Create app.py

app.py

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+import gradio as gr
+import torch
+from shap_e.diffusion.sample import sample_latents
+from shap_e.diffusion.gaussian_diffusion import diffusion_from_config
+from shap_e.models.download import load_model, load_config
+from shap_e.util.notebooks import decode_latent_mesh
+import zipfile
+import io
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load models
+transmitter = load_model("transmitter", device=device)
+xm = load_model("text300M", device=device)
+diffusion = diffusion_from_config(load_config("diffusion"))
+
+def generate_3d(prompt):
+    with torch.no_grad():
+        latents = sample_latents(
+            batch_size=1,
+            model=xm,
+            diffusion=diffusion,
+            guidance_scale=3.0,
+            model_kwargs=dict(texts=[prompt]),
+            progress=True,
+            clip_denoised=True,
+            use_fp16=True,
+            device=device,
+        )
+        mesh = decode_latent_mesh(xm, latents[0]).tri_mesh()
+
+        # Save zip
+        buf = io.BytesIO()
+        with zipfile.ZipFile(buf, "w") as z:
+            z.writestr("mesh.ply", mesh.to_ply())
+        buf.seek(0)
+        return buf
+
+demo = gr.Interface(
+    fn=generate_3d,
+    inputs=gr.Textbox(label="Prompt"),
+    outputs=gr.File(label="Generated 3D Model (.zip)")
+)
+
+demo.launch()