fix

api_routes.py

@@ -1497,7 +1497,7 @@ async def analyze_face(
             return JSONResponse(
                 content={
                     "success": False,
-                    "message": "请尝试上传清晰、无遮挡的正面照片",
+                    "message": "Please upload a clear front-facing photo without any obstructions.",
                     "face_count": 0,
                     "faces": [],
                 }
@@ -1559,6 +1559,31 @@ async def analyze_face(
         raise HTTPException(status_code=500, detail=f"分析过程中出现内部错误: {str(e)}")
 
 
+@api_router.post(path="/app/analyze", tags=["App专用"])
+@log_api_params
+async def app_analyze_face(
+    request: Request,
+    file: UploadFile = File(None),
+    files: list[UploadFile] = File(None),
+    images: str = Form(None),
+    nickname: str = Form(None, description="操作者昵称"),
+    model: ModelType = Query(
+        ModelType.HYBRID, description="选择使用的模型: howcuteami, deepface 或 hybrid"
+    ),
+):
+    """
+    App专用人脸分析接口，参数与原 /analyze 保持完全一致并透传。
+    """
+    return await analyze_face(
+        request=request,
+        file=file,
+        files=files,
+        images=images,
+        nickname=nickname,
+        model=model
+    )
+
+
 @api_router.post("/image_search", response_model=ImageFileList, tags=["图像搜索"])
 @log_api_params
 async def search_by_image(
@@ -1741,7 +1766,7 @@ async def list_outputs(
                                                  None) else ""
     nickname_filter = request.nickname.strip() if getattr(request, "nickname",
                                                           None) else None
-    
+
     # 业务逻辑：如果 search_type 为空，默认只显示非裁剪的人脸图
     is_cropped_filter = 0 if not search_type else None
 
@@ -1786,7 +1811,7 @@ async def list_outputs(
                     if category not in (
                     None, "all") and record_category != category:
                         continue
-                    
+
                     # 向量搜索结果也根据 is_cropped_filter 进行过滤
                     record_is_cropped = bool(record.get("is_cropped_face")) if record else "_face" in file_path
                     if is_cropped_filter is not None and record_is_cropped != bool(is_cropped_filter):
@@ -3626,7 +3651,7 @@ async def compress_image(
             # 异步执行图片向量化并入库，不阻塞主流程
             if CLIP_AVAILABLE:
                 asyncio.create_task(handle_image_vector_async(compressed_path, compressed_filename))
-            
+
             await _record_output_file(
                 file_path=compressed_path,
                 nickname=nickname,
@@ -4599,10 +4624,10 @@ async def face_similarity_verification(
                 h = region.get("h", 0)
                 x_end = min(source_image.shape[1], x + w)
                 y_end = min(source_image.shape[0], y + h)
-                
+
                 if x_end <= x or y_end <= y:
                     return source_image.copy(), False
-                
+
                 result_img = source_image.copy()
                 # 绘制绿色矩形框，厚度根据图片尺寸动态调整
                 thickness = max(2, int(round(min(source_image.shape[:2]) / 200)))
@@ -4656,7 +4681,7 @@ async def face_similarity_verification(
                 face_img2 = image2[y2:y2_end, x2:x2_end]
 
                 # face_path1 和 face_path2 已经通过 _get_save_path 生成
-                
+
                 # 直接保存裁剪图，不进行特征点绘制
                 if save_image_high_quality(face_img1, face_path1, quality=SAVE_QUALITY):
                     await _record_output_file(

face_analyzer.py

@@ -817,7 +817,7 @@ class EnhancedFaceAnalyzer:
         :return: 分析结果
         """
         if image is None:
-            raise ValueError("无效的图像输入")
+            raise ValueError("invalid image input")
 
         # 检测人脸
         face_boxes = self._detect_faces(image)
@@ -825,7 +825,7 @@ class EnhancedFaceAnalyzer:
         if not face_boxes:
             return {
                 "success": False,
-                "message": "请尝试上传清晰、无遮挡的正面照片",
+                "message": "Please upload a clear front-facing photo without any obstructions.",
                 "face_count": 0,
                 "faces": [],
                 "annotated_image": None,
@@ -834,7 +834,7 @@ class EnhancedFaceAnalyzer:
 
         results = {
             "success": True,
-            "message": f"成功检测到 {len(face_boxes)} 张人脸",
+            "message": f"success detected {len(face_boxes)} faces",
             "face_count": len(face_boxes),
             "faces": [],
             "model_used": model_type.value,