app.py

import torch
from diffusers import ShapEPipeline
from diffusers.utils import export_to_gif
import PIL.Image

def generate_3d_model(prompt, output_path="assistant_3d.gif"):
    """
    Generate a 3D model using ShapE optimized for CPU usage
    """
    try:
        # Force CPU and reduced precision
        pipe = ShapEPipeline.from_pretrained(
            "openai/shap-e",
            torch_dtype=torch.float32,
            low_cpu_mem_usage=True
        ).to("cpu")
        
        # Minimal generation settings to reduce memory usage
        outputs = pipe(
            prompt,
            num_inference_steps=32,  # Reduced from default
            frame_size=32,  # Smaller frame size
            guidance_scale=10.0,  # Reduced guidance scale
        )
        
        # Ensure we have PIL images
        if not isinstance(outputs.images[0], PIL.Image.Image):
            images = [PIL.Image.fromarray(img) for img in outputs.images]
        else:
            images = outputs.images
            
        # Save as GIF
        gif_path = export_to_gif(images, output_path)
        print(f"Successfully created GIF at: {gif_path}")
        return gif_path
        
    except Exception as e:
        print(f"Error during generation: {e}")
        print(f"Error type: {type(e)}")
        print(f"Full error details: {str(e)}")
        raise

if __name__ == "__main__":
    prompt = "A gentle AI voice assistant constructed from a circle ring and 3 lines that fly alongside the circle"  # Simplified prompt
    try:
        generate_3d_model(prompt)
    except Exception as e:
        print(f"Generation failed: {e}")