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| import gradio as gr | |
| import CaptchaCracker as cc | |
| # Target image data size | |
| img_width = 200 | |
| img_height = 50 | |
| # Target image label length | |
| max_length = 6 | |
| # Target image label component | |
| characters = {'0', '1', '2', '3', '4', '5', '6', '7', '8', '9'} | |
| # Model weight file path | |
| weights_path = "weights.h5" | |
| # Creating a model application instance | |
| AM = cc.ApplyModel(weights_path, img_width, img_height, max_length, characters) | |
| def inference(target_img_path): | |
| # Predicted value | |
| pred = AM.predict(target_img_path) | |
| return pred | |
| block = gr.Blocks() | |
| with block: | |
| gr.Markdown("Gradio Demo for WooilJeong/CaptchaCracker") | |
| with gr.Tabs(): | |
| with gr.TabItem("CaptchaCracker"): | |
| with gr.Row(): | |
| captchaimg = gr.inputs.Image(type="filepath") | |
| text = gr.outputs.Textbox() | |
| catch_run = gr.Button("Run") | |
| catch_run.click(inference, inputs=captchaimg, outputs=text) | |
| block.launch() |