updates

app.py

@@ -11,14 +11,25 @@ def predict(input_img):
 
 _HEADER_ = '''
 <h2>Toon3D: Seeing Cartoons from a New Perspective</h2>
-**Toon3D** lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions..
+Toon3D lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions.<br>
 Project page @ <a href='https://toon3d.studio/' target='_blank'>https://toon3d.studio/</a>
 
 **Important Notes:**
-- Our demo can export a .obj mesh with vertex colors or a .glb mesh now. If you prefer to export a .obj mesh with a **texture map**, please refer to our <a href='https://github.com/TencentARC/InstantMesh?tab=readme-ov-file#running-with-command-line' target='_blank'>Github Repo</a>.
-- The 3D mesh generation results highly depend on the quality of generated multi-view images. Please try a different **seed value** if the result is unsatisfying (Default: 42).
+- TODO 1
+- TODO 2
 '''
 
+def check_input_images(input_images):
+    if input_images is None:
+        raise gr.Error("No images uploaded!")
+    
+def process_images(input_images):
+
+    for image in input_images:
+        print(image)
+
+    return input_images
+
 gradio_app = gr.Interface(
     predict,
     inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
@@ -31,7 +42,24 @@ with gr.Blocks() as demo:
     with gr.Row(variant="panel"):
         with gr.Column():
             with gr.Row():
-                input = gr.File(file_count="directory")
+                input_images = gr.File(label="Upload Images", file_count="multiple", file_types=[".jpg", "jpeg", "png"])
+            with gr.Row():
+                process_data_button = gr.Button("Process Data", elem_id="process_data", variant="primary")
+            with gr.Row():
+                processed_data_zip = gr.File(label="Processed Data", file_count="single", file_types=[".zip"])
+        with gr.Column():
+            with gr.Row():
+                labeled_data = gr.File(label="Labeled Points", file_count="single", file_types=[".json"])
+
+
+
+# mv_images = gr.State()
+
+    process_data_button.click(fn=check_input_images, inputs=[input_images]).success(
+        fn=process_images,
+        inputs=[input_images],
+        outputs=[processed_data_zip],
+    )
 
 if __name__ == "__main__":
     demo.launch()