edit: 서비스파일 변경

.gitignore

@@ -148,3 +148,4 @@ log
 datas/*
 
 *result
+*.env

config.py

@@ -16,6 +16,8 @@ class Config:
         self.sftp_id = "fermat"
         self.sftp_pw = "1234"
         
+        self.api_path = "./gemini.env"
+                
     def set_dev(self):
         """
         개발용
@@ -28,6 +30,8 @@ class Config:
         self.sftp_pw = "fermat3514"
         self.db_pw = "1234"
         
+        self.api_path = "./gemini.env"
+        
         if not os.path.exists(self.remote_folder):
             os.makedirs(self.remote_folder)
             
@@ -37,6 +41,8 @@ class Config:
         
         self.db_pw = "Fermat3514!"
         
+        self.api_path = "../gemini.env"
+        
         if not os.path.exists(self.local_folder):
             os.makedirs(self.local_folder)
 

const.py

@@ -1,3 +1,7 @@
+from utils.api_key_manager import ApiKeyManager
+
 TEMP_FOLDER = "./temp"
 
 ILLEGAL_FILE_NAME = ['<', '>', ':', '"', '/', '\ ', '|', '?', '*']
+
+API_KEY_MANAGER = ApiKeyManager()

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_functions.py

@@ -127,7 +127,17 @@ def get_clip_info(model, query_image_path, item_info, top_k=4):
     #                         item_info=item_info,
     #                         index_type=model.value[1].index_type)
     
+    if os.path.exists(query_image_path):
         inference_times, result_img_paths, result_percents = vector_model.query_faiss(query_image_path, top_k=top_k)
+    elif query_image_path is None or query_image_path == "":
+        raise ValueError("query_image is None or empty.")
+    else:
+        inference_times, result_img_paths, result_percents = vector_model.query_faiss_image_data(query_image_path, top_k=top_k)
+    
+    for i in range(len(result_percents)):
+        if float(result_percents[i]) < 0.0:
+            result_percents[i] = None
+            result_img_paths[i] = None
 
     report_info = ReportInfo(
         feature_extraction_model=ReportInfoConst.feature_extraction_model,

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/faiss_similarity_search.py

@@ -53,7 +53,7 @@ from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS import feature_extraction_model
 # 사용할 이미지 임베딩 모델 클래스 추가
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.fem_openaiclipvit import FEOpenAIClipViT
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
-
+from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.utils import get_base64_bytes
 
 """
 Definition
@@ -219,6 +219,24 @@ class VectorSimilarity:
         feature_vectors = self.fem_model.image_embedding(image_data_np)
         return feature_vectors
     
+    def image_embedding_from_b64data(self, b64_data=None):
+        """
+        이미지 데이터(base64)에서 특징 벡터 추출
+        :param image_data_np: 이미지 데이터(numpy)
+        :return: 특징 벡터 or None
+        """
+        import io
+        feature_vectors = None
+
+        if b64_data is None:
+            log.error(f'invalid data[{b64_data}]')
+            return feature_vectors
+        
+        image = Image.open(io.BytesIO(b64_data)).convert("RGB")
+
+        feature_vectors = self.fem_model.image_embedding(image)
+        return feature_vectors
+
     def image_embedding_from_data(self, image_data_np=None):
         """
         이미지 데이터(numpy)에서 특징 벡터 추출
@@ -372,6 +390,53 @@ class VectorSimilarity:
                 
         return inference_times, result_img_paths, result_percents
     
+    def query_faiss_image_data(self, query_image_data=None, top_k=4):
+        
+        if os.path.exists(self.txt_file_path):
+            with open(self.txt_file_path, 'r') as f:
+                image_paths = [line.strip() for line in f.readlines()]
+        else:
+            logging.error("Image path list TXT file not found.")
+            image_paths = []
+            
+        b64_data = get_base64_bytes(query_image_data)
+
+        start_vector_time = datetime.now()
+        index = self.load_index(self.index_file_path)
+        query_vector = self.image_embedding_from_b64data(b64_data)
+        end_vector_time = datetime.now()
+        
+        diff_vector_time = self.time_diff(start_vector_time,end_vector_time)
+        
+        if self.index_type == INDEX_TYPE_COSINE:
+            faiss.normalize_L2(query_vector)
+        
+        start_search_time = datetime.now()
+        distances, indices = index.search(query_vector, top_k)
+        end_search_time = datetime.now()
+        
+        diff_search_time = self.time_diff(start_search_time,end_search_time)
+        diff_total_time = self.time_diff(start_vector_time,end_search_time)
+        inference_times = f"Total time - {diff_total_time}, vector_time - {diff_vector_time}, search_time - {diff_search_time}"
+        
+        result_img_paths = []
+        result_percents = []
+        
+        # 결과
+        # for i in range(top_k):
+        #     print(f"{i + 1}: {image_paths[indices[0][i]]}, Distance: {distances[0][i]}")
+        for idx, dist in zip(indices[0], distances[0]):
+            log.debug(f"{idx} (거리: {dist:.4f})")
+            
+            result_img_paths.append(image_paths[idx])
+
+            if self.index_type == INDEX_TYPE_COSINE:
+                result_percents.append(f"{dist*100:.2f}")
+            else:
+                result_percents.append(f"{((1 - dist)*100):.2f}")
+                
+        return inference_times, result_img_paths, result_percents
+
 # def test():
 #     """
 #     module test function

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/fem_openaiclipvit.py

@@ -31,6 +31,8 @@ import os, sys
 
 from transformers import CLIPProcessor, CLIPModel
 from huggingface_hub import login as huggingface_login
+from huggingface_hub import whoami, logout
+from huggingface_hub.utils import LocalTokenNotFoundError
 
 """
 Package: custom
@@ -104,8 +106,19 @@ class FEOpenAIClipViT(FEM.FeatureExtractionModel):
 
         # huggingface token
         if self.huggingface_token:
+            """
+            토큰이 있다면 로그인이 되어있는지 확인
+            안되어있다면 로그인 시도
+            """
+            try:
+                user_info = whoami()
+            except Exception as LocalTokenNotFoundError:
                 huggingface_login(fem_arguments.token)
                 
+            except Exception as e:
+                log.error(f'Huggingface login error: {e}')
+                raise e
+
         # model path check
         if not os.path.exists(fem_arguments.trained_model):
             self._download_model(fem_arguments.trained_model)

custom_apps/FEATURE_VECTOR_SIMILARITY_FAISS/utils.py

@@ -1,4 +1,5 @@
 import os
+import base64
 from pathlib import Path
 
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
@@ -7,6 +8,14 @@ def search_glass_parts(image_path_list):
     result = []
     
     for image_path in image_path_list:
+        
+        if image_path is None:
+            result.append(None)
+            continue
+        elif not os.path.exists(image_path):
+            result.append(None)
+            continue
+        
         parts_path = Path(os.path.join(os.path.dirname(os.path.dirname(image_path)),ImageDepths.parts))
         parts_files_generator = parts_path.rglob('*.png')
 
@@ -35,5 +44,29 @@ def file_name_to_parts(file_path):
         
     return result
 
+
+def get_base64_bytes(data: str) -> bytes:
+    """
+    문자열을 검사하여 유효한 Base64라면 디코딩된 bytes 데이터를 반환하고,
+    그렇지 않으면 ValueError 예외를 발생시킵니다.
+    """
+
+    try:
+        # 1. 입력 문자열 전처리 (공백 제거 등)
+        stripped_data = data.strip()
+        
+        # 2. bytes로 인코딩 (b64decode는 bytes-like object를 필요로 함)
+        encoded_input = stripped_data.encode('ascii')
+        
+        # 3. Base64 디코딩 수행 (validate=True로 엄격한 검사)
+        # 성공 시 b'...' 형태의 바이트 데이터가 생성됨
+        decoded_bytes = base64.b64decode(encoded_input, validate=True)
+        
+        return decoded_bytes
+        
+    except Exception as e:
+        # Base64 형식이 아니거나 패딩 오류 등 발생 시
+        raise ValueError(f"유효한 Base64 데이터가 아닙니다. 변환 불가: {e}")
+
 if __name__ == '__main__':
     print(file_name_to_parts(os.path.join(FAISS_VECTOR_PATH,"Glass_001",ImageDepths.parts,"Glass_001_Temple_L.png")))

custom_apps/gemini/main.py

@@ -7,14 +7,18 @@ from PIL import Image
 from io import BytesIO
 
 from main_rest.app.utils.date_utils import D
-from const import TEMP_FOLDER
+from const import TEMP_FOLDER, API_KEY_MANAGER
-
 
 def gemini_image(prompt, folder=None):
     from custom_logger.main_log import main_logger as LOG
     
     image_path = ''
-    client = genai.Client(api_key="AIzaSyCSw4pcPDYdAnjzBB7J9ZKXtRJJvunjWtA") # a2tec key
+    api_key = API_KEY_MANAGER.get_api_key()
+    
+    if api_key is None:
+        raise Exception("API 키 세팅 필요! - 서버를 다시 구동하거나, API키 파일을 확인")
+    
+    client = genai.Client(api_key=API_KEY_MANAGER.get_api_key()) # a2tec key
 
     for i in range(3):
         response = client.models.generate_content(

main_rest/app/main.py

@@ -34,7 +34,6 @@ from custom_logger.main_log import main_logger as LOG
 
 API_KEY_HEADER = APIKeyHeader(name='Authorization', auto_error=False)
 
-
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     # When service starts.
@@ -42,6 +41,10 @@ async def lifespan(app: FastAPI):
     
     import os
     import const
+    from const import API_KEY_MANAGER
+    
+    API_KEY_MANAGER.set_api_key()
+    
     if os.path.exists(const.TEMP_FOLDER):
         for _file in os.scandir(const.TEMP_FOLDER):
             os.remove(_file)

main_rest/app/models.py

@@ -659,8 +659,8 @@ class VectorGlassesImageResult(BaseModel):
 
 class VectorPartsImageResult(BaseModel):
     image : str | None = Field("", description='이미지 데이터', example='')
-    percents: float = Field(0.0, description='percents 값', example='')
+    percents: float | None = Field(0.0, description='percents 값', example='')
-    imageInfo : str = Field("", description='원본이미지 이름', example='')
+    imageInfo : str | None = Field("", description='원본이미지 이름', example='')
 #===============================================================================
 #===============================================================================
 #===============================================================================

main_rest/app/routes/services.py

@@ -507,9 +507,12 @@ async def vactor_vit_input_glasses_img_data(request: Request, request_body_info:
             raise Exception(f"indexType is invalid (current value = {request_body_info.indexType})")
         
         query_image_data = request_body_info.inputImage
-        query_image_path = os.path.join(TEMP_FOLDER, f'input_{D.date_file_name()}_query.png')
+        
-        os.makedirs(TEMP_FOLDER, exist_ok=True)
+        # query_image_path = os.path.join(TEMP_FOLDER, f'input_{D.date_file_name()}_query.png')
-        save_base64_as_image_file(request_body_info.inputImage ,query_image_path)
+        # os.makedirs(TEMP_FOLDER, exist_ok=True)
+        # save_base64_as_image_file(request_body_info.inputImage ,query_image_path)
+        
+        query_image_path = query_image_data
                
         vector_request_data = {'query_image_path' : query_image_path,
                                'index_type' : request_body_info.indexType, 
@@ -535,11 +538,19 @@ async def vactor_vit_input_glasses_img_data(request: Request, request_body_info:
         for img, percents, parts in zip(result_image_paths, result_percents, result_parts):
             
             b64_data = None
+            float_percent = None
+            img_info = None
+            
+            if img is not None:    
                 if os.path.exists(img):
                     b64_data = image_to_base64_string(img)
+                    img_info = os.path.split(img)[-1]
+
+            if percents is not None:                
+                if percents.isnumeric:
                     float_percent = float(percents)
             
-            info = M.VectorGlassesImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1], parts=parts)
+            info = M.VectorGlassesImageResult(image=b64_data, percents=float_percent, imageInfo=img_info, parts=parts)
             
             vector_image_results.append(info)
             
@@ -621,11 +632,19 @@ async def vactor_vit_input_parts_img_data(request: Request, request_body_info: M
         for img, percents in zip(result_image_paths, result_percents):
             
             b64_data = None
+            float_percent = None
+            img_info = None
+            
+            if img is not None:    
                 if os.path.exists(img):
                     b64_data = image_to_base64_string(img)
+                    img_info = os.path.split(img)[-1]
+
+            if percents is not None:                
+                if percents.isnumeric:
                     float_percent = float(percents)
             
-            info = M.VectorPartsImageResult(image=b64_data, percents=float_percent, imageInfo=os.path.split(img)[-1])
+            info = M.VectorPartsImageResult(image=b64_data, percents=float_percent, imageInfo=img_info)
             
             vector_image_results.append(info)
         

make_vector_files.py

@@ -1,10 +1,18 @@
-# from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_functions import get_clip_info
 import os
+import logging
+import matplotlib.pyplot as plt
+
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_similarity_search import VectorSimilarity
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.const import *
 from custom_apps.FEATURE_VECTOR_SIMILARITY_FAISS.faiss_functions import get_models, find_glass_folder_images, find_parts_folder_images
 from vector_rest.app import models as VM
 
+
+#log level
+os.environ["HF_HUB_VERBOSITY"] = "info"
+matplotlib_logger = logging.getLogger("matplotlib")
+matplotlib_logger.setLevel(logging.INFO)
+
 def make_image_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitModelType):
     model = get_models(index_type=index_type, model_type=model_type)
     
@@ -53,7 +61,7 @@ def make_vector_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitMo
     
     txt_file_path = make_image_files(item_info=item_info, index_type=index_type, model_type=model_type)
     
-    index_file_path = os.path.join(IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}_{DEFAULT_INDEX_NAME_SUFFIX}')
+    index_file_path = os.path.join(FAISS_VECTOR_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}_{DEFAULT_INDEX_NAME_SUFFIX}')
     
     if not os.path.exists(index_file_path):
         
@@ -66,12 +74,11 @@ def make_vector_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitMo
             
         vector_model.save_index_from_files(images_path_lists=image_lists,
                                 save_index_dir=FAISS_VECTOR_PATH,
-                                save_txt_dir=IMG_LIST_PATH,
                                 item_info=item_info,
                                 index_type=model.value[1].index_type)
         
     else:
-        temp_path = os.path.join(IMG_LIST_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}_{DEFAULT_INDEX_NAME_SUFFIX}.bak')
+        temp_path = os.path.join(FAISS_VECTOR_PATH,f'{model.name}_{os.path.basename(model.value[1].trained_model)}_{model.value[1].index_type}_{item_info}_{DEFAULT_INDEX_NAME_SUFFIX}.bak')
         
         try:
             os.rename(index_file_path, temp_path)
@@ -85,7 +92,6 @@ def make_vector_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitMo
                 
             vector_model.save_index_from_files(images_path_lists=image_lists,
                                     save_index_dir=FAISS_VECTOR_PATH,
-                                    save_txt_dir=IMG_LIST_PATH,
                                     item_info=item_info,
                                     index_type=model.value[1].index_type)
         except Exception as e:
@@ -102,7 +108,6 @@ def make_vector_files(item_info, index_type:VM.VitIndexType, model_type:VM.VitMo
 
 if __name__ == '__main__':
     
-    import time
     class_attributes = dict(VectorSearchItem.__dict__)
 
     pure_data_dict = {
@@ -113,5 +118,3 @@ if __name__ == '__main__':
     
     for item_key, item_value in pure_data_dict.items():
         make_vector_files(item_info=item_value, index_type=VM.VitIndexType.l2, model_type=VM.VitModelType.b32)
-        
-        time.sleep(5) # huggingface api 요청 제한 회피 위해 대기 TODO(jwkim) huggingface 로그인은 한번만 진행하게 변경

requirements_main.txt

@@ -13,6 +13,7 @@ pycryptodomex
 pycryptodome
 email-validator
 requests
+python-dotenv
 
 #imagen
 google-cloud-aiplatform

rest.main.service

@@ -0,0 +1,26 @@
+[Unit]
+Description=Main REST Service for Glasses AI
+After=rest.vector.service
+Requires=rest.vector.service
+
+[Service]
+User=user
+Group=user
+
+# User=fermat
+# Group=fermat
+
+WorkingDirectory=/home/user/a2tec/glasses_ai
+# WorkingDirectory=/home/fermat/project/glasses/rest
+
+# 51002 포트가 열릴 때까지 대기 (bash 내장 TCP 체크 활용)
+ExecStartPre=/bin/bash -c 'until timeout 1s bash -c "cat < /dev/null > /dev/tcp/localhost/51002"; do echo "Waiting for Vector Service on port 51002..."; sleep 2; done'
+
+# rest 가상환경 파이썬 사용
+ExecStart=/home/user/anaconda3/envs/rest/bin/python rest_main.py
+# ExecStart=/mnt/clover_1TB/anaconda_data/fm_rest/bin/python rest_main.py
+Restart=always
+RestartSec=5
+
+[Install]
+WantedBy=multi-user.target

rest.vector.service

@@ -0,0 +1,24 @@
+[Unit]
+Description=Vector Service for Glasses AI
+After=network-online.target
+Wants=network-online.target
+PartOf=rest.main.service
+
+[Service]
+User=user
+Group=user
+
+# User=fermat
+# Group=fermat
+
+WorkingDirectory=/home/user/a2tec/glasses_ai
+# WorkingDirectory=/home/fermat/project/glasses/rest
+
+ExecStart=/home/user/anaconda3/envs/rest_vector/bin/python rest_vector.py
+# ExecStart=/mnt/clover_1TB/anaconda_data/fm_rest_vector/bin/python rest_vector.py
+
+Restart=always
+RestartSec=3
+
+[Install]
+WantedBy=multi-user.target

utils/api_key_manager.py

@@ -0,0 +1,33 @@
+import os
+from dotenv import load_dotenv
+
+from config import rest_config
+from custom_logger.main_log import main_logger as LOG
+
+
+class ApiKeyManager:
+    
+    def __init__(self):
+        self.api_key = None
+        
+    def set_api_key(self, env_path=rest_config.api_path):
+        
+        if os.path.exists(env_path):
+            
+            load_dotenv(dotenv_path=env_path)
+            key = os.getenv("GEMINI_API_KEY")
+            
+            if key is None:
+                LOG.error(f"api key 파일에 GEMINI_API_KEY라는 변수가 없습니다")
+            elif key == "":
+                LOG.error(f"api key 파일에 내부 변수 GEMINI_API_KEY 값이 빈값입니다 값을 설정해주세요")
+            else:
+                self.api_key = key
+            
+        else:
+            LOG.error(f"api key 파일이 없습니다 : {os.path.abspath(env_path)}")
+        
+    def get_api_key(self):
+        if self.api_key is not None:
+            return self.api_key
+            

vector_rest/app/routes/services.py

@@ -84,8 +84,8 @@ async def vactor_vit(request: Request, request_body_info: M.VectorSearchVitReq):
     """
     response = M.VectorSearchVitRes()
     try:
-        if not os.path.exists(request_body_info.query_image_path):
+        # if not os.path.exists(request_body_info.query_image_path):
-            raise FileNotFoundError(f"File {request_body_info.query_image_path} does not exist.")
+        #     raise FileNotFoundError(f"File {request_body_info.query_image_path} does not exist.")
         
         model = get_models(index_type=request_body_info.index_type, model_type=request_body_info.model_type)