try that again last one was missing import spaces

app.py

@@ -1,4 +1,163 @@
-# In appTrellis_fileErrorsFIx.py
+# Version: 1.1.2 - API State Fix + DEBUG (Video Disabled) + Import Fix (2025-05-04)
+# Changes:
+# - ENSURED `import spaces` is present for the @spaces.GPU decorator.
+# - TEMPORARY DEBUGGING STEP: Commented out video rendering in `text_to_3d`
+#   and return None for video_path to isolate the "Session not found" error.
+# - 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  # <<<--- ENSURE THIS IMPORT IS PRESENT
+
+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
+# Ensure TMP_DIR is correctly defined relative to the script location
+# Using /tmp/ directly might be more robust in some container environments
+# TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+TMP_DIR = '/tmp/gradio_sessions' # Use standard /tmp directory
+print(f"Using temporary directory: {TMP_DIR}")
+os.makedirs(TMP_DIR, exist_ok=True)
+
+
+def start_session(req: gr.Request):
+    """Creates a temporary directory for the user session."""
+    try:
+        session_hash = req.session_hash
+        if not session_hash:
+             # Fallback or generate a temporary ID if session_hash is missing (might happen on first load?)
+             session_hash = f"no_session_{np.random.randint(10000, 99999)}"
+             print(f"Warning: No session_hash in request, using temporary ID: {session_hash}")
+
+        user_dir = os.path.join(TMP_DIR, str(session_hash))
+        os.makedirs(user_dir, exist_ok=True)
+        print(f"Started session, created directory: {user_dir}")
+    except Exception as e:
+        print(f"Error in start_session: {e}", file=sys.stderr)
+        # Decide if this is critical - maybe raise to prevent further issues?
+
+
+def end_session(req: gr.Request):
+    """Removes the temporary directory for the user session."""
+    try:
+        session_hash = req.session_hash
+        if not session_hash:
+            print("Warning: No session_hash in end_session request, cannot clean up.")
+            return
+
+        user_dir = os.path.join(TMP_DIR, str(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 or hash mismatch: {user_dir}")
+    except Exception as e:
+        print(f"Error in end_session: {e}", file=sys.stderr)
+
+
+def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
+    """Packs Gaussian and Mesh data into a serializable dictionary."""
+    print("[pack_state] Packing state to dictionary...")
+    try:
+        packed_data = {
+            'gaussian': {
+                **{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
+    except Exception as e:
+        print(f"Error during pack_state: {e}", file=sys.stderr)
+        traceback.print_exc()
+        raise  # Re-raise the error to be caught upstream
+
+
+def unpack_state(state_dict: dict) -> Tuple[Gaussian, edict]:
+    """Unpacks Gaussian and Mesh data from a dictionary."""
+    print("[unpack_state] Unpacking state from dictionary...")
+    try:
+        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
+    except Exception as e:
+        print(f"Error during unpack_state: {e}", file=sys.stderr)
+        traceback.print_exc()
+        raise # Re-raise the error
+
+
+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(
@@ -9,21 +168,27 @@ def text_to_3d(
     slat_guidance_strength: float,
     slat_sampling_steps: int,
     req: gr.Request,
-) -> Tuple[dict, str]: # Return type changed for clarity
+) -> Tuple[dict, Optional[str]]: # Return type changed Optional[str] for video path
     """
     Generates a 3D model (Gaussian and Mesh) from text and returns a
-    serializable state dictionary and a video preview path.
+    serializable state dictionary and potentially a video preview path.
+    >>> TEMPORARILY DISABLED VIDEO RENDERING FOR DEBUGGING <<<
     """
-    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}")
+    print(f"[text_to_3d - DEBUG MODE] Received prompt: '{prompt}', Seed: {seed}")
+    session_hash = req.session_hash
+    if not session_hash:
+        session_hash = f"no_session_{np.random.randint(10000, 99999)}" # Use consistent fallback
+        print(f"Warning: No session_hash in text_to_3d request, using temporary ID: {session_hash}")
+    user_dir = os.path.join(TMP_DIR, str(session_hash))
+    os.makedirs(user_dir, exist_ok=True) # Ensure it exists for this request
+    print(f"[text_to_3d - DEBUG MODE] User directory: {user_dir}")
 
     # --- Generation Pipeline ---
     try:
-        print("[text_to_3d] Running Trellis pipeline...")
+        print("[text_to_3d - DEBUG MODE] Running Trellis pipeline...")
+        # Add more specific pipeline settings if needed based on Trellis docs
         outputs = pipeline.run(
-            prompt,
+            prompt=prompt,
             seed=seed,
             formats=["gaussian", "mesh"], # Ensure both are generated
             sparse_structure_sampler_params={
@@ -34,54 +199,338 @@ def text_to_3d(
                 "steps": int(slat_sampling_steps), # Ensure steps are int
                 "cfg_strength": float(slat_guidance_strength),
             },
+            # device='cuda' # Explicitly specify device if needed
         )
-        print("[text_to_3d] Pipeline run completed.")
+        print("[text_to_3d - DEBUG MODE] Pipeline run completed.")
     except Exception as e:
-        print(f"❌ [text_to_3d] Pipeline error: {e}", file=sys.stderr)
+        print(f"❌ [text_to_3d - DEBUG MODE] Pipeline error: {e}", file=sys.stderr)
         traceback.print_exc()
-        raise gr.Error(f"Trellis pipeline failed: {e}")
+        raise gr.Error(f"Trellis pipeline failed during generation: {e}") # More specific error
 
     # --- Create Serializable State Dictionary ---
     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)
+        print(f"❌ [text_to_3d - DEBUG MODE] pack_state error: {e}", file=sys.stderr)
         traceback.print_exc()
-        raise gr.Error(f"Failed to pack state: {e}")
+        raise gr.Error(f"Failed to pack state after generation: {e}")
 
     # --- Render Video Preview (TEMPORARILY DISABLED FOR DEBUGGING) ---
-    video_path = None # Set path to None
+    video_path = None # Explicitly set path to None for this debug version
+    print("[text_to_3d - DEBUG MODE] Skipping video rendering.")
+    # --- Original Video Code Block Start (Keep commented for now) ---
     # 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}")
+    #     video_path_tmp = os.path.join(user_dir, 'sample.mp4') # Use temp name
+    #     imageio.mimsave(video_path_tmp, video, fps=15, quality=8) # Added quality setting
+    #     print(f"[text_to_3d] Video saved to: {video_path_tmp}")
+    #     video_path = video_path_tmp # Assign if successful
     # 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
-    print("[text_to_3d] Skipping video rendering for debugging.")
+    # --- Original Video Code Block End ---
+
+    # --- Cleanup and Return ---
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        print("[text_to_3d - DEBUG MODE] Cleared CUDA cache.")
+
+    # --- Return Serializable Dictionary and None Video Path ---
+    print("[text_to_3d - DEBUG MODE] Returning state dictionary and None video path.")
+    # Ensure state_dict is not None before returning
+    if state_dict is None:
+         raise gr.Error("Failed to create state dictionary.")
+    return state_dict, video_path
+
+
+@spaces.GPU(duration=120) # Increased duration slightly
+def extract_glb(
+    state_dict: dict, # <-- Accepts 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}")
+    session_hash = req.session_hash
+    if not session_hash:
+        session_hash = f"no_session_{np.random.randint(10000, 99999)}"
+        print(f"Warning: No session_hash in extract_glb request, using temporary ID: {session_hash}")
+
+    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(session_hash))
+    os.makedirs(user_dir, exist_ok=True) # Ensure it exists
+    print(f"[extract_glb] User directory: {user_dir}")
+
+    # --- Unpack state from the dictionary ---
+    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 during GLB extraction: {e}")
+
+    # --- Postprocessing and Export ---
+    try:
+        print("[extract_glb] Converting to GLB...")
+        # Ensure parameters have correct types
+        simplify_factor = float(mesh_simplify)
+        tex_size = int(texture_size)
+        glb = postprocessing_utils.to_glb(gs, mesh, simplify=simplify_factor, texture_size=tex_size, verbose=True)
+        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.")
+    # Ensure path is returned, even if export failed somehow (though error should raise first)
+    return glb_path, glb_path
+
+
+@spaces.GPU
+def extract_gaussian(
+    state_dict: dict, # <-- Accepts 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.")
+    session_hash = req.session_hash
+    if not session_hash:
+        session_hash = f"no_session_{np.random.randint(10000, 99999)}"
+        print(f"Warning: No session_hash in extract_gaussian request, using temporary ID: {session_hash}")
+
+    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(session_hash))
+    os.makedirs(user_dir, exist_ok=True) # Ensure it exists
+    print(f"[extract_gaussian] User directory: {user_dir}")
+
+    # --- Unpack state from the dictionary ---
+    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 during Gaussian extraction: {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("[text_to_3d] Cleared CUDA cache.")
+        print("[extract_gaussian] Cleared CUDA cache.")
+
+    # Return path twice for both Model3D and DownloadButton components
+    print("[extract_gaussian] Returning PLY path.")
+    # Ensure path is returned
+    return gaussian_path, gaussian_path
 
-    # --- Return Serializable Dictionary and None for Video Path ---
-    print("[text_to_3d] Returning state dictionary and None video path.")
-    return state_dict, video_path # Return dict and None video path
 
 # --- Gradio UI Definition ---
-# ... (rest of the file is the same, but you might want to adjust the output mapping if needed)
+print("Setting up Gradio Blocks interface...")
+# Define the interface layout
+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.
+    *(Note: Video preview is temporarily disabled for debugging)*
+    """)
+
+    # --- State Buffer ---
+    # This hidden component holds the dictionary linking generation and extraction.
+    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
+                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 component remains for layout but won't show anything in this debug version
+            video_output = gr.Video(label="Generated 3D Preview (DISABLED FOR DEBUG)", autoplay=False, loop=False, value=None, height=350)
+            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():
+                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...")
 
-# In the generate_btn.click handler, adjust the outputs if the video component causes issues:
-# Option 1: Keep Video component, it will just show nothing.
-# outputs=[output_buf, video_output], # This might be fine
+    # Handle session start/end
+    demo.load(start_session, inputs=None, outputs=None)
+    demo.unload(end_session, inputs=None, outputs=None)
 
-# Option 2: Use a dummy hidden component if video_output causes issues receiving None
-# outputs=[output_buf, gr.Textbox(visible=False)], # Example dummy
+    # --- Generate Button Click Flow ---
+    generate_event = generate_btn.click(
+        get_seed,
+        inputs=[randomize_seed, seed],
+        outputs=[seed],
+        api_name="get_seed"
+    ).then(
+        text_to_3d,
+        inputs=[text_prompt, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
+        # Output state_dict to buffer, output None to video component
+        outputs=[output_buf, video_output],
+        api_name="text_to_3d"
+    ).then(
+        # Function to update button interactivity after generation attempt
+        lambda: (
+            gr.Button(interactive=True),
+            gr.Button(interactive=True),
+            gr.DownloadButton(interactive=False),
+            gr.DownloadButton(interactive=False)
+        ),
+        inputs=None, # No inputs needed for the lambda
+        outputs=[extract_glb_btn, extract_gs_btn, download_glb, download_gs],
+    )
+
+    # --- Extract GLB Button Click Flow ---
+    extract_glb_event = extract_glb_btn.click(
+        extract_glb,
+        inputs=[output_buf, mesh_simplify, texture_size],
+        outputs=[model_output, download_glb],
+        api_name="extract_glb"
+    ).then(
+        lambda: gr.DownloadButton(interactive=True),
+        inputs=None,
+        outputs=[download_glb],
+    )
+
+    # --- Extract Gaussian Button Click Flow ---
+    extract_gs_event = extract_gs_btn.click(
+        extract_gaussian,
+        inputs=[output_buf],
+        outputs=[model_output, download_gs],
+        api_name="extract_gaussian"
+    ).then(
+        lambda: gr.DownloadButton(interactive=True),
+        inputs=None,
+        outputs=[download_gs],
+    )
+
+    # --- Clear Download Button Interactivity when model preview is cleared ---
+    model_output.clear(
+        lambda: (gr.DownloadButton(interactive=False), gr.DownloadButton(interactive=False)),
+        inputs=None,
+        outputs=[download_glb, download_gs]
+    )
+    # Also disable buttons if the (currently disabled) video output is cleared
+    video_output.clear(
+         lambda: (
+            gr.Button(interactive=False),
+            gr.Button(interactive=False),
+            gr.DownloadButton(interactive=False),
+            gr.DownloadButton(interactive=False)
+        ),
+        inputs=None,
+        outputs=[extract_glb_btn, extract_gs_btn, download_glb, download_gs],
+    )
+
+    print("Gradio interface setup complete.")
+
+
+# --- Launch the Gradio app ---
+# Main execution block
+if __name__ == "__main__":
+    print("Loading Trellis pipeline...")
+    pipeline_loaded = False
+    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.")
+        pipeline_loaded = True
+    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) # Exit if pipeline fails
+
+    if pipeline_loaded:
+        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
+        # Increased concurrency_limit and timeout for queue might help
+        demo.queue(
+             # default_concurrency_limit=5, # Adjust based on expected load and space resources
+             # api_open=True # Keep API accessible
+        ).launch(
+            # server_name="0.0.0.0", # Make accessible on local network
+            # share=False, # Set to True for public link if needed
+            debug=True, # Enable Gradio debug mode for more detailed logs
+            # prevent_thread_lock=True # May help with async issues in some cases
+        )
+        print("Gradio demo launched.")
+    else:
+         print("Gradio demo not launched due to pipeline loading failure.")