354 lines
13 KiB
Python
354 lines
13 KiB
Python
import os
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import logging
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import dashscope
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from dashscope import Generation
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# 修改导入语句，dashscope的API响应可能改变了结构
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from typing import List, Dict, Any, Optional
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import asyncio
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import httpx
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import json
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import re
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from app.core.config import settings
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# 导入 DashScope SDK
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try:
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from dashscope import MultiModalConversation
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from dashscope.api_entities.dashscope_response import DashScopeAPIResponse
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except ImportError:
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logging.error("请安装 DashScope SDK: pip install dashscope")
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raise
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logger = logging.getLogger(__name__)
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class DashScopeService:
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"""DashScope服务类，提供对DashScope API的调用封装"""
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def __init__(self):
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self.api_key = settings.DASHSCOPE_API_KEY
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# 配置DashScope
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dashscope.api_key = self.api_key
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# 配置API URL
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self.image_synthesis_url = "https://dashscope.aliyuncs.com/api/v1/services/aigc/image2image/image-synthesis"
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self.comment_url = "https://dashscope.aliyuncs.com/compatible-mode/v1"
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async def chat_completion(
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self,
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messages: List[Dict[str, str]],
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model: str = "qwen-max",
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max_tokens: int = 2048,
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temperature: float = 0.7,
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stream: bool = False
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):
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"""
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调用DashScope的大模型API进行对话
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Args:
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messages: 对话历史记录
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model: 模型名称
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max_tokens: 最大生成token数
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temperature: 温度参数，控制随机性
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stream: 是否流式输出
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Returns:
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ApiResponse: DashScope的API响应
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"""
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try:
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# 为了不阻塞FastAPI的异步性能，我们使用run_in_executor运行同步API
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loop = asyncio.get_event_loop()
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response = await loop.run_in_executor(
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None,
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lambda: Generation.call(
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model=model,
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messages=messages,
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max_tokens=max_tokens,
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temperature=temperature,
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result_format='message',
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stream=stream,
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)
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)
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if response.status_code != 200:
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logger.error(f"DashScope API请求失败，状态码：{response.status_code}, 错误信息：{response.message}")
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raise Exception(f"API调用失败: {response.message}")
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return response
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except Exception as e:
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logger.error(f"DashScope聊天API调用出错: {str(e)}")
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raise e
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async def generate_image(
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self,
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prompt: str,
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negative_prompt: Optional[str] = None,
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model: str = "stable-diffusion-xl",
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n: int = 1,
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size: str = "1024*1024"
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):
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"""
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调用DashScope的图像生成API
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Args:
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prompt: 生成图像的文本描述
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negative_prompt: 负面提示词
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model: 模型名称
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n: 生成图像数量
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size: 图像尺寸
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Returns:
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ApiResponse: DashScope的API响应
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"""
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try:
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# 构建请求参数
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params = {
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"model": model,
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"prompt": prompt,
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"n": n,
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"size": size,
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}
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if negative_prompt:
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params["negative_prompt"] = negative_prompt
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# 异步调用图像生成API
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loop = asyncio.get_event_loop()
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response = await loop.run_in_executor(
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None,
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lambda: dashscope.ImageSynthesis.call(**params)
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)
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if response.status_code != 200:
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logger.error(f"DashScope 图像生成API请求失败，状态码：{response.status_code}, 错误信息：{response.message}")
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raise Exception(f"图像生成API调用失败: {response.message}")
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return response
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except Exception as e:
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logger.error(f"DashScope图像生成API调用出错: {str(e)}")
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raise e
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async def generate_tryon(
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self,
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person_image_url: str,
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top_garment_url: Optional[str] = None,
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bottom_garment_url: Optional[str] = None,
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resolution: int = -1,
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restore_face: bool = True
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):
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"""
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调用阿里百炼平台的试衣服务
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Args:
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person_image_url: 人物图片URL
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top_garment_url: 上衣图片URL
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bottom_garment_url: 下衣图片URL
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resolution: 分辨率，-1表示自动
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restore_face: 是否修复面部
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Returns:
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Dict: 包含任务ID和请求ID的响应
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"""
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try:
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# 验证参数
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if not person_image_url:
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raise ValueError("人物图片URL不能为空")
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if not top_garment_url and not bottom_garment_url:
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raise ValueError("上衣和下衣图片至少需要提供一个")
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# 构建请求数据
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request_data = {
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"model": "aitryon",
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"input": {
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"person_image_url": person_image_url
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},
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"parameters": {
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"resolution": resolution,
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"restore_face": restore_face
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}
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}
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# 添加可选字段
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if top_garment_url:
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request_data["input"]["top_garment_url"] = top_garment_url
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if bottom_garment_url:
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request_data["input"]["bottom_garment_url"] = bottom_garment_url
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# 构建请求头
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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"X-DashScope-Async": "enable"
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}
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# 发送请求
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async with httpx.AsyncClient() as client:
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logger.info(f"发送试穿请求: {request_data}")
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response = await client.post(
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self.image_synthesis_url,
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json=request_data,
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headers=headers,
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timeout=30.0
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)
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response_data = response.json()
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logger.info(f"试穿API响应: {response_data}")
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if response.status_code == 200 or response.status_code == 202: # 202表示异步任务已接受
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# 提取任务ID，适应不同的API响应格式
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task_id = None
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if 'output' in response_data and 'task_id' in response_data['output']:
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task_id = response_data['output']['task_id']
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elif 'task_id' in response_data:
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task_id = response_data['task_id']
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if task_id:
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logger.info(f"试穿请求发送成功，任务ID: {task_id}")
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return {
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"task_id": task_id,
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"request_id": response_data.get('request_id'),
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"status": "processing"
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}
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else:
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# 如果没有任务ID，这可能是同步响应
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logger.info("收到同步响应，没有任务ID")
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return {
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"status": "completed",
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"result": response_data.get('output', {}),
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"request_id": response_data.get('request_id')
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}
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else:
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error_msg = f"试穿请求失败: {response.status_code} - {response.text}"
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logger.error(error_msg)
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raise Exception(error_msg)
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except Exception as e:
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logger.error(f"DashScope试穿API调用出错: {str(e)}")
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raise e
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async def check_tryon_status(self, task_id: str):
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"""
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检查试穿任务状态
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Args:
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task_id: 任务ID
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Returns:
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Dict: 任务状态信息
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"""
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try:
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# 构建请求头
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# 构建请求URL
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status_url = f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}"
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# 发送请求
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async with httpx.AsyncClient() as client:
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response = await client.get(
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status_url,
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headers=headers,
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timeout=30.0
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)
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if response.status_code == 200:
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response_data = response.json()
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print(response_data)
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status = response_data.get('output', {}).get('task_status', '')
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logger.info(f"试穿任务状态查询成功: {status}")
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# 检查是否完成并返回结果
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if status.lower() == 'succeeded':
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image_url = response_data.get('output', {}).get('image_url')
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if image_url:
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logger.info(f"试穿任务完成，结果URL: {image_url}")
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return {
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"status": status,
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"task_id": task_id,
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"image_url": image_url,
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"result": response_data.get('output', {})
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}
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return {
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"status": status,
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"task_id": task_id
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}
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else:
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error_msg = f"试穿任务状态查询失败: {response.status_code} - {response.text}"
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logger.error(error_msg)
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raise Exception(error_msg)
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except Exception as e:
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logger.error(f"查询试穿任务状态出错: {str(e)}")
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raise e
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# 传入图片url，通过 DashScope 的 qwen-vl-plus 模型，生成穿搭点评
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async def generate_dressing_comment(self, image_url: str):
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"""
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调用DashScope的qwen-vl-plus模型生成穿搭点评
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Args:
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image_url: 穿搭图片URL
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Returns:
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Dict: 包含任务ID和请求ID的响应
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"""
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try:
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# 构建请求头
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# 构建请求数据
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "你是一个穿搭点评师，请根据图片中的穿搭，给出穿搭点评，点评内容包括穿搭风格、穿搭亮点等。不超过 80字，并给出一个打分，满分100分。返回格式以json格式返回，返回内容包括：'comment': '穿搭点评', 'score': '打分'"
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},
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{
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"type": "image",
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"image": image_url
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}
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]
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}
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]
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#使用Dashscope sdk 进行请求
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loop = asyncio.get_event_loop()
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response = await loop.run_in_executor(
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None,
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lambda: dashscope.MultiModalConversation.call(
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api_key=self.api_key,
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model="qwen-vl-plus",
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messages=messages,
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result_format='json'
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)
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)
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logger.info(f"穿搭点评API响应: {response.output.choices[0].message.content}")
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content = response.output.choices[0].message.content
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# 将内容转换为json
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if isinstance(content, list) and len(content) > 0:
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text_content = ""
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for item in content:
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if isinstance(item, dict) and 'text' in item:
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text_content = item.get('text', '')
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print(f"提取的文本内容: {text_content}")
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# 只获取 ```json 和 ``` 之间的内容
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text_content = re.search(r'```json(.*)```', text_content, re.DOTALL).group(1)
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return json.loads(text_content)
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else:
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return None
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else:
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return None
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except Exception as e:
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logger.error(f"DashScope穿搭点评API调用出错: {str(e)}")
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raise e
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