Create app.py

app.py

@@ -0,0 +1,46 @@
+import torch  
+from meshgpt_pytorch import ( 
+    MeshTransformer,
+    mesh_render
+)
+import igl
+
+import gradio as gr
+import numpy as np 
+
+transformer = MeshTransformer.from_pretrained("MarcusLoren/MeshGPT-preview")
+
+def save_as_obj(file_path):
+    v, f = igl.read_triangle_mesh(file_path)
+    v, f, _, _ = igl.remove_unreferenced(v, f)
+    c, _ = igl.orientable_patches(f)
+    f, _ = igl.orient_outward(v, f, c)
+    igl.write_triangle_mesh(file_path, v, f)
+    return file_path
+
+def predict(text, num_input, num_temp):
+    transformer.eval()
+    labels = [label.strip() for label in text.split(',')] 
+    output = []
+    if num_input > 1:
+        for label in labels:
+            output.append((transformer.generate(texts = [label ] * num_input, temperature = num_temp)))   
+    else:
+        output.append((transformer.generate(texts = [label ] * num_input, temperature = num_temp)))   
+            
+    mesh_render.save_rendering('./render.obj', output) 
+    return save_as_obj('./render.obj')
+
+gradio_app = gr.Interface(
+    predict, 
+    inputs=[
+        gr.Textbox(label="Enter labels, separated by commas"),
+        gr.Number(value=1, label="Number of examples per input"),
+        gr.Slider(minimum=0, maximum=1, value=0, label="Temperature (0 to 1)")
+    ],
+    outputs=gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"),
+    title="MeshGPT Inference - (Rending doesn't work, please download for best result)",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch(share=False)