Daniel Bedeleanu
ea49cd6e4a
- Create backend/services/image_storage.py with 4 core functions:
- sanitize_filename(): remove unsafe chars, limit to 255 chars, convert to lowercase
- get_unique_filename(): handle collisions with UUID suffix (format: {name}_{uuid8}_{variant}.jpg)
- ensure_image_directories(): create /images/ root and category subdirs on startup
- save_image(): save bytes to /images/{category}/{filename}, returns relative path
- Create comprehensive test suite (22 tests) covering all functionality
- Integrate ensure_image_directories() into FastAPI startup event
- Directory structure: /images/{category}/{filename}
- Collision handling: auto-suffix with UUID if filename exists
- All tests passing, pathlib.Path for safe operations
586 lines
18 KiB
Python
586 lines
18 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Code generated by the Google Gen AI SDK generator DO NOT EDIT.
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import json
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import logging
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from typing import Any, Optional, TypeVar, Union
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from urllib.parse import urlencode
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from . import _api_module
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from . import _common
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from . import types
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from ._common import get_value_by_path as getv
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from ._common import set_value_by_path as setv
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logger = logging.getLogger('google_genai.operations')
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def _FetchPredictOperationParameters_to_vertex(
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['operation_name']) is not None:
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setv(to_object, ['operationName'], getv(from_object, ['operation_name']))
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if getv(from_object, ['resource_name']) is not None:
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setv(
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to_object,
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['_url', 'resourceName'],
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getv(from_object, ['resource_name']),
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)
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return to_object
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def _GetOperationParameters_to_mldev(
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['operation_name']) is not None:
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setv(
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to_object,
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['_url', 'operationName'],
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getv(from_object, ['operation_name']),
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)
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return to_object
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def _GetOperationParameters_to_vertex(
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['operation_name']) is not None:
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setv(
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to_object,
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['_url', 'operationName'],
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getv(from_object, ['operation_name']),
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)
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return to_object
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def _GetProjectOperationParameters_to_vertex(
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['operation_id']) is not None:
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setv(
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to_object, ['_url', 'operation_id'], getv(from_object, ['operation_id'])
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)
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return to_object
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class Operations(_api_module.BaseModule):
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def _get_videos_operation(
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self,
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*,
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operation_name: str,
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config: Optional[types.GetOperationConfigOrDict] = None,
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) -> Any:
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parameter_model = types._GetOperationParameters(
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operation_name=operation_name,
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config=config,
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)
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request_url_dict: Optional[dict[str, str]]
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if self._api_client.vertexai:
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request_dict = _GetOperationParameters_to_vertex(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = '{operationName}'.format_map(request_url_dict)
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else:
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path = '{operationName}'
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else:
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request_dict = _GetOperationParameters_to_mldev(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = '{operationName}'.format_map(request_url_dict)
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else:
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path = '{operationName}'
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query_params = request_dict.get('_query')
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if query_params:
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path = f'{path}?{urlencode(query_params)}'
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# TODO: remove the hack that pops config.
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request_dict.pop('config', None)
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http_options: Optional[types.HttpOptions] = None
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if (
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parameter_model.config is not None
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and parameter_model.config.http_options is not None
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):
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http_options = parameter_model.config.http_options
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request_dict = _common.convert_to_dict(request_dict)
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request_dict = _common.encode_unserializable_types(request_dict)
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response = self._api_client.request('get', path, request_dict, http_options)
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response_dict = {} if not response.body else json.loads(response.body)
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return response_dict
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def _fetch_predict_videos_operation(
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self,
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*,
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operation_name: str,
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resource_name: str,
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config: Optional[types.FetchPredictOperationConfigOrDict] = None,
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) -> Any:
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parameter_model = types._FetchPredictOperationParameters(
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operation_name=operation_name,
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resource_name=resource_name,
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config=config,
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)
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request_url_dict: Optional[dict[str, str]]
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if not self._api_client.vertexai:
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raise ValueError('This method is only supported in the Vertex AI client.')
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else:
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request_dict = _FetchPredictOperationParameters_to_vertex(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = '{resourceName}:fetchPredictOperation'.format_map(
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request_url_dict
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)
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else:
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path = '{resourceName}:fetchPredictOperation'
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query_params = request_dict.get('_query')
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if query_params:
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path = f'{path}?{urlencode(query_params)}'
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# TODO: remove the hack that pops config.
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request_dict.pop('config', None)
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http_options: Optional[types.HttpOptions] = None
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if (
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parameter_model.config is not None
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and parameter_model.config.http_options is not None
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):
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http_options = parameter_model.config.http_options
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request_dict = _common.convert_to_dict(request_dict)
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request_dict = _common.encode_unserializable_types(request_dict)
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response = self._api_client.request(
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'post', path, request_dict, http_options
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)
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response_dict = {} if not response.body else json.loads(response.body)
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return response_dict
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def _get(
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self,
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*,
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operation_id: str,
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config: Optional[types.GetOperationConfigOrDict] = None,
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) -> types.ProjectOperation:
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parameter_model = types._GetProjectOperationParameters(
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operation_id=operation_id,
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config=config,
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)
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request_url_dict: Optional[dict[str, str]]
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if not self._api_client.vertexai:
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raise ValueError('This method is only supported in the Vertex AI client.')
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else:
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request_dict = _GetProjectOperationParameters_to_vertex(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = 'operations/{operation_id}'.format_map(request_url_dict)
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else:
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path = 'operations/{operation_id}'
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query_params = request_dict.get('_query')
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if query_params:
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path = f'{path}?{urlencode(query_params)}'
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# TODO: remove the hack that pops config.
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request_dict.pop('config', None)
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http_options: Optional[types.HttpOptions] = None
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if (
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parameter_model.config is not None
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and parameter_model.config.http_options is not None
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):
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http_options = parameter_model.config.http_options
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request_dict = _common.convert_to_dict(request_dict)
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request_dict = _common.encode_unserializable_types(request_dict)
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response = self._api_client.request('get', path, request_dict, http_options)
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response_dict = {} if not response.body else json.loads(response.body)
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return_value = types.ProjectOperation._from_response(
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response=response_dict,
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kwargs={
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'config': {
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'response_schema': getattr(
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parameter_model.config, 'response_schema', None
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),
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'response_json_schema': getattr(
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parameter_model.config, 'response_json_schema', None
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),
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'include_all_fields': getattr(
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parameter_model.config, 'include_all_fields', None
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),
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}
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}
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if getattr(parameter_model, 'config', None)
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else {},
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)
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self._api_client._verify_response(return_value)
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return return_value
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T = TypeVar('T', bound=types.Operation)
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def get(
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self,
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operation: T,
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*,
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config: Optional[types.GetOperationConfigOrDict] = None,
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) -> T:
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"""Gets the status of a long-running operation.
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Args:
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operation (Operation): The operation instance to get the status for.
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config (GetOperationConfig): Configuration for getting the operation.
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Returns:
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Operation: The updated operation instance with the latest status or
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result.
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Usage:
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.. code-block:: python
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import time
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operation = client.models.generate_videos(
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model="veo-2.0-generate-001",
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source=types.GenerateVideosSource(
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prompt="A neon hologram of a cat driving at top speed",
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),
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)
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while not operation.done:
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time.sleep(10)
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operation = client.operations.get(operation)
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print(operation.result)
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"""
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# Currently, only GenerateVideosOperation is supported.
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# TODO(b/398040607): Support short form names
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operation_name = operation.name
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if not operation_name:
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raise ValueError('Operation name is empty.')
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# TODO(b/398233524): Cast operation types
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if self._api_client.vertexai:
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resource_name = operation_name.rpartition('/operations/')[0]
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http_options = types.HttpOptions()
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if isinstance(config, dict):
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dict_options = config.get('http_options', None)
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if dict_options is not None:
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http_options = types.HttpOptions(**dict(dict_options))
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elif isinstance(config, types.GetOperationConfig) and config is not None:
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http_options = (
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config.http_options
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if config.http_options is not None
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else types.HttpOptions()
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)
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fetch_operation_config = types.FetchPredictOperationConfig(
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http_options=http_options
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)
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response_dict = self._fetch_predict_videos_operation(
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operation_name=operation_name,
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resource_name=resource_name,
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config=fetch_operation_config,
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)
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response_operation = operation.from_api_response(
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response_dict, is_vertex_ai=True
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)
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self._api_client._verify_response(response_operation) # type: ignore [arg-type]
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return response_operation # type: ignore[no-any-return]
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else:
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response_dict = self._get_videos_operation(
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operation_name=operation_name,
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config=config,
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)
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response_operation = operation.from_api_response(
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response_dict, is_vertex_ai=False
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)
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self._api_client._verify_response(response_operation) # type: ignore [arg-type]
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return response_operation # type: ignore[no-any-return]
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class AsyncOperations(_api_module.BaseModule):
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async def _get_videos_operation(
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self,
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*,
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operation_name: str,
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config: Optional[types.GetOperationConfigOrDict] = None,
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) -> Any:
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parameter_model = types._GetOperationParameters(
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operation_name=operation_name,
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config=config,
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)
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request_url_dict: Optional[dict[str, str]]
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if self._api_client.vertexai:
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request_dict = _GetOperationParameters_to_vertex(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = '{operationName}'.format_map(request_url_dict)
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else:
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path = '{operationName}'
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else:
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request_dict = _GetOperationParameters_to_mldev(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = '{operationName}'.format_map(request_url_dict)
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else:
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path = '{operationName}'
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query_params = request_dict.get('_query')
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if query_params:
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path = f'{path}?{urlencode(query_params)}'
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# TODO: remove the hack that pops config.
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request_dict.pop('config', None)
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http_options: Optional[types.HttpOptions] = None
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if (
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parameter_model.config is not None
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and parameter_model.config.http_options is not None
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):
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http_options = parameter_model.config.http_options
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request_dict = _common.convert_to_dict(request_dict)
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request_dict = _common.encode_unserializable_types(request_dict)
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response = await self._api_client.async_request(
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'get', path, request_dict, http_options
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)
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response_dict = {} if not response.body else json.loads(response.body)
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return response_dict
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async def _fetch_predict_videos_operation(
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self,
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*,
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operation_name: str,
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resource_name: str,
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config: Optional[types.FetchPredictOperationConfigOrDict] = None,
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) -> Any:
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parameter_model = types._FetchPredictOperationParameters(
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operation_name=operation_name,
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resource_name=resource_name,
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config=config,
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)
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request_url_dict: Optional[dict[str, str]]
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if not self._api_client.vertexai:
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raise ValueError('This method is only supported in the Vertex AI client.')
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else:
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request_dict = _FetchPredictOperationParameters_to_vertex(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = '{resourceName}:fetchPredictOperation'.format_map(
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request_url_dict
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)
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else:
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path = '{resourceName}:fetchPredictOperation'
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query_params = request_dict.get('_query')
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if query_params:
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path = f'{path}?{urlencode(query_params)}'
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# TODO: remove the hack that pops config.
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request_dict.pop('config', None)
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http_options: Optional[types.HttpOptions] = None
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if (
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parameter_model.config is not None
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and parameter_model.config.http_options is not None
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):
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http_options = parameter_model.config.http_options
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request_dict = _common.convert_to_dict(request_dict)
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request_dict = _common.encode_unserializable_types(request_dict)
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response = await self._api_client.async_request(
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'post', path, request_dict, http_options
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)
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response_dict = {} if not response.body else json.loads(response.body)
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return response_dict
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async def _get(
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self,
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*,
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operation_id: str,
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config: Optional[types.GetOperationConfigOrDict] = None,
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) -> types.ProjectOperation:
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parameter_model = types._GetProjectOperationParameters(
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operation_id=operation_id,
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config=config,
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)
|
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request_url_dict: Optional[dict[str, str]]
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if not self._api_client.vertexai:
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raise ValueError('This method is only supported in the Vertex AI client.')
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else:
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request_dict = _GetProjectOperationParameters_to_vertex(parameter_model)
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request_url_dict = request_dict.get('_url')
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if request_url_dict:
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path = 'operations/{operation_id}'.format_map(request_url_dict)
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else:
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path = 'operations/{operation_id}'
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query_params = request_dict.get('_query')
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if query_params:
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path = f'{path}?{urlencode(query_params)}'
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# TODO: remove the hack that pops config.
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request_dict.pop('config', None)
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|
|
http_options: Optional[types.HttpOptions] = None
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if (
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parameter_model.config is not None
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and parameter_model.config.http_options is not None
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):
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http_options = parameter_model.config.http_options
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request_dict = _common.convert_to_dict(request_dict)
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request_dict = _common.encode_unserializable_types(request_dict)
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response = await self._api_client.async_request(
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'get', path, request_dict, http_options
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)
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response_dict = {} if not response.body else json.loads(response.body)
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return_value = types.ProjectOperation._from_response(
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response=response_dict,
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kwargs={
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'config': {
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'response_schema': getattr(
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parameter_model.config, 'response_schema', None
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),
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'response_json_schema': getattr(
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parameter_model.config, 'response_json_schema', None
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),
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'include_all_fields': getattr(
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parameter_model.config, 'include_all_fields', None
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),
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}
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}
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if getattr(parameter_model, 'config', None)
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else {},
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)
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self._api_client._verify_response(return_value)
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return return_value
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|
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T = TypeVar('T', bound=types.Operation)
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|
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async def get(
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self,
|
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operation: T,
|
|
*,
|
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config: Optional[types.GetOperationConfigOrDict] = None,
|
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) -> T:
|
|
"""Gets the status of a long-running operation.
|
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|
|
Args:
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operation (Operation): The operation instance to get the status for.
|
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config (GetOperationConfig): Configuration for getting the operation.
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Returns:
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Operation: The updated operation instance with the latest status or
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result.
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Usage:
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.. code-block:: python
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import asyncio
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operation = await client.aio.models.generate_videos(
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model="veo-2.0-generate-001",
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source=types.GenerateVideosSource(
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prompt="A neon hologram of a cat driving at top speed",
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),
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)
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while not operation.done:
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await asyncio.sleep(10)
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operation = await client.aio.operations.get(operation)
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print(operation.result)
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"""
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# Currently, only GenerateVideosOperation is supported.
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operation_name = operation.name
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if not operation_name:
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raise ValueError('Operation name is empty.')
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if self._api_client.vertexai:
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resource_name = operation_name.rpartition('/operations/')[0]
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http_options = types.HttpOptions()
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if isinstance(config, dict):
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dict_options = config.get('http_options', None)
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if dict_options is not None:
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http_options = types.HttpOptions(**dict(dict_options))
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elif isinstance(config, types.GetOperationConfig) and config is not None:
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http_options = (
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config.http_options
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if config.http_options is not None
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else types.HttpOptions()
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)
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fetch_operation_config = types.FetchPredictOperationConfig(
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http_options=http_options
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)
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response_dict = await self._fetch_predict_videos_operation(
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operation_name=operation_name,
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resource_name=resource_name,
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config=fetch_operation_config,
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)
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response_operation = operation.from_api_response(
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response_dict, is_vertex_ai=True
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)
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return response_operation # type: ignore[no-any-return]
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else:
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response_dict = await self._get_videos_operation(
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operation_name=operation_name,
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config=config,
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)
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response_operation = operation.from_api_response(
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response_dict, is_vertex_ai=False
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)
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return response_operation # type: ignore[no-any-return]
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