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Runtime error
| import gradio as gr | |
| import numpy as np | |
| import time | |
| def predict(x): | |
| return np.fliplr(x) | |
| def compress(): | |
| time.sleep(1) | |
| return 'The model has been compressed successfully.' | |
| with gr.Blocks() as demo: | |
| with gr.Column(): | |
| gr.Radio(["Image classification", "Object detection", "Semantic segmentation"], label="Tasks"), | |
| gr.Radio(["ResNet", "VGG", "MobileNet"], label="Models"), | |
| gr.Radio(["Weight quantization","Knowledge distillation","Network pruning", "Neural Architecture Search"], | |
| label="Compression methods"), | |
| gr.Radio(["Jetson Nano"], label="Deployments") | |
| compress_btn = gr.Button("compress") | |
| output_compress = gr.Textbox(lines=1, label="Model Compression Results", visible=False) | |
| with gr.Row(): | |
| Original_config = gr.Dataframe(headers=["#Params.(M)", "FLOPs(G)"], datatype=[ | |
| "str", "str"], row_count=1, value=[['63.8M','250G']], label="Original model config", visible=False) | |
| Compressed_config = gr.Dataframe(headers=["#Params.(M)", "FLOPs(G)"], datatype=[ | |
| "str", "str"], row_count=1, value=[['34.6M','126G']],label="Compressed model config", visible=False) | |
| with gr.Row(): | |
| input_predict = gr.Image(label="input") | |
| output_predict = gr.Image(label="output") | |
| predict_btn = gr.Button("predict") | |
| state = gr.State() | |
| compress_btn.click(fn=compress, inputs=None, | |
| outputs=output_compress, api_name="compress") | |
| compress_btn.click(lambda : (gr.Textbox.update(visible=True), "visible"), None, [output_compress, state]) | |
| output_compress.change(lambda: (Original_config.update(visible=True), "visible"), None, [Original_config, state]) | |
| output_compress.change(lambda: (Compressed_config.update(visible=True), "visible"), None, [Compressed_config, state]) | |
| predict_btn.click(fn=predict, inputs=input_predict, | |
| outputs=output_predict, api_name="predict") | |
| demo.launch(share=True) |