add flake8 and black

.vscode/settings.json

@@ -1,8 +1,4 @@
 {
-    "python.linting.pylint" : false,
-    "python.linting.flake8" : true,
-    "python.linting.enabled" : true,
-    "python.formatting.black" : true,
     "editor.formatOnSave": true,
     "python.linting.flake8Args":[
         "--max-line-length=88"
@@ -13,4 +9,6 @@
     "python.testing.pytestArgs": [
         "tests"
     ],
+    "black-formatter.importStrategy": "fromEnvironment",
+    "flake8.importStrategy": "fromEnvironment",
 }

app.py

@@ -2,7 +2,7 @@ import re
 
 import gradio as gr
 from pydantic import BaseModel
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import pipeline
 from loguru import logger
 
 # from pydantic import BaseModel
@@ -17,7 +17,7 @@ EN_SUMMARY_MODEL = "csebuetnlp/mT5_multilingual_XLSum"
 EN_SENTIMENT_MODEL = "distilbert-base-uncased-finetuned-sst-2-english"
 
 
-DEFAULT_EN_TEXT ="""Flags on official buildings are being flown at half-mast and a minute's silence will be observed at midday.
+DEFAULT_EN_TEXT = """Flags on official buildings are being flown at half-mast and a minute's silence will be observed at midday.
 Fourteen people were shot dead at the Faculty of Arts building of Charles University in the capital by a student who then killed himself.
 Police are working to uncover the motive behind the attack.
 It is one of the deadliest assaults by a lone gunman in Europe this century.
@@ -45,6 +45,7 @@ DEFAULT_RU_TEXT = """В результате взрыва на заправке,
 доноров для их пополнения на данный час тоже уже немало», — написало ведомство.
 """
 
+
 class Request(BaseModel):
     text: str
 
@@ -53,14 +54,16 @@ class Result(BaseModel):
     sentiment_score: float
     sentiment_label: str
     summary: str
-        
+
     def to_str(self):
-        return f"Summary:  {self.summary}\n Sentiment:  {self.sentiment_label} ({self.sentiment_score:.3f})"    
+        return f"Summary:  {self.summary}\nSentiment:  {self.sentiment_label} ({self.sentiment_score:.3f})"
+
 
 # class Response(BaseModel):
 #     results: List[Result] # list of Result objects
 
-class Summarizer():
+
+class Summarizer:
     ru_summary_pipe: pipeline
     ru_sentiment_pipe: pipeline
     en_summary_pipe: pipeline
@@ -68,65 +71,72 @@ class Summarizer():
     # sum_model_name = "csebuetnlp/mT5_multilingual_XLSum"
     # sum_tokenizer = AutoTokenizer.from_pretrained(sum_model_name)
     # sum_model = AutoModelForSeq2SeqLM.from_pretrained(sum_model_name)
-    
+
     def __init__(self) -> None:
-        sum_pipe = pipeline("summarization", model=RU_SUMMARY_MODEL, max_length=100, truncation=True)
+        sum_pipe = pipeline(
+            "summarization", model=RU_SUMMARY_MODEL, max_length=100, truncation=True
+        )
         self.ru_summary_pipe = sum_pipe
-        self.ru_sentiment_pipe = pipeline("sentiment-analysis", model=RU_SENTIMENT_MODEL)
+        self.ru_sentiment_pipe = pipeline(
+            "sentiment-analysis", model=RU_SENTIMENT_MODEL
+        )
         self.en_summary_pipe = sum_pipe
-        self.en_sentiment_pipe = pipeline("sentiment-analysis", model=EN_SENTIMENT_MODEL)
+        self.en_sentiment_pipe = pipeline(
+            "sentiment-analysis", model=EN_SENTIMENT_MODEL
+        )
 
     def mT5_summarize(self, text: str) -> str:
-        '''Handle text with mT5 model without pipeline'''
-        
-        WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
+        """Handle text with mT5 model without pipeline"""
+
+        def whitespace_handler(text: str):
+            return re.sub("\s+", " ", re.sub("\n+", " ", text.strip()))
 
         input_ids = self.sum_tokenizer(
-            [WHITESPACE_HANDLER(text)],
+            [whitespace_handler(text)],
             return_tensors="pt",
             padding="max_length",
             truncation=True,
-            max_length=512
+            max_length=512,
         )["input_ids"]
 
         output_ids = self.sum_model.generate(
-            input_ids=input_ids,
-            max_length=84,
-            no_repeat_ngram_size=2,
-            num_beams=4
+            input_ids=input_ids, max_length=84, no_repeat_ngram_size=2, num_beams=4
         )[0]
 
         summary = self.sum_tokenizer.decode(
-            output_ids,
-            skip_special_tokens=True,
-            clean_up_tokenization_spaces=False
+            output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
         )
 
         return summary
 
     def get_pipe(self, lang: str):
-        logger.info(f'Pipe language: {lang}')
-        summary = {'en': self.en_summary_pipe,
-                   'ru': self.ru_summary_pipe,}
-        sentiment = {'en': self.en_sentiment_pipe,
-                   'ru': self.ru_sentiment_pipe,}
+        logger.info(f"Pipe language: {lang}")
+        summary = {
+            "en": self.en_summary_pipe,
+            "ru": self.ru_summary_pipe,
+        }
+        sentiment = {
+            "en": self.en_sentiment_pipe,
+            "ru": self.ru_sentiment_pipe,
+        }
         return summary[lang], sentiment[lang]
 
-    def summarize(self, text: Request, lang: str = 'en') -> Result:
+    def summarize(self, req: Request, lang: str = "en") -> Result:
         sum_pipe, sent_pipe = self.get_pipe(lang)
-        response_summary = sum_pipe(text)
+        response_summary = sum_pipe(req.text)
         logger.info(response_summary)
-        response_sentiment = sent_pipe(text)
+        response_sentiment = sent_pipe(req.text)
         logger.info(response_sentiment)
-        result =  Result(
+        result = Result(
             summary=response_summary[0]["summary_text"],
             sentiment_label=response_sentiment[0]["label"],
             sentiment_score=response_sentiment[0]["score"],
         )
         return result
-    
-    def summ(self, text: Request, lang: str = 'en') -> str:
-        return self.summarize(text, lang).to_str()
+
+    def summ(self, req: Request, lang: str = "en") -> str:
+        return self.summarize(req, lang).to_str()
+
 
 if __name__ == "__main__":
     pipe = Summarizer()
@@ -134,20 +144,46 @@ if __name__ == "__main__":
     with gr.Blocks() as demo:
         with gr.Row():
             with gr.Column(scale=2, min_width=600):
-                en_sum_description=gr.Markdown(value=f"Model for Summary: {EN_SUMMARY_MODEL}")
-                en_sent_description=gr.Markdown(value=f"Model for Sentiment: {EN_SENTIMENT_MODEL}")
-                en_inputs=gr.Textbox(label="en_input", lines=5, value=DEFAULT_EN_TEXT, placeholder=DEFAULT_EN_TEXT)
-                en_lang=gr.Textbox(value='en',visible=False)
-                en_outputs=gr.Textbox(label="en_output", lines=5, placeholder="Summary and Sentiment would be here...")
+                en_sum_description = gr.Markdown(
+                    value=f"Model for Summary: {EN_SUMMARY_MODEL}"
+                )
+                en_sent_description = gr.Markdown(
+                    value=f"Model for Sentiment: {EN_SENTIMENT_MODEL}"
+                )
+                en_inputs = gr.Textbox(
+                    label="en_input",
+                    lines=5,
+                    value=DEFAULT_EN_TEXT,
+                    placeholder=DEFAULT_EN_TEXT,
+                )
+                en_lang = gr.Textbox(value="en", visible=False)
+                en_outputs = gr.Textbox(
+                    label="en_output",
+                    lines=5,
+                    placeholder="Summary and Sentiment would be here...",
+                )
                 en_inbtn = gr.Button("Proceed")
             with gr.Column(scale=2, min_width=600):
-                ru_sum_description=gr.Markdown(value=f"Model for Summary: {RU_SUMMARY_MODEL}")
-                ru_sent_description=gr.Markdown(value=f"Model for Sentiment: {RU_SENTIMENT_MODEL}")
-                ru_inputs=gr.Textbox(label="ru_input", lines=5, value=DEFAULT_RU_TEXT, placeholder=DEFAULT_RU_TEXT)
-                ru_lang=gr.Textbox(value='ru',visible=False)
-                ru_outputs=gr.Textbox(label="ru_output", lines=5, placeholder="Здесь будет обобщение и эмоциональный окрас текста...")
+                ru_sum_description = gr.Markdown(
+                    value=f"Model for Summary: {RU_SUMMARY_MODEL}"
+                )
+                ru_sent_description = gr.Markdown(
+                    value=f"Model for Sentiment: {RU_SENTIMENT_MODEL}"
+                )
+                ru_inputs = gr.Textbox(
+                    label="ru_input",
+                    lines=5,
+                    value=DEFAULT_RU_TEXT,
+                    placeholder=DEFAULT_RU_TEXT,
+                )
+                ru_lang = gr.Textbox(value="ru", visible=False)
+                ru_outputs = gr.Textbox(
+                    label="ru_output",
+                    lines=5,
+                    placeholder="Здесь будет обобщение и эмоциональный окрас текста...",
+                )
                 ru_inbtn = gr.Button("Запустить")
-                
+
         en_inbtn.click(
             pipe.summ,
             [en_inputs, en_lang],
@@ -158,4 +194,4 @@ if __name__ == "__main__":
             [ru_inputs, ru_lang],
             [ru_outputs],
         )
-    demo.launch(show_api=False)   
+    demo.launch(show_api=False)

main.py

@@ -1,43 +1,73 @@
 import gradio as gr
 from fastapi import FastAPI
-from typing import List
 from app import Summarizer, Request, Result
-from app import EN_SENTIMENT_MODEL, EN_SUMMARY_MODEL, RU_SENTIMENT_MODEL, RU_SUMMARY_MODEL
+from app import (
+    EN_SENTIMENT_MODEL,
+    EN_SUMMARY_MODEL,
+    RU_SENTIMENT_MODEL,
+    RU_SUMMARY_MODEL,
+)
 from app import DEFAULT_EN_TEXT, DEFAULT_RU_TEXT
 
 app = FastAPI()
 pipe = Summarizer()
 
+
 @app.post("/summ_ru", response_model=Result)
 async def ru_summ_api(request: Request):
-    results = pipe.summarize(request.text, lang='ru')
+    results = pipe.summarize(request.text, lang="ru")
     return results
 
 
-
 @app.post("/summ_en", response_model=Result)
-async def ru_summ_api(request: Request):
-    results = pipe.summarize(request.text, lang='en')
+async def en_summ_api(request: Request):
+    results = pipe.summarize(request.text, lang="en")
     return results
 
 
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column(scale=2, min_width=600):
-            en_sum_description=gr.Markdown(value=f"Model for Summary: {EN_SUMMARY_MODEL}")
-            en_sent_description=gr.Markdown(value=f"Model for Sentiment: {EN_SENTIMENT_MODEL}")
-            en_inputs=gr.Textbox(label="en_input", lines=5, value=DEFAULT_EN_TEXT, placeholder=DEFAULT_EN_TEXT)
-            en_lang=gr.Textbox(value='en',visible=False)
-            en_outputs=gr.Textbox(label="en_output", lines=5, placeholder="Summary and Sentiment would be here...")
+            en_sum_description = gr.Markdown(
+                value=f"Model for Summary: {EN_SUMMARY_MODEL}"
+            )
+            en_sent_description = gr.Markdown(
+                value=f"Model for Sentiment: {EN_SENTIMENT_MODEL}"
+            )
+            en_inputs = gr.Textbox(
+                label="en_input",
+                lines=5,
+                value=DEFAULT_EN_TEXT,
+                placeholder=DEFAULT_EN_TEXT,
+            )
+            en_lang = gr.Textbox(value="en", visible=False)
+            en_outputs = gr.Textbox(
+                label="en_output",
+                lines=5,
+                placeholder="Summary and Sentiment would be here...",
+            )
             en_inbtn = gr.Button("Proceed")
         with gr.Column(scale=2, min_width=600):
-            ru_sum_description=gr.Markdown(value=f"Model for Summary: {RU_SUMMARY_MODEL}")
-            ru_sent_description=gr.Markdown(value=f"Model for Sentiment: {RU_SENTIMENT_MODEL}")
-            ru_inputs=gr.Textbox(label="ru_input", lines=5, value=DEFAULT_RU_TEXT, placeholder=DEFAULT_RU_TEXT)
-            ru_lang=gr.Textbox(value='ru',visible=False)
-            ru_outputs=gr.Textbox(label="ru_output", lines=5, placeholder="Здесь будет обобщение и эмоциональный окрас текста...")
+            ru_sum_description = gr.Markdown(
+                value=f"Model for Summary: {RU_SUMMARY_MODEL}"
+            )
+            ru_sent_description = gr.Markdown(
+                value=f"Model for Sentiment: {RU_SENTIMENT_MODEL}"
+            )
+            ru_inputs = gr.Textbox(
+                label="ru_input",
+                lines=5,
+                value=DEFAULT_RU_TEXT,
+                placeholder=DEFAULT_RU_TEXT,
+            )
+            ru_lang = gr.Textbox(value="ru", visible=False)
+            ru_outputs = gr.Textbox(
+                label="ru_output",
+                lines=5,
+                placeholder="Здесь будет обобщение и эмоциональный окрас текста...",
+            )
             ru_inbtn = gr.Button("Запустить")
-            
+
     en_inbtn.click(
         pipe.summ,
         [en_inputs, en_lang],
@@ -49,7 +79,5 @@ with gr.Blocks() as demo:
         [ru_outputs],
     )
 
-# demo.launch(show_api=False)   
-
 # mounting at the root path
-app = gr.mount_gradio_app(app, demo, path="/")
+app = gr.mount_gradio_app(app, demo, path="/")

poetry.lock

@@ -91,6 +91,53 @@ docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-
 tests = ["attrs[tests-no-zope]", "zope-interface"]
 tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"]
 
+[[package]]
+name = "black"
+version = "24.1.1"
+description = "The uncompromising code formatter."
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "black-24.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:2588021038bd5ada078de606f2a804cadd0a3cc6a79cb3e9bb3a8bf581325a4c"},
+    {file = "black-24.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:1a95915c98d6e32ca43809d46d932e2abc5f1f7d582ffbe65a5b4d1588af7445"},
+    {file = "black-24.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2fa6a0e965779c8f2afb286f9ef798df770ba2b6cee063c650b96adec22c056a"},
+    {file = "black-24.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:5242ecd9e990aeb995b6d03dc3b2d112d4a78f2083e5a8e86d566340ae80fec4"},
+    {file = "black-24.1.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:fc1ec9aa6f4d98d022101e015261c056ddebe3da6a8ccfc2c792cbe0349d48b7"},
+    {file = "black-24.1.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:0269dfdea12442022e88043d2910429bed717b2d04523867a85dacce535916b8"},
+    {file = "black-24.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3d64db762eae4a5ce04b6e3dd745dcca0fb9560eb931a5be97472e38652a161"},
+    {file = "black-24.1.1-cp311-cp311-win_amd64.whl", hash = "sha256:5d7b06ea8816cbd4becfe5f70accae953c53c0e53aa98730ceccb0395520ee5d"},
+    {file = "black-24.1.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:e2c8dfa14677f90d976f68e0c923947ae68fa3961d61ee30976c388adc0b02c8"},
+    {file = "black-24.1.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a21725862d0e855ae05da1dd25e3825ed712eaaccef6b03017fe0853a01aa45e"},
+    {file = "black-24.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07204d078e25327aad9ed2c64790d681238686bce254c910de640c7cc4fc3aa6"},
+    {file = "black-24.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:a83fe522d9698d8f9a101b860b1ee154c1d25f8a82ceb807d319f085b2627c5b"},
+    {file = "black-24.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:08b34e85170d368c37ca7bf81cf67ac863c9d1963b2c1780c39102187ec8dd62"},
+    {file = "black-24.1.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7258c27115c1e3b5de9ac6c4f9957e3ee2c02c0b39222a24dc7aa03ba0e986f5"},
+    {file = "black-24.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40657e1b78212d582a0edecafef133cf1dd02e6677f539b669db4746150d38f6"},
+    {file = "black-24.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:e298d588744efda02379521a19639ebcd314fba7a49be22136204d7ed1782717"},
+    {file = "black-24.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:34afe9da5056aa123b8bfda1664bfe6fb4e9c6f311d8e4a6eb089da9a9173bf9"},
+    {file = "black-24.1.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:854c06fb86fd854140f37fb24dbf10621f5dab9e3b0c29a690ba595e3d543024"},
+    {file = "black-24.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3897ae5a21ca132efa219c029cce5e6bfc9c3d34ed7e892113d199c0b1b444a2"},
+    {file = "black-24.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:ecba2a15dfb2d97105be74bbfe5128bc5e9fa8477d8c46766505c1dda5883aac"},
+    {file = "black-24.1.1-py3-none-any.whl", hash = "sha256:5cdc2e2195212208fbcae579b931407c1fa9997584f0a415421748aeafff1168"},
+    {file = "black-24.1.1.tar.gz", hash = "sha256:48b5760dcbfe5cf97fd4fba23946681f3a81514c6ab8a45b50da67ac8fbc6c7b"},
+]
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+packaging = ">=22.0"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)", "aiohttp (>=3.7.4,!=3.9.0)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
 [[package]]
 name = "certifi"
 version = "2023.11.17"
@@ -373,6 +420,23 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
+[[package]]
+name = "flake8"
+version = "7.0.0"
+description = "the modular source code checker: pep8 pyflakes and co"
+category = "dev"
+optional = false
+python-versions = ">=3.8.1"
+files = [
+    {file = "flake8-7.0.0-py2.py3-none-any.whl", hash = "sha256:a6dfbb75e03252917f2473ea9653f7cd799c3064e54d4c8140044c5c065f53c3"},
+    {file = "flake8-7.0.0.tar.gz", hash = "sha256:33f96621059e65eec474169085dc92bf26e7b2d47366b70be2f67ab80dc25132"},
+]
+
+[package.dependencies]
+mccabe = ">=0.7.0,<0.8.0"
+pycodestyle = ">=2.11.0,<2.12.0"
+pyflakes = ">=3.2.0,<3.3.0"
+
 [[package]]
 name = "fonttools"
 version = "4.47.0"
@@ -979,6 +1043,18 @@ pillow = ">=8"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
 
+[[package]]
+name = "mccabe"
+version = "0.7.0"
+description = "McCabe checker, plugin for flake8"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "mccabe-0.7.0-py2.py3-none-any.whl", hash = "sha256:6c2d30ab6be0e4a46919781807b4f0d834ebdd6c6e3dca0bda5a15f863427b6e"},
+    {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
+]
+
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -1007,6 +1083,18 @@ files = [
 testing = ["astroid (>=2.0)", "coverage", "pylint (>=2.3.1,<2.4.0)", "pytest"]
 yaml = ["PyYAML (>=5.1.0)"]
 
+[[package]]
+name = "mypy-extensions"
+version = "1.0.0"
+description = "Type system extensions for programs checked with the mypy type checker."
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+files = [
+    {file = "mypy_extensions-1.0.0-py3-none-any.whl", hash = "sha256:4392f6c0eb8a5668a69e23d168ffa70f0be9ccfd32b5cc2d26a34ae5b844552d"},
+    {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"},
+]
+
 [[package]]
 name = "numpy"
 version = "1.26.2"
@@ -1259,6 +1347,18 @@ sql-other = ["SQLAlchemy (>=1.4.36)"]
 test = ["hypothesis (>=6.46.1)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)"]
 xml = ["lxml (>=4.8.0)"]
 
+[[package]]
+name = "pathspec"
+version = "0.12.1"
+description = "Utility library for gitignore style pattern matching of file paths."
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pathspec-0.12.1-py3-none-any.whl", hash = "sha256:a0d503e138a4c123b27490a4f7beda6a01c6f288df0e4a8b79c7eb0dc7b4cc08"},
+    {file = "pathspec-0.12.1.tar.gz", hash = "sha256:a482d51503a1ab33b1c67a6c3813a26953dbdc71c31dacaef9a838c4e29f5712"},
+]
+
 [[package]]
 name = "pillow"
 version = "10.1.0"
@@ -1327,6 +1427,22 @@ files = [
 docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "platformdirs"
+version = "4.1.0"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "platformdirs-4.1.0-py3-none-any.whl", hash = "sha256:11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380"},
+    {file = "platformdirs-4.1.0.tar.gz", hash = "sha256:906d548203468492d432bcb294d4bc2fff751bf84971fbb2c10918cc206ee420"},
+]
+
+[package.extras]
+docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
+
 [[package]]
 name = "pretrainedmodels"
 version = "0.7.4"
@@ -1365,6 +1481,18 @@ files = [
     {file = "protobuf-4.25.1.tar.gz", hash = "sha256:57d65074b4f5baa4ab5da1605c02be90ac20c8b40fb137d6a8df9f416b0d0ce2"},
 ]
 
+[[package]]
+name = "pycodestyle"
+version = "2.11.1"
+description = "Python style guide checker"
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pycodestyle-2.11.1-py2.py3-none-any.whl", hash = "sha256:44fe31000b2d866f2e41841b18528a505fbd7fef9017b04eff4e2648a0fadc67"},
+    {file = "pycodestyle-2.11.1.tar.gz", hash = "sha256:41ba0e7afc9752dfb53ced5489e89f8186be00e599e712660695b7a75ff2663f"},
+]
+
 [[package]]
 name = "pydantic"
 version = "2.5.3"
@@ -1515,6 +1643,18 @@ files = [
     {file = "pydub-0.25.1.tar.gz", hash = "sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f"},
 ]
 
+[[package]]
+name = "pyflakes"
+version = "3.2.0"
+description = "passive checker of Python programs"
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyflakes-3.2.0-py2.py3-none-any.whl", hash = "sha256:84b5be138a2dfbb40689ca07e2152deb896a65c3a3e24c251c5c62489568074a"},
+    {file = "pyflakes-3.2.0.tar.gz", hash = "sha256:1c61603ff154621fb2a9172037d84dca3500def8c8b630657d1701f026f8af3f"},
+]
+
 [[package]]
 name = "pygments"
 version = "2.17.2"
@@ -2290,6 +2430,18 @@ dev = ["tokenizers[testing]"]
 docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
 testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
 
+[[package]]
+name = "tomli"
+version = "2.0.1"
+description = "A lil' TOML parser"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
+    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
+]
+
 [[package]]
 name = "tomlkit"
 version = "0.12.0"
@@ -2683,4 +2835,4 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "a0a8f5d7bbfd3d08c83e048ee2f8e18cf31729338afa2484b5f817c387457622"
+content-hash = "5648f003dcb355b02c351874f25f204380d23b153110cec7f6177e81fd260dbf"

pyproject.toml

@@ -22,6 +22,8 @@ uvicorn = "^0.27.0"
 
 [tool.poetry.group.dev.dependencies]
 huggingface-hub = "^0.20.1"
+flake8 = "^7.0.0"
+black = "^24.1.1"
 
 [build-system]
 requires = ["poetry-core"]