feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/service/brand_dna/service.py

@@ -9,15 +9,16 @@ import torch.nn.functional as F
 import tritonclient.http as httpclient
 from minio import Minio
 
-from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, DESIGN_MODEL_URL, CATEGORY_PATH
+from app.core.config import DESIGN_MODEL_URL
+from app.core.config import settings
 from app.schemas.brand_dna import BrandDnaModel
-from app.service.attribute.config import local_debug_const, const
+from app.service.attribute.config import const
 from app.service.utils.generate_uuid import generate_uuid
 from app.service.utils.new_oss_client import oss_upload_image, oss_get_image
 
-logger = logging.getLogger()
+minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
-minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+logger = logging.getLogger()
 
 
 class BrandDna:
@@ -25,7 +26,7 @@ class BrandDna:
         self.sketch_bucket = "test"
         self.image_url = request_item.image_url
         self.is_brand_dna = request_item.is_brand_dna
-        self.attr_type = pd.read_csv(CATEGORY_PATH)
+        self.attr_type = pd.read_csv(settings.CATEGORY_PATH)
         # self.attr_type = pd.read_csv(r"E:\workspace\trinity_client_aida\app\service\attribute\config\descriptor\category\category_dis.csv")
         self.att_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
         self.seg_client = httpclient.InferenceServerClient(url='10.1.1.243:30000')

app/service/brand_dna/service_generate_brand_info.py

@@ -3,23 +3,25 @@ import logging
 import cv2
 import numpy as np
 import tritonclient.grpc as grpcclient
-from langchain.output_parsers import ResponseSchema, StructuredOutputParser
+from langchain_classic.output_parsers import ResponseSchema, StructuredOutputParser
 from langchain_community.chat_models import ChatTongyi
 from langchain_core.prompts import PromptTemplate
-# from langchain_openai import ChatOpenAI
 from minio import Minio
 from tritonclient.utils import np_to_triton_dtype
 
-from app.core.config import GI_MODEL_URL, MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, GI_MODEL_NAME
+from app.core.config import GI_MODEL_URL, GI_MODEL_NAME
 from app.schemas.brand_dna import GenerateBrandModel
 from app.service.utils.generate_uuid import generate_uuid
 from app.service.utils.new_oss_client import oss_upload_image
 
+from app.core.config import settings
+
 
 class GenerateBrandInfo:
     def __init__(self, request_data):
         # minio client init
-        self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
+        self.generate_logo_prompt = None
+        self.minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
         # user info init
         self.user_id = request_data.user_id
@@ -55,7 +57,7 @@ class GenerateBrandInfo:
         return self.result_data
 
     def llm_generate_brand_info(self):
-        output = self.model(self._input.to_messages())
+        output = self.model.invoke(self._input.to_messages())
         brand_data = self.output_parser.parse(output.content)
         self.result_data = brand_data
         self.generate_logo_prompt = brand_data['brand_logo_prompt']
@@ -87,8 +89,8 @@ class GenerateBrandInfo:
     def upload_logo_image(self, image, object_name):
         try:
             _, img_byte_array = cv2.imencode('.jpg', image)
-            object_name = f'{self.user_id}/{self.category}/{object_name}'
-            req = oss_upload_image(oss_client=self.minio_client, bucket="aida-users", object_name=object_name, image_bytes=img_byte_array)
+            object_name = f'{self.user_id}/{self.category}/{object_name}.jpg'
+            oss_upload_image(oss_client=self.minio_client, bucket="aida-users", object_name=object_name, image_bytes=img_byte_array)
             image_url = f"aida-users/{object_name}"
             return image_url
         except Exception as e:

app/service/brand_dna/test.py

@@ -1,32 +0,0 @@
-from dotenv import load_dotenv
-from langchain.output_parsers import StructuredOutputParser, ResponseSchema
-from langchain_core.prompts import PromptTemplate
-from langchain_openai import ChatOpenAI
-
-# 加载.env文件的环境变量
-load_dotenv()
-
-# 创建一个大语言模型，model指定了大语言模型的种类
-model = ChatOpenAI(model="qwen2.5-14b-instruct")
-
-# 想要接收的响应模式
-response_schemas = [
-    ResponseSchema(name="brand_name", description="Brand name."),
-    ResponseSchema(name="brand_slogan", description="Brand slogan."),
-    ResponseSchema(name="brand_logo_prompt", description="prompt required for brand logo generation.")
-]
-output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
-format_instructions = output_parser.get_format_instructions()
-prompt = PromptTemplate(
-    template="你是一个时装品牌的设计师。根据用户输入提取出brand name，brand slogan,brand logo 描述。如果没有以上内容，需要你根据用户输入随意发挥。随后根据brand logo 描述生成一个prompt，这个prompt用于生成模型.\n{format_instructions}\n{question}",
-    input_variables=["question"],
-    partial_variables={"format_instructions": format_instructions}
-)
-_input = prompt.format_prompt(question="brand name: cat home")
-
-output = model(_input.to_messages())
-brand_data = output_parser.parse(output.content)
-
-
-def generate_logo(bucket_name, object_name, prompt):
-    pass