import os
import subprocess
import sys
from concurrent.futures import ThreadPoolExecutor

# Enable fast downloads
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
os.environ["HF_XET_HIGH_PERFORMANCE"] = "1"

# Disable torch.compile / dynamo before any torch import
os.environ["TORCH_COMPILE_DISABLE"] = "1"
os.environ["TORCHDYNAMO_DISABLE"] = "1"

# Install xformers for memory-efficient attention
subprocess.run([sys.executable, "-m", "pip", "install", "xformers==0.0.32.post2", "--no-build-isolation"], check=False)

# Clone LTX-2 repo at a pinned compatible commit and install packages
LTX_REPO_URL = "https://github.com/Lightricks/LTX-2.git"
LTX_REPO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "LTX-2")
LTX_COMMIT = "ae855f8538843825f9015a419cf4ba5edaf5eec2"

if os.path.exists(LTX_REPO_DIR):
    print(f"Removing existing repo at {LTX_REPO_DIR}...")
    subprocess.run(["rm", "-rf", LTX_REPO_DIR], check=True)

print(f"Cloning {LTX_REPO_URL}...")
subprocess.run(["git", "clone", LTX_REPO_URL, LTX_REPO_DIR], check=True)

print(f"Checking out commit {LTX_COMMIT}...")
subprocess.run(["git", "-C", LTX_REPO_DIR, "checkout", LTX_COMMIT], check=True)

print("Installing ltx-core and ltx-pipelines from pinned repo commit...")
subprocess.run(
    [
        sys.executable, "-m", "pip", "install",
        "--force-reinstall", "--no-deps",
        "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-core"),
        "-e", os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines"),
    ],
    check=True,
)

sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-pipelines", "src"))
sys.path.insert(0, os.path.join(LTX_REPO_DIR, "packages", "ltx-core", "src"))

import logging
import random
import tempfile
from pathlib import Path

import torch
torch._dynamo.config.suppress_errors = True
torch._dynamo.config.disable = True

# Critical workaround: Replace inference_mode with no_grad
# Avoids "inference tensor" failures in spatial upsampler and VAE decoder
torch.inference_mode = torch.no_grad

import spaces
import gradio as gr
import numpy as np
from huggingface_hub import hf_hub_download, snapshot_download

from ltx_pipelines.distilled import DistilledPipeline
from ltx_pipelines.utils.args import ImageConditioningInput
from ltx_pipelines.utils.media_io import encode_video

# Patch attention backend into the LTX attention module.
import torch.nn.functional as F
from ltx_core.model.transformer import attention as _attn_mod

def _sdpa_as_mea(query, key, value, attn_bias=None, scale=None, **kwargs):
    # xformers memory_efficient_attention: (B, S, H, D) -> (B, S, H, D)
    # torch SDPA:                          (B, H, S, D) -> (B, H, S, D)
    q, k, v = query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2)
    return F.scaled_dot_product_attention(q, k, v, scale=scale).transpose(1, 2)

_cap = torch.cuda.get_device_capability() if torch.cuda.is_available() else (0, 0)
_use_xformers = False
if _cap < (12, 0):
    try:
        from xformers.ops import memory_efficient_attention as _mea
        _attn_mod.memory_efficient_attention = _mea
        _use_xformers = True
        print(f"[ATTN] Using xformers memory_efficient_attention")
    except Exception as e:
        print(f"[ATTN] xformers unavailable ({e}), falling back to SDPA")

if not _use_xformers:
    _attn_mod.memory_efficient_attention = _sdpa_as_mea
    print(f"[ATTN] Using SDPA fallback (sm_{_cap[0]}{_cap[1]})")

logging.getLogger().setLevel(logging.INFO)

MAX_SEED = np.iinfo(np.int32).max
DEFAULT_PROMPT = (
    "An astronaut hatches from a fragile egg on the surface of the Moon, "
    "the shell cracking and peeling apart in gentle low-gravity motion. "
    "Fine lunar dust lifts and drifts outward with each movement, floating "
    "in slow arcs before settling back onto the ground."
)
DEFAULT_FRAME_RATE = 24.0

# Resolution presets: (width, height)
RESOLUTIONS = {
    "high": {"16:9": (1024, 640), "9:16": (640, 1024), "1:1": (1024, 1024)},
    "low": {"16:9": (512, 320), "9:16": (320, 512), "1:1": (512, 512)},
}


# Model repos
LTX_MODEL_REPO = "Lightricks/LTX-2.3"
GEMMA_REPO = "Lightricks/gemma-3-12b-it-qat-q4_0-unquantized"

# Download model checkpoints in parallel for speed
print("=" * 80)
print("Downloading Element-8 (pre-distilled LTX) + Gemma (parallel)...")
print("=" * 80)

def download_checkpoint():
    # Use pre-distilled LTX checkpoint - no LoRA needed
    return hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-22b-distilled.safetensors")

def download_upsampler():
    return hf_hub_download(repo_id=LTX_MODEL_REPO, filename="ltx-2.3-spatial-upscaler-x2-1.0.safetensors")

def download_gemma():
    return snapshot_download(repo_id=GEMMA_REPO)

with ThreadPoolExecutor(max_workers=3) as executor:
    future_checkpoint = executor.submit(download_checkpoint)
    future_upsampler = executor.submit(download_upsampler)
    future_gemma = executor.submit(download_gemma)

    checkpoint_path = future_checkpoint.result()
    spatial_upsampler_path = future_upsampler.result()
    gemma_root = future_gemma.result()

print(f"Checkpoint: {checkpoint_path}")
print(f"Spatial upsampler: {spatial_upsampler_path}")
print(f"Gemma root: {gemma_root}")

# Initialize pipeline with pre-distilled checkpoint (no LoRA needed)
pipeline = DistilledPipeline(
    distilled_checkpoint_path=checkpoint_path,
    spatial_upsampler_path=spatial_upsampler_path,
    gemma_root=gemma_root,
    loras=(),
)

# Preload all models for ZeroGPU tensor packing
print("Preloading all pipeline components via model_ledger...")

# DistilledPipeline uses model_ledger similar to other pipelines
ledger = pipeline.model_ledger
_transformer = ledger.transformer()
_video_encoder = ledger.video_encoder()
_video_decoder = ledger.video_decoder()
_spatial_upsampler = ledger.spatial_upsampler()
_text_encoder = ledger.text_encoder()
_embeddings_processor = ledger.gemma_embeddings_processor()
_audio_encoder = ledger.audio_encoder()
_audio_decoder = ledger.audio_decoder()
_vocoder = ledger.vocoder()

# Replace ledger methods with lambdas returning preloaded instances
ledger.transformer = lambda: _transformer
ledger.video_encoder = lambda: _video_encoder
ledger.video_decoder = lambda: _video_decoder
ledger.spatial_upsampler = lambda: _spatial_upsampler
ledger.text_encoder = lambda: _text_encoder
ledger.gemma_embeddings_processor = lambda: _embeddings_processor
ledger.audio_encoder = lambda: _audio_encoder
ledger.audio_decoder = lambda: _audio_decoder
ledger.vocoder = lambda: _vocoder

print("All models preloaded!")

print("=" * 80)
print("Pipeline ready!")
print("=" * 80)


def log_memory(tag: str):
    if torch.cuda.is_available():
        allocated = torch.cuda.memory_allocated() / 1024**3
        peak = torch.cuda.max_memory_allocated() / 1024**3
        free, total = torch.cuda.mem_get_info()
        print(f"[VRAM {tag}] allocated={allocated:.2f}GB peak={peak:.2f}GB free={free / 1024**3:.2f}GB total={total / 1024**3:.2f}GB")


def detect_aspect_ratio(image) -> str:
    if image is None:
        return "16:9"
    if hasattr(image, "size"):
        w, h = image.size
    elif hasattr(image, "shape"):
        h, w = image.shape[:2]
    else:
        return "16:9"
    ratio = w / h
    candidates = {"16:9": 16 / 9, "9:16": 9 / 16, "1:1": 1.0}
    return min(candidates, key=lambda k: abs(ratio - candidates[k]))


def on_image_upload(first_image, last_image, high_res):
    ref_image = first_image if first_image is not None else last_image
    aspect = detect_aspect_ratio(ref_image)
    tier = "high" if high_res else "low"
    w, h = RESOLUTIONS[tier][aspect]
    return gr.update(value=w), gr.update(value=h)


def on_highres_toggle(first_image, last_image, high_res):
    ref_image = first_image if first_image is not None else last_image
    aspect = detect_aspect_ratio(ref_image)
    tier = "high" if high_res else "low"
    w, h = RESOLUTIONS[tier][aspect]
    return gr.update(value=w), gr.update(value=h)


DEFAULT_NEGATIVE_PROMPT = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走, blurry, glasses, deformed, subtitles, text, captions, worst quality, low quality, inconsistent motion, jittery, distorted"


def remove_music_demucs(input_video_path: str, output_video_path: str) -> bool:
    """Remove background music from video using Demucs, keeping only vocals."""
    import subprocess
    import tempfile
    from pathlib import Path

    try:
        with tempfile.TemporaryDirectory() as tmpdir:
            tmpdir = Path(tmpdir)

            # Extract audio from video
            audio_in = tmpdir / "audio.wav"
            extract_cmd = [
                'ffmpeg', '-y', '-i', input_video_path,
                '-vn', '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2',
                str(audio_in)
            ]
            result = subprocess.run(extract_cmd, capture_output=True, text=True)
            if result.returncode != 0:
                print(f"[demucs] Failed to extract audio: {result.stderr[-200:]}")
                return False

            print(f"[demucs] Running music separation...")

            import soundfile as sf
            from demucs.pretrained import get_model
            from demucs.apply import apply_model

            # Load model (cached after first run)
            model = get_model('htdemucs')
            model.to('cuda')
            model.eval()

            # Load audio
            data, sr = sf.read(str(audio_in))
            wav = torch.from_numpy(data.T).float()
            if wav.dim() == 1:
                wav = wav.unsqueeze(0)

            # Resample if needed
            if sr != model.samplerate:
                import torchaudio
                wav = torchaudio.functional.resample(wav, sr, model.samplerate)

            wav = wav.unsqueeze(0).to('cuda')

            # Separate sources
            with torch.no_grad():
                sources = apply_model(model, wav, overlap=0.25, progress=False)

            # Keep only vocals (index 3)
            vocals = sources[0, 3].cpu()

            # Save vocals
            audio_out = tmpdir / "vocals.wav"
            audio_np = vocals.numpy().T
            sf.write(str(audio_out), audio_np, model.samplerate)

            print(f"[demucs] Merging vocals back with video...")
            merge_cmd = [
                'ffmpeg', '-y',
                '-i', input_video_path,
                '-i', str(audio_out),
                '-c:v', 'copy',
                '-map', '0:v:0', '-map', '1:a:0',
                '-c:a', 'aac', '-b:a', '128k',
                '-shortest',
                output_video_path
            ]
            result = subprocess.run(merge_cmd, capture_output=True, text=True)
            if result.returncode != 0:
                print(f"[demucs] Failed to merge: {result.stderr[-200:]}")
                return False

            print(f"[demucs] Successfully removed music")
            return True

    except Exception as e:
        print(f"[demucs] Error: {e}")
        import traceback
        traceback.print_exc()
        return False


def apply_gaussian_blur(video_tensor: torch.Tensor, blur_amount: int) -> torch.Tensor:
    """Apply Gaussian blur to video tensor. Video shape: [frames, H, W, C]"""
    if blur_amount <= 0:
        return video_tensor

    from torchvision.transforms.functional import gaussian_blur

    # Ensure kernel size is odd and at least 3
    kernel_size = blur_amount * 2 + 1
    sigma = blur_amount / 2.0

    # Video tensor is [frames, H, W, C], but gaussian_blur expects [batch, C, H, W]
    # Permute to [frames, C, H, W]
    video_tensor = video_tensor.permute(0, 3, 1, 2)

    blurred = gaussian_blur(video_tensor, kernel_size=[kernel_size, kernel_size], sigma=[sigma, sigma])

    # Permute back to [frames, H, W, C]
    blurred = blurred.permute(0, 2, 3, 1)

    return blurred


def loop_clips_with_audio_track(clip_paths: list[str], audio_path: str) -> str:
    """Loop video clips to match audio duration. CPU work - free."""
    import subprocess
    from pydub import AudioSegment

    try:
        # Get audio duration
        audio = AudioSegment.from_file(audio_path)
        audio_duration = len(audio) / 1000.0  # Convert to seconds

        # Get total clips duration
        clips_duration = 0.0
        for clip in clip_paths:
            probe = subprocess.run([
                'ffprobe', '-v', 'error', '-show_entries', 'format=duration',
                '-of', 'default=noprint_wrappers=1:nokey=1', clip
            ], capture_output=True, text=True, check=True)
            clips_duration += float(probe.stdout.strip())

        # Calculate loop count
        loop_count = int(audio_duration / clips_duration) + 1

        print(f"[loop] Audio: {audio_duration:.2f}s, Clips: {clips_duration:.2f}s, Loops: {loop_count}")

        # Create concat file with loops
        concat_file = tempfile.mktemp(suffix=".txt")
        with open(concat_file, 'w') as f:
            for _ in range(loop_count):
                for clip in clip_paths:
                    f.write(f"file '{clip}'\n")

        # Concat videos
        concat_video = tempfile.mktemp(suffix=".mp4")
        result = subprocess.run([
            'ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i', concat_file,
            '-c', 'copy', concat_video
        ], capture_output=True, text=True)

        if result.returncode != 0:
            raise Exception(f"Concat failed: {result.stderr[-200:]}")

        # Replace audio and trim to audio duration
        final_video = tempfile.mktemp(suffix=".mp4")
        result = subprocess.run([
            'ffmpeg', '-y',
            '-i', concat_video,
            '-i', audio_path,
            '-map', '0:v:0', '-map', '1:a:0',
            '-c:v', 'copy', '-c:a', 'aac', '-b:a', '192k',
            '-t', str(audio_duration),
            '-shortest',
            final_video
        ], capture_output=True, text=True)

        if result.returncode != 0:
            raise Exception(f"Audio merge failed: {result.stderr[-200:]}")

        print(f"[loop] Created looped video: {audio_duration:.2f}s")
        return final_video

    except Exception as e:
        print(f"[loop] Error: {e}")
        import traceback
        traceback.print_exc()
        return clip_paths[0] if clip_paths else None


def transcribe_with_whisper_gpu(video_path: str, model_size: str = "small") -> list[dict]:
    """Transcribe video audio with Whisper on GPU (already inside GPU context). Returns segments with timestamps."""
    import whisper

    try:
        print(f"[whisper] Loading {model_size} model on GPU...")
        model = whisper.load_model(model_size).to('cuda')

        print(f"[whisper] Transcribing audio on GPU...")
        result = model.transcribe(video_path, word_timestamps=True, fp16=True)

        print(f"[whisper] Transcription complete: {len(result['segments'])} segments")
        return result['segments']
    except Exception as e:
        print(f"[whisper] Error: {e}")
        import traceback
        traceback.print_exc()
        return []


def create_beautiful_ass_subtitles(segments: list[dict], output_path: str, video_width: int, video_height: int):
    """Create elegant animated ASS subtitles with universal language support."""

    # Download Noto Sans - supports all languages (Latin, CJK, Tamil, etc.)
    import urllib.request
    font_url = "https://github.com/google/fonts/raw/main/ofl/notosans/NotoSans-SemiBold.ttf"
    font_path = "/tmp/NotoSans-SemiBold.ttf"
    font_name = "Noto Sans SemiBold"

    try:
        if not os.path.exists(font_path):
            urllib.request.urlretrieve(font_url, font_path)
    except:
        font_name = "Arial"  # Fallback

    # ASS subtitle header with beautiful styling
    ass_content = f"""[Script Info]
Title: Elegant Subtitles
ScriptType: v4.00+
WrapStyle: 0
PlayResX: {video_width}
PlayResY: {video_height}
ScaledBorderAndShadow: yes

[V4+ Styles]
Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
Style: Default,{font_name},{int(video_height * 0.05)},&H00FFFFFF,&H000000FF,&H00000000,&H80000000,0,0,0,0,100,100,0,0,1,2,1,5,10,10,{int(video_height * 0.42)},1

[Events]
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
"""

    # Add each segment with fade animation
    for seg in segments:
        start_time = format_ass_time(seg['start'])
        end_time = format_ass_time(seg['end'])
        text = seg['text'].strip()

        # Add fade in/out animation
        fade_duration = 200  # ms
        animated_text = f"{{\\fad({fade_duration},{fade_duration})}}{text}"

        ass_content += f"Dialogue: 0,{start_time},{end_time},Default,,0,0,0,,{animated_text}\n"

    with open(output_path, 'w', encoding='utf-8') as f:
        f.write(ass_content)

    print(f"[subtitles] Created ASS file with {len(segments)} segments")


def format_ass_time(seconds: float) -> str:
    """Convert seconds to ASS timestamp format (h:mm:ss.cc)."""
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    centisecs = int((seconds % 1) * 100)
    return f"{hours}:{minutes:02d}:{secs:02d}.{centisecs:02d}"


def burn_subtitles_and_watermark(video_path: str, output_path: str, subtitle_path: str = None, watermark_path: str = None):
    """Burn subtitles and/or watermark into video using FFmpeg. CPU work - free."""
    import subprocess
    import tempfile

    try:
        current_video = video_path

        # Step 1: Apply watermark if needed (first pass)
        if watermark_path and os.path.exists(watermark_path):
            print(f"[burn] Pass 1: Overlaying watermark...")

            # Get video duration for looping watermark
            probe = subprocess.run([
                'ffprobe', '-v', 'error', '-show_entries', 'format=duration',
                '-of', 'default=noprint_wrappers=1:nokey=1', current_video
            ], capture_output=True, text=True, check=True)
            video_duration = float(probe.stdout.strip())

            temp_watermarked = tempfile.mktemp(suffix=".mp4")

            cmd = [
                'ffmpeg', '-y', '-i', current_video,
                '-loop', '1', '-t', str(video_duration), '-i', watermark_path,
                '-filter_complex', '[1:v][0:v]scale2ref[ovr][base];[base][ovr]overlay=0:0[vout]',
                '-map', '[vout]', '-map', '0:a?',
                '-c:a', 'copy', '-pix_fmt', 'yuv420p',
                temp_watermarked
            ]

            result = subprocess.run(cmd, capture_output=True, text=True)
            if result.returncode != 0:
                raise Exception(f"Watermark pass failed: {result.stderr[-200:]}")

            current_video = temp_watermarked
            print(f"[burn] Watermark applied")

        # Step 2: Apply subtitles if needed (second pass)
        if subtitle_path and os.path.exists(subtitle_path):
            print(f"[burn] Pass 2: Burning subtitles from {subtitle_path}...")

            # Escape the subtitle path for FFmpeg (replace : with \\: and \ with /)
            subtitle_path_escaped = subtitle_path.replace('\\', '/').replace(':', '\\:')

            cmd = [
                'ffmpeg', '-y', '-i', current_video,
                '-vf', f"subtitles='{subtitle_path_escaped}':force_style='FontName=Noto Sans SemiBold'",
                '-c:a', 'copy', '-pix_fmt', 'yuv420p',
                output_path
            ]

            print(f"[burn] FFmpeg command: {' '.join(cmd)}")
            result = subprocess.run(cmd, capture_output=True, text=True)
            if result.returncode != 0:
                print(f"[burn] Subtitle stderr: {result.stderr}")
                raise Exception(f"Subtitle pass failed: {result.stderr[-500:]}")

            print(f"[burn] Subtitles burned successfully")
        elif current_video != video_path:
            # Only watermark was applied, move temp file to output
            import shutil
            shutil.move(current_video, output_path)
        else:
            # Nothing to do, just copy
            import shutil
            shutil.copy2(video_path, output_path)

        print(f"[burn] Successfully burned subtitles/watermark")
        return True

    except Exception as e:
        print(f"[burn] Error: {e}")
        import traceback
        traceback.print_exc()
        return False


@spaces.GPU(duration=90)
@torch.inference_mode()
def generate_video(
    first_image,
    last_image,
    prompts: list[str],
    duration: float,
    enhance_prompt: bool = True,
    seed: int = 42,
    randomize_seed: bool = True,
    height: int = 320,
    width: int = 512,
    negative_prompt: str = DEFAULT_NEGATIVE_PROMPT,
    blur_amount: int = 0,
    remove_music: bool = False,
    add_subtitles: bool = False,
    audio_track = None,
    progress=gr.Progress(track_tqdm=True),
):
    try:
        torch.cuda.reset_peak_memory_stats()
        log_memory("start")

        base_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
        generated_clips = []

        # Generate multiple clips in one GPU session (one per prompt)
        for clip_idx, prompt in enumerate(prompts):
            current_seed = base_seed + clip_idx
            print(f"[GPU] Generating clip {clip_idx + 1}/{len(prompts)}, prompt: {prompt[:50]}..., seed={current_seed}")

            frame_rate = DEFAULT_FRAME_RATE
            num_frames = int(duration * frame_rate) + 1
            num_frames = ((num_frames - 1 + 7) // 8) * 8 + 1

            print(f"Generating: {height}x{width}, {num_frames} frames ({duration}s), seed={current_seed}")

            images = []
            output_dir = Path("outputs")
            output_dir.mkdir(exist_ok=True)

            if first_image is not None:
                temp_first_path = output_dir / f"temp_first_{current_seed}.jpg"
                if hasattr(first_image, "save"):
                    first_image.save(temp_first_path)
                else:
                    temp_first_path = Path(first_image)
                images.append(ImageConditioningInput(path=str(temp_first_path), frame_idx=0, strength=1.0))

            if last_image is not None:
                temp_last_path = output_dir / f"temp_last_{current_seed}.jpg"
                if hasattr(last_image, "save"):
                    last_image.save(temp_last_path)
                else:
                    temp_last_path = Path(last_image)
                images.append(ImageConditioningInput(path=str(temp_last_path), frame_idx=num_frames - 1, strength=1.0))

            from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number

            tiling_config = TilingConfig.default()
            video_chunks_number = get_video_chunks_number(num_frames, tiling_config)

            log_memory("before pipeline call")

            # Run inference - DistilledPipeline has simpler API
            video_frames_iter, audio = pipeline(
                prompt=prompt,
                seed=current_seed,
                height=int(height),
                width=int(width),
                num_frames=num_frames,
                frame_rate=frame_rate,
                images=images,
                enhance_prompt=enhance_prompt,
            )

            # Collect video frames
            frames = [frame for frame in video_frames_iter]
            video_tensor = torch.cat(frames, dim=0) if len(frames) > 1 else frames[0]

            log_memory("after pipeline call")

            # Apply Gaussian blur if requested (for censoring/teaser effect)
            if blur_amount > 0:
                print(f"Applying Gaussian blur (amount={blur_amount})...")
                video_tensor = apply_gaussian_blur(video_tensor, blur_amount)
                log_memory("after blur")

            output_path = tempfile.mktemp(suffix=".mp4")
            encode_video(
                video=video_tensor,
                fps=frame_rate,
                audio=audio,
                output_path=output_path,
                video_chunks_number=video_chunks_number,
            )

            log_memory("after encode_video")

            # Remove background music if requested
            if remove_music:
                print(f"Removing background music with Demucs...")
                processed_path = tempfile.mktemp(suffix=".mp4")
                success = remove_music_demucs(output_path, processed_path)
                if success:
                    output_path = processed_path
                    log_memory("after demucs")
                else:
                    print(f"Warning: Music removal failed, using original video")

            generated_clips.append(str(output_path))

        # Transcribe with Whisper if requested (still within GPU context)
        subtitle_segments = []
        if add_subtitles and audio_track:
            print("[GPU] Transcribing audio track with Whisper...")
            # Transcribe the audio track file, not the generated video (which has no audio yet)
            subtitle_segments = transcribe_with_whisper_gpu(audio_track, model_size="small")
            log_memory("after whisper")
        elif add_subtitles and not audio_track:
            print("[GPU] Warning: Subtitles requested but no audio track provided - skipping transcription")

        # Return all generated clips and subtitle segments
        return generated_clips, subtitle_segments, base_seed

    except Exception as e:
        import traceback
        log_memory("on error")
        print(f"Error: {str(e)}\n{traceback.format_exc()}")
        return [], [], base_seed


def full_generation_process(
    first_image,
    last_image,
    prompt1: str,
    prompt2: str,
    prompt3: str,
    duration: float,
    enhance_prompt: bool,
    seed: int,
    randomize_seed: bool,
    height: int,
    width: int,
    negative_prompt: str,
    blur_amount: int,
    remove_music: bool,
    add_subtitles: bool,
    watermark,
    audio_track,
    progress=gr.Progress(track_tqdm=True),
):
    """Main entry point: generates clips (GPU) then optionally loops with audio (CPU)."""

    # Collect non-empty prompts
    prompts = [p.strip() for p in [prompt1, prompt2, prompt3] if p and p.strip()]
    if not prompts:
        return None, seed

    print(f"Generating {len(prompts)} clip(s)")

    # Phase 1: Generate clips + transcribe (GPU time counted)
    clips, subtitle_segments, final_seed = generate_video(
        first_image, last_image, prompts, duration, enhance_prompt,
        seed, randomize_seed, height, width, negative_prompt,
        blur_amount, remove_music, add_subtitles, audio_track, progress
    )

    if not clips:
        return None, final_seed

    # Phase 2: CPU work (free) - loop clips with audio if provided
    if audio_track and len(clips) > 1:
        print("[CPU] Looping clips to match audio duration...")
        final_video = loop_clips_with_audio_track(clips, audio_track)
    elif len(clips) == 1:
        final_video = clips[0]
    else:
        final_video = clips[0]

    # Phase 3: CPU work (free) - add subtitles and/or watermark
    if add_subtitles or watermark:
        print("[CPU] Adding subtitles/watermark...")

        # Use subtitle segments from GPU transcription
        subtitle_file = None
        if add_subtitles and subtitle_segments:
            subtitle_file = tempfile.mktemp(suffix=".ass")
            create_beautiful_ass_subtitles(subtitle_segments, subtitle_file, int(width), int(height))
            print(f"[subtitles] Created subtitle file: {subtitle_file}, exists: {os.path.exists(subtitle_file)}")
            if os.path.exists(subtitle_file):
                with open(subtitle_file, 'r') as f:
                    print(f"[subtitles] File size: {len(f.read())} bytes")

        # Burn subtitles and/or watermark
        output_with_extras = tempfile.mktemp(suffix=".mp4")
        success = burn_subtitles_and_watermark(final_video, output_with_extras, subtitle_file, watermark)
        if success:
            final_video = output_with_extras

    return final_video, final_seed


with gr.Blocks(title="Element-8 Video", delete_cache=(3600, 7200)) as demo:  # cleanup: check every 1h, delete files >2h old
    gr.Markdown("# Element-8: Fast Video Generation with Frame Conditioning")
    gr.Markdown(
        "High quality video + audio generation with first and last frame conditioning. "
        "Pre-distilled LTX model for fast inference. "
        "[[code]](https://github.com/Lightricks/LTX-2)"
    )

    with gr.Row():
        with gr.Column():
            with gr.Row():
                first_image = gr.Image(label="First Frame (Optional)", type="pil")
                last_image = gr.Image(label="Last Frame (Optional)", type="pil")
            prompt1 = gr.Textbox(
                label="Prompt 1",
                value="Make this image come alive with cinematic motion, smooth animation",
                lines=2,
                placeholder="First prompt (required)",
            )
            prompt2 = gr.Textbox(
                label="Prompt 2 (Optional)",
                value="",
                lines=2,
                placeholder="Second prompt (leave empty if not needed)",
            )
            prompt3 = gr.Textbox(
                label="Prompt 3 (Optional)",
                value="",
                lines=2,
                placeholder="Third prompt (leave empty if not needed)",
            )
            duration = gr.Slider(label="Duration (seconds)", minimum=1.0, maximum=10.0, value=3.0, step=0.1)
            audio_track = gr.Audio(label="Audio Track (Optional) - loops clips to match duration", type="filepath", sources=["upload"])

            generate_btn = gr.Button("Generate Video", variant="primary", size="lg")

            with gr.Accordion("Advanced Settings", open=False):
                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, value=10, step=1)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                with gr.Row():
                    width = gr.Number(label="Width", value=512, precision=0)
                    height = gr.Number(label="Height", value=320, precision=0)
                with gr.Row():
                    enhance_prompt = gr.Checkbox(label="Enhance Prompt", value=False)
                    high_res = gr.Checkbox(label="High Resolution", value=False)
                with gr.Row():
                    blur_amount = gr.Number(label="Blur (0=off, 36=heavy)", value=0, precision=0)
                    remove_music = gr.Checkbox(label="Remove Music", value=False)
                with gr.Row():
                    add_subtitles = gr.Checkbox(label="Add Subtitles (Whisper)", value=False)
                    watermark = gr.File(label="Watermark PNG (full-video size, position in your editor)", file_types=[".png"])
                negative_prompt = gr.Textbox(
                    label="Negative Prompt",
                    value=DEFAULT_NEGATIVE_PROMPT,
                    lines=3,
                    placeholder="What to avoid in the generated video...",
                )

        with gr.Column():
            output_video = gr.Video(label="Generated Video", autoplay=True)

    gr.Examples(
        examples=[
            [
                None,
                "pinkknit.jpg",
                "The camera falls downward through darkness as if dropped into a tunnel. "
                "As it slows, five friends wearing pink knitted hats and sunglasses lean "
                "over and look down toward the camera with curious expressions. The lens "
                "has a strong fisheye effect, creating a circular frame around them. They "
                "crowd together closely, forming a symmetrical cluster while staring "
                "directly into the lens.",
                "",
                "",
                3.0,
                False,
                42,
                True,
                1024,
                1024,
            ],
        ],
        inputs=[
            first_image, last_image, prompt1, prompt2, prompt3, duration,
            enhance_prompt, seed, randomize_seed, height, width,
        ],
    )

    first_image.change(
        fn=on_image_upload,
        inputs=[first_image, last_image, high_res],
        outputs=[width, height],
    )

    last_image.change(
        fn=on_image_upload,
        inputs=[first_image, last_image, high_res],
        outputs=[width, height],
    )

    high_res.change(
        fn=on_highres_toggle,
        inputs=[first_image, last_image, high_res],
        outputs=[width, height],
    )

    generate_btn.click(
        fn=full_generation_process,
        inputs=[
            first_image, last_image, prompt1, prompt2, prompt3, duration, enhance_prompt,
            seed, randomize_seed, height, width, negative_prompt, blur_amount, remove_music,
            add_subtitles, watermark, audio_track,
        ],
        outputs=[output_video, seed],
    )


css = """
.fillable{max-width: 1200px !important}
"""

if __name__ == "__main__":
    demo.launch(theme=gr.themes.Citrus(), css=css)