handler.py

import torch
import base64
import os
from PIL import Image
from io import BytesIO
from trellis.pipelines import TrellisImageTo3DPipeline
from trellis.utils import postprocessing_utils

class EndpointHandler:
    def __init__(self, model_dir):
        # Load the pipeline from the local directory
        self.pipeline = TrellisImageTo3DPipeline.from_pretrained("microsoft/TRELLIS-image-large")
        self.pipeline.cuda()
        
    def __call__(self, data):
        """
        Args:
            data (:obj:`dict`):
                - "inputs": The base64 encoded image or URL.
                - "params": Dictionary of generation parameters (optional).
        """
        inputs = data.pop("inputs", data)
        params = data.pop("parameters", {})

        # Decode image
        image = Image.open(BytesIO(base64.b64decode(inputs)))
        
        # Run Inference
        # Note: You can adjust 'steps' or 'cfg' via params
        outputs = self.pipeline(
            image,
            num_samples=1,
            return_flags=["mesh"],
            **params
        )

        # Process mesh to GLB
        mesh = outputs['mesh'][0]
        glb_io = BytesIO()
        mesh.export(glb_io, file_type='glb')
        glb_io.seek(0)

        # Encode GLB to base64 for the response
        return {
            "mesh_base64": base64.b64encode(glb_io.getvalue()).decode("utf-8"),
            "format": "glb"
        }