commit

app.py

@@ -1,7 +1,141 @@
+import os
 import gradio as gr
+from gradio_client import Client, handle_file
+from gradio_imageslider import ImageSlider
+from pathlib import Path
+from gradio.utils import get_cache_folder
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+class Examples(gr.helpers.Examples):
+    def __init__(self, *args, cached_folder=None, **kwargs):
+        super().__init__(*args, **kwargs, _initiated_directly=False)
+        if cached_folder is not None:
+            self.cached_folder = cached_folder
+            # self.cached_file = Path(self.cached_folder) / "log.csv"
+        self.create()
+
+
+client = Client("Canyu/Diception",
+                max_workers=3)
+
+
+def process_image_check(path_input):
+    if path_input is None:
+        raise gr.Error(
+            "Missing image in the left pane: please upload an image first."
+        )
+
+def infer_image_matting(matting_image_input):
+    return client.predict(
+      file=handle_file(matting_image_input),
+      api_name="/infer_image_matting"
+    )
+
+def clear_cache():
+    return None, None
+
+def run_demo_server():
+    gradio_theme = gr.themes.Default()
+    with gr.Blocks(
+        theme=gradio_theme,
+        title="Matting",
+    ) as demo:
+        with gr.Row():
+            gr.Markdown("# Matting Demo")
+        with gr.Row():
+            gr.Markdown("### Due to the GPU quota limit, if an error occurs, please wait for 5 minutes before retrying.")
+        with gr.Row():
+            with gr.Column():
+                matting_image_input = gr.Image(
+                    label="Input Image",
+                    type="filepath",
+                )
+                with gr.Row():
+                    matting_image_submit_btn = gr.Button(
+                        value="Estimate Matting", variant="primary"
+                    )
+                    matting_image_reset_btn = gr.Button(value="Reset")
+                
+                with gr.Row():
+                    img_clear_button = gr.Button("Clear Cache")
+                
+            with gr.Column():
+                # matting_image_output = gr.Image(label='Output')
+                matting_image_output =  gr.Image(label='Matting Output')
+                    
+                        #     label="Matting Output",
+                        #     type="filepath",
+                        #     show_download_button=True,
+                        #     show_share_button=True,
+                        #     interactive=False,
+                        #     elem_classes="slider",
+                        #     position=0.25,
+                        # )
+                
+            
+
+        img_clear_button.click(clear_cache, outputs=[matting_image_input, matting_image_output])
+
+        matting_image_submit_btn.click(
+            fn=process_image_check,
+            inputs=matting_image_input,
+            outputs=None,
+            preprocess=False,
+            queue=False,
+        ).success(
+            # fn=process_pipe_matting,
+            fn=infer_image_matting,
+            inputs=[
+                matting_image_input,
+            ],
+            outputs=[matting_image_output],
+            concurrency_limit=1,
+        )
+
+        matting_image_reset_btn.click(
+            fn=lambda: (
+                None,
+                None,
+            ),
+            inputs=[],
+            outputs=[
+                matting_image_input,
+                matting_image_output,
+            ],
+            queue=False,
+        )
+
+        gr.Examples(
+            fn=infer_image_matting,
+            examples=[
+                "assets/person.jpg",              
+            ],
+            inputs=[matting_image_input],
+            outputs=[matting_image_output],
+            cache_examples=True,
+            # cache_examples=False,
+            # cached_folder="cache_dir",
+        )
+        
+    demo.queue(
+        api_open=False,
+    ).launch()
+
+
+if __name__ == '__main__':
+
+    # 在公共 Space 中，通过 gradio.client 调用私有 Space
+    def call_private_space(input_text):
+        # 加载私有 Space（假设是私有 Space 的名称）
+        app = gr.Interface.load("Canyu/Diception", use_auth_token=True)
+
+        # 调用私有 Space 中的函数
+        output = app(input_text)
+        
+        return output
+
+    # 创建一个公共 Space 界面，允许用户输入数据并调用私有 Space 中的函数
+    interface = gr.Interface(fn=call_private_space, inputs="text", outputs="text")
+
+    # 启动公共 Space
+    interface.launch()