Fix lint

app/Controllers/admin.py

@@ -1,6 +1,9 @@
-from fastapi import APIRouter
+from fastapi import APIRouter, Depends
+
+from app.Services.authentication import force_admin_token_verify
+
+admin_router = APIRouter(dependencies=[Depends(force_admin_token_verify)])
 
-admin_router = APIRouter()
 
 def add_image_info():
-    pass
+    pass

app/Controllers/search.py

@@ -131,14 +131,14 @@ async def randomPick(
     return SearchApiResponse(result=result, message=f"Successfully get {len(result)} results.", query_id=uuid4())
 
 
-@searchRouter.get("/recall/{queryId}", description="Recall the query with given queryId")
-async def recallQuery(queryId: str):
+@searchRouter.get("/recall/{query_id}", description="Recall the query with given queryId")
+async def recallQuery(query_id: str):
     raise NotImplementedError()
 
 
 async def process_advanced_and_combined_search_query(model: Union[AdvancedSearchModel, CombinedSearchModel],
                                                      basis: Union[SearchBasisParams, SearchCombinedParams],
-                                                     filter: FilterParams,
+                                                     filter_param: FilterParams,
                                                      paging: SearchPagingParams) -> List[SearchResult]:
     if basis.basis == SearchBasisEnum.ocr:
         positive_vectors = [transformers_service.get_bert_vector(t) for t in model.criteria]
@@ -151,7 +151,7 @@ async def process_advanced_and_combined_search_query(model: Union[AdvancedSearch
                                            positive_vectors=positive_vectors,
                                            negative_vectors=negative_vectors,
                                            mode=model.mode,
-                                           filter_param=filter,
+                                           filter_param=filter_param,
                                            with_vectors=True if isinstance(basis, SearchCombinedParams) else False,
                                            top_k=paging.count,
                                            skip=paging.skip)

app/Services/transformers_service.py

@@ -8,7 +8,7 @@
 from torch import FloatTensor, no_grad
 from transformers import CLIPProcessor, CLIPModel, BertTokenizer, BertModel
 
-from app.config import config, environment
+from app.config import config
 
 
 class TransformersService:

app/Services/vector_db_context.py

@@ -1,6 +1,7 @@
+from typing import Optional
+
 import numpy
 from loguru import logger
-from typing import Optional
 from qdrant_client import AsyncQdrantClient
 from qdrant_client.http import models
 from qdrant_client.http.models import PointStruct
@@ -30,8 +31,7 @@ async def retrieve_by_id(self, id: str, with_vectors=False) -> ImageData:
                                       numpy.array(result[0].vector, dtype=numpy.float32) if with_vectors else None)
 
     async def querySearch(self, query_vector, query_vector_name: str = IMG_VECTOR,
-                          top_k=10, skip=0, filter_param: FilterParams | None = None) -> list[
-        SearchResult]:
+                          top_k=10, skip=0, filter_param: FilterParams | None = None) -> list[SearchResult]:
         logger.info("Querying Qdrant... top_k = {}", top_k)
         result = await self.client.search(collection_name=self.collection_name,
                                           query_vector=(query_vector_name, query_vector),
@@ -57,7 +57,7 @@ async def querySimilar(self,
         _strategy = None if mode is None else (RecommendStrategy.AVERAGE_VECTOR if
                                                mode == SearchModelEnum.average else RecommendStrategy.BEST_SCORE)
         # since only combined_search need return vectors, We can define _combined_search_need_vectors like below
-        _combined_search_need_vectors = [self.IMG_VECTOR if query_vector_name == self.TEXT_VECTOR else self.IMG_VECTOR]\
+        _combined_search_need_vectors = [self.IMG_VECTOR if query_vector_name == self.TEXT_VECTOR else self.IMG_VECTOR] \
             if with_vectors else None
         logger.info("Querying Qdrant... top_k = {}", top_k)
         result = await self.client.recommend(collection_name=self.collection_name,
@@ -71,15 +71,19 @@ async def querySimilar(self,
                                              offset=skip,
                                              with_payload=True)
         logger.success("Query completed!")
-        result_transform = lambda t: SearchResult(
-            img=ImageData.from_payload(
-                t.id,
-                t.payload,
-                numpy.array(t.vector['image_vector']) if t.vector and 'image_vector' in t.vector else None,
-                numpy.array(t.vector['text_contain_vector']) if t.vector and 'text_contain_vector' in t.vector else None
-            ),
-            score=t.score
-        )
+
+        def result_transform(t):
+            return SearchResult(
+                img=ImageData.from_payload(
+                    t.id,
+                    t.payload,
+                    numpy.array(t.vector['image_vector']) if t.vector and 'image_vector' in t.vector else None,
+                    numpy.array(
+                        t.vector['text_contain_vector']) if t.vector and 'text_contain_vector' in t.vector else None
+                ),
+                score=t.score
+            )
+
         return [result_transform(t) for t in result]
 
     async def insertItems(self, items: list[ImageData]):

requirements.txt

@@ -9,8 +9,8 @@ pydantic-settings
 # AI - Manually install cuda-capable pytorch
 torch
 torchvision
-transformers
-pillow
+transformers>4.35.2
+pillow>9.3.0
 numpy
 
 # OCR - you can choose other option if necessary, or completely disable it if you don't need this feature

scripts/local_indexing.py

@@ -1,11 +1,4 @@
-
-if __name__ == '__main__':
-    import sys
-
-    sys.path.insert(1, './')
-
 import argparse
-import asyncio
 from datetime import datetime
 from pathlib import Path
 from shutil import copy2
@@ -26,14 +19,14 @@ def parse_args():
     return parser.parse_args()
 
 
-def copy_and_index(filePath: Path) -> ImageData | None:
+def copy_and_index(file_path: Path) -> ImageData | None:
     try:
-        img = Image.open(filePath)
+        img = Image.open(file_path)
     except Exception as e:
-        logger.error("Error when opening image {}: {}", filePath, e)
+        logger.error("Error when opening image {}: {}", file_path, e)
         return None
     id = uuid4()
-    img_ext = filePath.suffix
+    img_ext = file_path.suffix
     image_ocr_result = None
     text_contain_vector = None
     [width, height] = img.size
@@ -46,7 +39,7 @@ def copy_and_index(filePath: Path) -> ImageData | None:
             else:
                 image_ocr_result = None
     except Exception as e:
-        logger.error("Error when processing image {}: {}", filePath, e)
+        logger.error("Error when processing image {}: {}", file_path, e)
         return None
     imgdata = ImageData(id=id,
                         url=f'/static/{id}{img_ext}',
@@ -59,7 +52,7 @@ def copy_and_index(filePath: Path) -> ImageData | None:
                         ocr_text=image_ocr_result)
 
     # copy to static
-    copy2(filePath, Path(config.static_file.path) / f'{id}{img_ext}')
+    copy2(file_path, Path(config.static_file.path) / f'{id}{img_ext}')
     return imgdata
 
 
@@ -88,8 +81,3 @@ async def main(args):
         logger.info("Upload {} element to database", len(buffer))
         await db_context.insertItems(buffer)
         logger.success("Indexing completed! {} images indexed", counter)
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    asyncio.run(main(args))