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Configuration error
Configuration error
Luca Vivona
commited on
Commit
•
7a0af64
1
Parent(s):
f670280
⚙️ updated examples
Browse files
backend/src/example/examples.py
CHANGED
@@ -1,14 +1,15 @@
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import gradio as gr
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import PIL
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from helper.compiler import GradioCompiler, register
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"""
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@
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class Pictionary:
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def __init__(self) -> None:
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@@ -45,7 +46,7 @@ class Pictionary:
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return confidences
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"""
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@
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class HelloWorld_2_0:
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@register(inputs=["text", "text", gr.Radio(["morning", "evening", "night"])], outputs="text")
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@
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class FSD:
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def get_new_val(self, old_val, nc):
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carr = np.array(pixels / np.max(pixels, axis=(0, 1)) * 255, dtype=np.uint8)
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return [PIL.Image.fromarray(carr), self.palette_reduce(img, nc) ]
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@
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class C:
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def Hello(self):
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def Greeting(self, name):
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return self.Hello() + " " + name
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class stock_forecast:
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def __init__(self):
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import gradio as gr
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import sys
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import PIL
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sys.path.insert(0, "../resources")
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from resources.module import GradioModule, register
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"""
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@GradioModule
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class Pictionary:
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def __init__(self) -> None:
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return confidences
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"""
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@GradioModule
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class HelloWorld_2_0:
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@register(inputs=["text", "text", gr.Radio(["morning", "evening", "night"])], outputs="text")
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@GradioModule
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class FSD:
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def get_new_val(self, old_val, nc):
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carr = np.array(pixels / np.max(pixels, axis=(0, 1)) * 255, dtype=np.uint8)
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return [PIL.Image.fromarray(carr), self.palette_reduce(img, nc) ]
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@register(inputs=[gr.Image(), gr.Image(), gr.Slider(0.00, 16)], outputs=gr.Gallery())
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def examples(self, img, img2, nc, ) -> 'list[PIL.Image.Image]':
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pixels = np.array(img, dtype=float) / 255
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new_height, new_width, _ = img.shape
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for row in range(new_height):
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for col in range(new_width):
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old_val = pixels[row, col].copy()
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new_val = self.get_new_val(old_val, nc)
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pixels[row, col] = new_val
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err = old_val - new_val
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if col < new_width - 1:
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pixels[row, col + 1] += err * 7 / 16
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if row < new_height - 1:
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if col > 0:
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pixels[row + 1, col - 1] += err * 3 / 16
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pixels[row + 1, col] += err * 5 / 16
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if col < new_width - 1:
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pixels[row + 1, col + 1] += err * 1 / 16
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carr = np.array(pixels / np.max(pixels, axis=(0, 1)) * 255, dtype=np.uint8)
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return [PIL.Image.fromarray(carr), self.palette_reduce(img, nc) ]
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@GradioModule
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class C:
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def Hello(self):
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def Greeting(self, name):
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return self.Hello() + " " + name
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@GradioModule
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class stock_forecast:
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def __init__(self):
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