From 40e38e66c5dcc48548c2d619ebf22e82bd9a3f8c Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 7 Jun 2024 11:38:52 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 packages/jupyter-ai/jupyter_ai/chat_handlers/base.py    | 6 +++---
 packages/jupyter-ai/jupyter_ai/chat_handlers/default.py | 4 +++-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/packages/jupyter-ai/jupyter_ai/chat_handlers/base.py b/packages/jupyter-ai/jupyter_ai/chat_handlers/base.py
index 437dfd68f..97392168a 100644
--- a/packages/jupyter-ai/jupyter_ai/chat_handlers/base.py
+++ b/packages/jupyter-ai/jupyter_ai/chat_handlers/base.py
@@ -1,8 +1,8 @@
 import argparse
+import contextlib
 import os
 import time
 import traceback
-import contextlib
 from typing import (
     TYPE_CHECKING,
     Awaitable,
@@ -21,9 +21,9 @@
 from jupyter_ai.models import (
     AgentChatMessage,
     ChatMessage,
+    ClosePendingMessage,
     HumanChatMessage,
     PendingMessage,
-    ClosePendingMessage,
 )
 from jupyter_ai_magics import Persona
 from jupyter_ai_magics.providers import BaseProvider
@@ -199,7 +199,7 @@ def reply(self, response: str, human_msg: Optional[HumanChatMessage] = None):
 
             handler.broadcast_message(agent_msg)
             break
-    
+
     def start_pending(self, text: str, ellipsis: bool = True) -> str:
         """
         Sends a pending message to the client.
diff --git a/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py b/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py
index a09660837..dddbf1417 100644
--- a/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py
+++ b/packages/jupyter-ai/jupyter_ai/chat_handlers/default.py
@@ -48,5 +48,7 @@ def create_llm_chain(
     async def process_message(self, message: HumanChatMessage):
         self.get_llm_chain()
         with self.pending(PENDING_MESSAGE):
-            response = await self.llm_chain.apredict(input=message.body, stop=["\nHuman:"])
+            response = await self.llm_chain.apredict(
+                input=message.body, stop=["\nHuman:"]
+            )
         self.reply(response, message)