fix: pre-commit에 isort, flake8 추가

.github/workflows/pre-commit.yml

@@ -1,29 +1,59 @@
-name: Pre-commit Black Check
+name: Pre-commit Auto-fix and Comment
 
 on:
   push:
-    branches:
-      - master
+    branches: [ master ]
   pull_request:
-    branches:
-      - master
+    branches: [ master ]
 
 jobs:
   pre-commit:
-    name: Run Pre-commit Hooks
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          fetch-depth: 0
+          ref: ${{ github.head_ref || github.ref_name }}
 
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
           python-version: "3.x"
 
       - name: Install pre-commit
-        run: pip install pre-commit
+        run: python -m pip install --upgrade pre-commit
+
+      - name: Run pre-commit (auto-fix)
+        id: precommit_run
+        continue-on-error: true
+        run: |
+          pre-commit run --all-files --verbose --hook-stage manual
 
-      - name: Run pre-commit
-        run: pre-commit run --all-files --verbose
+      - name: Commit & push if changed
+        run: |
+          git config user.name  "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          if ! git diff --quiet; then
+            git add -u
+            git commit -m "style: apply pre-commit fixes (black, isort)" || true
+            git push
+            echo "FIXES_PUSHED=true" >> $GITHUB_ENV
+          fi
+
+      - name: Comment on PR
+        if: env.FIXES_PUSHED == 'true' && github.event_name == 'pull_request'
+        uses: actions/github-script@v7
+        with:
+          script: |
+            github.rest.issues.createComment({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              issue_number: context.issue.number,
+              body: "✅ Pre-commit hooks fixed formatting (black, isort) and the bot pushed the changes."
+            })

.pre-commit-config.yaml

@@ -1,5 +1,16 @@
 repos:
+  - repo: https://github.com/pycqa/isort
+    rev: 6.0.0
+    hooks:
+      - id: isort
   - repo: https://github.com/psf/black
     rev: 25.1.0
     hooks:
       - id: black
+  - repo: https://github.com/pycqa/flake8
+    rev: 7.1.2
+    hooks:
+      - id: flake8
+        args:
+          - --ignore=E501,E722,E262,F401,F841,W503,E226
+          - --max-line-length=88

cli/__init__.py

@@ -1,5 +1,7 @@
-import click
 import subprocess
+
+import click
+
 from llm_utils.tools import set_gms_server
 
 

data_utils/datahub_source.py

@@ -1,13 +1,18 @@
-from datahub.metadata.schema_classes import DatasetPropertiesClass, SchemaMetadataClass
-from datahub.emitter.rest_emitter import DatahubRestEmitter
-from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
-from datahub.metadata.schema_classes import UpstreamLineageClass
 from collections import defaultdict
+
 import requests
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+from datahub.metadata.schema_classes import (
+    DatasetPropertiesClass,
+    SchemaMetadataClass,
+    UpstreamLineageClass,
+)
+
 from data_utils.queries import (
-    ROOT_GLOSSARY_NODES_QUERY,
     GLOSSARY_NODE_QUERY,
     LIST_QUERIES_QUERY,
+    ROOT_GLOSSARY_NODES_QUERY,
 )
 
 

evaluation/gen_answer.py

@@ -1,10 +1,9 @@
+import uuid
 from argparse import ArgumentParser
-from langchain_core.messages import HumanMessage
-
-from utils import load_question_json, save_answer_json
 
+from langchain_core.messages import HumanMessage
 from tqdm import tqdm
-import uuid
+from utils import load_question_json, save_answer_json
 
 from llm_utils.graph import builder
 

evaluation/gen_persona.py

@@ -1,12 +1,12 @@
 import os
+from argparse import ArgumentParser
 
-from utils import save_persona_json, pretty_print_persona
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai.chat_models import ChatOpenAI
 from persona_class import PersonaList
+from utils import pretty_print_persona, save_persona_json
 
 from llm_utils.tools import _get_table_info
-from langchain_openai.chat_models import ChatOpenAI
-from langchain_core.prompts import ChatPromptTemplate
-from argparse import ArgumentParser
 
 
 def get_table_des_string(tables_desc):

evaluation/gen_question.py

@@ -1,10 +1,10 @@
-from utils import load_persona_json, save_question_json
+import os
+from argparse import ArgumentParser
+
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai.chat_models import ChatOpenAI
 from tqdm import tqdm
-
-from argparse import ArgumentParser
-import os
+from utils import load_persona_json, save_question_json
 
 
 def get_persona_prompt(persona):

evaluation/persona_class.py

@@ -1,6 +1,7 @@
-from pydantic import BaseModel
 from typing import List
 
+from pydantic import BaseModel
+
 
 class Persona(BaseModel):
     name: str

evaluation/utils.py

@@ -1,7 +1,8 @@
 import json
-from persona_class import PersonaList
-from glob import glob
 import os
+from glob import glob
+
+from persona_class import PersonaList
 
 
 def save_persona_json(data, filepath):

interface/lang2sql.py

@@ -1,15 +1,15 @@
-import streamlit as st
-from langchain_core.messages import HumanMessage
-from llm_utils.graph import builder
-from langchain.chains.sql_database.prompt import SQL_PROMPTS
 import os
 from typing import Union
-import pandas as pd
 
+import pandas as pd
+import streamlit as st
 from clickhouse_driver import Client
-from llm_utils.connect_db import ConnectDB
 from dotenv import load_dotenv
+from langchain.chains.sql_database.prompt import SQL_PROMPTS
+from langchain_core.messages import HumanMessage
 
+from llm_utils.connect_db import ConnectDB
+from llm_utils.graph import builder
 
 # Clickhouse 연결
 db = ConnectDB()

interface/viz_eval.py

@@ -1,9 +1,9 @@
-import streamlit as st
-import json
 import glob
-import pandas as pd
+import json
 import os
 
+import pandas as pd
+import streamlit as st
 
 st.set_page_config(layout="wide", page_title="Lang2SQL 평가 시각화")
 

llm_utils/chains.py

@@ -1,15 +1,16 @@
 import os
+
+from dotenv import load_dotenv
 from langchain_core.prompts import (
     ChatPromptTemplate,
     MessagesPlaceholder,
     SystemMessagePromptTemplate,
 )
 
-from .llm_factory import get_llm
-
-from dotenv import load_dotenv
 from prompt.template_loader import get_prompt_template
 
+from .llm_factory import get_llm
+
 env_path = os.path.join(os.getcwd(), ".env")
 
 if os.path.exists(env_path):

llm_utils/connect_db.py

@@ -1,5 +1,6 @@
 import os
 from typing import Union
+
 import pandas as pd
 from clickhouse_driver import Client
 from dotenv import load_dotenv

llm_utils/graph.py

@@ -1,20 +1,17 @@
-import os
 import json
+import os
 
-from typing_extensions import TypedDict, Annotated
+from langchain.chains.sql_database.prompt import SQL_PROMPTS
 from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
-from langchain.chains.sql_database.prompt import SQL_PROMPTS
 from pydantic import BaseModel, Field
-from .llm_factory import get_llm
-
-from llm_utils.chains import (
-    query_refiner_chain,
-    query_maker_chain,
-)
+from typing_extensions import Annotated, TypedDict
 
+from llm_utils.chains import query_maker_chain, query_refiner_chain
 from llm_utils.tools import get_info_from_db
 
+from .llm_factory import get_llm
+
 # 노드 식별자 정의
 QUERY_REFINER = "query_refiner"
 GET_TABLE_INFO = "get_table_info"

llm_utils/llm_factory.py

@@ -4,7 +4,8 @@
 
 from dotenv import load_dotenv
 from langchain.llms.base import BaseLanguageModel
-from langchain_aws import ChatBedrockConverse, BedrockEmbeddings
+from langchain_aws import BedrockEmbeddings, ChatBedrockConverse
+from langchain_community.llms.bedrock import Bedrock
 from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
 from langchain_huggingface import (
     ChatHuggingFace,
@@ -13,12 +14,11 @@
 )
 from langchain_ollama import ChatOllama, OllamaEmbeddings
 from langchain_openai import (
+    AzureChatOpenAI,
     AzureOpenAIEmbeddings,
     ChatOpenAI,
-    AzureChatOpenAI,
     OpenAIEmbeddings,
 )
-from langchain_community.llms.bedrock import Bedrock
 
 # .env 파일 로딩
 load_dotenv()

llm_utils/prompts_class.py

@@ -1,6 +1,6 @@
-from langchain.chains.sql_database.prompt import SQL_PROMPTS
 import os
 
+from langchain.chains.sql_database.prompt import SQL_PROMPTS
 from langchain_core.prompts import load_prompt
 
 

llm_utils/tools.py

@@ -1,11 +1,11 @@
 import os
-from typing import List, Dict, Optional, TypeVar, Callable, Iterable, Any
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any, Callable, Dict, Iterable, List, Optional, TypeVar
 
 from langchain.schema import Document
+from tqdm import tqdm
 
 from data_utils.datahub_source import DatahubMetadataFetcher
-from tqdm import tqdm
-from concurrent.futures import ThreadPoolExecutor
 
 T = TypeVar("T")
 R = TypeVar("R")

setup.py

@@ -1,5 +1,5 @@
 # setup.py
-from setuptools import setup, find_packages
+from setuptools import find_packages, setup
 
 with open("docs/README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()