file upload

app.py

@@ -7,14 +7,31 @@ pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotd
 @spaces.GPU
 def predict(input_img):
     predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
+    return input_img, {p["label"]: p["score"] for p in predictions}
+
+_HEADER_ = '''
+<h2>Toon3D: Seeing Cartoons from a New Perspective</h2>
+**Toon3D** lifts cartoons into 3D via aligning and warping backprojected monocular depth predictions..
+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).
+'''
 
 gradio_app = gr.Interface(
     predict,
     inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
     outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+    title="Toon3D",
 )
 
+with gr.Blocks() as demo:
+    gr.Markdown(_HEADER_)
+    with gr.Row(variant="panel"):
+        with gr.Column():
+            with gr.Row():
+                input = gr.File(file_count="directory")
+
 if __name__ == "__main__":
     gradio_app.launch()