From 197deea887fa147c647f23dd054d07116da484c7 Mon Sep 17 00:00:00 2001
From: mandu <jys0972@gmail.com>
Date: Thu, 11 Jul 2024 20:37:42 +0900
Subject: [PATCH 1/3] =?UTF-8?q?feat(recommend=5Fservice.py)=20=EC=B6=94?=
 =?UTF-8?q?=EC=B2=9C=20=EC=8B=9C=EC=8A=A4=ED=85=9C=20=EA=B5=AC=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/recommend/recommend_service.py | 199 +++++++++++++++++++++++++++++
 1 file changed, 199 insertions(+)
 create mode 100644 app/recommend/recommend_service.py

diff --git a/app/recommend/recommend_service.py b/app/recommend/recommend_service.py
new file mode 100644
index 0000000..3b5e1d2
--- /dev/null
+++ b/app/recommend/recommend_service.py
@@ -0,0 +1,199 @@
+# pylint: disable=missing-module-docstring, missing-module-docstring, attribute-defined-outside-init, unnecessary-comprehension, not-callable, consider-using-f-string, unused-variable
+
+import asyncio
+import warnings
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+from fastapi import Depends
+
+from app.database.session import get_db_session
+from app.model.crawled_article import Articles
+from app.service.article_manage_service import ArticleManageService
+from lightfm import LightFM
+from lightfm.data import Dataset  # pylint: disable=E0611
+
+warnings.filterwarnings("ignore")
+
+
+def articles_to_dataframe(articles: list[Articles]) -> pd.DataFrame:
+    # 객체 리스트를 딕셔너리 리스트로 변환
+    articles_dict_list = [
+        {
+            "article_id": article.id,
+            article.category: 1,
+            "created_at": article.created_at,
+        }
+        for article in articles
+    ]
+
+    # pandas DataFrame 생성
+    df = pd.DataFrame(articles_dict_list)
+    return df
+
+
+class ArticleDataInfo:
+    def __init__(self, article_id, category, created_at):
+        self.article_data = pd.DataFrame(
+            {
+                "article_id": article_id,
+                "경제 및 기업": [0],
+                "정치 및 사회": [0],
+                "기술 및 문화": [0],
+                "스포츠 및 여가": [0],
+                "오피니언 및 분석": [0],
+                "created at": [created_at],
+            }
+        )
+        self.article_data.iloc[0][category] = 1
+
+
+class InteractionDataInfo:
+    def __init__(self, user_id, article_id, duration_time):
+        self.interaction_data = pd.DataFrame(
+            {
+                "user_id": [user_id],
+                "article_id": [article_id],
+                "duration_time": [duration_time],
+            }
+        )
+
+
+class RecommendService:
+    # pylint: disable=too-many-instance-attributes
+    def __init__(self):
+        self.set_user_datas("user_classification.csv")
+        asyncio.run(self.set_article_datas())
+        self.set_interaction_datas("data/interaction_data.csv")
+
+    def set_user_datas(self, user_data_path):
+        self.user_data_path = user_data_path
+        self.user_datas = pd.read_csv(user_data_path)
+
+    async def set_article_datas(self):
+        session = Depends(get_db_session)
+        articles = await ArticleManageService().get_all_articles(session=session)
+        self.article_datas = pd.get_dummies(articles_to_dataframe(articles))
+
+    def set_interaction_datas(self, interaction_data_path):
+        self.interaction_data_path = interaction_data_path
+        self.interaction_datas = pd.read_csv(interaction_data_path)
+
+    def make_dataset(self):
+        self.user_datas = pd.get_dummies(self.user_datas)
+        self.user_features_col = self.user_datas.drop(
+            columns=["classification_id"]
+        ).columns.values
+        self.user_feat = self.user_datas.drop(columns=["classification_id"]).to_dict(
+            orient="records"
+        )
+
+        self.item_features = self.article_datas
+        self.item_features_col = self.item_features.drop(
+            columns=["article_id", "created at"]
+        ).columns.values
+        self.item_feat = self.item_features.drop(
+            columns=["article_id", "created at"]
+        ).to_dict(orient="records")
+
+        self.dataset = Dataset()
+        self.dataset.fit(
+            users=[x for x in self.user_datas["classification_id"]],
+            items=[x for x in self.article_datas["article_id"]],
+            item_features=self.item_features_col,
+            user_features=self.user_features_col,
+        )
+
+        self.item_features = self.dataset.build_item_features(
+            (x, y) for x, y in zip(self.item_features["article_id"], self.item_feat)
+        )
+        self.user_features = self.dataset.build_user_features(
+            (x, y) for x, y in zip(self.user_datas["classification_id"], self.user_feat)
+        )
+
+        (self.interactions, self.weights) = self.dataset.build_interactions(
+            (x, y, z * self.get_time_weight(y))
+            for x, y, z in zip(
+                self.interaction_datas["classification_id"],
+                self.interaction_datas["article_id"],
+                self.interaction_datas["duration_time"],
+            )
+        )
+
+        num_users, num_items = self.dataset.interactions_shape()
+        print("Num users: {}, num_items {}.".format(num_users, num_items))
+
+    def make_model(
+        self,
+        n_components: int = 30,
+        loss: str = "warp",
+        epoch: int = 30,
+        num_thread: int = 4,
+    ):
+        self.n_components = n_components
+        self.loss = loss
+        self.epoch = epoch
+        self.num_thread = num_thread
+        self.model = LightFM(
+            no_components=self.n_components, loss=self.loss, random_state=1616
+        )
+
+    def fit_model(self):
+        self.make_dataset()
+        self.make_model()
+        self.model.fit(
+            self.interactions,
+            user_features=self.user_features,
+            item_features=self.item_features,
+            epochs=self.epoch,
+            num_threads=self.num_thread,
+            sample_weight=self.weights,
+        )
+
+    def get_top_n_articles(self, user_id: int, article_num: int):
+        item_ids = np.arange(self.interactions.shape[1])  # 예측할 아이템 ID 배열
+
+        predictions = self.model.predict(user_id, item_ids)
+        top_items = self.article_datas.iloc[np.argsort(-predictions)[:article_num]]
+        return top_items
+
+    def similar_items(self, item_id, N=10):
+        item_bias, item_representations = self.model.get_item_representations(
+            features=self.item_features
+        )
+
+        scores = item_representations.dot(item_representations[item_id, :])
+        best = np.argpartition(scores, -N)[-N:]
+
+        return self.article_datas.iloc[best]
+
+    def get_time_weight(self, article_id):
+        today = datetime.now().date()
+        date_obj = datetime.strptime(
+            self.article_datas[self.article_datas["article_id"] == article_id][
+                "created at"
+            ].iloc[0],
+            "%Y-%m-%d",
+        ).date()
+        difference = today - date_obj
+        return max(1 - ((difference.days // 30) / 5), 0)
+
+    def fit_model_partialy(self):
+        self.make_dataset()
+        self.model.fit_partial(self.interactions, item_features=self.item_features)
+
+    def add_interaction_data(self, interaction_data: InteractionDataInfo):
+        df = pd.read_csv(self.interaction_data_path)
+        df = pd.concat([df, interaction_data.interaction_data])
+        df.to_csv(self.interaction_data_path, index=False)
+        print("interaction is added")
+
+
+def main():
+    recommendService = RecommendService()
+    recommendService.fit_model()
+    user_id = 1
+    print(recommendService.get_top_n_articles(user_id, 5))
+
+    return "goof"

From 98b4b01fcac434ae58da0d5f6772eb5fcba1d107 Mon Sep 17 00:00:00 2001
From: mandu <jys0972@gmail.com>
Date: Thu, 11 Jul 2024 20:38:29 +0900
Subject: [PATCH 2/3] =?UTF-8?q?create(user=5Fclassification.csv)=20:=20?=
 =?UTF-8?q?=EC=9C=A0=EC=A0=80=20=EB=B6=84=EB=A5=98=20=EC=A0=95=EC=9D=98=20?=
 =?UTF-8?q?=ED=85=8C=EC=9D=B4=EB=B8=94?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 app/recommend/user_classification.csv | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 app/recommend/user_classification.csv

diff --git a/app/recommend/user_classification.csv b/app/recommend/user_classification.csv
new file mode 100644
index 0000000..398400a
--- /dev/null
+++ b/app/recommend/user_classification.csv
@@ -0,0 +1,21 @@
+classification_id,sex,issue finder,lifestyle consumer,entertainer,tech specialist,professionals
+1,F,1,1,1,0,0
+2,F,1,1,0,1,0
+3,F,1,1,0,0,1
+4,F,1,0,1,1,0
+5,F,1,0,1,0,1
+6,F,1,0,0,1,1
+7,F,0,1,1,1,0
+8,F,0,1,1,0,1
+9,F,0,1,0,1,1
+10,F,0,0,1,1,1
+11,M,1,1,1,0,0
+12,M,1,1,0,1,0
+13,M,1,1,0,0,1
+14,M,1,0,1,1,0
+15,M,1,0,1,0,1
+16,M,1,0,0,1,1
+17,M,0,1,1,1,0
+18,M,0,1,1,0,1
+19,M,0,1,0,1,1
+20,M,0,0,1,1,1

From 77ef491b3b0fab8e7ada0496ba0bff1e04c85743 Mon Sep 17 00:00:00 2001
From: stopmin <geemin0@naver.com>
Date: Thu, 11 Jul 2024 23:04:22 +0900
Subject: [PATCH 3/3] =?UTF-8?q?python=20=EB=B2=84=EC=A0=84=203.11.4?=
 =?UTF-8?q?=EB=A1=9C=20=EC=88=98=EC=A0=95=20-=20lightfm,=20pandas,=20numpy?=
 =?UTF-8?q?=20=EC=A2=85=EC=86=8D=EC=84=B1=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 Dockerfile   |   2 +-
 Pipfile      |   7 +-
 Pipfile.lock | 537 ++++++++++++++++++++++++++++++++++++++++-----------
 3 files changed, 435 insertions(+), 111 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index d2d2c33..69da693 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM --platform=linux/amd64 python:3.12.4-slim-bookworm
+FROM --platform=linux/amd64 python:3.11.4-slim-bookworm
 
 WORKDIR /app
 
diff --git a/Pipfile b/Pipfile
index cdd0161..d109294 100644
--- a/Pipfile
+++ b/Pipfile
@@ -19,6 +19,9 @@ aiohttp = "*"
 aiosmtplib = "*"
 schedule = "*"
 feedparser = "*"
+numpy = "*"
+pandas = "*"
+lightfm = "*"
 
 [dev-packages]
 flake8 = "*"
@@ -30,5 +33,5 @@ pylint = "*"
 python-dotenv = "*"
 
 [requires]
-python_version = "3.12"
-python_full_version = "3.12.4"
+python_version = "3.11"
+python_full_version = "3.11.4"
diff --git a/Pipfile.lock b/Pipfile.lock
index 155211c..4decaf1 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,12 +1,12 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "1a8e880d24fe4f7a7f2ba47534ff161b542f6b3036de0e649c4b0c4c79473860"
+            "sha256": "0bc0e447f68081f98544da4d9de5559d7392e588f8c0a77511c5e064597ea5cd"
         },
         "pipfile-spec": 6,
         "requires": {
-            "python_full_version": "3.12.4",
-            "python_version": "3.12"
+            "python_full_version": "3.11.4",
+            "python_version": "3.11"
         },
         "sources": [
             {
@@ -142,6 +142,14 @@
             "markers": "python_version >= '3.8'",
             "version": "==4.4.0"
         },
+        "async-timeout": {
+            "hashes": [
+                "sha256:4640d96be84d82d02ed59ea2b7105a0f7b33abe8703703cd0ab0bf87c427522f",
+                "sha256:7405140ff1230c310e51dc27b3145b9092d659ce68ff733fb0cefe3ee42be028"
+            ],
+            "markers": "python_full_version < '3.12.0'",
+            "version": "==4.0.3"
+        },
         "asyncpg": {
             "hashes": [
                 "sha256:0009a300cae37b8c525e5b449233d59cd9868fd35431abc470a3e364d2b85cb9",
@@ -209,11 +217,107 @@
         },
         "certifi": {
             "hashes": [
-                "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516",
-                "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"
+                "sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b",
+                "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"
             ],
             "markers": "python_version >= '3.6'",
-            "version": "==2024.6.2"
+            "version": "==2024.7.4"
+        },
+        "charset-normalizer": {
+            "hashes": [
+                "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
+                "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
+                "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786",
+                "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8",
+                "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09",
+                "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185",
+                "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574",
+                "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e",
+                "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519",
+                "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898",
+                "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269",
+                "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3",
+                "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f",
+                "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6",
+                "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8",
+                "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a",
+                "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73",
+                "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc",
+                "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714",
+                "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2",
+                "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc",
+                "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce",
+                "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d",
+                "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e",
+                "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6",
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+                "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4",
+                "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77",
+                "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d",
+                "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0",
+                "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed",
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+                "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac",
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+                "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2",
+                "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db",
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+                "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99",
+                "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c",
+                "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d",
+                "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811",
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+                "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03",
+                "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b",
+                "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04",
+                "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c",
+                "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001",
+                "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458",
+                "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389",
+                "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99",
+                "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985",
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+                "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238",
+                "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f",
+                "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d",
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+                "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a",
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@@ -511,6 +615,21 @@
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@@ -698,14 +817,66 @@
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@@ -874,6 +1082,14 @@
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+                "sha256:b5923f48cb840380f9854339176ef21763118a7300a88203ccd0bdd26e58527b",
+                "sha256:bbc0471b5f22c11c389075d091d3885693fd3f5e9a54ce051b46308bc787e5d4",
+                "sha256:bff2438ea1330e06e53c424893ec0072640dac00f29c6a43a575cbae4c99b2b9",
+                "sha256:c40003d880f39c11c1edbae8144e3813904b10514cd3d3d00c277ae996488cdb",
+                "sha256:d91db2c41dd6c20646af280355d41dfa1ec7eead235642178bd57635a3f82209",
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+            "markers": "python_version >= '3.10'",
+            "version": "==1.14.0"
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         "sgmllib3k": {
             "hashes": [
                 "sha256:7868fb1c8bfa764c1ac563d3cf369c381d1325d36124933a726f29fcdaa812e9"
@@ -1062,6 +1351,14 @@
             "markers": "python_version >= '3.7'",
             "version": "==1.5.4"
         },
+        "six": {
+            "hashes": [
+                "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
+                "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"
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+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==1.16.0"
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         "sniffio": {
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@@ -1142,6 +1439,14 @@
             "markers": "python_version >= '3.8'",
             "version": "==0.37.2"
         },
+        "threadpoolctl": {
+            "hashes": [
+                "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107",
+                "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==3.5.0"
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         "tqdm": {
             "hashes": [
                 "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644",
@@ -1166,6 +1471,14 @@
             "markers": "python_version >= '3.8'",
             "version": "==4.12.2"
         },
+        "tzdata": {
+            "hashes": [
+                "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd",
+                "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"
+            ],
+            "markers": "python_version >= '2'",
+            "version": "==2024.1"
+        },
         "ujson": {
             "hashes": [
                 "sha256:0de4971a89a762398006e844ae394bd46991f7c385d7a6a3b93ba229e6dac17e",
@@ -1250,6 +1563,14 @@
             "markers": "python_version >= '3.8'",
             "version": "==5.10.0"
         },
+        "urllib3": {
+            "hashes": [
+                "sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472",
+                "sha256:dd505485549a7a552833da5e6063639d0d177c04f23bc3864e41e5dc5f612168"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2.2.2"
+        },
         "uvicorn": {
             "extras": [
                 "standard"
@@ -1649,11 +1970,11 @@
         },
         "identify": {
             "hashes": [
-                "sha256:37d93f380f4de590500d9dba7db359d0d3da95ffe7f9de1753faa159e71e7dfa",
-                "sha256:e5e00f54165f9047fbebeb4a560f9acfb8af4c88232be60a488e9b68d122745d"
+                "sha256:cb171c685bdc31bcc4c1734698736a7d5b6c8bf2e0c15117f4d469c8640ae5cf",
+                "sha256:e79ae4406387a9d300332b5fd366d8994f1525e8414984e1a59e058b2eda2dd0"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==2.5.36"
+            "version": "==2.6.0"
         },
         "isort": {
             "hashes": [
@@ -1811,11 +2132,11 @@
         },
         "tomlkit": {
             "hashes": [
-                "sha256:af914f5a9c59ed9d0762c7b64d3b5d5df007448eb9cd2edc8a46b1eafead172f",
-                "sha256:eef34fba39834d4d6b73c9ba7f3e4d1c417a4e56f89a7e96e090dd0d24b8fb3c"
+                "sha256:08ad192699734149f5b97b45f1f18dad7eb1b6d16bc72ad0c2335772650d7b72",
+                "sha256:7075d3042d03b80f603482d69bf0c8f345c2b30e41699fd8883227f89972b264"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==0.12.5"
+            "markers": "python_version >= '3.8'",
+            "version": "==0.13.0"
         },
         "virtualenv": {
             "hashes": [