析生成的 Wiki 页面...")
sample_pages = []

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

if not file_blocks:
    print("  [WARNING] 未找到标准文件块格式，尝试宽松匹配...")
    file_blocks = re.findall(r'FILE: (.*?)\n(.*?)(?=(?:FILE:|$))', generation_result, re.DOTALL)

print(f"  解析到 {len(file_blocks)} 个文件块")

for i, block in enumerate(file_blocks):
    if isinstance(block, tuple):
        file_path = block[0].strip()
        file_content = block[1].strip()
    else:
        continue

    # 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 [{i+1}/{len(file_blocks)}]: {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=== B 组测试完成 ===")
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": "B-real",
    "mode": "Single-Step",
    "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),
    "pagesGenerated": len(sample_pages),
    "model": selected_model,
    "rawContentLength": len(generation_result),
    "status": "success"
}

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"\n[OK] 元数据已保存到: {metadata_file}")
print(f"\n输出文件位置:")
print(f"  原始生成: {raw_output_file}")
print(f"  Wiki 页面: {output_dir}/wiki/")
print(f"  元数据: {metadata_file}")
print(f"\n接下来可以运行评估脚本对比 A 组和 B 组的结果：")
print(f"  python tools/experiments/wiki-generation-compare/scripts/evaluate-real.py")