feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/api/api_brand_dna_initialize.py

@@ -1,34 +1,25 @@
 import io
 import logging
-import sys
-import time
-from typing import List
-from collections import defaultdict
-import numpy as np
-from apscheduler.schedulers.background import BackgroundScheduler
-from apscheduler.triggers.cron import CronTrigger
-from fastapi import HTTPException, APIRouter
-
-import pymysql
-from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX
-from minio import Minio
-import torch
-from torchvision import models, transforms
-from PIL import Image
 import os
+import sys
+from collections import defaultdict
+
+import numpy as np
+import pymysql
+import torch
+from PIL import Image
+from fastapi import HTTPException, APIRouter
 from fastapi.responses import JSONResponse
+from minio import Minio
+from torchvision import models, transforms
+
+from app.core.mysql_config import DB_CONFIG
+from app.core.new_config import settings
 
 sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
 logger = logging.getLogger()
 router = APIRouter()
-
-# MinIO 配置
-minio_client = Minio(
-    "www.minio.aida.com.hk:12024",
-    access_key="admin",
-    secret_key="Aidlab123123!",
-    secure=True
-)
+minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
 transform = transforms.Compose([
     transforms.Resize((224, 224)),
@@ -67,8 +58,8 @@ def extract_feature_vector_from_resnet(sketch_path: str) -> np.ndarray:
 
 
 # 预加载
-BRAND_FEATURES = np.load(f'{RECOMMEND_PATH_PREFIX}brand_feature.npy', allow_pickle=True).item()
-SYSTEM_FEATURES = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item()
+BRAND_FEATURES = np.load(f'{settings.RECOMMEND_PATH_PREFIX}brand_feature.npy', allow_pickle=True).item()
+SYSTEM_FEATURES = np.load(f'{settings.RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item()
 
 
 def save_sketch_to_iid():
@@ -76,11 +67,11 @@ def save_sketch_to_iid():
         sketch_path: iid
         for iid, sketch_path in enumerate(SYSTEM_FEATURES.keys(), start=1)
     }
-    np.save(f"{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy", sketch_to_iid)
+    np.save(f"{settings.RECOMMEND_PATH_PREFIX}sketch_to_iid.npy", sketch_to_iid)
 
 
 def load_sketch_to_iid():
-    path = f"{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy"
+    path = f"{settings.RECOMMEND_PATH_PREFIX}sketch_to_iid.npy"
     if os.path.exists(path):
         return np.load(path, allow_pickle=True).item()
     save_sketch_to_iid()
@@ -90,7 +81,7 @@ def load_sketch_to_iid():
 sketch_to_iid = load_sketch_to_iid()
 
 
-def getNewCategory(gender: str, sketch_category: str) -> str:
+def get_new_category(gender: str, sketch_category: str) -> str:
     return f"{gender.lower()}_{sketch_category.lower()}"
 
 
@@ -103,8 +94,8 @@ def get_category_from_path(path: str) -> str:
 
 def load_brand_matrix():
     """单独加载 brand_matrix 和 brand_index_map"""
-    mat_path = f"{RECOMMEND_PATH_PREFIX}brand_matrix.npy"
-    idx_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy"
+    mat_path = f"{settings.RECOMMEND_PATH_PREFIX}brand_matrix.npy"
+    idx_path = f"{settings.RECOMMEND_PATH_PREFIX}brand_index_map.npy"
     try:
         matrix = np.load(mat_path)
         index_map = np.load(idx_path, allow_pickle=True).item()
@@ -113,11 +104,19 @@ def load_brand_matrix():
         index_map = {}
     return matrix, index_map
 
+
 def cosine_similarity(vec1, vec2):
     """计算余弦相似度（增加零值处理）"""
     norm = np.linalg.norm(vec1) * np.linalg.norm(vec2)
     return np.dot(vec1, vec2) / (norm + 1e-10) if norm != 0 else 0.0
 
+
+def getNewCategory(gender, sketch_category):
+    print(gender)
+    print(sketch_category)
+    return "None"
+
+
 def calculate_brand_matrix(sketch_data, brand_id: int) -> np.ndarray:
     # 1. 收集品牌-分类-特征
     brand_feature = defaultdict(lambda: defaultdict(list))
@@ -164,11 +163,11 @@ def calculate_brand_matrix(sketch_data, brand_id: int) -> np.ndarray:
             brand_matrix[row_idx, sketch_index[iid]] = cos_sim
 
     # 7. 持久化
-    np.save(f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy", brand_matrix)
-    np.save(f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy", brand_index_map)
+    np.save(f"{settings.RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy", brand_matrix)
+    np.save(f"{settings.RECOMMEND_PATH_PREFIX}brand_index_map.npy", brand_index_map)
 
     # 返回该品牌对应行
-    return brand_matrix[row_idx:row_idx+1]
+    return brand_matrix[row_idx:row_idx + 1]
 
 
 @router.get("/brand_dna_initialize/{brand_id}")
@@ -178,14 +177,12 @@ async def brand_dna_initialize(brand_id: int):
         conn = pymysql.connect(**DB_CONFIG)
         cursor = conn.cursor()
         cursor.execute("""
-            SELECT id, img_url, gender, category
-              FROM product_image_attribute
-             WHERE library_id IN (
-                 SELECT library_id
-                   FROM brand_rel_library
-                  WHERE brand_id = %s
-             )
-        """, (brand_id,))
+                       SELECT id, img_url, gender, category
+                       FROM product_image_attribute
+                       WHERE library_id IN (SELECT library_id
+                                            FROM brand_rel_library
+                                            WHERE brand_id = %s)
+                       """, (brand_id,))
         sketch_data = cursor.fetchall()
 
         # 触发计算并持久化，若内部出错会抛异常