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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 from Hugging Face
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}")
# Example image URL (replace this with a suitable example)
example_image_url = "https://huggingface.co/datasets/nateraw/image-folder/resolve/main/example_1.png"
# Function to load example image from URL
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:
# 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 logic
return "3D model generated from input image!"
except Exception as e:
return f"Error during inference: {str(e)}"
# Load the example image for the Gradio interface
example_image = load_example_image()
# Gradio interface setup
interface = gr.Interface(
fn=image_to_3d,
inputs=gr.Image(type="pil", label="Upload an Image or use Example"),
outputs="text", # Placeholder output (replace with 3D rendering if needed)
title="OpenLRM Mix-Large 1.1 - Image to 3D",
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 if loaded
)
# Launch the Gradio interface
interface.launch()
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