app.py

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
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 (change this to a suitable 2D image URL)
example_image_url = "https://example.com/path-to-your-example-image.jpg"

# Load example image
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
        return "3D model generated!"  
    except Exception as e:
        return f"Error during inference: {str(e)}"

# Gradio interface
example_image = load_example_image()

interface = gr.Interface(
    fn=image_to_3d,
    inputs=gr.Image(type="pil", label="Upload an Image or use Example"),
    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.",
    examples=[[example_image]] if example_image else None  # Include the example image
)

# Launch the Gradio interface
interface.launch()