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app/test/google_oss.py

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+import asyncio
+from google.cloud import storage
+from google.oauth2 import service_account
+from vertexai.generative_models import Part
+import os
+import mimetypes
+from typing import List
+from google.cloud import aiplatform
+
+# 替换为您的 GCS Bucket 名称和目标文件夹
+GCS_BUCKET_NAME = "aida-test-vertex-ai-bucket"
+GCS_BLOB_FOLDER = "user_456/"
+KEY_FILE_PATH = "/workspace/lc_stylist_agent/request.json"
+# 1. 从 JSON 文件加载凭证
+aiplatform.init(
+    project='aida-461108',
+    location='us-central1'
+)
+
+
+class MyVertexAIClient:
+    def __init__(self):
+        try:
+            self.credentials = service_account.Credentials.from_service_account_file(KEY_FILE_PATH)
+        except Exception as e:
+            # 这里的异常处理应根据实际情况调整
+            raise RuntimeError(f"Failed to load credentials from file {KEY_FILE_PATH}: {e}")
+        # 初始化 GCS 客户端 (可以在 setup 或 __init__ 中完成)
+        self.gcs_client = storage.Client(
+            project=self.credentials.project_id,
+            credentials=self.credentials
+        )
+        self.uploaded_gcs_uris: List[str] = []  # 用于记录和后续清理
+
+    # --- 辅助方法：安全地执行同步 GCS 上传 ---
+    def _upload_to_gcs_sync(self, bucket_name: str, blob_name: str, local_file_path: str) -> str:
+        """同步方法：将文件上传到 GCS 并返回 GCS URI。"""
+        bucket = self.gcs_client.bucket(bucket_name)
+        blob = bucket.blob(blob_name)
+        blob.upload_from_filename(local_file_path)
+
+        gcs_uri = f"gs://{bucket_name}/{blob_name}"
+        return gcs_uri
+
+    # --- 目标方法：改写 gemini_client.aio.files.upload ---
+    async def upload_file_to_vertex_part(self, merged_image_path: str) -> Part:
+        """
+        [改写目标] 异步地将本地文件上传到 GCS，并返回 Vertex AI Part 对象。
+
+        :param merged_image_path: 本地文件的路径。
+        :return: 包含 GCS URI 的 Vertex AI Part 对象。
+        """
+        # 1. 确定 GCS 路径
+        file_name = os.path.basename(merged_image_path)
+        # 使用唯一标识符以防冲突，这里简化为文件名
+        blob_name = GCS_BLOB_FOLDER + file_name
+
+        # 2. 异步执行 GCS 上传 (将同步 I/O 放入线程池)
+        loop = asyncio.get_event_loop()
+        gcs_uri = await loop.run_in_executor(
+            None,  # 使用默认线程池
+            self._upload_to_gcs_sync,
+            GCS_BUCKET_NAME,
+            blob_name,
+            merged_image_path
+        )
+
+        # 3. 记录 GCS URI 以供后续清理
+        self.uploaded_gcs_uris.append(gcs_uri)
+
+        # 4. 确定 MIME 类型
+        mime_type, _ = mimetypes.guess_type(merged_image_path)
+        if not mime_type:
+            mime_type = 'application/octet-stream'  # 提供默认值
+
+        # 5. 创建 Vertex AI Part 对象
+        # 这是 Vertex AI SDK 传递文件的标准方式
+        vertex_part = Part.from_uri(
+            uri=gcs_uri,
+            mime_type=mime_type
+        )
+
+        return vertex_part
+
+    def _list_clear_object(self, bucket_name: str, prefix: str, delimiter=None):
+        blobs = self.gcs_client.list_blobs(
+            bucket_name, prefix=prefix, delimiter=delimiter
+        )
+        for blob in blobs:
+            blob.delete()
+            print(f"  ✅ 已删除: {blob.name}")
+
+
+# --- 原始代码的调用改写 ---
+
+# 假设 self.vertex_client 是 MyVertexAIClient 的实例
+# original: myfile = await self.gemini_client.aio.files.upload(file=merged_image_path)
+# original: content_parts.append(myfile)
+
+# 改写后:
+merged_image_path = "/workspace/lc_stylist_agent/app/core/data/outfit_output/35e8626c-943f-4a3f-a0cc-1280d8bcf84d.jpg"
+
+
+async def run():
+    vertex_ai_server = MyVertexAIClient()
+    # file_name = os.path.basename(merged_image_path)
+    # blob_name = GCS_BLOB_FOLDER + file_name
+
+    # vertex_part = vertex_ai_server._upload_to_gcs_sync(GCS_BUCKET_NAME, blob_name, merged_image_path)
+    # print(vertex_part)
+
+    vertex_ai_server._list_clear_object(GCS_BUCKET_NAME, "user_456")
+
+
+# content_parts.append(vertex_part)
+asyncio.run(run())