app.py

# Version: 1.1.0 - API State Fix (2025-05-04)
# Changes:
# - Modified `text_to_3d` to explicitly return the serializable `state_dict` from `pack_state`
#   as the first return value. This ensures the dictionary is available via the API.
# - Modified `extract_glb` and `extract_gaussian` to accept `state_dict: dict` as their first argument
#   instead of relying on the implicit `gr.State` object type when called via API.
# - Kept Gradio UI bindings (`outputs=[output_buf, ...]`, `inputs=[output_buf, ...]`)
#   so the UI continues to function by passing the dictionary through output_buf.
# - Added minor safety checks and logging.

import gradio as gr
import spaces

import os
import shutil
os.environ['TOKENIZERS_PARALLELISM'] = 'true'
# Fix potential SpConv issue if needed, try 'hash' or 'native'
# os.environ.setdefault('SPCONV_ALGO', 'native') # Use setdefault to avoid overwriting if already set
os.environ['SPCONV_ALGO'] = 'native' # Direct set as per original

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


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)


def start_session(req: gr.Request):
    """Creates a temporary directory for the user session."""
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    print(f"Started session, created directory: {user_dir}")


def end_session(req: gr.Request):
    """Removes the temporary directory for the user session."""
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    if os.path.exists(user_dir):
      try:
          shutil.rmtree(user_dir)
          print(f"Ended session, removed directory: {user_dir}")
      except OSError as e:
          print(f"Error removing tmp directory {user_dir}: {e.strerror}", file=sys.stderr)
    else:
        print(f"Ended session, directory already removed: {user_dir}")


def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
    """Packs Gaussian and Mesh data into a serializable dictionary."""
    # Ensure tensors are on CPU and converted to numpy before returning the dict
    print("[pack_state] Packing state to dictionary...")
    packed_data = {
        'gaussian': {
            # Spread init_params first to ensure correct types
            **{k: v for k, v in gs.init_params.items()}, # Ensure init_params are included
            '_xyz': gs._xyz.detach().cpu().numpy(),
            '_features_dc': gs._features_dc.detach().cpu().numpy(),
            '_scaling': gs._scaling.detach().cpu().numpy(),
            '_rotation': gs._rotation.detach().cpu().numpy(),
            '_opacity': gs._opacity.detach().cpu().numpy(),
        },
        'mesh': {
            'vertices': mesh.vertices.detach().cpu().numpy(),
            'faces': mesh.faces.detach().cpu().numpy(),
        },
    }
    print(f"[pack_state] Dictionary created. Keys: {list(packed_data.keys())}, Gaussian points: {len(packed_data['gaussian']['_xyz'])}, Mesh vertices: {len(packed_data['mesh']['vertices'])}")
    return packed_data


def unpack_state(state_dict: dict) -> Tuple[Gaussian, edict]:
    """Unpacks Gaussian and Mesh data from a dictionary."""
    print("[unpack_state] Unpacking state from dictionary...")
    if not isinstance(state_dict, dict) or 'gaussian' not in state_dict or 'mesh' not in state_dict:
        raise ValueError("Invalid state_dict structure passed to unpack_state.")

    # Ensure the device is correctly set when unpacking
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    print(f"[unpack_state] Using device: {device}")

    gauss_data = state_dict['gaussian']
    mesh_data = state_dict['mesh']

    # Recreate Gaussian object using parameters stored during packing
    gs = Gaussian(
        aabb=gauss_data.get('aabb'), # Use .get for safety
        sh_degree=gauss_data.get('sh_degree'),
        mininum_kernel_size=gauss_data.get('mininum_kernel_size'),
        scaling_bias=gauss_data.get('scaling_bias'),
        opacity_bias=gauss_data.get('opacity_bias'),
        scaling_activation=gauss_data.get('scaling_activation'),
    )
    # Load tensors, ensuring they are created on the correct device
    gs._xyz = torch.tensor(gauss_data['_xyz'], device=device, dtype=torch.float32)
    gs._features_dc = torch.tensor(gauss_data['_features_dc'], device=device, dtype=torch.float32)
    gs._scaling = torch.tensor(gauss_data['_scaling'], device=device, dtype=torch.float32)
    gs._rotation = torch.tensor(gauss_data['_rotation'], device=device, dtype=torch.float32)
    gs._opacity = torch.tensor(gauss_data['_opacity'], device=device, dtype=torch.float32)
    print(f"[unpack_state] Gaussian unpacked. Points: {gs.get_xyz.shape[0]}")

    # Recreate mesh object using edict for compatibility if needed elsewhere
    mesh = edict(
        vertices=torch.tensor(mesh_data['vertices'], device=device, dtype=torch.float32),
        faces=torch.tensor(mesh_data['faces'], device=device, dtype=torch.int64), # Faces are typically long/int64
    )
    print(f"[unpack_state] Mesh unpacked. Vertices: {mesh.vertices.shape[0]}, Faces: {mesh.faces.shape[0]}")

    return gs, mesh


def get_seed(randomize_seed: bool, seed: int) -> int:
    """Gets a seed value, randomizing if requested."""
    new_seed = np.random.randint(0, MAX_SEED) if randomize_seed else seed
    print(f"[get_seed] Randomize: {randomize_seed}, Input Seed: {seed}, Output Seed: {new_seed}")
    return int(new_seed) # Ensure it's a standard int


@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]: # Return type changed for clarity
    """
    Generates a 3D model (Gaussian and Mesh) from text and returns a
    serializable state dictionary and a video preview path.
    """
    print(f"[text_to_3d] Received prompt: '{prompt}', Seed: {seed}")
    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    print(f"[text_to_3d] User directory: {user_dir}")

    # --- Generation Pipeline ---
    try:
        print("[text_to_3d] Running Trellis pipeline...")
        outputs = pipeline.run(
            prompt,
            seed=seed,
            formats=["gaussian", "mesh"], # Ensure both are generated
            sparse_structure_sampler_params={
                "steps": int(ss_sampling_steps), # Ensure steps are int
                "cfg_strength": float(ss_guidance_strength),
            },
            slat_sampler_params={
                "steps": int(slat_sampling_steps), # Ensure steps are int
                "cfg_strength": float(slat_guidance_strength),
            },
        )
        print("[text_to_3d] Pipeline run completed.")
    except Exception as e:
        print(f"❌ [text_to_3d] Pipeline error: {e}", file=sys.stderr)
        traceback.print_exc()
        # Return an empty dict and maybe an error indicator path or None?
        # For now, re-raise to signal failure clearly upstream.
        raise gr.Error(f"Trellis pipeline failed: {e}")

    # --- Create Serializable State Dictionary --- VITAL CHANGE for API
    # This dictionary holds the necessary data for later extraction.
    try:
        state_dict = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
    except Exception as e:
        print(f"❌ [text_to_3d] pack_state error: {e}", file=sys.stderr)
        traceback.print_exc()
        raise gr.Error(f"Failed to pack state: {e}")

    # --- Render Video Preview ---
    try:
        print("[text_to_3d] Rendering video preview...")
        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']
        # Ensure video frames are uint8
        video = [np.concatenate([v.astype(np.uint8), vg.astype(np.uint8)], axis=1) for v, vg in zip(video, video_geo)]
        video_path = os.path.join(user_dir, 'sample.mp4')
        imageio.mimsave(video_path, video, fps=15, quality=8) # Added quality setting
        print(f"[text_to_3d] Video saved to: {video_path}")
    except Exception as e:
        print(f"❌ [text_to_3d] Video rendering/saving error: {e}", file=sys.stderr)
        traceback.print_exc()
        # Still return state_dict, but maybe signal video error? Return None for path.
        video_path = None # Indicate video failure

    # --- Cleanup and Return ---
    # Clear CUDA cache if GPU was used
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        print("[text_to_3d] Cleared CUDA cache.")

    # --- Return Serializable Dictionary and Video Path --- VITAL CHANGE for API
    print("[text_to_3d] Returning state dictionary and video path.")
    return state_dict, video_path


@spaces.GPU(duration=120) # Increased duration slightly
def extract_glb(
    state_dict: dict, # <-- VITAL CHANGE: Accept the dictionary directly
    mesh_simplify: float,
    texture_size: int,
    req: gr.Request,
) -> Tuple[str, str]:
    """
    Extracts a GLB file from the provided 3D model state dictionary.
    """
    print(f"[extract_glb] Received request. Simplify: {mesh_simplify}, Texture Size: {texture_size}")
    if not isinstance(state_dict, dict):
        print("❌ [extract_glb] Error: Invalid state_dict received (not a dictionary).")
        raise gr.Error("Invalid state data received. Please generate the model first.")

    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    print(f"[extract_glb] User directory: {user_dir}")

    # --- Unpack state from the dictionary --- VITAL CHANGE for API
    try:
        gs, mesh = unpack_state(state_dict)
    except Exception as e:
        print(f"❌ [extract_glb] unpack_state error: {e}", file=sys.stderr)
        traceback.print_exc()
        raise gr.Error(f"Failed to unpack state: {e}")

    # --- Postprocessing and Export ---
    try:
        print("[extract_glb] Converting to GLB...")
        glb = postprocessing_utils.to_glb(gs, mesh, simplify=float(mesh_simplify), texture_size=int(texture_size), verbose=True) # Verbose for debugging
        glb_path = os.path.join(user_dir, 'sample.glb')
        print(f"[extract_glb] Exporting GLB to: {glb_path}")
        glb.export(glb_path)
        print("[extract_glb] GLB exported successfully.")
    except Exception as e:
        print(f"❌ [extract_glb] GLB conversion/export error: {e}", file=sys.stderr)
        traceback.print_exc()
        raise gr.Error(f"Failed to extract GLB: {e}")

    # --- Cleanup and Return ---
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        print("[extract_glb] Cleared CUDA cache.")

    # Return path twice for both Model3D and DownloadButton components
    print("[extract_glb] Returning GLB path.")
    return glb_path, glb_path


@spaces.GPU
def extract_gaussian(
    state_dict: dict, # <-- VITAL CHANGE: Accept the dictionary directly
    req: gr.Request
) -> Tuple[str, str]:
    """
    Extracts a PLY (Gaussian) file from the provided 3D model state dictionary.
    """
    print("[extract_gaussian] Received request.")
    if not isinstance(state_dict, dict):
        print("❌ [extract_gaussian] Error: Invalid state_dict received (not a dictionary).")
        raise gr.Error("Invalid state data received. Please generate the model first.")

    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
    os.makedirs(user_dir, exist_ok=True)
    print(f"[extract_gaussian] User directory: {user_dir}")

    # --- Unpack state from the dictionary --- VITAL CHANGE for API
    try:
        gs, _ = unpack_state(state_dict) # Only need Gaussian part
    except Exception as e:
        print(f"❌ [extract_gaussian] unpack_state error: {e}", file=sys.stderr)
        traceback.print_exc()
        raise gr.Error(f"Failed to unpack state: {e}")

    # --- Export PLY ---
    try:
        gaussian_path = os.path.join(user_dir, 'sample.ply')
        print(f"[extract_gaussian] Saving PLY to: {gaussian_path}")
        gs.save_ply(gaussian_path)
        print("[extract_gaussian] PLY saved successfully.")
    except Exception as e:
        print(f"❌ [extract_gaussian] PLY saving error: {e}", file=sys.stderr)
        traceback.print_exc()
        raise gr.Error(f"Failed to extract Gaussian PLY: {e}")

    # --- Cleanup and Return ---
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        print("[extract_gaussian] Cleared CUDA cache.")

    # Return path twice for both Model3D and DownloadButton components
    print("[extract_gaussian] Returning PLY path.")
    return gaussian_path, gaussian_path


# --- Gradio UI Definition ---
print("Setting up Gradio Blocks interface...")
with gr.Blocks(delete_cache=(600, 600), title="TRELLIS Text-to-3D") 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 preview.
    * Adjust extraction settings if desired.
    * Click "Extract GLB" or "Extract Gaussian" to get the downloadable 3D file.
    """)

    # --- State Buffer ---
    # This hidden component will hold the dictionary returned by text_to_3d,
    # acting as the state link between generation and extraction for the UI/API.
    output_buf = gr.State()

    with gr.Row():
        with gr.Column(scale=1): # Input column
            text_prompt = gr.Textbox(label="Text Prompt", lines=5, placeholder="e.g., a cute red dragon")

            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("--- \n **Stage 1: Sparse Structure Generation**")
                with gr.Row():
                    ss_guidance_strength = gr.Slider(0.0, 15.0, label="Guidance Strength", value=7.5, step=0.1)
                    ss_sampling_steps = gr.Slider(10, 50, label="Sampling Steps", value=25, step=1)
                gr.Markdown("--- \n **Stage 2: Structured Latent Generation**")
                with gr.Row():
                    slat_guidance_strength = gr.Slider(0.0, 15.0, label="Guidance Strength", value=7.5, step=0.1)
                    slat_sampling_steps = gr.Slider(10, 50, label="Sampling Steps", value=25, step=1)

            generate_btn = gr.Button("Generate 3D Preview", variant="primary")

            with gr.Accordion(label="GLB Extraction Settings", open=True): # Open by default
                # Tooltips added for clarity
                mesh_simplify = gr.Slider(0.9, 0.99, label="Simplify Factor", value=0.95, step=0.01, info="Higher value = less simplification (more polys)")
                texture_size = gr.Slider(512, 2048, label="Texture Size (pixels)", value=1024, step=512, info="Size of the generated texture map")

            with gr.Row():
                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
                extract_gs_btn = gr.Button("Extract Gaussian (PLY)", interactive=False)
            gr.Markdown("""
                        *NOTE: Gaussian file (.ply) can be very large (~50MB+) and may take time to process/download.*
                        """)

        with gr.Column(scale=1): # Output column
            video_output = gr.Video(label="Generated 3D Preview (Geometry | Texture)", autoplay=True, loop=True, height=350) # Slightly larger height
            model_output = gr.Model3D(label="Extracted Model Preview", height=350, clear_color=[0.95, 0.95, 0.95, 1.0]) # Light background

            with gr.Row():
                # Link download button visibility/interactivity to model_output potentially
                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
                download_gs = gr.DownloadButton(label="Download Gaussian (PLY)", interactive=False)

    # --- Event Handlers ---
    print("Defining Gradio event handlers...")

    # Handle session start/end
demo.load(start_session)
demo.unload(end_session)

    # --- Generate Button Click Flow ---
    # 1. Get Seed -> 2. Run text_to_3d -> 3. Enable extraction buttons
    generate_event = generate_btn.click(
        get_seed,
        inputs=[randomize_seed, seed],
        outputs=[seed],
        api_name="get_seed" # Optional API name
    ).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], # output_buf receives state_dict
        api_name="text_to_3d"
    ).then(
        lambda: ( # Return tuple for multiple outputs
            gr.Button(interactive=True),
            gr.Button(interactive=True),
            gr.DownloadButton(interactive=False), # Ensure download buttons are disabled initially
            gr.DownloadButton(interactive=False)
        ),
        outputs=[extract_glb_btn, extract_gs_btn, download_glb, download_gs], # Update interactivity
    )

    # --- Clear video/model outputs if prompt changes (optional, prevents confusion)
    # text_prompt.change(lambda: (None, None, gr.Button(interactive=False), gr.Button(interactive=False)), outputs=[video_output, model_output, extract_glb_btn, extract_gs_btn])

    # --- Extract GLB Button Click Flow ---
    # 1. Run extract_glb -> 2. Update Model3D and Download Button
    extract_glb_event = extract_glb_btn.click(
        extract_glb,
        inputs=[output_buf, mesh_simplify, texture_size], # Pass the state_dict via output_buf
        outputs=[model_output, download_glb], # Returns path to both
        api_name="extract_glb"
    ).then(
        lambda: gr.DownloadButton(interactive=True), # Enable download button
        outputs=[download_glb],
    )

    # --- Extract Gaussian Button Click Flow ---
    # 1. Run extract_gaussian -> 2. Update Model3D and Download Button
    extract_gs_event = extract_gs_btn.click(
        extract_gaussian,
        inputs=[output_buf], # Pass the state_dict via output_buf
        outputs=[model_output, download_gs], # Returns path to both
        api_name="extract_gaussian"
    ).then(
        lambda: gr.DownloadButton(interactive=True), # Enable download button
        outputs=[download_gs],
    )

    # --- Clear Download Button Interactivity when model preview is cleared ---
    # This might be redundant if generate disables them, but adds safety
    model_output.clear(
        lambda: (gr.DownloadButton(interactive=False), gr.DownloadButton(interactive=False)),
        outputs=[download_glb, download_gs]
    )
    video_output.clear( # Also disable extraction if video is cleared (e.g., new generation starts)
         lambda: (
            gr.Button(interactive=False),
            gr.Button(interactive=False),
            gr.DownloadButton(interactive=False),
            gr.DownloadButton(interactive=False)
        ),
        outputs=[extract_glb_btn, extract_gs_btn, download_glb, download_gs],
    )

    print("Gradio interface setup complete.")


# --- Launch the Gradio app ---
if __name__ == "__main__":
    print("Loading Trellis pipeline...")
    try:
        # Ensure model/variant matches requirements, use revision if needed
        pipeline = TrellisTextTo3DPipeline.from_pretrained(
            "JeffreyXiang/TRELLIS-text-xlarge",
            # revision="main", # Specify if needed
            torch_dtype=torch.float16 # Use float16 if GPU supports it for less memory
        )
        # Move to GPU if available
        if torch.cuda.is_available():
            pipeline = pipeline.to("cuda")
            print("✅ Trellis pipeline loaded successfully to GPU.")
        else:
            print("⚠️ WARNING: CUDA not available, running on CPU (will be very slow).")
            print("✅ Trellis pipeline loaded successfully to CPU.")
    except Exception as e:
        print(f"❌ Failed to load Trellis pipeline: {e}", file=sys.stderr)
        traceback.print_exc()
        # Exit if pipeline is critical for the app to run
        print("❌ Exiting due to pipeline load failure.")
        sys.exit(1)

    print("Launching Gradio demo...")
    # Set share=True if you need a public link (e.g., for testing from outside local network)
    # Set server_name="0.0.0.0" to allow access from local network IP
    demo.queue().launch( # Use queue for potentially long-running tasks
        # server_name="0.0.0.0",
        # share=False,
        debug=True # Enable debug mode for more logs
    )
    print("Gradio demo launched.")