# from __future__ import annotations

# import json
# import typing
# import aiohttp

# from .. import runner
# from ...core import app, entities as core_entities
# from .. import entities as llm_entities

# api_url = "请求地址/v1"
# api_key = "请求key"
# user_name = "dify-plugin"
# # 需要在dify的自定义字段中另外设置context和system_prompt
# headers = {
#     "Authorization": f"Bearer {api_key}",
#     "Content-Type": "application/json"
# }


# def get_content_text(content):
#     if isinstance(content, list):
#         return " ".join(str(element) if element.image_url is None else " " for element in content)
#     elif isinstance(content, str):
#         return content  
#     else:
#         return ""


# @runner.runner_class("dify-api")
# class DifyAPIRunner(runner.RequestRunner):
#     """Dify API 对话请求器
#     """

#     async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
#         """运行请求"""
#         await query.use_model.requester.preprocess(query)

#         # 构建系统提示词
#         prompt_messages = query.prompt.messages.copy()
#         system_prompt = "\n".join(
#             f"{msg.role}: {get_content_text(msg.content)}" for msg in prompt_messages if msg.content
#         )

#         # 构建上下文
#         previous_messages = query.messages.copy()
#         user_message = [query.user_message]

#         # 检查 user_message 中的 image_url
#         image_urls = [element.image_url.url for element in query.user_message.content if element.type == 'image_url' and element.image_url is not None]

#         if len(image_urls) > 10:
#             raise ValueError("仅可包含最多10张图片")

#         data = {}
#         if image_urls:
#             data["files"] = [
#                 {
#                     "type": "image",
#                     "transfer_method": "remote_url",
#                     "url": url
#                 } for url in image_urls
#             ]
#         else:
#             data["files"] = []

#         # 继续处理其他逻辑
#         all_messages = previous_messages + user_message

#         context = "\n".join(
#             f"{msg.role}: {get_content_text(msg.content)}" for msg in all_messages if msg.content
#         )

#         # 构建请求数据
#         data.update({
#             "inputs": {
#                 "context": context,
#                 "system_prompt": system_prompt,
#                 "files": data["files"]
#             },
#             "query": get_content_text(query.user_message.content),
#             "response_mode": "blocking",
#             "conversation_id": "",
#             "user": user_name
            
#         })

#         async with aiohttp.ClientSession() as session:
#             try:
#                 async with session.post(api_url + "/chat-messages", headers=headers, json=data) as response:
#                     response_data = await response.json()
#                     response.raise_for_status()

#                     # 处理响应数据
#                     content_elements = [llm_entities.ContentElement.from_text(response_data.get("answer", ""))]

#                     msg = llm_entities.Message(
#                         role="assistant",
#                         content=content_elements
#                     )
#                     yield msg
#             except aiohttp.ClientResponseError as http_err:
#                 if response.status == 404:
#                     error_message = "对话不存在"
#                 elif response.status == 400:
#                     error_code = response_data.get("code")
#                     if error_code == "invalid_param":
#                         error_message = "传入参数异常"
#                     elif error_code == "app_unavailable":
#                         error_message = "App 配置不可用"
#                     elif error_code == "provider_not_initialize":
#                         error_message = "无可用模型凭据配置"
#                     elif error_code == "provider_quota_exceeded":
#                         error_message = "模型调用额度不足"
#                     elif error_code == "model_currently_not_support":
#                         error_message = "当前模型不可用"
#                     elif error_code == "completion_request_error":
#                         error_message = "文本生成失败"
#                 elif response.status == 500:
#                     error_message = "服务内部异常"
#                 else:
#                     error_message = f"HTTP error occurred: {http_err}"
#                 raise ValueError(error_message)
#             except Exception as err:
#                 raise ValueError(f"An error occurred: {err}")


# # @runner.runner_class("local-agent")
# # class LocalAgentRunner(runner.RequestRunner):
# #     """本地Agent请求运行器
# #     """

# #     async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
# #         """运行请求
# #         """
# #         await query.use_model.requester.preprocess(query)

# #         pending_tool_calls = []

# #         req_messages = query.prompt.messages.copy() + query.messages.copy() + [query.user_message]

# #         # 首次请求
# #         try:
# #             msg = await query.use_model.requester.call(query.use_model, req_messages, query.use_funcs)
# #             if "answer" not in msg.content:
# #                 raise ValueError("请求失败：返回内容不含answer")
# #         except Exception as e:
# #             err_msg = llm_entities.Message(
# #                 role="system", content=f"请求失败：{e}"
# #             )
# #             yield err_msg
# #             return

# #         yield msg

# #         pending_tool_calls = msg.tool_calls

# #         req_messages.append(msg)

# #         # 持续请求，只要还有待处理的工具调用就继续处理调用
# #         while pending_tool_calls:
# #             for tool_call in pending_tool_calls:
# #                 try:
# #                     func = tool_call.function
                    
# #                     parameters = json.loads(func.arguments)

# #                     func_ret = await self.ap.tool_mgr.execute_func_call(
# #                         query, func.name, parameters
# #                     )

# #                     msg = llm_entities.Message(
# #                         role="tool", content=json.dumps(func_ret, ensure_ascii=False), tool_call_id=tool_call.id
# #                     )

# #                     yield msg

# #                     req_messages.append(msg)
# #                 except Exception as e:
# #                     # 工具调用出错，添加一个报错信息到 req_messages
# #                     err_msg = llm_entities.Message(
# #                         role="tool", content=f"err: {e}", tool_call_id=tool_call.id
# #                     )

# #                     yield err_msg

# #                     req_messages.append(err_msg)

# #             # 处理完所有调用，再次请求
# #             try:
# #                 msg = await query.use_model.requester.call(query.use_model, req_messages, query.use_funcs)
# #                 if "answer" not in msg.content:
# #                     raise ValueError("请求失败：返回内容不含answer")
# #             except Exception as e:
# #                 err_msg = llm_entities.Message(
# #                     role="system", content=f"请求失败：{e}"
# #                 )
# #                 yield err_msg
# #                 return

# #             yield msg

# #             pending_tool_calls = msg.tool_calls

# #             req_messages.append(msg)