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| import numpy as np | |
| import gradio as gr | |
| def sepia(input_img, strength): | |
| sepia_filter = strength * np.array( | |
| [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]] | |
| ) + (1-strength) * np.identity(3) | |
| sepia_img = input_img.dot(sepia_filter.T) | |
| sepia_img /= sepia_img.max() | |
| return sepia_img | |
| callback = gr.CSVLogger() | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| img_input = gr.Image() | |
| strength = gr.Slider(0, 1, 0.5) | |
| img_output = gr.Image() | |
| with gr.Row(): | |
| btn = gr.Button("Flag") | |
| # This needs to be called at some point prior to the first call to callback.flag() | |
| callback.setup([img_input, strength, img_output], "flagged_data_points") | |
| img_input.change(sepia, [img_input, strength], img_output) | |
| strength.change(sepia, [img_input, strength], img_output) | |
| # We can choose which components to flag -- in this case, we'll flag all of them | |
| btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, _preprocess=False) | |
| if __name__ == "__main__": | |
| demo.launch() | |