app.py

import gradio as gr
import spaces

import os
import shutil
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
os.environ['SPCONV_ALGO'] = 'native'
from typing import *
import torch
import numpy as np
import imageio
from easydict import EasyDict as edict
from trellis.pipelines import TrellisTextTo3DPipeline
from trellis.representations import Gaussian, MeshExtractResult
from trellis.utils import render_utils, postprocessing_utils

import traceback
import sys

# --- FastAPI / Threading Imports ---
import threading
import uvicorn
import logging
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel


MAX_SEED = np.iinfo(np.int32).max
TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
os.makedirs(TMP_DIR, exist_ok=True)

# --- Global Pipeline Variable ---
pipeline = None

# --- Logging Setup ---
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def start_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    
    
def end_session(req: gr.Request):
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    shutil.rmtree(user_dir)


def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
    return {
        'gaussian': {
            **gs.init_params,
            '_xyz': gs._xyz.cpu().numpy(),
            '_features_dc': gs._features_dc.cpu().numpy(),
            '_scaling': gs._scaling.cpu().numpy(),
            '_rotation': gs._rotation.cpu().numpy(),
            '_opacity': gs._opacity.cpu().numpy(),
        },
        'mesh': {
            'vertices': mesh.vertices.cpu().numpy(),
            'faces': mesh.faces.cpu().numpy(),
        },
    }
    
    
def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
    gs = Gaussian(
        aabb=state['gaussian']['aabb'],
        sh_degree=state['gaussian']['sh_degree'],
        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
        scaling_bias=state['gaussian']['scaling_bias'],
        opacity_bias=state['gaussian']['opacity_bias'],
        scaling_activation=state['gaussian']['scaling_activation'],
    )
    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
    
    mesh = edict(
        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
    )
    
    return gs, mesh


def get_seed(randomize_seed: bool, seed: int) -> int:
    """
    Get the random seed.
    """
    return np.random.randint(0, MAX_SEED) if randomize_seed else seed


@spaces.GPU
def text_to_3d(
    prompt: str,
    seed: int,
    ss_guidance_strength: float,
    ss_sampling_steps: int,
    slat_guidance_strength: float,
    slat_sampling_steps: int,
    req: gr.Request,
) -> Tuple[dict, str]:
    """
    Convert an text prompt to a 3D model.

    Args:
        prompt (str): The text prompt.
        seed (int): The random seed.
        ss_guidance_strength (float): The guidance strength for sparse structure generation.
        ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
        slat_guidance_strength (float): The guidance strength for structured latent generation.
        slat_sampling_steps (int): The number of sampling steps for structured latent generation.

    Returns:
        dict: The information of the generated 3D model.
        str: The path to the video of the 3D model.
    """
    # --- Determine user_dir robustly ---
    session_hash_str = str(req.session_hash) if hasattr(req, 'session_hash') and req.session_hash else f"api_call_{np.random.randint(10000)}"
    user_dir = os.path.join(TMP_DIR, session_hash_str)
    os.makedirs(user_dir, exist_ok=True) # Ensure directory exists

    outputs = pipeline.run(
        prompt,
        seed=seed,
        formats=["gaussian", "mesh"],
        sparse_structure_sampler_params={
            "steps": ss_sampling_steps,
            "cfg_strength": ss_guidance_strength,
        },
        slat_sampler_params={
            "steps": slat_sampling_steps,
            "cfg_strength": slat_guidance_strength,
        },
    )
    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
    video_path = os.path.join(user_dir, 'sample.mp4')
    try:
        imageio.mimsave(video_path, video, fps=15) # Now the directory should exist
    except FileNotFoundError:
         print(f"ERROR: Directory {user_dir} still not found before mimsave!", file=sys.stderr)
         # Decide if we should raise or return an error state?
         # Returning a dummy path might hide the error, so let's raise for now
         raise
    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
    torch.cuda.empty_cache()
    return state, video_path


@spaces.GPU(duration=90)
def extract_glb(
    state: dict,
    mesh_simplify: float,
    texture_size: int,
    req: gr.Request,
) -> Tuple[str, str]:
    """
    Extract a GLB file from the 3D model.

    Args:
        state (dict): The state of the generated 3D model.
        mesh_simplify (float): The mesh simplification factor.
        texture_size (int): The texture resolution.

    Returns:
        str: The path to the extracted GLB file.
    """
    # --- Determine user_dir robustly ---
    session_hash_str = str(req.session_hash) if hasattr(req, 'session_hash') and req.session_hash else f"api_call_{np.random.randint(10000)}"
    user_dir = os.path.join(TMP_DIR, session_hash_str)
    os.makedirs(user_dir, exist_ok=True) # Ensure directory exists

    gs, mesh = unpack_state(state)
    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
    glb_path = os.path.join(user_dir, 'sample.glb')
    try:
        glb.export(glb_path) # Now the directory should exist
    except FileNotFoundError:
        print(f"ERROR: Directory {user_dir} still not found before glb.export!", file=sys.stderr)
        raise
    torch.cuda.empty_cache()
    return glb_path, glb_path


@spaces.GPU
def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
    """
    Extract a Gaussian file from the 3D model.

    Args:
        state (dict): The state of the generated 3D model.

    Returns:
        str: The path to the extracted Gaussian file.
    """
    # --- Determine user_dir robustly ---
    session_hash_str = str(req.session_hash) if hasattr(req, 'session_hash') and req.session_hash else f"api_call_{np.random.randint(10000)}"
    user_dir = os.path.join(TMP_DIR, session_hash_str)
    os.makedirs(user_dir, exist_ok=True) # Ensure directory exists

    gs, _ = unpack_state(state)
    gaussian_path = os.path.join(user_dir, 'sample.ply')
    try:
        gs.save_ply(gaussian_path) # Now the directory should exist
    except FileNotFoundError:
        print(f"ERROR: Directory {user_dir} still not found before gs.save_ply!", file=sys.stderr)
        raise
    torch.cuda.empty_cache()
    return gaussian_path, gaussian_path


# --- FastAPI App Setup ---
api_app = FastAPI()

class GenerateRequest(BaseModel):
    prompt: str
    seed: int = 0 # Default seed
    mesh_simplify: float = 0.95 # Default simplify factor
    texture_size: int = 1024 # Default texture size
    # Add other generation parameters if needed

@api_app.post("/api/generate-sync")
@spaces.GPU(duration=300) # Allow longer duration for API calls
async def generate_sync_api(request_data: GenerateRequest):
    global pipeline # Access the globally initialized pipeline
    if pipeline is None:
        logger.error("API Error: Pipeline not initialized")
        raise HTTPException(status_code=503, detail="Pipeline not ready")

    prompt = request_data.prompt
    seed = request_data.seed
    mesh_simplify = request_data.mesh_simplify
    texture_size = request_data.texture_size
    # Extract other params if added to GenerateRequest

    logger.info(f"API /generate-sync received prompt: {prompt}")

    try:
        # --- Determine a unique temporary directory for this API call ---
        api_call_hash = f"api_sync_{np.random.randint(100000)}"
        user_dir = os.path.join(TMP_DIR, api_call_hash)
        os.makedirs(user_dir, exist_ok=True)
        logger.info(f"API using temp dir: {user_dir}")

        # --- Stage 1: Run the text-to-3D pipeline ---
        logger.info("API running pipeline...")
        # Use default values for parameters not exposed in the simple API for now
        outputs = pipeline.run(
            prompt,
            seed=seed,
            formats=["gaussian", "mesh"],
            sparse_structure_sampler_params={"steps": 25, "cfg_strength": 7.5},
            slat_sampler_params={"steps": 25, "cfg_strength": 7.5},
        )
        gs = outputs['gaussian'][0]
        mesh = outputs['mesh'][0]
        logger.info("API pipeline finished.")
        torch.cuda.empty_cache()

        # --- Stage 2: Extract GLB ---
        logger.info("API extracting GLB...")
        glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
        glb_path = os.path.join(user_dir, 'generated_sync.glb')
        glb.export(glb_path)
        logger.info(f"API GLB exported to: {glb_path}")
        torch.cuda.empty_cache()

        # Return the absolute path within the container
        return {"status": "success", "glb_path": os.path.abspath(glb_path)}

    except Exception as e:
        logger.error(f"API /generate-sync error: {str(e)}", exc_info=True)
        # Clean up temp dir on error if it exists
        if os.path.exists(user_dir):
            try:
                shutil.rmtree(user_dir)
            except Exception as cleanup_e:
                logger.error(f"API Error cleaning up dir {user_dir}: {cleanup_e}")
        raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
    # Note: We don't automatically clean up the user_dir on success,
    # as the file needs to be accessible for download by the calling server.
    # A separate cleanup mechanism might be needed eventually.


# --- Gradio Blocks Definition ---
with gr.Blocks(delete_cache=(600, 600)) as demo:
    gr.Markdown("""
    ## Text to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
    * Type a text prompt and click "Generate" to create a 3D asset.
    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
    """)
    
    with gr.Row():
        with gr.Column():
            text_prompt = gr.Textbox(label="Text Prompt", lines=5)
            
            with gr.Accordion(label="Generation Settings", open=False):
                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                gr.Markdown("Stage 1: Sparse Structure Generation")
                with gr.Row():
                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, step=1)
                gr.Markdown("Stage 2: Structured Latent Generation")
                with gr.Row():
                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=25, step=1)

            generate_btn = gr.Button("Generate")
            
            with gr.Accordion(label="GLB Extraction Settings", open=False):
                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
            
            with gr.Row():
                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
                extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
            gr.Markdown("""
                        *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
                        """)

        with gr.Column():
            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
            model_output = gr.Model3D(label="Extracted GLB/Gaussian", height=300)
            
            with gr.Row():
                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)  
    
    output_buf = gr.State()

    # Handlers
    demo.load(start_session)
    demo.unload(end_session)

    generate_btn.click(
        get_seed,
        inputs=[randomize_seed, seed],
        outputs=[seed],
    ).then(
        text_to_3d,
        inputs=[text_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
        outputs=[output_buf, video_output],
    ).then(
        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
        outputs=[extract_glb_btn, extract_gs_btn],
    )

    video_output.clear(
        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
        outputs=[extract_glb_btn, extract_gs_btn],
    )

    extract_glb_btn.click(
        extract_glb,
        inputs=[output_buf, mesh_simplify, texture_size],
        outputs=[model_output, download_glb],
    ).then(
        lambda: gr.Button(interactive=True),
        outputs=[download_glb],
    )
    
    extract_gs_btn.click(
        extract_gaussian,
        inputs=[output_buf],
        outputs=[model_output, download_gs],
    ).then(
        lambda: gr.Button(interactive=True),
        outputs=[download_gs],
    )

    model_output.clear(
        lambda: gr.Button(interactive=False),
        outputs=[download_glb],
    )
    

# --- Functions to Run FastAPI in Background ---
def run_api():
    """Run the FastAPI server."""
    uvicorn.run(api_app, host="0.0.0.0", port=8000) # Run on port 8000

def start_api_thread():
    """Start the API server in a background thread."""
    api_thread = threading.Thread(target=run_api, daemon=True)
    api_thread.start()
    logger.info("Started FastAPI server thread on port 8000")
    return api_thread


# Launch the Gradio app and FastAPI server
if __name__ == "__main__":
    logger.info("Initializing Trellis Pipeline...")
    # Make pipeline global so API endpoint can access it
    pipeline = TrellisTextTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-text-xlarge")
    pipeline.cuda()
    logger.info("Trellis Pipeline Initialized.")

    # Start the background API server
    start_api_thread()

    # Launch the Gradio interface (blocking call)
    logger.info("Launching Gradio Demo...")
    demo.launch()