File size: 5,631 Bytes
c3f69c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 |
import gradio as gr
from datetime import datetime
import os
import random
import string
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def random_plot():
start_year = 2020
x = np.arange(start_year, start_year + 5)
year_count = x.shape[0]
plt_format = "-"
fig = plt.figure()
ax = fig.add_subplot(111)
series = np.arange(0, year_count, dtype=float)
series = series**2
series += np.random.rand(year_count)
ax.plot(x, series, plt_format)
return fig
images = [
"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
]
file_dir = os.path.join(os.path.dirname(__file__), "..", "kitchen_sink", "files")
model3d_dir = os.path.join(os.path.dirname(__file__), "..", "model3D", "files")
highlighted_text_output_1 = [
{
"entity": "I-LOC",
"score": 0.9988978,
"index": 2,
"word": "Chicago",
"start": 5,
"end": 12,
},
{
"entity": "I-MISC",
"score": 0.9958592,
"index": 5,
"word": "Pakistani",
"start": 22,
"end": 31,
},
]
highlighted_text_output_2 = [
{
"entity": "I-LOC",
"score": 0.9988978,
"index": 2,
"word": "Chicago",
"start": 5,
"end": 12,
},
{
"entity": "I-LOC",
"score": 0.9958592,
"index": 5,
"word": "Pakistan",
"start": 22,
"end": 30,
},
]
highlighted_text = "Does Chicago have any Pakistani restaurants"
def random_model3d():
model_3d = random.choice(
[os.path.join(model3d_dir, model) for model in os.listdir(model3d_dir) if model != "source.txt"]
)
return model_3d
components = [
gr.Textbox(value=lambda: datetime.now(), label="Current Time"),
gr.Number(value=lambda: random.random(), label="Random Percentage"),
gr.Slider(minimum=0, maximum=100, randomize=True, label="Slider with randomize"),
gr.Slider(
minimum=0,
maximum=1,
value=lambda: random.random(),
label="Slider with value func",
),
gr.Checkbox(value=lambda: random.random() > 0.5, label="Random Checkbox"),
gr.CheckboxGroup(
choices=["a", "b", "c", "d"],
value=lambda: random.choice(["a", "b", "c", "d"]),
label="Random CheckboxGroup",
),
gr.Radio(
choices=list(string.ascii_lowercase),
value=lambda: random.choice(string.ascii_lowercase),
),
gr.Dropdown(
choices=["a", "b", "c", "d", "e"],
value=lambda: random.choice(["a", "b", "c"]),
),
gr.Image(
value=lambda: random.choice(images)
),
gr.Video(value=lambda: os.path.join(file_dir, "world.mp4")),
gr.Audio(value=lambda: os.path.join(file_dir, "cantina.wav")),
gr.File(
value=lambda: random.choice(
[os.path.join(file_dir, img) for img in os.listdir(file_dir)]
)
),
gr.Dataframe(
value=lambda: pd.DataFrame({"random_number_rows": range(5)}, columns=["one", "two", "three"]) # type: ignore
),
gr.ColorPicker(value=lambda: random.choice(["#000000", "#ff0000", "#0000FF"])),
gr.Label(value=lambda: random.choice(["Pedestrian", "Car", "Cyclist"])),
gr.HighlightedText(
value=lambda: random.choice(
[
{"text": highlighted_text, "entities": highlighted_text_output_1},
{"text": highlighted_text, "entities": highlighted_text_output_2},
]
),
),
gr.JSON(value=lambda: random.choice([{"a": 1}, {"b": 2}])),
gr.HTML(
value=lambda: random.choice(
[
'<p style="color:red;">I am red</p>',
'<p style="color:blue;">I am blue</p>',
]
)
),
gr.Gallery(
value=lambda: images
),
gr.Model3D(value=random_model3d),
gr.Plot(value=random_plot),
gr.Markdown(value=lambda: f"### {random.choice(['Hello', 'Hi', 'Goodbye!'])}"),
]
def evaluate_values(*args):
are_false = []
for a in args:
if isinstance(a, (pd.DataFrame, np.ndarray)):
are_false.append(not a.any().any()) # type: ignore
elif isinstance(a, str) and a.startswith("#"):
are_false.append(a == "#000000")
else:
are_false.append(not a)
return all(are_false)
with gr.Blocks() as demo:
for i, component in enumerate(components):
component.label = f"component_{str(i).zfill(2)}"
component.render()
clear = gr.ClearButton(value="Clear", components=components)
result = gr.Textbox(label="Are all cleared?")
hide = gr.Button(value="Hide")
reveal = gr.Button(value="Reveal")
clear_button_and_components = components + [clear]
hide.click(
lambda: [c.__class__(visible=False) for c in clear_button_and_components],
inputs=[],
outputs=clear_button_and_components
)
reveal.click(
lambda: [c.__class__(visible=True) for c in clear_button_and_components],
inputs=[],
outputs=clear_button_and_components
)
get_value = gr.Button(value="Get Values")
get_value.click(evaluate_values, components, result)
if __name__ == "__main__":
demo.launch()
|