test

app.py

@@ -193,70 +193,87 @@ class Handler:
         # print("Shapes:", img.shape, basevq_rec.shape, vqgan_rec.shape, revq_rec.shape)
         # return img, basevq_rec, vqgan_rec, revq_rec
         return basevq_rec, vqgan_rec, optvq_rec
-    
+
 def draw_process(x, y, std):
     img = (np.random.rand(256, 256, 3) * 255).astype(np.uint8)
     return img, img
 
-if __name__ == "__main__":
-    # create the model handler
-    # handler = Handler(device=device)
+demo2 = gr.Interface(
+    fn=draw_process,
+    inputs=[
+        gr.Slider(label="x", value=0, minimum=-10, maximum=10, step=0.1),
+        gr.Slider(label="y", value=0, minimum=-10, maximum=10, step=0.1),
+        gr.Slider(label="std", value=1, minimum=0, maximum=5, step=0.1)
+    ],
+    outputs=[
+        gr.Image(label="NN", type="numpy"),
+        gr.Image(label="OptVQ", type="numpy")
+    ],
+    title="Demo 2: 2D Matching Visualization",
+    description="Visualize nearest neighbor vs. optimal transport matching for synthetic 2D data."
+)
 
-    print("Creating Gradio interface...")
+demo2.launch()
 
-    demo2 = gr.Interface(
-    fn=draw_process,
-        inputs=[
-            gr.Slider(label="x", value=0, minimum=-10, maximum=10, step=0.1),
-            gr.Slider(label="y", value=0, minimum=-10, maximum=10, step=0.1),
-            gr.Slider(label="std", value=1, minimum=0, maximum=5, step=0.1)
-        ],
-        outputs=[
-            gr.Image(label="NN", type="numpy"),
-            gr.Image(label="OptVQ", type="numpy")
-        ],
-        title="Demo 2: 2D Matching Visualization",
-        description="Visualize nearest neighbor vs. optimal transport matching for synthetic 2D data."
-    )
+# if __name__ == "__main__":
+#     # create the model handler
+#     # handler = Handler(device=device)
 
-    # 合并两个 interface 成 Tabbed UI
-    demo = gr.TabbedInterface(
-        interface_list=[demo2],
-        tab_names=["2D Matching"]
-    )
+#     print("Creating Gradio interface...")
 
-    demo.launch()
-
-    # create the interface
-    # with gr.Blocks() as demo:
-        # gr.Textbox(value="This demo shows the image reconstruction comparison between ReVQ and other methods. The input image is resized to 256 x 256 and then fed into the models. The output images are the reconstructed images from the latent codes.", label="Demo 1: Image reconstruction results")
-        # with gr.Row():
-        #     with gr.Column():
-        #         image_input = gr.Image(label="Input data", image_mode="RGB", type="numpy")
-        #         btn_demo1 = gr.Button(value="Run reconstruction")
-        #     image_basevq = gr.Image(label="BaseVQ rec.")
-        #     image_vqgan = gr.Image(label="VQGAN rec.")
-        #     image_revq = gr.Image(label="ReVQ rec.")
-        # btn_demo1.click(fn=handler.process_image, inputs=[image_input], outputs=[image_basevq, image_vqgan, image_revq])
-
-        # gr.Textbox(value="This demo shows the 2D visualizations of nearest neighbor and optimal transport (OptVQ) methods. The data points are randomly generated from a normal distribution, and the matching results are shown as arrows with different colors.", label="Demo 2: 2D visualizations of matching results")
-        # gr.Markdown("### Demo 2: 2D visualizations of matching results\n"
-        #         "This demo shows the 2D visualizations of nearest neighbor and optimal transport (OptVQ) methods. "
-        #         "The data points are randomly generated from a normal distribution, and the matching results are shown as arrows with different colors.")
-        # with gr.Row():
-        #     with gr.Column():
-        #         input_x = gr.Slider(label="x", value=0, minimum=-10, maximum=10, step=0.1)
-        #         input_y = gr.Slider(label="y", value=0, minimum=-10, maximum=10, step=0.1)
-        #         input_std = gr.Slider(label="std", value=1, minimum=0, maximum=5, step=0.1)
-        #         btn_demo2 = gr.Button(value="Run 2D example")
-        #     output_nn = gr.Image(label="NN", interactive=False, type="numpy")
-        #     output_optvq = gr.Image(label="OptVQ", interactive=False, type="numpy")
+#     demo2 = gr.Interface(
+#     fn=draw_process,
+#         inputs=[
+#             gr.Slider(label="x", value=0, minimum=-10, maximum=10, step=0.1),
+#             gr.Slider(label="y", value=0, minimum=-10, maximum=10, step=0.1),
+#             gr.Slider(label="std", value=1, minimum=0, maximum=5, step=0.1)
+#         ],
+#         outputs=[
+#             gr.Image(label="NN", type="numpy"),
+#             gr.Image(label="OptVQ", type="numpy")
+#         ],
+#         title="Demo 2: 2D Matching Visualization",
+#         description="Visualize nearest neighbor vs. optimal transport matching for synthetic 2D data."
+#     )
+
+#     # 合并两个 interface 成 Tabbed UI
+#     demo = gr.TabbedInterface(
+#         interface_list=[demo2],
+#         tab_names=["2D Matching"]
+#     )
+
+#     demo.launch()
+
+#     # create the interface
+#     # with gr.Blocks() as demo:
+#         # gr.Textbox(value="This demo shows the image reconstruction comparison between ReVQ and other methods. The input image is resized to 256 x 256 and then fed into the models. The output images are the reconstructed images from the latent codes.", label="Demo 1: Image reconstruction results")
+#         # with gr.Row():
+#         #     with gr.Column():
+#         #         image_input = gr.Image(label="Input data", image_mode="RGB", type="numpy")
+#         #         btn_demo1 = gr.Button(value="Run reconstruction")
+#         #     image_basevq = gr.Image(label="BaseVQ rec.")
+#         #     image_vqgan = gr.Image(label="VQGAN rec.")
+#         #     image_revq = gr.Image(label="ReVQ rec.")
+#         # btn_demo1.click(fn=handler.process_image, inputs=[image_input], outputs=[image_basevq, image_vqgan, image_revq])
+
+#         # gr.Textbox(value="This demo shows the 2D visualizations of nearest neighbor and optimal transport (OptVQ) methods. The data points are randomly generated from a normal distribution, and the matching results are shown as arrows with different colors.", label="Demo 2: 2D visualizations of matching results")
+#         # gr.Markdown("### Demo 2: 2D visualizations of matching results\n"
+#         #         "This demo shows the 2D visualizations of nearest neighbor and optimal transport (OptVQ) methods. "
+#         #         "The data points are randomly generated from a normal distribution, and the matching results are shown as arrows with different colors.")
+#         # with gr.Row():
+#         #     with gr.Column():
+#         #         input_x = gr.Slider(label="x", value=0, minimum=-10, maximum=10, step=0.1)
+#         #         input_y = gr.Slider(label="y", value=0, minimum=-10, maximum=10, step=0.1)
+#         #         input_std = gr.Slider(label="std", value=1, minimum=0, maximum=5, step=0.1)
+#         #         btn_demo2 = gr.Button(value="Run 2D example")
+#         #     output_nn = gr.Image(label="NN", interactive=False, type="numpy")
+#         #     output_optvq = gr.Image(label="OptVQ", interactive=False, type="numpy")
         
-        # # set the function
-        # input_x.change(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
-        # input_y.change(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
-        # input_std.change(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
-        # btn_demo2.click(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
-        # btn_demo2.click(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
-
-    # demo.launch()
+#         # # set the function
+#         # input_x.change(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
+#         # input_y.change(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
+#         # input_std.change(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
+#         # btn_demo2.click(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
+#         # btn_demo2.click(fn=draw_process, inputs=[input_x, input_y, input_std], outputs=[output_nn, output_optvq])
+
+#     # demo.launch()