app.py

app.py

@@ -0,0 +1,43 @@
+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)