Update app.py
Browse files
app.py
CHANGED
@@ -4,6 +4,8 @@ from PIL import Image
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import torch
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import requests
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from io import BytesIO
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# Load model and processor from Hugging Face
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def load_model_and_processor():
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@@ -36,38 +38,42 @@ def image_to_3d(image):
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return "Model or processor not loaded."
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try:
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# Preprocess
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inputs = processor(images=image, return_tensors="pt")
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# Run inference
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with torch.no_grad():
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outputs = model(**inputs)
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#
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except Exception as e:
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return f"Error during inference: {str(e)}"
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# Load the example image for the Gradio interface
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example_image = load_example_image()
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# Description of image types to upload
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image_type_description = """
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Upload a clear image of a single object with minimal background distractions for best results. Example image types:
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- Objects such as cars, furniture, geometric shapes, or architectural structures.
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"""
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# Gradio interface setup
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interface = gr.Interface(
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fn=image_to_3d,
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inputs=gr.Image(type="pil", label="Upload an Image"),
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outputs="
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title="OpenLRM Mix-Large 1.1 - Image to 3D",
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description="Upload an image to generate a 3D model using OpenLRM Mix-Large 1.1.",
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examples=[[example_image]] if example_image else None,
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theme="compact"
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allow_flagging="never"
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)
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# Display a suggestion below the upload widget
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interface.launch(
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import torch
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import requests
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from io import BytesIO
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import trimesh
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import plotly.graph_objects as go
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# Load model and processor from Hugging Face
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def load_model_and_processor():
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return "Model or processor not loaded."
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try:
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# Preprocess input image
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inputs = processor(images=image, return_tensors="pt")
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# Run inference
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with torch.no_grad():
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outputs = model(**inputs)
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# Convert outputs to a 3D mesh (replace with actual logic based on model output)
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# Assuming 'vertices' and 'faces' are returned by the model (adjust as needed)
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vertices = outputs['vertices'].numpy() # Placeholder for vertex output
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faces = outputs['faces'].numpy() # Placeholder for face output
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# Create a mesh using trimesh
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mesh = trimesh.Trimesh(vertices=vertices, faces=faces)
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# Visualize the mesh using Plotly
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fig = go.Figure(data=[go.Mesh3d(x=vertices[:,0], y=vertices[:,1], z=vertices[:,2],
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i=faces[:,0], j=faces[:,1], k=faces[:,2])])
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return fig # return the figure for display
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except Exception as e:
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return f"Error during inference: {str(e)}"
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# Load the example image for the Gradio interface
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example_image = load_example_image()
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# Gradio interface setup
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interface = gr.Interface(
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fn=image_to_3d,
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inputs=gr.Image(type="pil", label="Upload an Image"),
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outputs=gr.Plot(label="3D Model"),
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title="OpenLRM Mix-Large 1.1 - Image to 3D",
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description="Upload an image to generate a 3D model using OpenLRM Mix-Large 1.1.",
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examples=[[example_image]] if example_image else None,
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theme="compact"
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)
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# Display a suggestion below the upload widget
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interface.launch()
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