from diffusers_helper.hf_login import login

import os
import threading
import time
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
import json

os.environ['HF_HOME'] = os.path.abspath(os.path.realpath(os.path.join(os.path.dirname(__file__), './hf_download')))

import gradio as gr
import torch
import traceback
import einops
import safetensors.torch as sf
import numpy as np
import math

# Check if running in Hugging Face Space
IN_HF_SPACE = os.environ.get('SPACE_ID') is not None

# Track GPU availability
GPU_AVAILABLE = False
GPU_INITIALIZED = False
last_update_time = time.time()

# If running in a HF Space, import spaces
if IN_HF_SPACE:
    try:
        import spaces
        print("Running inside a Hugging Face Space, 'spaces' module imported.")
        
        try:
            GPU_AVAILABLE = torch.cuda.is_available()
            print(f"GPU available: {GPU_AVAILABLE}")
            if GPU_AVAILABLE:
                print(f"GPU device name: {torch.cuda.get_device_name(0)}")
                print(f"GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9} GB")
                
                # Small GPU operation test
                test_tensor = torch.zeros(1, device='cuda') + 1
                del test_tensor
                print("GPU test operation succeeded.")
            else:
                print("Warning: CUDA says it's available, but no GPU device was detected.")
        except Exception as e:
            GPU_AVAILABLE = False
            print(f"Error checking GPU: {e}")
            print("Falling back to CPU mode.")
    except ImportError:
        print("Could not import 'spaces' module. Possibly not in a HF Space.")
        GPU_AVAILABLE = torch.cuda.is_available()

from PIL import Image
from diffusers import AutoencoderKLHunyuanVideo
from transformers import (
    LlamaModel, 
    CLIPTextModel, 
    LlamaTokenizerFast, 
    CLIPTokenizer, 
    SiglipImageProcessor, 
    SiglipVisionModel
)
from diffusers_helper.hunyuan import (
    encode_prompt_conds, 
    vae_decode, 
    vae_encode, 
    vae_decode_fake
)
from diffusers_helper.utils import (
    save_bcthw_as_mp4, 
    crop_or_pad_yield_mask, 
    soft_append_bcthw, 
    resize_and_center_crop, 
    generate_timestamp
)
from diffusers_helper.models.hunyuan_video_packed import HunyuanVideoTransformer3DModelPacked
from diffusers_helper.pipelines.k_diffusion_hunyuan import sample_hunyuan
from diffusers_helper.memory import (
    cpu,
    gpu,
    get_cuda_free_memory_gb,
    move_model_to_device_with_memory_preservation,
    offload_model_from_device_for_memory_preservation,
    fake_diffusers_current_device,
    DynamicSwapInstaller,
    unload_complete_models,
    load_model_as_complete
)
from diffusers_helper.thread_utils import AsyncStream, async_run
from diffusers_helper.gradio.progress_bar import make_progress_bar_css, make_progress_bar_html
from diffusers_helper.clip_vision import hf_clip_vision_encode

outputs_folder = './outputs/'
os.makedirs(outputs_folder, exist_ok=True)

# Manage GPU memory if not in HF Space
if not IN_HF_SPACE:
    try:
        if torch.cuda.is_available():
            free_mem_gb = get_cuda_free_memory_gb(gpu)
            print(f'Free VRAM: {free_mem_gb} GB')
        else:
            free_mem_gb = 6.0
            print("CUDA not available, using default memory setting.")
    except Exception as e:
        free_mem_gb = 6.0
        print(f"Error getting CUDA memory: {e}, using default=6GB")
    high_vram = free_mem_gb > 60
    print(f'High-VRAM mode: {high_vram}')
else:
    print("Using default memory settings in a HF Space.")
    try:
        if GPU_AVAILABLE:
            free_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9 * 0.9
            high_vram = free_mem_gb > 10
        else:
            free_mem_gb = 6.0
            high_vram = False
    except Exception as e:
        print(f"Error retrieving GPU memory: {e}")
        free_mem_gb = 6.0
        high_vram = False
    print(f'GPU mem: {free_mem_gb:.2f} GB, high_vram={high_vram}')

models = {}
cpu_fallback_mode = not GPU_AVAILABLE


def load_models():
    """
    Load the entire pipeline models (VAE, text encoders, image encoder, transformer).
    """
    global models, cpu_fallback_mode, GPU_INITIALIZED
    
    if GPU_INITIALIZED:
        print("Models are already loaded. Skipping duplicate loading.")
        return models
    
    print("Starting model load...")

    try:
        device = 'cuda' if (GPU_AVAILABLE and not cpu_fallback_mode) else 'cpu'
        model_device = 'cpu'

        dtype = torch.float16 if GPU_AVAILABLE else torch.float32
        transformer_dtype = torch.bfloat16 if GPU_AVAILABLE else torch.float32

        print(f"Device: {device}, VAE/encoders dtype={dtype}, transformer dtype={transformer_dtype}")

        try:
            text_encoder = LlamaModel.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo", 
                subfolder='text_encoder', 
                torch_dtype=dtype
            ).to(model_device)
            text_encoder_2 = CLIPTextModel.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo", 
                subfolder='text_encoder_2', 
                torch_dtype=dtype
            ).to(model_device)
            tokenizer = LlamaTokenizerFast.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo",
                subfolder='tokenizer'
            )
            tokenizer_2 = CLIPTokenizer.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo",
                subfolder='tokenizer_2'
            )
            vae = AutoencoderKLHunyuanVideo.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo",
                subfolder='vae',
                torch_dtype=dtype
            ).to(model_device)

            feature_extractor = SiglipImageProcessor.from_pretrained(
                "lllyasviel/flux_redux_bfl", 
                subfolder='feature_extractor'
            )
            image_encoder = SiglipVisionModel.from_pretrained(
                "lllyasviel/flux_redux_bfl",
                subfolder='image_encoder',
                torch_dtype=dtype
            ).to(model_device)

            # Use a custom rotating-landscape model (for example)
            transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained(
                "tori29umai/FramePackI2V_HY_rotate_landscape", 
                torch_dtype=transformer_dtype
            ).to(model_device)

            print("All models loaded successfully.")
        except Exception as e:
            print(f"Error loading models: {e}")
            print("Retry with float32 on CPU.")
            dtype = torch.float32
            transformer_dtype = torch.float32
            cpu_fallback_mode = True

            text_encoder = LlamaModel.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo", 
                subfolder='text_encoder', 
                torch_dtype=dtype
            ).to('cpu')
            text_encoder_2 = CLIPTextModel.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo", 
                subfolder='text_encoder_2', 
                torch_dtype=dtype
            ).to('cpu')
            tokenizer = LlamaTokenizerFast.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo",
                subfolder='tokenizer'
            )
            tokenizer_2 = CLIPTokenizer.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo",
                subfolder='tokenizer_2'
            )
            vae = AutoencoderKLHunyuanVideo.from_pretrained(
                "hunyuanvideo-community/HunyuanVideo",
                subfolder='vae',
                torch_dtype=dtype
            ).to('cpu')

            feature_extractor = SiglipImageProcessor.from_pretrained(
                "lllyasviel/flux_redux_bfl", 
                subfolder='feature_extractor'
            )
            image_encoder = SiglipVisionModel.from_pretrained(
                "lllyasviel/flux_redux_bfl",
                subfolder='image_encoder',
                torch_dtype=dtype
            ).to('cpu')

            transformer = HunyuanVideoTransformer3DModelPacked.from_pretrained(
                "tori29umai/FramePackI2V_HY_rotate_landscape", 
                torch_dtype=transformer_dtype
            ).to('cpu')

            print("Models loaded in CPU-only fallback mode.")

        vae.eval()
        text_encoder.eval()
        text_encoder_2.eval()
        image_encoder.eval()
        transformer.eval()

        if not high_vram or cpu_fallback_mode:
            vae.enable_slicing()
            vae.enable_tiling()

        transformer.high_quality_fp32_output_for_inference = True
        print("transformer.high_quality_fp32_output_for_inference = True")

        if not cpu_fallback_mode:
            transformer.to(dtype=transformer_dtype)
            vae.to(dtype=dtype)
            image_encoder.to(dtype=dtype)
            text_encoder.to(dtype=dtype)
            text_encoder_2.to(dtype=dtype)

        vae.requires_grad_(False)
        text_encoder.requires_grad_(False)
        text_encoder_2.requires_grad_(False)
        image_encoder.requires_grad_(False)
        transformer.requires_grad_(False)

        if torch.cuda.is_available() and not cpu_fallback_mode:
            try:
                if not high_vram:
                    DynamicSwapInstaller.install_model(transformer, device=device)
                    DynamicSwapInstaller.install_model(text_encoder, device=device)
                else:
                    text_encoder.to(device)
                    text_encoder_2.to(device)
                    image_encoder.to(device)
                    vae.to(device)
                    transformer.to(device)
                print(f"Successfully moved models to {device}")
            except Exception as e:
                print(f"Error moving models to {device}: {e}")
                print("Falling back to CPU.")
                cpu_fallback_mode = True

        models_local = {
            'text_encoder': text_encoder,
            'text_encoder_2': text_encoder_2,
            'tokenizer': tokenizer,
            'tokenizer_2': tokenizer_2,
            'vae': vae,
            'feature_extractor': feature_extractor,
            'image_encoder': image_encoder,
            'transformer': transformer
        }
        
        GPU_INITIALIZED = True
        models.update(models_local)
        print(f"Model load complete. Mode: {'CPU' if cpu_fallback_mode else 'GPU'}")
        return models
    except Exception as e:
        print(f"Unexpected error in load_models(): {e}")
        traceback.print_exc()
        cpu_fallback_mode = True
        return {}


# Use GPU decorator if in HF Space
if IN_HF_SPACE and 'spaces' in globals() and GPU_AVAILABLE:
    try:
        @spaces.GPU
        def initialize_models():
            global GPU_INITIALIZED
            try:
                result = load_models()
                GPU_INITIALIZED = True
                return result
            except Exception as e:
                print(f"Error in @spaces.GPU model init: {e}")
                global cpu_fallback_mode
                cpu_fallback_mode = True
                return load_models()
    except Exception as e:
        print(f"Error creating spaces.GPU decorator: {e}")
        def initialize_models():
            return load_models()
else:
    def initialize_models():
        return load_models()


def get_models():
    """
    Retrieve the global models or load them if not yet loaded.
    """
    global models
    model_loading_key = "__model_loading__"

    if not models:
        if model_loading_key in globals():
            print("Models are loading. Please wait.")
            import time
            start_time = time.time()
            while (not models) and (model_loading_key in globals()):
                time.sleep(0.5)
                if time.time() - start_time > 60:
                    print("Timed out waiting for model load.")
                    break
            if models:
                return models
        try:
            globals()[model_loading_key] = True
            if IN_HF_SPACE and 'spaces' in globals() and GPU_AVAILABLE and not cpu_fallback_mode:
                try:
                    print("Loading models via @spaces.GPU")
                    models_local = initialize_models()
                    models.update(models_local)
                except Exception as e:
                    print(f"GPU decorator load error: {e}, fallback to direct load.")
                    models_local = load_models()
                    models.update(models_local)
            else:
                models_local = load_models()
                models.update(models_local)
        except Exception as e:
            print(f"Unexpected error while loading models: {e}")
            models.clear()
        finally:
            if model_loading_key in globals():
                del globals()[model_loading_key]
    return models


# Predefined resolutions for a rotating-landscape scenario
PREDEFINED_RESOLUTIONS = [
    (416, 960), (448, 864), (480, 832), (512, 768), (544, 704), 
    (576, 672), (608, 640), (640, 608), (672, 576), (704, 544), 
    (768, 512), (832, 480), (864, 448), (960, 416)
]

def find_closest_aspect_ratio(width, height, target_resolutions):
    """
    Find the resolution in 'target_resolutions' whose aspect ratio
    is closest to the original image aspect ratio (width/height).
    """
    original_aspect = width / height
    min_diff = float('inf')
    closest_resolution = None
    
    for tw, th in target_resolutions:
        target_aspect = tw / th
        diff = abs(original_aspect - target_aspect)
        if diff < min_diff:
            min_diff = diff
            closest_resolution = (tw, th)
    return closest_resolution


stream = AsyncStream()

@torch.no_grad()
def worker(
    input_image, 
    prompt, 
    n_prompt, 
    seed, 
    total_second_length, 
    latent_window_size, 
    steps, 
    cfg, 
    gs, 
    rs, 
    gpu_memory_preservation, 
    use_teacache
):
    """
    Background worker that performs the actual generation.
    """
    global last_update_time
    last_update_time = time.time()

    # For demonstration, limit max length to 3 seconds
    total_second_length = min(total_second_length, 3.0)
    
    try:
        models_local = get_models()
        if not models_local:
            err_msg = "Failed to load models. Check logs for details."
            print(err_msg)
            stream.output_queue.push(('error', err_msg))
            stream.output_queue.push(('end', None))
            return
        
        text_encoder = models_local['text_encoder']
        text_encoder_2 = models_local['text_encoder_2']
        tokenizer = models_local['tokenizer']
        tokenizer_2 = models_local['tokenizer_2']
        vae = models_local['vae']
        feature_extractor = models_local['feature_extractor']
        image_encoder = models_local['image_encoder']
        transformer = models_local['transformer']
    except Exception as e:
        err = f"Error retrieving models: {e}"
        print(err)
        traceback.print_exc()
        stream.output_queue.push(('error', err))
        stream.output_queue.push(('end', None))
        return

    device = 'cuda' if (GPU_AVAILABLE and not cpu_fallback_mode) else 'cpu'
    print(f"Inference device: {device}")

    # Adjust parameters if in CPU fallback
    if cpu_fallback_mode:
        print("CPU fallback mode: using smaller parameters for performance.")
        latent_window_size = min(latent_window_size, 5)
        steps = min(steps, 15)
        total_second_length = min(total_second_length, 2.0)

    total_latent_sections = (total_second_length * 30) / (latent_window_size * 4)
    total_latent_sections = int(max(round(total_latent_sections), 1))

    job_id = generate_timestamp()
    last_output_filename = None
    history_pixels = None
    history_latents = None
    total_generated_latent_frames = 0

    stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting ...'))))

    try:
        if not high_vram and not cpu_fallback_mode:
            try:
                unload_complete_models(
                    text_encoder, text_encoder_2, image_encoder, vae, transformer
                )
            except Exception as e:
                print(f"Error unloading models: {e}")

        # Text encode
        last_update_time = time.time()
        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Encoding text ...'))))

        try:
            if not high_vram and not cpu_fallback_mode:
                fake_diffusers_current_device(text_encoder, device)
                load_model_as_complete(text_encoder_2, target_device=device)

            llama_vec, clip_l_pooler = encode_prompt_conds(prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)
            if cfg == 1:
                llama_vec_n, clip_l_pooler_n = torch.zeros_like(llama_vec), torch.zeros_like(clip_l_pooler)
            else:
                llama_vec_n, clip_l_pooler_n = encode_prompt_conds(n_prompt, text_encoder, text_encoder_2, tokenizer, tokenizer_2)

            llama_vec, llama_attention_mask = crop_or_pad_yield_mask(llama_vec, length=512)
            llama_vec_n, llama_attention_mask_n = crop_or_pad_yield_mask(llama_vec_n, length=512)
        except Exception as e:
            err = f"Text encoding error: {e}"
            print(err)
            traceback.print_exc()
            stream.output_queue.push(('error', err))
            stream.output_queue.push(('end', None))
            return

        # Process input image
        try:
            H, W, C = input_image.shape
            target_w, target_h = find_closest_aspect_ratio(W, H, PREDEFINED_RESOLUTIONS)
            
            # If CPU fallback, scale down
            if cpu_fallback_mode:
                scale_factor = min(320 / target_h, 320 / target_w)
                target_h = int(target_h * scale_factor)
                target_w = int(target_w * scale_factor)
            
            print(f"Original image: {W}x{H}, resizing to: {target_w}x{target_h}")
            input_image_np = resize_and_center_crop(input_image, target_width=target_w, target_height=target_h)
            Image.fromarray(input_image_np).save(os.path.join(outputs_folder, f'{job_id}.png'))

            input_image_pt = torch.from_numpy(input_image_np).float() / 127.5 - 1
            input_image_pt = input_image_pt.permute(2, 0, 1)[None, :, None]
        except Exception as e:
            err = f"Image processing error: {e}"
            print(err)
            traceback.print_exc()
            stream.output_queue.push(('error', err))
            stream.output_queue.push(('end', None))
            return

        # VAE encode
        last_update_time = time.time()
        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'VAE encoding ...'))))

        try:
            if not high_vram and not cpu_fallback_mode:
                load_model_as_complete(vae, target_device=device)
            start_latent = vae_encode(input_image_pt, vae)
        except Exception as e:
            err = f"VAE encode error: {e}"
            print(err)
            traceback.print_exc()
            stream.output_queue.push(('error', err))
            stream.output_queue.push(('end', None))
            return

        # CLIP Vision
        last_update_time = time.time()
        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'CLIP Vision encoding ...'))))

        try:
            if not high_vram and not cpu_fallback_mode:
                load_model_as_complete(image_encoder, target_device=device)
            image_encoder_output = hf_clip_vision_encode(input_image_np, feature_extractor, image_encoder)
            image_encoder_last_hidden_state = image_encoder_output.last_hidden_state
        except Exception as e:
            err = f"CLIP Vision encode error: {e}"
            print(err)
            traceback.print_exc()
            stream.output_queue.push(('error', err))
            stream.output_queue.push(('end', None))
            return

        # Convert dtype
        try:
            llama_vec = llama_vec.to(transformer.dtype)
            llama_vec_n = llama_vec_n.to(transformer.dtype)
            clip_l_pooler = clip_l_pooler.to(transformer.dtype)
            clip_l_pooler_n = clip_l_pooler_n.to(transformer.dtype)
            image_encoder_last_hidden_state = image_encoder_last_hidden_state.to(transformer.dtype)
        except Exception as e:
            err = f"Data type conversion error: {e}"
            print(err)
            traceback.print_exc()
            stream.output_queue.push(('error', err))
            stream.output_queue.push(('end', None))
            return

        # Sampling
        last_update_time = time.time()
        stream.output_queue.push(('progress', (None, '', make_progress_bar_html(0, 'Starting sampling...'))))

        rnd = torch.Generator("cpu").manual_seed(seed)
        num_frames = latent_window_size * 4 - 3

        try:
            history_latents = torch.zeros(
                size=(1, 16, 1 + 2 + 16, target_h // 8, target_w // 8), 
                dtype=torch.float32
            ).cpu()
            history_pixels = None
            total_generated_latent_frames = 0
        except Exception as e:
            err = f"Error initializing history latents: {e}"
            print(err)
            traceback.print_exc()
            stream.output_queue.push(('error', err))
            stream.output_queue.push(('end', None))
            return

        latent_paddings = list(reversed(range(total_latent_sections)))
        if total_latent_sections > 4:
            latent_paddings = [3] + [2]*(total_latent_sections - 3) + [1, 0]

        for latent_padding in latent_paddings:
            last_update_time = time.time()
            is_last_section = (latent_padding == 0)
            latent_padding_size = latent_padding * latent_window_size

            if stream.input_queue.top() == 'end':
                if history_pixels is not None and total_generated_latent_frames > 0:
                    try:
                        final_name = os.path.join(outputs_folder, f'{job_id}_final_{total_generated_latent_frames}.mp4')
                        save_bcthw_as_mp4(history_pixels, final_name, fps=30, crf=18)
                        stream.output_queue.push(('file', final_name))
                    except Exception as e:
                        print(f"Error saving final partial video: {e}")
                stream.output_queue.push(('end', None))
                return

            print(f'latent_padding_size = {latent_padding_size}, is_last_section={is_last_section}')

            try:
                indices = torch.arange(0, sum([1, latent_padding_size, latent_window_size, 1, 2, 16])).unsqueeze(0)
                (
                    cidx_pre,
                    blank_indices,
                    latent_indices,
                    cidx_post,
                    cidx_2x,
                    cidx_4x
                ) = indices.split([1, latent_padding_size, latent_window_size, 1, 2, 16], dim=1)
                clean_latent_indices = torch.cat([cidx_pre, cidx_post], dim=1)

                clean_latents_pre = start_latent.to(history_latents)
                c_latents_post, c_latents_2x, c_latents_4x = history_latents[:, :, :1 + 2 + 16].split([1, 2, 16], dim=2)
                clean_latents = torch.cat([clean_latents_pre, c_latents_post], dim=2)
            except Exception as e:
                err = f"Error preparing sampling data: {e}"
                print(err)
                traceback.print_exc()
                if last_output_filename:
                    stream.output_queue.push(('file', last_output_filename))
                continue

            if not high_vram and not cpu_fallback_mode:
                try:
                    unload_complete_models()
                    move_model_to_device_with_memory_preservation(
                        transformer, target_device=device, preserved_memory_gb=gpu_memory_preservation
                    )
                except Exception as e:
                    print(f"Error moving transformer to GPU: {e}")

            if use_teacache and not cpu_fallback_mode:
                try:
                    transformer.initialize_teacache(enable_teacache=True, num_steps=steps)
                except Exception as e:
                    print(f"Error initializing TeaCache: {e}")
                    transformer.initialize_teacache(enable_teacache=False)
            else:
                transformer.initialize_teacache(enable_teacache=False)

            def callback(d):
                global last_update_time
                last_update_time = time.time()
                try:
                    if stream.input_queue.top() == 'end':
                        stream.output_queue.push(('end', None))
                        raise KeyboardInterrupt('User requested stop.')
                    preview_latents = d['denoised']
                    preview_latents = vae_decode_fake(preview_latents)
                    preview_img = (preview_latents * 255.0).cpu().numpy().clip(0,255).astype(np.uint8)
                    preview_img = einops.rearrange(preview_img, 'b c t h w -> (b h) (t w) c')

                    curr_step = d['i'] + 1
                    percentage = int(100.0 * curr_step / steps)
                    hint = f'Sampling {curr_step}/{steps}'
                    desc = f'Generated frames so far: {int(max(0, total_generated_latent_frames * 4 - 3))}'
                    bar_html = make_progress_bar_html(percentage, hint)
                    stream.output_queue.push(('progress', (preview_img, desc, bar_html)))
                except KeyboardInterrupt:
                    raise
                except Exception as exc:
                    print(f"Error in sampling callback: {exc}")
                return

            try:
                print(f"Sampling: device={device}, dtype={transformer.dtype}, teacache={use_teacache}")
                try:
                    generated_latents = sample_hunyuan(
                        transformer=transformer,
                        sampler='unipc',
                        width=target_w,
                        height=target_h,
                        frames=num_frames,
                        real_guidance_scale=cfg,
                        distilled_guidance_scale=gs,
                        guidance_rescale=rs,
                        num_inference_steps=steps,
                        generator=rnd,
                        prompt_embeds=llama_vec,
                        prompt_embeds_mask=llama_attention_mask,
                        prompt_poolers=clip_l_pooler,
                        negative_prompt_embeds=llama_vec_n,
                        negative_prompt_embeds_mask=llama_attention_mask_n,
                        negative_prompt_poolers=clip_l_pooler_n,
                        device=device,
                        dtype=transformer.dtype,
                        image_embeddings=image_encoder_last_hidden_state,
                        latent_indices=latent_indices,
                        clean_latents=clean_latents,
                        clean_latent_indices=clean_latent_indices,
                        clean_latents_2x=c_latents_2x,
                        clean_latent_2x_indices=cidx_2x,
                        clean_latents_4x=c_latents_4x,
                        clean_latent_4x_indices=cidx_4x,
                        callback=callback
                    )
                except KeyboardInterrupt as e:
                    print(f"User interrupt: {e}")
                    if last_output_filename:
                        stream.output_queue.push(('file', last_output_filename))
                        err_msg = "User stopped generation; partial video returned."
                    else:
                        err_msg = "User stopped generation; no video produced."
                    stream.output_queue.push(('error', err_msg))
                    stream.output_queue.push(('end', None))
                    return
            except Exception as e:
                print(f"Error during sampling: {e}")
                traceback.print_exc()
                if last_output_filename:
                    stream.output_queue.push(('file', last_output_filename))
                    err_msg = f"Sampling error; partial video returned: {e}"
                    stream.output_queue.push(('error', err_msg))
                else:
                    err_msg = f"Sampling error; no video produced: {e}"
                    stream.output_queue.push(('error', err_msg))
                stream.output_queue.push(('end', None))
                return

            try:
                if is_last_section:
                    generated_latents = torch.cat([start_latent.to(generated_latents), generated_latents], dim=2)
                total_generated_latent_frames += int(generated_latents.shape[2])
                history_latents = torch.cat([generated_latents.to(history_latents), history_latents], dim=2)
            except Exception as e:
                err = f"Error merging latent outputs: {e}"
                print(err)
                traceback.print_exc()
                if last_output_filename:
                    stream.output_queue.push(('file', last_output_filename))
                stream.output_queue.push(('error', err))
                stream.output_queue.push(('end', None))
                return

            if not high_vram and not cpu_fallback_mode:
                try:
                    offload_model_from_device_for_memory_preservation(
                        transformer, target_device=device, preserved_memory_gb=8
                    )
                    load_model_as_complete(vae, target_device=device)
                except Exception as e:
                    print(f"Error managing model memory: {e}")

            try:
                real_history_latents = history_latents[:, :, :total_generated_latent_frames]
            except Exception as e:
                err = f"Error slicing latents history: {e}"
                print(err)
                if last_output_filename:
                    stream.output_queue.push(('file', last_output_filename))
                continue

            try:
                if history_pixels is None:
                    history_pixels = vae_decode(real_history_latents, vae).cpu()
                else:
                    section_latent_frames = (latent_window_size * 2 + 1) if is_last_section else (latent_window_size * 2)
                    overlapped_frames = latent_window_size * 4 - 3
                    current_pixels = vae_decode(real_history_latents[:, :, :section_latent_frames], vae).cpu()
                    history_pixels = soft_append_bcthw(current_pixels, history_pixels, overlapped_frames)

                output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
                save_bcthw_as_mp4(history_pixels, output_filename, fps=30, crf=18)
                last_output_filename = output_filename
                stream.output_queue.push(('file', output_filename))
            except Exception as e:
                print(f"Error decoding/saving video: {e}")
                traceback.print_exc()
                if last_output_filename:
                    stream.output_queue.push(('file', last_output_filename))
                err = f"Error decoding/saving video: {e}"
                stream.output_queue.push(('error', err))
                continue

            if is_last_section:
                break
    except Exception as e:
        print(f"Outer error: {e}, type={type(e)}")
        traceback.print_exc()
        if not high_vram and not cpu_fallback_mode:
            try:
                unload_complete_models(
                    text_encoder, text_encoder_2, image_encoder, vae, transformer
                )
            except Exception as ue:
                print(f"Unload error: {ue}")
        if last_output_filename:
            stream.output_queue.push(('file', last_output_filename))
        err = f"Error in worker: {e}"
        stream.output_queue.push(('error', err))

    print("Worker finished, pushing end.")
    stream.output_queue.push(('end', None))


# Create a processing function with or without the HF Spaces GPU decorator
if IN_HF_SPACE and 'spaces' in globals():
    @spaces.GPU
    def process_with_gpu(input_image, prompt, n_prompt, seed, total_second_length, use_teacache):
        global stream
        assert input_image is not None, "No input image provided."

        # Fix certain parameters for simplicity
        latent_window_size = 9
        steps = 25
        cfg = 1.0
        gs = 10.0
        rs = 0.0
        gpu_memory_preservation = 6

        yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
        try:
            stream = AsyncStream()
            async_run(
                worker,
                input_image, prompt, n_prompt, seed,
                total_second_length, latent_window_size, steps,
                cfg, gs, rs, gpu_memory_preservation, use_teacache
            )

            output_filename = None
            prev_output_filename = None
            error_message = None

            while True:
                try:
                    flag, data = stream.output_queue.next()
                    if flag == 'file':
                        output_filename = data
                        prev_output_filename = output_filename
                        yield output_filename, gr.update(), gr.update(), '', gr.update(interactive=False), gr.update(interactive=True)
                    elif flag == 'progress':
                        preview, desc, html = data
                        yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
                    elif flag == 'error':
                        error_message = data
                        print(f"Received error: {error_message}")
                    elif flag == 'end':
                        if output_filename is None and prev_output_filename is not None:
                            output_filename = prev_output_filename
                        if error_message:
                            yield output_filename, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
                        else:
                            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
                        break
                except Exception as e:
                    print(f"Error processing output: {e}")
                    if (time.time() - last_update_time) > 60:
                        print(f"No updates for {(time.time()-last_update_time):.1f}s, likely hung.")
                        if prev_output_filename:
                            yield prev_output_filename, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
                        else:
                            yield None, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
                        break
        except Exception as e:
            print(f"Error starting process: {e}")
            traceback.print_exc()
            yield None, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
    
    process = process_with_gpu
else:
    def process(input_image, prompt, n_prompt, seed, total_second_length, use_teacache):
        global stream
        assert input_image is not None, "No input image provided."

        latent_window_size = 9
        steps = 25
        cfg = 1.0
        gs = 10.0
        rs = 0.0
        gpu_memory_preservation = 6

        yield None, None, '', '', gr.update(interactive=False), gr.update(interactive=True)
        try:
            stream = AsyncStream()
            async_run(
                worker,
                input_image, prompt, n_prompt, seed,
                total_second_length, latent_window_size, steps,
                cfg, gs, rs, gpu_memory_preservation, use_teacache
            )

            output_filename = None
            prev_output_filename = None
            error_message = None

            while True:
                try:
                    flag, data = stream.output_queue.next()
                    if flag == 'file':
                        output_filename = data
                        prev_output_filename = output_filename
                        yield output_filename, gr.update(), gr.update(), '', gr.update(interactive=False), gr.update(interactive=True)
                    elif flag == 'progress':
                        preview, desc, html = data
                        yield gr.update(), gr.update(visible=True, value=preview), desc, html, gr.update(interactive=False), gr.update(interactive=True)
                    elif flag == 'error':
                        error_message = data
                        print(f"Received error: {error_message}")
                    elif flag == 'end':
                        if output_filename is None and prev_output_filename is not None:
                            output_filename = prev_output_filename
                        if error_message:
                            yield output_filename, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
                        else:
                            yield output_filename, gr.update(visible=False), gr.update(), '', gr.update(interactive=True), gr.update(interactive=False)
                        break
                except Exception as e:
                    print(f"Error processing output: {e}")
                    if (time.time() - last_update_time) > 60:
                        print(f"No updates for {(time.time()-last_update_time):.1f}s, likely hung.")
                        if prev_output_filename:
                            yield prev_output_filename, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
                        else:
                            yield None, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)
                        break
        except Exception as e:
            print(f"Error starting process: {e}")
            traceback.print_exc()
            yield None, gr.update(visible=False), gr.update(), gr.update(interactive=True), gr.update(interactive=False)


def end_process():
    """
    Stop generation by pushing 'end' signal into the queue.
    """
    print("User clicked the stop button, sending 'end' signal...")
    global stream
    if 'stream' in globals() and stream is not None:
        try:
            current_top = stream.input_queue.top()
            print(f"Queue top signal: {current_top}")
        except Exception as e:
            print(f"Error checking queue status: {e}")
        try:
            stream.input_queue.push('end')
            print("Successfully pushed 'end' signal.")
        except Exception as e:
            print(f"Error pushing 'end' signal: {e}")
    else:
        print("Warning: 'stream' is not initialized; cannot stop.")
    return None


quick_prompts = [
    ["The camera smoothly orbits around the center of the scene, keeping the center point fixed and always in view"]
]

def make_custom_css():
    base_progress_css = make_progress_bar_css()
    enhanced_css = """
    body {
        background: #f9fafb !important;
        font-family: "Noto Sans", sans-serif;
    }
    #app-container {
        max-width: 1200px;
        margin: 0 auto;
        padding: 1rem;
        position: relative;
    }
    h1 {
        font-size: 2rem;
        text-align: center;
        margin-bottom: 1rem;
        color: #2d3748;
        font-weight: 700;
    }
    .start-btn, .stop-btn {
        min-height: 45px;
        font-size: 1rem;
        font-weight: 600;
    }
    .start-btn {
        background-color: #3182ce !important;
        color: #fff !important;
    }
    .stop-btn {
        background-color: #e53e3e !important;
        color: #fff !important;
    }
    .button-container button:hover {
        filter: brightness(0.95);
    }
    .preview-container, .video-container {
        border: 1px solid #cbd5e0;
        border-radius: 8px;
        overflow: hidden;
    }
    .progress-container {
        margin-top: 15px;
        margin-bottom: 15px;
    }
    .error-message {
        background-color: #fff5f5;
        border: 1px solid #fed7d7;
        color: #e53e3e;
        padding: 10px;
        border-radius: 4px;
        margin-top: 10px;
    }
    .error-icon {
        color: #e53e3e;
        margin-right: 8px;
    }
    #error-message {
        color: #ff4444;
        font-weight: bold;
        padding: 10px;
        border-radius: 4px;
        margin-top: 10px;
    }
    @media (max-width: 768px) {
        #app-container {
            padding: 0.5rem;
        }
        .mobile-full-width {
            flex-direction: column !important;
        }
        .mobile-full-width > .gr-block {
            width: 100% !important;
        }
    }
    """
    return base_progress_css + enhanced_css

css = make_custom_css()

block = gr.Blocks(css=css).queue()
with block:
    gr.HTML("<h1>FramePack Rotate-Landscape - Generate Rotating Landscape Video</h1>")
    
    with gr.Row(elem_classes="mobile-full-width"):
        with gr.Column(scale=1):
            input_image = gr.Image(
                sources='upload',
                type="numpy",
                label="Upload Image",
                height=320
            )

            prompt = gr.Textbox(
                label="Prompt",
                value='The camera smoothly orbits around the center of the scene...',
            )

            example_quick_prompts = gr.Dataset(
                samples=quick_prompts,
                label="Quick Prompts",
                samples_per_page=1000,
                components=[prompt]
            )
            example_quick_prompts.click(
                lambda x: x[0],
                inputs=[example_quick_prompts],
                outputs=prompt,
                show_progress=False,
                queue=False
            )

            with gr.Row(elem_classes="button-container"):
                start_button = gr.Button(
                    value="Generate",
                    elem_classes="start-btn",
                    variant="primary"
                )
                end_button = gr.Button(
                    value="Stop",
                    elem_classes="stop-btn",
                    interactive=False
                )

            use_teacache = gr.Checkbox(
                label="Use TeaCache",
                value=True,
                info="Faster speed, but possibly worse finger/hand generation."
            )
            n_prompt = gr.Textbox(label="Negative Prompt", value="", visible=False)
            seed = gr.Number(label="Seed", value=31337, precision=0)
            total_second_length = gr.Slider(
                label="Video length (max 3 seconds)",
                minimum=0.5, maximum=3, value=1.0, step=0.1
            )

        with gr.Column(scale=1):
            preview_image = gr.Image(
                label="Preview",
                height=200,
                visible=False,
                elem_classes="preview-container"
            )
            result_video = gr.Video(
                label="Generated Video",
                autoplay=True,
                loop=True,
                show_share_button=True,
                height=512,
                elem_classes="video-container"
            )
            gr.HTML("""
            <div>
                Note: Due to reversed sampling, ending actions may appear before starting actions. If the start action is missing, please wait for further frames.
            </div>
            """)

            with gr.Group(elem_classes="progress-container"):
                progress_desc = gr.Markdown('')
                progress_bar = gr.HTML('')

            error_message = gr.HTML('', elem_id='error-message', visible=True)
    
    # Inputs
    ips = [input_image, prompt, n_prompt, seed, total_second_length, use_teacache]
    start_button.click(
        fn=process,
        inputs=ips,
        outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button]
    )
    end_button.click(fn=end_process)

block.launch()