app.py

import gradio as gr
from transformers import AutoModel, AutoProcessor
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")

# 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

# 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"
)

interface.launch()