noni27 commited on
Commit
98be1d8
·
verified ·
1 Parent(s): 033a8fa

Update app.py

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Files changed (1) hide show
  1. app.py +5 -5
app.py CHANGED
@@ -251,8 +251,8 @@ def main_fun(image_pil, x_units, y_units, text_prompt, box_threshold, text_thres
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  output = inpainted_shifted + shifted_image
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  output = Image.fromarray(output)
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  # visualize_results(image_pil, output, 'shifted')
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-
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- return segmented_image, output
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  import gradio as gr
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@@ -263,9 +263,9 @@ with image_blocks as demo:
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  with gr.Column():
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  image = gr.Image(sources=['upload'], type="pil", label="Upload")
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  # with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
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- text_prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', show_label=True)
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- x_units = gr.Slider(minimum=50, maximum=300, step=10, value=10, label="x_units")
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- y_units = gr.Slider(minimum=30, maximum=300, step=10, value=5, label="y_units")
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  with gr.Accordion("Advanced options", open=False) as advanced_options:
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  box_threshold = gr.Slider(
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  label="Box Threshold", minimum=0.0, maximum=1.0, value=0.23, step=0.01
 
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  output = inpainted_shifted + shifted_image
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  output = Image.fromarray(output)
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  # visualize_results(image_pil, output, 'shifted')
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+ segmented_image = Image.fromarray(segmented_image)
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+ return segmented_image.resize((512, 512)), output.resize((512, 512))
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  import gradio as gr
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  with gr.Column():
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  image = gr.Image(sources=['upload'], type="pil", label="Upload")
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  # with gr.Row(elem_id="prompt-container").style(mobile_collapse=False, equal_height=True):
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+ text_prompt = gr.Textbox(placeholder = 'Your prompt (what you want in place of what is erased)', label="Object class", show_label=True)
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+ x_units = gr.Slider(minimum=0, maximum=300, step=10, value=100, label="x_units")
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+ y_units = gr.Slider(minimum=0, maximum=300, step=10, value=0, label="y_units")
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  with gr.Accordion("Advanced options", open=False) as advanced_options:
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  box_threshold = gr.Slider(
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  label="Box Threshold", minimum=0.0, maximum=1.0, value=0.23, step=0.01