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import gradio as gr
from transformers import AutoModel, AutoProcessor
from PIL import Image
import torch
# Load model and processor
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):
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", # 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()
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