import numpy as np
from PIL import Image
import os
import asyncio
import hashlib
import json
import aiohttp
import logging
import base64
from openai import OpenAI, BadRequestError
from Config.Config import (
    ALY_LLM_API_KEY, VL_MODEL_NAME, VL_MODEL_NAME_AD
)
from Apps.AiTeJiYiChong.Config.Setting import (
    SAFE_EXCLUDE_RATIO, FALLBACK_WIDTH, FALLBACK_HEIGHT,
    BOTTOM_SAFE_EXCLUDE_RATIO
)

logger = logging.getLogger(__name__)


class ReadImageKit:
    _client = OpenAI(
        api_key=ALY_LLM_API_KEY,
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
    )

    # 通用回退设备信息，仅在无法动态获取设备信息时使用
    _FALLBACK_DEVICE_INFO = {
        "displayWidth": FALLBACK_WIDTH,
        "displayHeight": FALLBACK_HEIGHT,
        "productName": "generic"
    }

    _prompt = (
        "仅输出JSON数组（不含任何说明文字），按从左到右、从上到下的顺序识别图片中由【绿色方框】标识的充电站区域。识别规则如下：\n"
        "1. 必须是图中用绿色实线方框圈出的区域。\n"
        "2. 每一个卡片区域必须同时具备以下所有要素，否则严禁识别：\n"
        "   - 场站名称 (station_name)；\n"
        "   - 距离信息 (distance, 例如 '2.82km' 或 '90m')，通常位于卡片右侧蓝色胶囊区域内；\n"
        "   - 金额/电费 (price，例如 '1.2500')，通常以红色字体显示；\n"
        "   - 充电枪信息 (piles，包含'快'或'慢'的类型、总枪数和空闲枪数，例如 '快 4/4')。\n"
        "3. 如果绿色方框内缺少上述任何一项要素，说明它不是真正的场站卡片，请直接跳过。\n"
        "\n"
        "JSON对象字段要求：\n"
        "1. b_use: 状态标识（1或0）。如果场站名称为灰色或带有“暂停使用”等标签，则为0，否则为1。\n"
        "2. station_name: 场站名称；\n"
        "3. price: 一度电的价格（数字，如 1.2500）；\n"
        "4. piles: 充电枪列表 [{type: '快', free: 4, total: 4}]；\n"
        "5. parking: 停车费用描述（通常在蓝色'P'图标后，例如 '免费停车三小时'）；\n"
        "6. distance: 距离信息字符串（例如 '2.82km' 或 '90m'）；\n"
        "7. bounds: {x1,y1,x2,y2} 区域像素坐标（0-1000）；\n"
        "8. bounds_norm: {left,top,right,bottom} 归一化坐标(0-1)；\n"
        "9. station_name_bounds: 场站名称文字区域坐标 {x1,y1,x2,y2}（0-1000）；\n"
        "10. station_name_bounds_norm: 场站名称文字归一化坐标(0-1)。\n"
        "\n"
        "重要约束：\n"
        "A. 严禁识别未被绿色方框圈出的区域。如顶部的“长春市”选择、顶部的搜索框、以及中间的“推荐站点”等标签。\n"
        "B. 真正的场站卡片在绿色方框内包含：场站名称、金额（红色文字）、距离（右侧蓝色背景内）、充电枪状态（绿色或蓝色徽章，格式为 闲x/x 或 x/x）。\n"
        "C. 严禁将顶部的功能图标（如我的订单、收藏站点等）误认为场站卡片。\n"
        "\n"
        "严格返回纯JSON格式。"
    )

    @staticmethod
    def _extract_json(text: str) -> str:
        if not text:
            return "[]"

        cleaned = text.strip()
        if "```" in cleaned:
            lines = []
            for line in cleaned.splitlines():
                if line.strip().startswith("```"):
                    continue
                lines.append(line)
            cleaned = "\n".join(lines).strip()

        decoder = json.JSONDecoder()

        pos = 0
        while pos < len(cleaned):
            idx_dict = cleaned.find("{", pos)
            idx_list = cleaned.find("[", pos)

            candidates = [i for i in (idx_dict, idx_list) if i != -1]
            if not candidates:
                break

            start = min(candidates)
            snippet = cleaned[start:]
            try:
                _, end = decoder.raw_decode(snippet)
                return snippet[:end]
            except json.JSONDecodeError:
                pos = start + 1
                continue

        return "[]"

    @classmethod
    async def get_stations_from_image(cls, image_path: str, device_info=None):
        """
        使用 Qwen-VL 模型从截图中识别充电站列表
        """
        if device_info is None:
            device_info = cls._FALLBACK_DEVICE_INFO

        if not os.path.exists(image_path):
            logger.error(f"Image not found: {image_path}")
            return []

        # 将图片转换为 Base64
        with open(image_path, "rb") as image_file:
            encoded_image = base64.b64encode(image_file.read()).decode("utf-8")

        try:
            response = await asyncio.to_thread(
                cls._client.chat.completions.create,
                model=VL_MODEL_NAME,
                messages=[
                    {
                        "role": "user",
                        "content": [
                            {"type": "text", "text": cls._prompt},
                            {
                                "type": "image_url",
                                "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"},
                            },
                        ],
                    }
                ],
                max_tokens=2000,
                temperature=0.01
            )

            content = response.choices[0].message.content
            logger.info(f"VL Model Response: {content}")

            json_str = cls._extract_json(content)
            stations = json.loads(json_str)

            # 后处理：如果 bounds 是归一化的，则转换为像素坐标（如果需要）
            # 或者如果 bounds 是 0-1000 的，则保持原样或按需转换
            return stations

        except Exception as e:
            logger.error(f"Error calling VL model: {e}")
            return []