Format The Codebase

- black formatting
- isort formatting

demo/calculator/run.py

@@ -1,5 +1,6 @@
 import gradio as gr
 
+
 def calculator(num1, operation, num2):
     if operation == "add":
         return num1 + num2
@@ -10,7 +11,9 @@ def calculator(num1, operation, num2):
     elif operation == "divide":
         return num1 / num2
 
-iface = gr.Interface(calculator, 
+
+iface = gr.Interface(
+    calculator,
     ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"],
     "number",
     examples=[

demo/calculator_live/run.py

@@ -1,5 +1,6 @@
 import gradio as gr
 
+
 def calculator(num1, operation, num2):
     if operation == "add":
         return num1 + num2
@@ -10,10 +11,12 @@ def calculator(num1, operation, num2):
     elif operation == "divide":
         return num1 / num2
 
-iface = gr.Interface(calculator, 
+
+iface = gr.Interface(
+    calculator,
     ["number", gr.inputs.Radio(["add", "subtract", "multiply", "divide"]), "number"],
     "number",
-    live=True
+    live=True,
 )
 
 if __name__ == "__main__":

demo/chatbot/run.py

@@ -1,10 +1,12 @@
-import gradio as gr
 import random
 
+import gradio as gr
+
+
 def chat(message, history):
     history = history or []
     if message.startswith("How many"):
-        response = random.randint(1,10)
+        response = random.randint(1, 10)
     elif message.startswith("How"):
         response = random.choice(["Great", "Good", "Okay", "Bad"])
     elif message.startswith("Where"):
@@ -19,11 +21,19 @@ def chat(message, history):
     html += "</div>"
     return html, history
 
-iface = gr.Interface(chat, ["text", "state"], ["html", "state"], css="""
+
+iface = gr.Interface(
+    chat,
+    ["text", "state"],
+    ["html", "state"],
+    css="""
     .chatbox {display:flex;flex-direction:column}
     .user_msg, .resp_msg {padding:4px;margin-bottom:4px;border-radius:4px;width:80%}
     .user_msg {background-color:cornflowerblue;color:white;align-self:start}
     .resp_msg {background-color:lightgray;align-self:self-end}
-""", allow_screenshot=False, allow_flagging="never")
+""",
+    allow_screenshot=False,
+    allow_flagging="never",
+)
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()

demo/diff_texts/run.py

@@ -1,20 +1,25 @@
-import gradio as gr
 from difflib import Differ
 
+import gradio as gr
+
+
 def diff_texts(text1, text2):
     d = Differ()
     return [
-        (token[2:], token[0] if token[0] != " " else None) for token in d.compare(text1, text2)
+        (token[2:], token[0] if token[0] != " " else None)
+        for token in d.compare(text1, text2)
     ]
 
+
 iface = gr.Interface(
     diff_texts,
     [
         gr.inputs.Textbox(
-            lines=3, default="The quick brown fox jumped over the lazy dogs."),
-        gr.inputs.Textbox(
-            lines=3, default="The fast brown fox jumps over lazy dogs."),
+            lines=3, default="The quick brown fox jumped over the lazy dogs."
+        ),
+        gr.inputs.Textbox(lines=3, default="The fast brown fox jumps over lazy dogs."),
     ],
-    gr.outputs.HighlightedText())
+    gr.outputs.HighlightedText(),
+)
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()

demo/digit_classifier/run.py

@@ -1,10 +1,14 @@
+import os
+from urllib.request import urlretrieve
+
 import tensorflow as tf
+
 import gradio
 import gradio as gr
-from urllib.request import urlretrieve
-import os
 
-urlretrieve("https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5", "mnist-model.h5")
+urlretrieve(
+    "https://gr-models.s3-us-west-2.amazonaws.com/mnist-model.h5", "mnist-model.h5"
+)
 model = tf.keras.models.load_model("mnist-model.h5")
 
 
@@ -13,11 +17,14 @@ def recognize_digit(image):
     prediction = model.predict(image).tolist()[0]
     return {str(i): prediction[i] for i in range(10)}
 
-im = gradio.inputs.Image(shape=(28, 28), image_mode='L', invert_colors=False, source="canvas")
+
+im = gradio.inputs.Image(
+    shape=(28, 28), image_mode="L", invert_colors=False, source="canvas"
+)
 
 iface = gr.Interface(
-    recognize_digit, 
-    im, 
+    recognize_digit,
+    im,
     gradio.outputs.Label(num_top_classes=3),
     live=True,
     interpretation="default",
@@ -28,4 +35,3 @@ def recognize_digit(image):
 
 if __name__ == "__main__":
     iface.launch()
-

demo/disease_report/run.py

@@ -1,9 +1,12 @@
-import gradio as gr
-import numpy as np
-from fpdf import FPDF
 import os
 import tempfile
 
+import numpy as np
+from fpdf import FPDF
+
+import gradio as gr
+
+
 def disease_report(img, scan_for, generate_report):
     results = []
     for i, mode in enumerate(["Red", "Green", "Blue"]):
@@ -16,28 +19,30 @@ def disease_report(img, scan_for, generate_report):
         pdf = FPDF()
         pdf.add_page()
         pdf.set_font("Arial", size=15)
-        pdf.cell(200, 10, txt="Disease Report", 
-                ln=1, align='C')
-        pdf.cell(200, 10, txt="A Gradio Demo.",
-                ln=2, align='C')
+        pdf.cell(200, 10, txt="Disease Report", ln=1, align="C")
+        pdf.cell(200, 10, txt="A Gradio Demo.", ln=2, align="C")
         pdf.output(report)
     return results, report if generate_report else None
 
-iface = gr.Interface(disease_report, 
+
+iface = gr.Interface(
+    disease_report,
     [
-        "image", 
-        gr.inputs.CheckboxGroup(["Cancer", "Rash", "Heart Failure", "Stroke", "Diabetes", "Pneumonia"]),
-        "checkbox"
+        "image",
+        gr.inputs.CheckboxGroup(
+            ["Cancer", "Rash", "Heart Failure", "Stroke", "Diabetes", "Pneumonia"]
+        ),
+        "checkbox",
     ],
     [
         gr.outputs.Carousel(["text", "image"], label="Disease"),
-        gr.outputs.File(label="Report")
+        gr.outputs.File(label="Report"),
     ],
     title="Disease Report",
     description="Upload an Xray and select the diseases to scan for.",
     theme="grass",
     flagging_options=["good", "bad", "etc"],
-    allow_flagging="auto"
+    allow_flagging="auto",
 )
 
 if __name__ == "__main__":

demo/filter_records/run.py

@@ -1,19 +1,25 @@
 import gradio as gr
 
+
 def filter_records(records, gender):
-    return records[records['gender'] == gender]
+    return records[records["gender"] == gender]
+
 
-iface = gr.Interface(filter_records,
-  [
-    gr.inputs.Dataframe(headers=["name", "age", "gender"], datatype=["str", "number", "str"], row_count=5), 
-    gr.inputs.Dropdown(["M", "F", "O"])
-  ],
-  "dataframe",
-  description="Enter gender as 'M', 'F', or 'O' for other."
+iface = gr.Interface(
+    filter_records,
+    [
+        gr.inputs.Dataframe(
+            headers=["name", "age", "gender"],
+            datatype=["str", "number", "str"],
+            row_count=5,
+        ),
+        gr.inputs.Dropdown(["M", "F", "O"]),
+    ],
+    "dataframe",
+    description="Enter gender as 'M', 'F', or 'O' for other.",
 )
 
 iface.test_launch()
 
 if __name__ == "__main__":
     iface.launch()
-

demo/form_graph/run.py

@@ -1,8 +1,10 @@
-import gradio as gr
 import random
+
 import matplotlib.pyplot as plt
 import numpy as np
 
+import gradio as gr
+
 
 def plot_forecast(final_year, companies, noise, show_legend, point_style):
     start_year = 2020
@@ -22,18 +24,19 @@ def plot_forecast(final_year, companies, noise, show_legend, point_style):
     return fig
 
 
-iface = gr.Interface(plot_forecast,
-             [
-                 gr.inputs.Radio([2025, 2030, 2035, 2040],
-                                 label="Project to:"),
-                 gr.inputs.CheckboxGroup(
-                     ["Google", "Microsoft", "Gradio"], label="Company Selection"),
-                 gr.inputs.Slider(1, 100, label="Noise Level"),
-                 gr.inputs.Checkbox(label="Show Legend"),
-                 gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
-             ],
-             gr.outputs.Image(plot=True, label="forecast") 
-             )
+iface = gr.Interface(
+    plot_forecast,
+    [
+        gr.inputs.Radio([2025, 2030, 2035, 2040], label="Project to:"),
+        gr.inputs.CheckboxGroup(
+            ["Google", "Microsoft", "Gradio"], label="Company Selection"
+        ),
+        gr.inputs.Slider(1, 100, label="Noise Level"),
+        gr.inputs.Checkbox(label="Show Legend"),
+        gr.inputs.Dropdown(["cross", "line", "circle"], label="Style"),
+    ],
+    gr.outputs.Image(plot=True, label="forecast"),
+)
 
 if __name__ == "__main__":
     iface.launch()

demo/fraud_detector/run.py

@@ -1,34 +1,38 @@
-import gradio as gr
-import pandas as pd
 import random
 
+import pandas as pd
+
+import gradio as gr
+
 
 def fraud_detector(card_activity, categories, sensitivity):
     activity_range = random.randint(0, 100)
-    drop_columns = [column for column in ["retail", "food", "other"] if column not in categories]
+    drop_columns = [
+        column for column in ["retail", "food", "other"] if column not in categories
+    ]
     if len(drop_columns):
         card_activity.drop(columns=drop_columns, inplace=True)
-    return card_activity, card_activity, {"fraud": activity_range / 100., "not fraud": 1 - activity_range / 100.}
+    return (
+        card_activity,
+        card_activity,
+        {"fraud": activity_range / 100.0, "not fraud": 1 - activity_range / 100.0},
+    )
 
 
-iface = gr.Interface(fraud_detector,
-                     [
-                         gr.inputs.Timeseries(
-                             x="time",
-                             y=["retail", "food", "other"]
-                         ),
-                         gr.inputs.CheckboxGroup(["retail", "food", "other"], default=[
-                             "retail", "food", "other"]),
-                         gr.inputs.Slider(1, 3)
-                     ],
-                     [
-                         "dataframe",
-                         gr.outputs.Timeseries(
-                             x="time",
-                             y=["retail", "food", "other"]
-                         ),
-                         gr.outputs.Label(label="Fraud Level"),
-                     ]
-                     )
+iface = gr.Interface(
+    fraud_detector,
+    [
+        gr.inputs.Timeseries(x="time", y=["retail", "food", "other"]),
+        gr.inputs.CheckboxGroup(
+            ["retail", "food", "other"], default=["retail", "food", "other"]
+        ),
+        gr.inputs.Slider(1, 3),
+    ],
+    [
+        "dataframe",
+        gr.outputs.Timeseries(x="time", y=["retail", "food", "other"]),
+        gr.outputs.Label(label="Fraud Level"),
+    ],
+)
 if __name__ == "__main__":
     iface.launch()