Update app.py

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 from transformers import AutoModel, AutoProcessor
 from PIL import Image
 import torch
+import requests
+from io import BytesIO
 
 # Load model and processor
 try:
@@ -10,6 +12,19 @@ try:
 except Exception as e:
     print(f"Error loading model or processor: {e}")
 
+# Example image URL (change this to a suitable 2D image URL)
+example_image_url = "https://example.com/path-to-your-example-image.jpg"
+
+# Load example image
+def load_example_image():
+    try:
+        response = requests.get(example_image_url)
+        image = Image.open(BytesIO(response.content))
+        return image
+    except Exception as e:
+        print(f"Error loading example image: {e}")
+        return None
+
 # Define function to generate 3D output from 2D image
 def image_to_3d(image):
     try:
@@ -26,13 +41,17 @@ def image_to_3d(image):
         return f"Error during inference: {str(e)}"
 
 # Gradio interface
+example_image = load_example_image()
+
 interface = gr.Interface(
     fn=image_to_3d,
-    inputs=gr.Image(type="pil"),
+    inputs=gr.Image(type="pil", label="Upload an Image or use Example"),
     outputs="text",  # Placeholder output (you can modify this for 3D)
     title="OpenLRM Mix-Large 1.1 - Image to 3D",
-    description="Upload an image to generate a 3D model using OpenLRM Mix-Large 1.1."
+    description="Upload an image to generate a 3D model using OpenLRM Mix-Large 1.1.",
+    examples=[[example_image]] if example_image else None  # Include the example image
 )
 
 # Launch the Gradio interface
 interface.launch()
+