import subprocess
# not sure why it works in the original space but says "pip not found" in mine
#subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

import os
from huggingface_hub import snapshot_download, hf_hub_download

# Configuration for data paths
DATA_ROOT = os.path.normpath(os.getenv('DATA_ROOT', '.'))
WAN_MODELS_PATH = os.path.join(DATA_ROOT, 'wan_models')
OTHER_MODELS_PATH = os.path.join(DATA_ROOT, 'other_models')

snapshot_download(
    repo_id="Wan-AI/Wan2.1-T2V-1.3B",
    local_dir=os.path.join(WAN_MODELS_PATH, "Wan2.1-T2V-1.3B"),
    local_dir_use_symlinks=False,
    resume_download=True,
    repo_type="model" 
)

hf_hub_download(
    repo_id="gdhe17/Self-Forcing",
    filename="checkpoints/self_forcing_dmd.pt",
    local_dir=OTHER_MODELS_PATH,              
    local_dir_use_symlinks=False 
)
import re
import random
import argparse
import hashlib
import urllib.request
import time
from PIL import Image
import torch
import gradio as gr
from omegaconf import OmegaConf
from tqdm import tqdm
import imageio
import av
import uuid
import tempfile
import shutil
from pathlib import Path
from typing import Dict, Any, List, Optional, Tuple, Union

from pipeline import CausalInferencePipeline
from demo_utils.constant import ZERO_VAE_CACHE
from demo_utils.vae_block3 import VAEDecoderWrapper
from utils.wan_wrapper import WanDiffusionWrapper, WanTextEncoder

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM #, BitsAndBytesConfig
import numpy as np

device = "cuda" if torch.cuda.is_available() else "cpu"

# LoRA Storage Configuration
STORAGE_PATH = Path(DATA_ROOT) / "storage"
LORA_PATH = STORAGE_PATH / "loras"
OUTPUT_PATH = STORAGE_PATH / "output"

# Create necessary directories
STORAGE_PATH.mkdir(parents=True, exist_ok=True)
LORA_PATH.mkdir(parents=True, exist_ok=True)
OUTPUT_PATH.mkdir(parents=True, exist_ok=True)

# Global variables for LoRA management
current_lora_id = None
current_lora_path = None

# --- Argument Parsing ---
parser = argparse.ArgumentParser(description="Gradio Demo for Self-Forcing with Frame Streaming")
parser.add_argument('--port', type=int, default=7860, help="Port to run the Gradio app on.")
parser.add_argument('--host', type=str, default='0.0.0.0', help="Host to bind the Gradio app to.")
parser.add_argument("--checkpoint_path", type=str, default=os.path.join(OTHER_MODELS_PATH, 'checkpoints', 'self_forcing_dmd.pt'), help="Path to the model checkpoint.")
parser.add_argument("--config_path", type=str, default='./configs/self_forcing_dmd.yaml', help="Path to the model config.")
parser.add_argument('--share', action='store_true', help="Create a public Gradio link.")
parser.add_argument('--trt', action='store_true', help="Use TensorRT optimized VAE decoder.")
parser.add_argument('--fps', type=float, default=15.0, help="Playback FPS for frame streaming.")
args = parser.parse_args()

gpu = "cuda"

try:
    config = OmegaConf.load(args.config_path)
    default_config = OmegaConf.load("configs/default_config.yaml")
    config = OmegaConf.merge(default_config, config)
except FileNotFoundError as e:
    print(f"Error loading config file: {e}\n. Please ensure config files are in the correct path.")
    exit(1)

# Initialize Models
print("Initializing models...")
text_encoder = WanTextEncoder()
transformer = WanDiffusionWrapper(is_causal=True)

try:
    state_dict = torch.load(args.checkpoint_path, map_location="cpu")
    transformer.load_state_dict(state_dict.get('generator_ema', state_dict.get('generator')))
except FileNotFoundError as e:
    print(f"Error loading checkpoint: {e}\nPlease ensure the checkpoint '{args.checkpoint_path}' exists.")
    exit(1)

text_encoder.eval().to(dtype=torch.float16).requires_grad_(False)
transformer.eval().to(dtype=torch.float16).requires_grad_(False)

text_encoder.to(gpu)
transformer.to(gpu)

APP_STATE = {
    "torch_compile_applied": False,
    "fp8_applied": False,
    "current_use_taehv": False,
    "current_vae_decoder": None,
}

# I've tried to enable it, but I didn't notice a significant performance improvement..
ENABLE_TORCH_COMPILATION = False

# “default”: The default mode, used when no mode parameter is specified. It provides a good balance between performance and overhead.
# “reduce-overhead”: Minimizes Python-related overhead using CUDA graphs. However, it may increase memory usage.
# “max-autotune”: Uses Triton or template-based matrix multiplications on supported devices. It takes longer to compile but optimizes for the fastest possible execution. On GPUs it enables CUDA graphs by default.
# “max-autotune-no-cudagraphs”: Similar to “max-autotune”, but without CUDA graphs.
TORCH_COMPILATION_MODE = "default"

# Apply torch.compile for maximum performance
if not APP_STATE["torch_compile_applied"] and ENABLE_TORCH_COMPILATION:
    print("🚀 Applying torch.compile for speed optimization...")
    transformer.compile(mode=TORCH_COMPILATION_MODE)
    APP_STATE["torch_compile_applied"] = True
    print("✅ torch.compile applied to transformer")

def upload_lora_file(file: tempfile._TemporaryFileWrapper) -> Tuple[str, str]:
    """Upload a LoRA file and return a hash-based ID for future reference"""
    if file is None:
        return "", ""
        
    try:
        # Calculate SHA256 hash of the file
        sha256_hash = hashlib.sha256()
        with open(file.name, "rb") as f:
            for chunk in iter(lambda: f.read(4096), b""):
                sha256_hash.update(chunk)
        file_hash = sha256_hash.hexdigest()
        
        # Create destination path using hash
        dest_path = LORA_PATH / f"{file_hash}.safetensors"
        
        # Check if file already exists
        if dest_path.exists():
            print(f"LoRA file already exists!")
            return file_hash, file_hash
        
        # Copy the file to the destination
        shutil.copy(file.name, dest_path)
        
        print(f"LoRA file uploaded!")
        return file_hash, file_hash
    except Exception as e:
        print(f"Error uploading LoRA file: {e}")
        raise gr.Error(f"Failed to upload LoRA file: {str(e)}")

def get_lora_file_path(lora_id: Optional[str]) -> Optional[Path]:
    """Get the path to a LoRA file from its hash-based ID"""
    if not lora_id:
        return None
        
    # Check if file exists
    lora_path = LORA_PATH / f"{lora_id}.safetensors"
    if lora_path.exists():
        return lora_path
    
    return None

def manage_lora_weights(lora_id: Optional[str], lora_weight: float) -> Tuple[bool, Optional[Path]]:
    """Manage LoRA weights for the transformer model"""
    global current_lora_id, current_lora_path
    
    # Determine if we should use LoRA
    using_lora = lora_id is not None and lora_id.strip() != "" and lora_weight > 0
    
    # If not using LoRA but we have one loaded, clear it
    if not using_lora and current_lora_id is not None:
        print(f"Clearing current LoRA")
        current_lora_id = None
        current_lora_path = None
        return False, None
    
    # If using LoRA, check if we need to change weights
    if using_lora:
        lora_path = get_lora_file_path(lora_id)
        
        if not lora_path:
            print(f"A LoRA file with this ID was found. Using base model instead.")
            
            # If we had a LoRA loaded, clear it
            if current_lora_id is not None:
                print(f"Clearing current LoRA")
                current_lora_id = None
                current_lora_path = None
                
            return False, None
        
        # If LoRA ID changed, update
        if lora_id != current_lora_id:
            print(f"Loading LoRA..")
            current_lora_id = lora_id
            current_lora_path = lora_path
        else:
            print(f"Using a LoRA!")
        
        return True, lora_path
    
    return False, None

def frames_to_ts_file(frames, filepath, fps = 15):
    """
    Convert frames directly to .ts file using PyAV.
    
    Args:
        frames: List of numpy arrays (HWC, RGB, uint8)
        filepath: Output file path
        fps: Frames per second
    
    Returns:
        The filepath of the created file
    """
    if not frames:
        return filepath
    
    height, width = frames[0].shape[:2]
    
    # Create container for MPEG-TS format
    container = av.open(filepath, mode='w', format='mpegts')
    
    # Add video stream with optimized settings for streaming
    stream = container.add_stream('h264', rate=fps)
    stream.width = width
    stream.height = height
    stream.pix_fmt = 'yuv420p'
    
    # Optimize for low latency streaming
    stream.options = {
        'preset': 'ultrafast',
        'tune': 'zerolatency', 
        'crf': '23',
        'profile': 'baseline',
        'level': '3.0'
    }
    
    try:
        for frame_np in frames:
            frame = av.VideoFrame.from_ndarray(frame_np, format='rgb24')
            frame = frame.reformat(format=stream.pix_fmt)
            for packet in stream.encode(frame):
                container.mux(packet)
        
        for packet in stream.encode():
            container.mux(packet)
            
    finally:
        container.close()
    
    return filepath

def initialize_vae_decoder(use_taehv=False, use_trt=False):
    if use_trt:
        from demo_utils.vae import VAETRTWrapper
        print("Initializing TensorRT VAE Decoder...")
        vae_decoder = VAETRTWrapper()
        APP_STATE["current_use_taehv"] = False
    elif use_taehv:
        print("Initializing TAEHV VAE Decoder...")
        from demo_utils.taehv import TAEHV
        taehv_checkpoint_path = "checkpoints/taew2_1.pth"
        if not os.path.exists(taehv_checkpoint_path):
            print(f"Downloading TAEHV checkpoint to {taehv_checkpoint_path}...")
            os.makedirs("checkpoints", exist_ok=True)
            download_url = "https://github.com/madebyollin/taehv/raw/main/taew2_1.pth"
            try:
                urllib.request.urlretrieve(download_url, taehv_checkpoint_path)
            except Exception as e:
                raise RuntimeError(f"Failed to download taew2_1.pth: {e}")
        
        class DotDict(dict): __getattr__ = dict.get
        
        class TAEHVDiffusersWrapper(torch.nn.Module):
            def __init__(self):
                super().__init__()
                self.dtype = torch.float16
                self.taehv = TAEHV(checkpoint_path=taehv_checkpoint_path).to(self.dtype)
                self.config = DotDict(scaling_factor=1.0)
            def decode(self, latents, return_dict=None):
                return self.taehv.decode_video(latents, parallel=not LOW_MEMORY).mul_(2).sub_(1)
        
        vae_decoder = TAEHVDiffusersWrapper()
        APP_STATE["current_use_taehv"] = True
    else:
        print("Initializing Default VAE Decoder...")
        vae_decoder = VAEDecoderWrapper()
        try:
            vae_state_dict = torch.load(os.path.join(WAN_MODELS_PATH, 'Wan2.1-T2V-1.3B', 'Wan2.1_VAE.pth'), map_location="cpu")
            decoder_state_dict = {k: v for k, v in vae_state_dict.items() if 'decoder.' in k or 'conv2' in k}
            vae_decoder.load_state_dict(decoder_state_dict)
        except FileNotFoundError:
            print("Warning: Default VAE weights not found.")
        APP_STATE["current_use_taehv"] = False

    vae_decoder.eval().to(dtype=torch.float16).requires_grad_(False).to(gpu)
    
    # Apply torch.compile to VAE decoder if enabled (following demo.py pattern)
    if APP_STATE["torch_compile_applied"] and not use_taehv and not use_trt:
        print("🚀 Applying torch.compile to VAE decoder...")
        vae_decoder.compile(mode=TORCH_COMPILATION_MODE)
        print("✅ torch.compile applied to VAE decoder")
    
    APP_STATE["current_vae_decoder"] = vae_decoder
    print(f"✅ VAE decoder initialized: {'TAEHV' if use_taehv else 'Default VAE'}")

# Initialize with default VAE
initialize_vae_decoder(use_taehv=False, use_trt=args.trt)

pipeline = CausalInferencePipeline(
    config, device=gpu, generator=transformer, text_encoder=text_encoder, 
    vae=APP_STATE["current_vae_decoder"]
)

pipeline.to(dtype=torch.float16).to(gpu)

@torch.no_grad()
def video_generation_handler_streaming(prompt, seed=42, fps=15, width=400, height=224, duration=5, lora_id=None, lora_weight=0.0):
    """
    Generator function that yields .ts video chunks using PyAV for streaming.
    """
    if seed == -1: 
        seed = random.randint(0, 2**32 - 1)
    
    # print(f"🎬 Starting PyAV streaming: seed: {seed}, duration: {duration}s")

    # Handle LoRA weights
    using_lora, lora_path = manage_lora_weights(lora_id, lora_weight)
    if using_lora:
        print(f"🎨 Using LoRA with weight factor {lora_weight}")
    else:
        print("🎨 Using base model (no LoRA)")

    # Setup
    conditional_dict = text_encoder(text_prompts=[prompt])
    for key, value in conditional_dict.items():
        conditional_dict[key] = value.to(dtype=torch.float16)
    
    rnd = torch.Generator(gpu).manual_seed(int(seed))
    pipeline._initialize_kv_cache(1, torch.float16, device=gpu)
    pipeline._initialize_crossattn_cache(1, torch.float16, device=gpu)
    noise = torch.randn([1, 21, 16, 60, 104], device=gpu, dtype=torch.float16, generator=rnd)
    
    vae_cache, latents_cache = None, None
    if not APP_STATE["current_use_taehv"] and not args.trt:
        vae_cache = [c.to(device=gpu, dtype=torch.float16) for c in ZERO_VAE_CACHE]

    # Calculate number of blocks based on duration
    # Current setup generates approximately 5 seconds with 7 blocks
    # So we scale proportionally
    base_duration = 5.0  # seconds
    base_blocks = 8
    num_blocks = max(1, int(base_blocks * duration / base_duration))
    
    current_start_frame = 0
    all_num_frames = [pipeline.num_frame_per_block] * num_blocks
    
    total_frames_yielded = 0
    
    # Ensure temp directory exists
    os.makedirs("gradio_tmp", exist_ok=True)

    # Generation loop
    for idx, current_num_frames in enumerate(all_num_frames):
        print(f"📦 Processing block {idx+1}/{num_blocks}")
        
        noisy_input = noise[:, current_start_frame : current_start_frame + current_num_frames]

        # Denoising steps
        for step_idx, current_timestep in enumerate(pipeline.denoising_step_list):
            timestep = torch.ones([1, current_num_frames], device=noise.device, dtype=torch.int64) * current_timestep
            _, denoised_pred = pipeline.generator(
                noisy_image_or_video=noisy_input, conditional_dict=conditional_dict,
                timestep=timestep, kv_cache=pipeline.kv_cache1,
                crossattn_cache=pipeline.crossattn_cache,
                current_start=current_start_frame * pipeline.frame_seq_length
            )
            if step_idx < len(pipeline.denoising_step_list) - 1:
                next_timestep = pipeline.denoising_step_list[step_idx + 1]
                noisy_input = pipeline.scheduler.add_noise(
                    denoised_pred.flatten(0, 1), torch.randn_like(denoised_pred.flatten(0, 1)),
                    next_timestep * torch.ones([1 * current_num_frames], device=noise.device, dtype=torch.long)
                ).unflatten(0, denoised_pred.shape[:2])

        if idx < len(all_num_frames) - 1:
            pipeline.generator(
                noisy_image_or_video=denoised_pred, conditional_dict=conditional_dict,
                timestep=torch.zeros_like(timestep), kv_cache=pipeline.kv_cache1,
                crossattn_cache=pipeline.crossattn_cache,
                current_start=current_start_frame * pipeline.frame_seq_length,
            )

        # Decode to pixels
        if args.trt:
            pixels, vae_cache = pipeline.vae.forward(denoised_pred.half(), *vae_cache)
        elif APP_STATE["current_use_taehv"]:
            if latents_cache is None: 
                latents_cache = denoised_pred
            else:
                denoised_pred = torch.cat([latents_cache, denoised_pred], dim=1)
                latents_cache = denoised_pred[:, -3:]
            pixels = pipeline.vae.decode(denoised_pred)
        else:
            pixels, vae_cache = pipeline.vae(denoised_pred.half(), *vae_cache)
            
        # Handle frame skipping
        if idx == 0 and not args.trt: 
            pixels = pixels[:, 3:]
        elif APP_STATE["current_use_taehv"] and idx > 0: 
            pixels = pixels[:, 12:]

        print(f"🔍 DEBUG Block {idx}: Pixels shape after skipping: {pixels.shape}")

        # Process all frames from this block at once
        all_frames_from_block = []
        for frame_idx in range(pixels.shape[1]):
            frame_tensor = pixels[0, frame_idx]
            
            # Convert to numpy (HWC, RGB, uint8)
            frame_np = torch.clamp(frame_tensor.float(), -1., 1.) * 127.5 + 127.5
            frame_np = frame_np.to(torch.uint8).cpu().numpy()
            frame_np = np.transpose(frame_np, (1, 2, 0))  # CHW -> HWC
            
            all_frames_from_block.append(frame_np)
            total_frames_yielded += 1
            
            # Yield status update for each frame (cute tracking!)
            blocks_completed = idx
            current_block_progress = (frame_idx + 1) / pixels.shape[1]
            total_progress = (blocks_completed + current_block_progress) / num_blocks * 100
            
            # Cap at 100% to avoid going over
            total_progress = min(total_progress, 100.0)
            
            frame_status_html = (
                f"<div style='padding: 10px; border: 1px solid #ddd; border-radius: 8px; font-family: sans-serif;'>"
                f"  <p style='margin: 0 0 8px 0; font-size: 16px; font-weight: bold;'>Generating Video...</p>"
                f"  <div style='background: #e9ecef; border-radius: 4px; width: 100%; overflow: hidden;'>"
                f"    <div style='width: {total_progress:.1f}%; height: 20px; background-color: #0d6efd; transition: width 0.2s;'></div>"
                f"  </div>"
                f"  <p style='margin: 8px 0 0 0; color: #555; font-size: 14px; text-align: right;'>"
                f"    Block {idx+1}/{num_blocks}   |   Frame {total_frames_yielded}   |   {total_progress:.1f}%"
                f"  </p>"
                f"</div>"
            )
            
            # Yield None for video but update status (frame-by-frame tracking)
            yield None, frame_status_html

        # Encode entire block as one chunk
        if all_frames_from_block:
            print(f"📹 Encoding block {idx} with {len(all_frames_from_block)} frames")
            
            try:
                chunk_uuid = str(uuid.uuid4())[:8]
                ts_filename = f"block_{idx:04d}_{chunk_uuid}.ts"
                ts_path = os.path.join("gradio_tmp", ts_filename)
                
                frames_to_ts_file(all_frames_from_block, ts_path, fps)
                
                # Calculate final progress for this block
                total_progress = (idx + 1) / num_blocks * 100
                
                # Yield the actual video chunk
                yield ts_path, gr.update()
                
            except Exception as e:
                print(f"⚠️ Error encoding block {idx}: {e}")
                import traceback
                traceback.print_exc()
                    
        current_start_frame += current_num_frames
    
    # Final completion status
    final_status_html = (
        f"<div style='padding: 16px; border: 1px solid #198754; background: linear-gradient(135deg, #d1e7dd, #f8f9fa); border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);'>"
        f"  <div style='display: flex; align-items: center; margin-bottom: 8px;'>"
        f"    <span style='font-size: 24px; margin-right: 12px;'>🎉</span>"
        f"    <h4 style='margin: 0; color: #0f5132; font-size: 18px;'>Stream Complete!</h4>"
        f"  </div>"
        f"  <div style='background: rgba(255,255,255,0.7); padding: 8px; border-radius: 4px;'>"
        f"    <p style='margin: 0; color: #0f5132; font-weight: 500;'>"
        f"      📊 Generated {total_frames_yielded} frames across {num_blocks} blocks"
        f"    </p>"
        f"    <p style='margin: 4px 0 0 0; color: #0f5132; font-size: 14px;'>"
        f"      🎬 Playback: {fps} FPS • 📁 Format: MPEG-TS/H.264"
        f"    </p>"
        f"  </div>"
        f"</div>"
    )
    yield None, final_status_html
    print(f"✅ PyAV streaming complete! {total_frames_yielded} frames across {num_blocks} blocks")

# --- Gradio UI Layout ---
with gr.Blocks(title="Wan2.1 1.3B LoRA Self-Forcing streaming demo") as demo:
    gr.Markdown("# 🚀 Run Any LoRA in near real-time!")
    gr.Markdown("Real-time video generation with distilled Wan2-1 1.3B and LoRA [[Model]](https://huggingface.co/gdhe17/Self-Forcing), [[Project page]](https://self-forcing.github.io), [[Paper]](https://huggingface.co/papers/2506.08009)")
    
    with gr.Tabs():
        # LoRA Upload Tab
        with gr.TabItem("1️⃣ Upload LoRA"):
            gr.Markdown("## Upload LoRA Weights")
            gr.Markdown("Upload your custom LoRA weights file to use for generation. The file will be automatically stored and you'll receive a unique hash-based ID.")
            
            with gr.Row():
                lora_file = gr.File(label="LoRA File (safetensors format)")
                
            with gr.Row():
                lora_id_output = gr.Textbox(label="LoRA Hash ID (use this in the generation tab)", interactive=False)
        
        # Video Generation Tab
        with gr.TabItem("2️⃣ Generate Video"):
            with gr.Row():
                with gr.Column(scale=2):
                    with gr.Group():
                        prompt = gr.Textbox(
                            label="Prompt", 
                            placeholder="A stylish woman walks down a Tokyo street...", 
                            lines=4,
                            value=""
                        )

                    start_btn = gr.Button("🎬 Start Streaming", variant="primary", size="lg")
                    
                    gr.Markdown("### ⚙️ Settings")
                    with gr.Row():
                        seed = gr.Number(
                            label="Seed", 
                            value=-1, 
                            info="Use -1 for random seed",
                            precision=0
                        )
                        fps = gr.Slider(
                            label="Playback FPS", 
                            minimum=1, 
                            maximum=30, 
                            value=args.fps, 
                            step=1,
                            visible=False,
                            info="Frames per second for playback"
                        )
                    
                    with gr.Row():
                        duration = gr.Slider(
                            label="Duration (seconds)",
                            minimum=1,
                            maximum=5,
                            value=3,
                            step=1,
                            info="Video duration in seconds"
                        )
                    
                    with gr.Row():
                        width = gr.Slider(
                            label="Width", 
                            minimum=224, 
                            maximum=720, 
                            value=400, 
                            step=8,
                            info="Video width in pixels (8px steps)"
                        )
                        height = gr.Slider(
                            label="Height", 
                            minimum=224, 
                            maximum=720, 
                            value=224, 
                            step=8,
                            info="Video height in pixels (8px steps)"
                        )
                    
                    gr.Markdown("### 🎨 LoRA Settings")
                    lora_id = gr.Textbox(
                        label="LoRA ID (from upload tab)",
                        placeholder="Enter your LoRA ID here...",
                    )
                    
                    lora_weight = gr.Slider(
                        label="LoRA Weight",
                        minimum=0.0,
                        maximum=1.0,
                        step=0.01,
                        value=1.0,
                        info="Strength of LoRA influence"
                    )
                    
                with gr.Column(scale=3):
                    gr.Markdown("### 📺 Video Stream")

                    streaming_video = gr.Video(
                        label="Live Stream",
                        streaming=True,
                        loop=True,
                        height=400,
                        autoplay=True,
                        show_label=False
                    )
                    
                    status_display = gr.HTML(
                        value=(
                            "<div style='text-align: center; padding: 20px; color: #666; border: 1px dashed #ddd; border-radius: 8px;'>"
                            "🎬 Ready to start streaming...<br>"
                            "<small>Configure your prompt and click 'Start Streaming'</small>"
                            "</div>"
                        ),
                        label="Generation Status"
                    )

    # Connect the generator to the streaming video
    start_btn.click(
        fn=video_generation_handler_streaming,
        inputs=[prompt, seed, fps, width, height, duration, lora_id, lora_weight],
        outputs=[streaming_video, status_display]
    )
    
    # Connect LoRA upload to both display fields
    lora_file.change(
        fn=upload_lora_file,
        inputs=[lora_file],
        outputs=[lora_id_output, lora_id]
    )


# --- Launch App ---
if __name__ == "__main__":
    if os.path.exists("gradio_tmp"):
        import shutil
        shutil.rmtree("gradio_tmp")
    os.makedirs("gradio_tmp", exist_ok=True)
    
    print("🚀 Starting Self-Forcing Streaming Demo")
    print(f"📁 Temporary files will be stored in: gradio_tmp/")
    print(f"🎯 Chunk encoding: PyAV (MPEG-TS/H.264)")
    print(f"⚡ GPU acceleration: {gpu}")
    
    demo.queue().launch(
        server_name=args.host, 
        server_port=args.port, 
        share=args.share,
        show_error=True,
        max_threads=40,
        mcp_server=True
    )