Updated black cuasing some reformatting (#218)

03_scaling_out/youtube_face_detection.py

@@ -85,7 +85,7 @@ def detect_faces(fn, start, stop):
     for img in clip.iter_frames():
         gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-        for (x, y, w, h) in faces:
+        for x, y, w, h in faces:
             cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
         imgs.append(img)
 

06_gpu_and_ml/text-to-pokemon/text_to_pokemon/pokemon_naming.py

@@ -5,13 +5,12 @@ import dataclasses
 import json
 import time
 import urllib.request
+from typing import Any
 
 import modal
 
 from . import config
 
-from typing import Any
-
 rnn_image = modal.Image.debian_slim().pip_install(
     "keras",
     "pandas",
@@ -117,7 +116,6 @@ def generate_names(
             if len(gen_name) > MAX_NAME_LEN:
                 continue
             elif len(gen_name) >= MIN_NAME_LEN:
-
                 # Only allow new and unique names
                 if gen_name not in training_names and gen_name not in new_names:
                     new_names.add(gen_name)
@@ -191,9 +189,8 @@ def train_rnn(
     training_names: list[str],
     max_sequence_len: int,
 ):
+    from keras.layers import LSTM, Dense
     from keras.models import Sequential
-    from keras.layers import Dense
-    from keras.layers import LSTM
     from keras.optimizers import RMSprop
 
     epochs = 100  # Number of times we train on our full data

misc/news_summarizer.py

@@ -80,7 +80,6 @@ class NYArticle:
     secret=modal.Secret.from_name("nytimes"), image=stub["scraping_image"]
 )
 def latest_science_stories(n_stories: int = 5) -> List[NYArticle]:
-
     # query api for latest science articles
     params = {
         "api-key": os.environ["NYTIMES_API_KEY"],
@@ -116,7 +115,6 @@ def latest_science_stories(n_stories: int = 5) -> List[NYArticle]:
 
 @stub.function(image=stub["scraping_image"])
 def scrape_nyc_article(url: str) -> str:
-
     print(f"Scraping article => {url}")
 
     # fetch article; simulate desktop browser
@@ -151,7 +149,6 @@ def scrape_nyc_article(url: str) -> str:
     memory=4096,
 )
 def summarize_article(text: str) -> str:
-
     print(f"Summarizing text with {len(text)} characters.")
 
     # summarize text