Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from model import CRM
+from inference import generate3d
+import numpy as np
+
+# Load model
+crm_path = "CRM.pth"  # Make sure the model is uploaded to the Space
+model = CRM(torch.load(crm_path, map_location="cpu"))
+model = model.to("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def generate_3d(image_path, seed=1234, scale=5.5, step=30):
+    image = Image.open(image_path).convert("RGB")
+    np_img = np.array(image)
+    glb_path = generate3d(model, np_img, np_img, "cuda:0" if torch.cuda.is_available() else "cpu")
+    return glb_path
+
+iface = gr.Interface(
+    fn=generate_3d,
+    inputs=gr.Image(type="filepath"),
+    outputs=gr.Model3D(),
+    title="Convolutional Reconstruction Model (CRM)",
+    description="Upload an image to generate a 3D model."
+)
+
+iface.launch()