#!/usr/bin/env python
# -*- coding: utf-8 -*-

import os
import base64
import json
import requests
from dotenv import load_dotenv
from app.api.baidu_image_search import BaiduImageSearch
from app.api.image_utils import ImageUtils
from app.api.type_manager_mongo import TypeManagerMongo

# 加载环境变量
load_dotenv()

class AzureOpenAI:
    """Azure OpenAI API封装类，用于与GPT-4o多模态模型交互"""
    
    def __init__(self):
        """初始化，获取API密钥和端点"""
        self.api_key = os.getenv('AZURE_OPENAI_API_KEY')
        self.endpoint = os.getenv('AZURE_OPENAI_ENDPOINT')
        self.api_version = os.getenv('AZURE_OPENAI_API_VERSION')
        self.deployment_name = os.getenv('AZURE_OPENAI_DEPLOYMENT_NAME')
        self.baidu_image_search = BaiduImageSearch()
        self.type_manager = TypeManagerMongo()
        
        # 检查配置是否存在
        if not self.api_key or not self.endpoint or not self.api_version or not self.deployment_name:
            raise ValueError("Azure OpenAI配置不完整，请检查.env文件")
    
    def _encode_image(self, image_path):
        """
        将图片编码为base64格式
        
        Args:
            image_path: 图片路径
            
        Returns:
            str: base64编码的图片数据
        """
        with open(image_path, "rb") as image_file:
            return base64.b64encode(image_file.read()).decode('utf-8')
    
    def _get_image_type_description(self, image_path):
        """
        获取图片类型和描述
        
        Args:
            image_path: 图片路径
            
        Returns:
            tuple: (类型, 描述)
        """
        try:
            # 使用百度图像搜索API搜索图片
            search_result = self.baidu_image_search.search_image(image_path=image_path)
            
            # 使用方法三确定最可信的类型
            reliable_types = self.baidu_image_search._determine_most_reliable_types(search_result.get('result', []))
            image_type = reliable_types.get('method3', '')
            
            # 首先从本地类型管理器获取描述信息
            description = self.type_manager.get_description(image_type)
            
            # 如果本地没有描述信息，尝试从搜索结果中获取
            if not description:
                for item in search_result.get('result', []):
                    brief = item.get('brief', '{}')
                    if isinstance(brief, str):
                        try:
                            brief_dict = json.loads(brief)
                            if brief_dict.get('type') == image_type:
                                description = brief_dict.get('description', '')
                                if description:
                                    # 将获取到的描述信息保存到本地
                                    self.type_manager.add_type(image_type, description)
                                    break
                        except:
                            continue
            
            print(f"获取到的图片类型: {image_type}, 描述长度: {len(description)}")
            return image_type, description
        except Exception as e:
            print(f"获取图片类型和描述失败: {str(e)}")
            return "", ""
    
    def chat_with_image(self, image_path, message, conversation_history=None, robot_info=None):
        """
        使用图片和消息与GPT-4o多模态模型进行对话
        
        Args:
            image_path: 图片路径
            message: 用户消息
            conversation_history: 对话历史记录
            robot_info: 机器人角色信息，包含name和background
            
        Returns:
            dict: 模型响应
        """
        if conversation_history is None:
            conversation_history = []
        
        # 获取图片类型和描述
        image_type, description = self._get_image_type_description(image_path)
        
        # 编码图片
        base64_image = self._encode_image(image_path)
        
        # 构建系统提示
        if robot_info and robot_info.get('name') and robot_info.get('background'):
            # 如果有机器人角色信息，使用机器人角色
            system_message = f"你是{robot_info['name']}，一个能够分析图片并回答问题的角色。\n\n你的背景故事：{robot_info['background']}\n\n在对话中，你应该始终保持这个角色的身份和特点，用第一人称回答问题。"
        else:
            # 默认智能助手
            system_message = "你是一个智能助手，能够分析图片并回答问题。"
        
        # 添加图片类型和描述信息
        if image_type and description:
            system_message += f"\n\n这是一张{image_type}的图片。\n描述信息：{description}\n\n请基于这些信息和图片内容回答用户的问题。"
        
        # 打印系统提示
        print("\nimage_type and description:")
        print(f"{image_type} - {description}")
        print(f"系统提示: {system_message}...（总长度：{len(system_message)}）")
        
        # 构建消息历史
        messages = [
            {"role": "system", "content": system_message}
        ]
        
        # 添加对话历史
        for msg in conversation_history:
            messages.append(msg)
        
        # 添加当前消息和图片
        messages.append({
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": message
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base64_image}"
                    }
                }
            ]
        })
        
        # 构建API请求
        headers = {
            "Content-Type": "application/json",
            "api-key": self.api_key
        }
        
        payload = {
            "messages": messages,
            "max_tokens": 2000,
            "temperature": 0.7,
            "top_p": 0.95,
            "stream": False
        }
        
        # 发送请求
        url = f"{self.endpoint}/openai/deployments/{self.deployment_name}/chat/completions?api-version={self.api_version}"
        response = requests.post(url, headers=headers, json=payload)
        
        if response.status_code == 200:
            return response.json()
        else:
            raise Exception(f"Azure OpenAI API请求失败: {response.text}")