[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/linear_regression.py

@@ -20,7 +20,7 @@ def collect_dataset():
     response = requests.get(
         "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
         "master/Week1/ADRvsRating.csv",
-        timeout=10
+        timeout=10,
     )
     lines = response.text.splitlines()
     data = []

web_programming/covid_stats_via_xpath.py

@@ -18,7 +18,9 @@ class CovidData(NamedTuple):
 
 def covid_stats(url: str = "https://www.worldometers.info/coronavirus/") -> CovidData:
     xpath_str = '//div[@class = "maincounter-number"]/span/text()'
-    return CovidData(*html.fromstring(requests.get(url, timeout=10).content).xpath(xpath_str))
+    return CovidData(
+        *html.fromstring(requests.get(url, timeout=10).content).xpath(xpath_str)
+    )
 
 
 fmt = """Total COVID-19 cases in the world: {}

web_programming/crawl_google_scholar_citation.py

@@ -11,7 +11,9 @@ def get_citation(base_url: str, params: dict) -> str:
     """
     Return the citation number.
     """
-    soup = BeautifulSoup(requests.get(base_url, params=params, timeout=10).content, "html.parser")
+    soup = BeautifulSoup(
+        requests.get(base_url, params=params, timeout=10).content, "html.parser"
+    )
     div = soup.find("div", attrs={"class": "gs_ri"})
     anchors = div.find("div", attrs={"class": "gs_fl"}).find_all("a")
     return anchors[2].get_text()

web_programming/current_stock_price.py

@@ -4,7 +4,9 @@
 
 def stock_price(symbol: str = "AAPL") -> str:
     url = f"https://finance.yahoo.com/quote/{symbol}?p={symbol}"
-    yahoo_finance_source = requests.get(url, headers={"USER-AGENT": "Mozilla/5.0"}, timeout=10).text
+    yahoo_finance_source = requests.get(
+        url, headers={"USER-AGENT": "Mozilla/5.0"}, timeout=10
+    ).text
     soup = BeautifulSoup(yahoo_finance_source, "html.parser")
     specific_fin_streamer_tag = soup.find("fin-streamer", {"data-test": "qsp-price"})
 

web_programming/download_images_from_google_query.py

@@ -39,7 +39,9 @@ def download_images_from_google_query(query: str = "dhaka", max_images: int = 5)
         "ijn": "0",
     }
 
-    html = requests.get("https://www.google.com/search", params=params, headers=headers, timeout=10)
+    html = requests.get(
+        "https://www.google.com/search", params=params, headers=headers, timeout=10
+    )
     soup = BeautifulSoup(html.text, "html.parser")
     matched_images_data = "".join(
         re.findall(r"AF_initDataCallback\(([^<]+)\);", str(soup.select("script")))

web_programming/fetch_anime_and_play.py

@@ -82,7 +82,9 @@ def search_anime_episode_list(episode_endpoint: str) -> list:
 
     request_url = f"{BASE_URL}{episode_endpoint}"
 
-    response = requests.get(url=request_url, headers={"UserAgent": UserAgent().chrome}, timeout=10)
+    response = requests.get(
+        url=request_url, headers={"UserAgent": UserAgent().chrome}, timeout=10
+    )
     response.raise_for_status()
 
     soup = BeautifulSoup(response.text, "html.parser")

web_programming/fetch_jobs.py

@@ -13,7 +13,9 @@
 
 
 def fetch_jobs(location: str = "mumbai") -> Generator[tuple[str, str], None, None]:
-    soup = BeautifulSoup(requests.get(url + location, timeout=10).content, "html.parser")
+    soup = BeautifulSoup(
+        requests.get(url + location, timeout=10).content, "html.parser"
+    )
     # This attribute finds out all the specifics listed in a job
     for job in soup.find_all("div", attrs={"data-tn-component": "organicJob"}):
         job_title = job.find("a", attrs={"data-tn-element": "jobTitle"}).text.strip()

web_programming/get_amazon_product_data.py

@@ -24,7 +24,9 @@ def get_amazon_product_data(product: str = "laptop") -> DataFrame:
         ),
         "Accept-Language": "en-US, en;q=0.5",
     }
-    soup = BeautifulSoup(requests.get(url, headers=header, timeout=10).text, features="lxml")
+    soup = BeautifulSoup(
+        requests.get(url, headers=header, timeout=10).text, features="lxml"
+    )
     # Initialize a Pandas dataframe with the column titles
     data_frame = DataFrame(
         columns=[

web_programming/random_anime_character.py

@@ -21,7 +21,9 @@ def random_anime_character() -> tuple[str, str, str]:
     """
     Returns the Title, Description, and Image Title of a random anime character .
     """
-    soup = BeautifulSoup(requests.get(URL, headers=headers, timeout=10).text, "html.parser")
+    soup = BeautifulSoup(
+        requests.get(URL, headers=headers, timeout=10).text, "html.parser"
+    )
     title = soup.find("meta", attrs={"property": "og:title"}).attrs["content"]
     image_url = soup.find("meta", attrs={"property": "og:image"}).attrs["content"]
     description = soup.find("p", id="description").get_text()

web_programming/recaptcha_verification.py

@@ -56,7 +56,9 @@ def login_using_recaptcha(request):
     client_key = request.POST.get("g-recaptcha-response")
 
     # post recaptcha response to Google's recaptcha api
-    response = requests.post(url, data={"secret": secret_key, "response": client_key}, timeout=10)
+    response = requests.post(
+        url, data={"secret": secret_key, "response": client_key}, timeout=10
+    )
     # if the recaptcha api verified our keys
     if response.json().get("success", False):
         # authenticate the user

web_programming/slack_message.py

@@ -5,7 +5,9 @@
 
 def send_slack_message(message_body: str, slack_url: str) -> None:
     headers = {"Content-Type": "application/json"}
-    response = requests.post(slack_url, json={"text": message_body}, headers=headers, timeout=10)
+    response = requests.post(
+        slack_url, json={"text": message_body}, headers=headers, timeout=10
+    )
     if response.status_code != 200:
         msg = (
             "Request to slack returned an error "