Update app.py

app.py

@@ -4,23 +4,35 @@ from PIL import Image
 import torch
 
 # Load model and processor
-model = AutoModel.from_pretrained("zxhezexin/openlrm-mix-large-1.1")
-processor = AutoProcessor.from_pretrained("zxhezexin/openlrm-mix-large-1.1")
+try:
+    model = AutoModel.from_pretrained("zxhezexin/openlrm-mix-large-1.1")
+    processor = AutoProcessor.from_pretrained("zxhezexin/openlrm-mix-large-1.1")
+except Exception as e:
+    print(f"Error loading model or processor: {e}")
 
 # Define function to generate 3D output from 2D image
 def image_to_3d(image):
-    inputs = processor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    # This is placeholder logic; you'd need to process the outputs appropriately
-    return "3D Output Generated"  # Replace with actual visualization code
+    try:
+        # Preprocess the input image
+        inputs = processor(images=image, return_tensors="pt")
+        
+        # Run inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # Placeholder return, replace this with actual 3D visualization
+        return "3D model generated!"  
+    except Exception as e:
+        return f"Error during inference: {str(e)}"
 
 # Gradio interface
 interface = gr.Interface(
     fn=image_to_3d,
     inputs=gr.Image(type="pil"),
-    outputs="text",  # Replace with "3D" if you can visualize the output
-    title="OpenLRM Mix-Large 1.1 - Image to 3D"
+    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."
 )
 
+# Launch the Gradio interface
 interface.launch()