Fix style

chapters/en/chapter9/section2.mdx

@@ -10,9 +10,11 @@ Let's get started with a simple “Hello World” example to get familiar with t
 ```py
 import gradio as gr
 
+
 def greet(name):
     return "Hello " + name
 
+
 gr.Interface(fn=greet, inputs="text", outputs="text").launch()
 ```
 
@@ -38,9 +40,11 @@ Take a look at the example below:
 ```py
 import gradio as gr
 
+
 def greet(name):
     return "Hello " + name
 
+
 # We instantiate the Textbox class
 textbox = gr.inputs.Textbox(label="Type your name here:", placeholder="John Doe", lines=2)
 
@@ -68,10 +72,11 @@ First, we define a prediction function that takes in a text prompt and returns t
 ```py
 from transformers import pipeline
 
-model = pipeline('text-generation')
+model = pipeline("text-generation")
+
 
 def predict(prompt):
-    completion = model(prompt)[0]['generated_text']
+    completion = model(prompt)[0]["generated_text"]
     return completion
 ```
 

chapters/en/chapter9/section3.mdx

@@ -49,11 +49,13 @@ shortcut `"audio"`.
 import numpy as np
 import gradio as gr
 
+
 def reverse_audio(audio):
     sr, data = audio
     reversed_audio = (sr, np.flipud(data))
     return reversed_audio
 
+
 mic = gr.inputs.Audio(source="microphone", type="numpy", label="Speak here...")
 gr.Interface(reverse_audio, mic, "audio").launch()
 ```
@@ -82,6 +84,7 @@ import gradio as gr
 
 notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
 
+
 def generate_tone(note, octave, duration):
     sr = 48000
     a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
@@ -91,6 +94,7 @@ def generate_tone(note, octave, duration):
     audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
     return (sr, audio)
 
+
 gr.Interface(
     generate_tone,
     [
@@ -136,21 +140,25 @@ import gradio as gr
 
 model = pipeline("automatic-speech-recognition")
 
+
 def transcribe_audio(mic=None, file=None):
-  if mic is not None:
-    audio = mic
-  elif file is not None:
-    audio = file
-  else:
-    return("You must either provide a mic recording or a file")
-  transcription = model(audio)["text"]
-  return transcription
+    if mic is not None:
+        audio = mic
+    elif file is not None:
+        audio = file
+    else:
+        return "You must either provide a mic recording or a file"
+    transcription = model(audio)["text"]
+    return transcription
+
 
 gr.Interface(
-  fn=transcribe_audio,
-  inputs=[gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-          gr.inputs.Audio(source ="upload", type="filepath", optional=True)],
-  outputs="text",
+    fn=transcribe_audio,
+    inputs=[
+        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
+        gr.inputs.Audio(source="upload", type="filepath", optional=True),
+    ],
+    outputs="text",
 ).launch()
 ```
 

chapters/en/chapter9/section4.mdx

@@ -83,29 +83,30 @@ import torch
 import gradio as gr
 from torch import nn
 
-LABELS = Path('class_names.txt').read_text().splitlines()
+LABELS = Path("class_names.txt").read_text().splitlines()
 
 model = nn.Sequential(
-    nn.Conv2d(1, 32, 3, padding='same'),
+    nn.Conv2d(1, 32, 3, padding="same"),
     nn.ReLU(),
     nn.MaxPool2d(2),
-    nn.Conv2d(32, 64, 3, padding='same'),
+    nn.Conv2d(32, 64, 3, padding="same"),
     nn.ReLU(),
     nn.MaxPool2d(2),
-    nn.Conv2d(64, 128, 3, padding='same'),
+    nn.Conv2d(64, 128, 3, padding="same"),
     nn.ReLU(),
     nn.MaxPool2d(2),
     nn.Flatten(),
     nn.Linear(1152, 256),
     nn.ReLU(),
     nn.Linear(256, len(LABELS)),
 )
-state_dict = torch.load('pytorch_model.bin', map_location='cpu')
+state_dict = torch.load("pytorch_model.bin", map_location="cpu")
 model.load_state_dict(state_dict, strict=False)
 model.eval()
 
+
 def predict(im):
-    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
+    x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.0
     with torch.no_grad():
         out = model(x)
     probabilities = torch.nn.functional.softmax(out[0], dim=0)
@@ -116,14 +117,15 @@ def predict(im):
 Now that we have a `predict()` function. The next step is to define and launch our gradio interface:
 ```py
 interface = gr.Interface(
-    predict, 
-    inputs='sketchpad', 
-    outputs='label', 
-    theme="huggingface", 
-    title="Sketch Recognition", 
-    description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!", 
-    article = "<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
-    live=True)
+    predict,
+    inputs="sketchpad",
+    outputs="label",
+    theme="huggingface",
+    title="Sketch Recognition",
+    description="Who wants to play Pictionary? Draw a common object like a shovel or a laptop, and the algorithm will guess in real time!",
+    article="<p style='text-align: center'>Sketch Recognition | Demo Model</p>",
+    live=True,
+)
 interface.launch(share=True)
 ```
 

chapters/en/chapter9/section5.mdx

@@ -15,11 +15,19 @@ title = "GPT-J-6B"
 description = "Gradio Demo for GPT-J 6B, a transformer model trained using Ben Wang's Mesh Transformer JAX. 'GPT-J' refers to the class of model, while '6B' represents the number of trainable parameters. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
 article = "<p style='text-align: center'><a href='https://github.com/kingoflolz/mesh-transformer-jax' target='_blank'>GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model</a></p>"
 examples = [
-    ['The tower is 324 metres (1,063 ft) tall,'],
+    ["The tower is 324 metres (1,063 ft) tall,"],
     ["The Moon's orbit around Earth has"],
-    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"]
+    ["The smooth Borealis basin in the Northern Hemisphere covers 40%"],
 ]
-gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", inputs=gr.inputs.Textbox(lines=5, label="Input Text"),title=title,description=description,article=article, examples=examples,enable_queue=True).launch()
+gr.Interface.load(
+    "huggingface/EleutherAI/gpt-j-6B",
+    inputs=gr.inputs.Textbox(lines=5, label="Input Text"),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    enable_queue=True,
+).launch()
 ```
 
 The code above will produce the interface below:
@@ -46,7 +54,9 @@ by overriding any of the
 parameters. Here, we add a title and get it to work with a webcam instead:
 
 ```py
-gr.Interface.load("spaces/abidlabs/remove-bg", inputs="webcam", title="Remove your webcam background!").launch()
+gr.Interface.load(
+    "spaces/abidlabs/remove-bg", inputs="webcam", title="Remove your webcam background!"
+).launch()
 ```
 
 <iframe src="https://hf.space/gradioiframe/dawood/Remove-bg/+" frameBorder="0" height="550" title="Gradio app" class="container p-0 flex-grow space-iframe" allow="accelerometer; ambient-light-sensor; autoplay; battery; camera; document-domain; encrypted-media; fullscreen; geolocation; gyroscope; layout-animations; legacy-image-formats; magnetometer; microphone; midi; oversized-images; payment; picture-in-picture; publickey-credentials-get; sync-xhr; usb; vr ; wake-lock; xr-spatial-tracking" sandbox="allow-forms allow-modals allow-popups allow-popups-to-escape-sandbox allow-same-origin allow-scripts allow-downloads"></iframe>

chapters/en/chapter9/section6.mdx

@@ -81,9 +81,9 @@ def classify_image(inp):
 image = gr.inputs.Image(shape=(224, 224))
 label = gr.outputs.Label(num_top_classes=3)
 
-title="Gradio Image Classifiction + Interpretation Example"
+title = "Gradio Image Classifiction + Interpretation Example"
 gr.Interface(
-    fn=classify_image, inputs=image, outputs=label, interpretation="default",title=title
+    fn=classify_image, inputs=image, outputs=label, interpretation="default", title=title
 ).launch()
 ```
 
@@ -126,9 +126,9 @@ def classify_image(inp):
 image = gr.inputs.Image(shape=(224, 224))
 label = gr.outputs.Label(num_top_classes=3)
 
-title="Gradio Image Classifiction + Interpretation Example"
+title = "Gradio Image Classifiction + Interpretation Example"
 gr.Interface(
-    fn=classify_image, inputs=image, outputs=label, interpretation="default",title=title
+    fn=classify_image, inputs=image, outputs=label, interpretation="default", title=title
 ).launch(auth=("admin", "pass1234"))
 ```