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app.py

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+import torch
+import gradio as gr
+from shap_e.diffusion.sample import sample_latents
+from shap_e.models.download import load_model
+from shap_e.util.notebooks import decode_latent_mesh
+from PIL import Image
+
+# pick device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# load models once
+print("Loading Shap-E models (this may take a bit)...")
+xm = load_model('transmitter', device=device)
+im_model = load_model('image300M', device=device)
+
+def generate_3d(image):
+    """Takes an uploaded image and returns path to generated 3D model"""
+    img = image.convert("RGB")
+
+    # generate latents
+    latents = sample_latents(
+        batch_size=1,
+        model=im_model,
+        guidance_scale=3.0,
+        model_kwargs=dict(images=[img]),
+        device=device
+    )
+
+    # decode into mesh
+    mesh = decode_latent_mesh(xm, latents[0])
+
+    # save model
+    output_path = "output.obj"
+    with open(output_path, "w") as f:
+        mesh.write_obj(f)
+
+    return output_path
+
+# Gradio UI
+demo = gr.Interface(
+    fn=generate_3d,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.File(),
+    title="Shap-E: 2D → 3D Model",
+    description="Upload a 2D image and download a generated 3D model (.obj)"
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+torch
+torchvision
+torchaudio
+git+https://github.com/openai/shap-e.git
+Pillow
+gradio