import os

os.environ.setdefault("HF_HOME", "/tmp/.cache/huggingface")
os.environ.setdefault("HF_MODULES_CACHE", "/tmp/hf_modules")
os.environ.setdefault("MPLCONFIGDIR", "/tmp/matplotlib")
os.environ.setdefault("GRADIO_SSR_MODE", "false")
os.environ.setdefault("TORCH_COMPILE_DISABLE", "1")
os.environ.setdefault("TORCHDYNAMO_DISABLE", "1")

for _path in (
    os.environ["HF_HOME"],
    os.environ["HF_MODULES_CACHE"],
    os.environ["MPLCONFIGDIR"],
):
    os.makedirs(_path, exist_ok=True)

import random
import gc
import tempfile
import time

import gradio as gr
import imageio.v3 as iio
import numpy as np
import spaces
import torch
from huggingface_hub import hf_hub_download
from PIL import Image, ImageOps
from safetensors.torch import load_file

from diffusers import LTX2InContextPipeline, LTX2LatentUpsamplePipeline
from diffusers.pipelines.ltx2.latent_upsampler import LTX2LatentUpsamplerModel
from diffusers.pipelines.ltx2.pipeline_ltx2_condition import LTX2VideoCondition
from diffusers.pipelines.ltx2.pipeline_ltx2_ic_lora import LTX2ReferenceCondition
from diffusers.pipelines.ltx2.utils import DISTILLED_SIGMA_VALUES, STAGE_2_DISTILLED_SIGMA_VALUES
from diffusers.utils import encode_video, load_video


BASE_MODEL = "diffusers/LTX-2.3-Distilled-Diffusers"
EDIT_REPO = "Alissonerdx/EditAnything"
MOTION_LORA = "edit_anything_30k_v0.1_motion_transfer_r128.safetensors"
PROMPT_LORA = "edit_anything_v1.1_r256.safetensors"
UPSAMPLER_REPO = "dg845/LTX-2.3-Spatial-Upsampler-Diffusers"

FPS = 24
NUM_STEPS = len(DISTILLED_SIGMA_VALUES)
MAX_SEED = np.iinfo(np.int32).max
HF_TOKEN = os.environ.get("HF_TOKEN")

MODE_MOTION = "Motion Transfer (v0.1)"
MODE_PROMPT = "Prompt Edit (v1.1)"
MODE_REF = "Ref V2V (experimental, not enabled)"

ADAPTERS = {
    MODE_MOTION: ("motion_v01_r128", MOTION_LORA),
    MODE_PROMPT: ("prompt_v11_r256", PROMPT_LORA),
}

RES_PRESETS = {
    "Fast (768x448)": (768, 448),
    "Quality (960x544)": (960, 544),
}
FRAME_CHOICES = [49, 73, 97, 121]
EXAMPLE_DIR = "examples"
UNUSED_FIRST_FRAME_EXAMPLE = f"{EXAMPLE_DIR}/motion_edited_first_frame.png"

PROMPT_EDIT_EXAMPLES = [
    [
        MODE_PROMPT,
        f"{EXAMPLE_DIR}/prompt_add_source.mp4",
        "Add",
        "Add a golden retriever sitting on the grass beside the suitcase, to the right of the woman on the park bench.",
        "",
    ],
    [
        MODE_PROMPT,
        f"{EXAMPLE_DIR}/prompt_remove_source.mp4",
        "Remove",
        "Remove the woman walking on the path.",
        "",
    ],
    [
        MODE_PROMPT,
        f"{EXAMPLE_DIR}/prompt_replace_source.mp4",
        "Replace",
        "Replace the large red triangular sculpture in the background with a stone fountain spraying water in the plaza.",
        "",
    ],
    [
        MODE_PROMPT,
        f"{EXAMPLE_DIR}/prompt_style_source.mp4",
        "Style",
        "",
        "Watercolor Painting",
    ],
]

MOTION_TRANSFER_EXAMPLES = [
    [
        MODE_MOTION,
        f"{EXAMPLE_DIR}/motion_guide_source.mp4",
        f"{EXAMPLE_DIR}/motion_edited_first_frame.png",
        "Change the dancer into a copper-red-haired dancer wearing a loose white sweater and dark pants on the same rocky beach.",
        "",
    ],
]

APP_THEME = gr.themes.Soft(
    primary_hue="orange",
    secondary_hue="sky",
    neutral_hue="slate",
    spacing_size="sm",
    radius_size="sm",
    text_size="sm",
    font=gr.themes.GoogleFont("Inter"),
).set(
    button_primary_background_fill="#f97316",
    button_primary_background_fill_hover="#ea580c",
    button_primary_text_color="#ffffff",
    block_border_width="1px",
    block_shadow="none",
)

CUSTOM_CSS = """
:root {
  --ea-max-width: 1280px;
}
html,
body,
gradio-app {
  overflow-x: hidden !important;
}
.gradio-container,
.gradio-container * {
  box-sizing: border-box;
}
.gradio-container {
  width: min(var(--ea-max-width), calc(100vw - 32px)) !important;
  max-width: var(--ea-max-width) !important;
  margin: 0 auto !important;
  color: #111827;
  min-width: 0 !important;
}
.ea-main-row {
  align-items: flex-start !important;
  gap: 0.75rem !important;
  width: 100% !important;
  min-width: 0 !important;
}
.ea-main-row > *,
.ea-input-column,
.ea-output-column {
  min-width: 0 !important;
  max-width: 100% !important;
}
.ea-header {
  margin: 0 0 14px;
}
.ea-header h1 {
  font-size: clamp(1.45rem, 2vw, 2rem);
  line-height: 1.12;
  margin-bottom: 0.35rem;
  letter-spacing: 0;
}
.ea-header p {
  color: #64748b;
  margin: 0;
}
.ea-column-title h2,
.ea-column-title h3 {
  font-size: 0.95rem;
  line-height: 1.25;
  margin: 0 0 0.25rem;
}
.ea-hint p,
.ea-task-hint p {
  color: #64748b;
  font-size: 0.9rem;
  margin: 0;
}
.ea-output-column {
  position: sticky;
  top: 12px;
  align-self: start;
}
.ea-generate button {
  min-height: 48px;
  font-weight: 700;
  letter-spacing: 0;
}
.edit-type-selector .wrap {
  display: flex !important;
  gap: 0.45rem;
  flex-wrap: wrap !important;
}
.edit-type-selector [role="radiogroup"] {
  display: flex !important;
  flex-wrap: wrap !important;
  gap: 0.45rem !important;
}
.edit-type-selector label {
  flex: 1 1 120px;
  min-width: 0;
  border-radius: 8px !important;
  border: 1px solid #cbd5e1 !important;
  padding: 0.5rem 0.72rem !important;
  background: #ffffff !important;
}
.edit-type-selector label:has(input:checked) {
  border-color: #f97316 !important;
  background: #fff7ed !important;
  color: #9a3412 !important;
  font-weight: 700;
}
.ea-examples table {
  font-size: 0.82rem;
}
.ea-examples .table-wrap {
  max-height: 320px;
  overflow: auto;
  max-width: 100%;
}
footer {
  display: none !important;
}
@media (max-width: 1000px) {
  .gradio-container {
    padding-top: 52px !important;
    padding-left: 14px !important;
    padding-right: 14px !important;
    overflow-x: hidden !important;
  }
  .ea-output-column {
    position: static;
  }
}
@media (max-width: 760px) {
  .gradio-container {
    width: 100% !important;
    max-width: 100vw !important;
    padding: 56px 16px 24px !important;
  }
  .ea-header {
    padding-top: 0 !important;
    max-width: 100% !important;
  }
  .ea-main-row {
    display: flex !important;
    flex-direction: column !important;
    gap: 0.85rem !important;
  }
  .ea-main-row > *,
  .ea-input-column,
  .ea-output-column,
  .gradio-container .block,
  .gradio-container .form,
  .gradio-container .panel,
  .gradio-container .tabs,
  .gradio-container .tabitem {
    width: 100% !important;
    max-width: 100% !important;
    min-width: 0 !important;
    flex: 1 1 auto !important;
  }
  .ea-output-column {
    margin-top: 0.25rem;
  }
  .edit-type-selector .wrap,
  .edit-type-selector [role="radiogroup"] {
    width: 100% !important;
    max-width: 100% !important;
  }
  .edit-type-selector label {
    flex: 1 1 100% !important;
    max-width: 100% !important;
    min-height: 46px;
    display: flex !important;
    align-items: center;
  }
  .ea-generate button {
    min-height: 52px;
  }
  .ea-examples .table-wrap {
    overflow-x: auto;
  }
  .ea-examples table {
    min-width: 560px;
  }
}
@media (max-width: 1280px) {
  .ea-header {
    padding-top: 52px;
    max-width: calc(100vw - 28px);
  }
  .ea-header h1,
  .ea-header p,
  .ea-hint p,
  .ea-task-hint p {
    max-width: calc(100vw - 28px);
    white-space: normal;
    overflow-wrap: anywhere;
  }
  .edit-type-selector .wrap,
  .edit-type-selector [role="radiogroup"] {
    max-width: calc(100vw - 40px);
  }
  .edit-type-selector label {
    flex: 0 1 calc(50vw - 34px);
    max-width: calc(50vw - 34px);
  }
}
@media (max-width: 760px) {
  .ea-header {
    padding-top: 52px !important;
  }
  .edit-type-selector .wrap,
  .edit-type-selector [role="radiogroup"] {
    max-width: 100% !important;
  }
  .edit-type-selector label {
    flex: 1 1 100% !important;
    max-width: 100% !important;
  }
}
"""


@spaces.GPU(duration=1)
def _zerogpu_probe():
    return "ready"


print("Loading LTX-2.3 distilled diffusers pipeline...", flush=True)
pipe = LTX2InContextPipeline.from_pretrained(BASE_MODEL, torch_dtype=torch.bfloat16)
pipe.to("cuda")
pipe.vae.enable_tiling()

print("Loading Edit Anything standard LoRAs...", flush=True)
for adapter_name, filename in ADAPTERS.values():
    lora_path = hf_hub_download(EDIT_REPO, filename, token=HF_TOKEN)
    lora_state = load_file(lora_path)
    alpha_keys = [key for key in lora_state if key.endswith(".alpha")]
    if alpha_keys:
        print(f"Filtering {len(alpha_keys)} LoRA alpha tensors from {filename}.", flush=True)
        lora_state = {key: value for key, value in lora_state.items() if key not in alpha_keys}
    pipe.load_lora_weights(lora_state, adapter_name=adapter_name)
    del lora_state
    gc.collect()
pipe.set_adapters(ADAPTERS[MODE_PROMPT][0], 1.0)

print("Loading stage-2 spatial latent upsampler...", flush=True)
_upsampler = LTX2LatentUpsamplerModel.from_pretrained(
    UPSAMPLER_REPO,
    subfolder="latent_upsampler",
    torch_dtype=torch.bfloat16,
)
_upsampler.to("cuda")
upsample_pipe = LTX2LatentUpsamplePipeline(vae=pipe.vae, latent_upsampler=_upsampler)
print("Pipeline ready.", flush=True)


def _src_fps(path, default=FPS):
    try:
        return float(iio.immeta(path, plugin="pyav").get("fps", default)) or default
    except Exception:
        return default


def _probe_video(path):
    frames = load_video(path)
    if not frames:
        raise gr.Error("Could not read frames from the uploaded video.")
    return frames


def _pick_resolution(first_frame, preset):
    width, height = RES_PRESETS[preset]
    if first_frame.height > first_frame.width:
        width, height = height, width
    return width, height


def _load_frames(path, num_frames, width, height):
    frames = _probe_video(path)
    source_fps = _src_fps(path)
    out = []
    for i in range(num_frames):
        idx = min(int(round(i / FPS * source_fps)), len(frames) - 1)
        frame = frames[idx].convert("RGB")
        out.append(ImageOps.fit(frame, (width, height), Image.LANCZOS))
    return out


def _prepare_first_frame(image, width, height):
    if image is None:
        raise gr.Error("Motion Transfer needs an externally edited first frame.")
    if not isinstance(image, Image.Image):
        image = Image.fromarray(np.asarray(image))
    return ImageOps.fit(image.convert("RGB"), (width, height), Image.LANCZOS)


def _compose_prompt(mode, edit_type, prompt, style_name):
    prompt = (prompt or "").strip()
    style_name = (style_name or "").strip()

    if mode == MODE_REF:
        raise gr.Error(
            "Ref V2V is not enabled in this diffusers Space. Its .module.safetensors "
            "sidecar installs custom AdaLN, role embedding, and ref-attention branches "
            "through BFSnodes; those branches are not standard diffusers LoRA adapters."
        )

    if mode == MODE_PROMPT and edit_type == "Style":
        style = style_name or prompt
        if not style:
            raise gr.Error("Style mode needs a style name, for example 'Watercolor Painting'.")
        if style.lower().startswith("convert the video into"):
            return style
        return f"Convert the video into a {style} style."

    if not prompt:
        raise gr.Error("Enter an edit prompt.")
    return prompt


def _duration(*args, **kwargs):
    preset = next((a for a in args if isinstance(a, str) and a in RES_PRESETS), "Fast (768x448)")
    num_frames = next((a for a in args if isinstance(a, int) and a in FRAME_CHOICES), 73)
    per_frame = 1.05 if "Quality" in str(preset) else 0.75
    return int(15 + int(num_frames) * per_frame)


def _export(video_np, audio, path):
    kwargs = {}
    if audio is not None:
        kwargs = {
            "audio": audio[0].float().cpu(),
            "audio_sample_rate": pipe.vocoder.config.output_sampling_rate,
        }
    encode_video(video_np, fps=FPS, output_path=path, **kwargs)


def _set_adapter(mode, scale):
    adapter_name = ADAPTERS[mode][0]
    pipe.set_adapters(adapter_name, float(scale))
    return adapter_name


def _run_two_stage(
    prompt,
    reference_conditions,
    conditions,
    width,
    height,
    num_frames,
    seed,
    adapter_name,
    lora_scale,
    conditioning_attention_strength,
):
    pipe.set_adapters(adapter_name, float(lora_scale))
    generator = torch.Generator(device="cuda").manual_seed(int(seed))

    video_latent, audio_latent = pipe(
        prompt=prompt,
        negative_prompt="",
        reference_conditions=reference_conditions,
        conditions=conditions,
        reference_downscale_factor=1,
        conditioning_attention_strength=float(conditioning_attention_strength),
        width=width,
        height=height,
        num_frames=num_frames,
        frame_rate=FPS,
        num_inference_steps=NUM_STEPS,
        sigmas=DISTILLED_SIGMA_VALUES,
        guidance_scale=1.0,
        stg_scale=0.0,
        audio_guidance_scale=1.0,
        audio_stg_scale=0.0,
        generator=generator,
        output_type="latent",
        return_dict=False,
    )

    up_latent = upsample_pipe(latents=video_latent, output_type="latent", return_dict=False)[0]

    pipe.disable_lora()
    try:
        video_out, audio_out = pipe(
            prompt=prompt,
            negative_prompt="",
            latents=up_latent,
            audio_latents=audio_latent,
            width=width * 2,
            height=height * 2,
            num_frames=num_frames,
            frame_rate=FPS,
            num_inference_steps=len(STAGE_2_DISTILLED_SIGMA_VALUES),
            sigmas=STAGE_2_DISTILLED_SIGMA_VALUES,
            noise_scale=STAGE_2_DISTILLED_SIGMA_VALUES[0],
            guidance_scale=1.0,
            stg_scale=0.0,
            audio_guidance_scale=1.0,
            audio_stg_scale=0.0,
            generator=generator,
            output_type="np",
            return_dict=False,
        )
    finally:
        pipe.set_adapters(adapter_name, float(lora_scale))

    return video_out, audio_out


@spaces.GPU(duration=_duration, size="xlarge")
@torch.inference_mode()
def edit_anything(
    mode,
    video,
    edited_first_frame,
    edit_type,
    prompt,
    style_name,
    preset,
    num_frames,
    seed,
    randomize_seed,
    lora_scale,
    guide_strength,
    source_attention,
    progress=gr.Progress(track_tqdm=True),
):
    if video is None:
        raise gr.Error("Upload a source video.")
    if mode not in ADAPTERS and mode != MODE_REF:
        raise gr.Error("Choose a supported edit mode.")

    final_prompt = _compose_prompt(mode, edit_type, prompt, style_name)
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    seed = int(seed)
    num_frames = int(num_frames)

    progress(0.03, desc="Preparing source frames")
    first = _probe_video(video)[0].convert("RGB")
    width, height = _pick_resolution(first, preset)
    guide_frames = _load_frames(video, num_frames, width, height)

    reference_conditions = [
        LTX2ReferenceCondition(frames=guide_frames, strength=float(guide_strength))
    ]
    conditions = None
    edited_anchor = None

    if mode == MODE_MOTION:
        edited_anchor = _prepare_first_frame(edited_first_frame, width, height)
        conditions = [LTX2VideoCondition(frames=edited_anchor, index=0, strength=1.0)]

    adapter_name = _set_adapter(mode, lora_scale)
    started = time.perf_counter()

    progress(0.12, desc="Running LTX-2.3 stage 1")
    video_out, audio_out = _run_two_stage(
        prompt=final_prompt,
        reference_conditions=reference_conditions,
        conditions=conditions,
        width=width,
        height=height,
        num_frames=num_frames,
        seed=seed,
        adapter_name=adapter_name,
        lora_scale=lora_scale,
        conditioning_attention_strength=source_attention,
    )

    progress(0.92, desc="Encoding output video")
    result = np.clip(video_out[0], 0, 1).astype(np.float32)
    if edited_anchor is not None and len(result) > 0:
        result[0] = (
            np.array(edited_anchor.resize((width * 2, height * 2), Image.LANCZOS)).astype(np.float32) / 255.0
        )

    out_path = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False).name
    _export(result, audio_out, out_path)

    elapsed = time.perf_counter() - started
    print(
        f"[METRIC] mode={mode!r} frames={num_frames} preset={preset!r} "
        f"seed={seed} elapsed_s={elapsed:.2f}",
        flush=True,
    )
    details = (
        f"Seed: {seed}\n"
        f"Prompt: {final_prompt}\n"
        f"Mode: {mode}\n"
        f"Elapsed seconds: {elapsed:.2f}"
    )
    return out_path, seed, details


def _mode_hint(mode):
    if mode == MODE_MOTION:
        return (
            "Use a guide video plus one edited first frame. The frame anchors appearance; the video supplies motion."
        )
    if mode == MODE_PROMPT:
        return "Upload a source video. Choose one focused edit task."
    return (
        "Ref V2V is disabled in this diffusers build because it requires BFSnodes sidecar module injection."
    )


def _edit_type_hint(edit_type):
    if edit_type == "Add":
        return "Describe what to add and where it should appear."
    if edit_type == "Remove":
        return "Name what to remove. Short prompts work best."
    if edit_type == "Replace":
        return "Describe what changes and where."
    return "Enter a style name, e.g. Watercolor or Vintage Film."


EDIT_PLACEHOLDERS = {
    "Add": "Add a cat beside the suitcase.",
    "Remove": "Remove the woman walking on the path.",
    "Replace": "Replace the statue with a man.",
    "Style": "",
}


def _mode_updates(mode):
    return _mode_hint(mode), gr.update(visible=(mode == MODE_MOTION))


def _edit_type_updates(edit_type):
    return (
        _edit_type_hint(edit_type),
        gr.update(
            visible=(edit_type != "Style"),
            placeholder=EDIT_PLACEHOLDERS.get(edit_type, EDIT_PLACEHOLDERS["Replace"]),
        ),
        gr.update(visible=(edit_type == "Style")),
    )


def _run_prompt_example(mode, video, edit_type, prompt, style_name):
    return edit_anything(
        mode,
        video,
        UNUSED_FIRST_FRAME_EXAMPLE,
        edit_type,
        prompt,
        style_name,
        "Fast (768x448)",
        49,
        42,
        False,
        1.0,
        1.0,
        1.0,
    )


def _run_motion_example(mode, video, edited_first_frame, prompt, style_name):
    return edit_anything(
        mode,
        video,
        edited_first_frame,
        "Replace",
        prompt,
        style_name,
        "Fast (768x448)",
        49,
        42,
        False,
        1.0,
        1.0,
        1.0,
    )


with gr.Blocks(
    title="Edit Anything LTX-2.3",
    theme=APP_THEME,
    css=CUSTOM_CSS,
) as demo:
    gr.Markdown(
        "# Edit Anything LTX-2.3\n"
        "Prompt and first-frame video edits powered by Edit Anything LoRAs on LTX-2.3 Distilled.",
        elem_classes=["ea-header"],
    )

    with gr.Row(elem_classes=["ea-main-row"]):
        with gr.Column(scale=1, min_width=0, elem_classes=["ea-input-column"]):
            gr.Markdown("## Inputs", elem_classes=["ea-column-title"])
            mode_hint = gr.Markdown(_mode_hint(MODE_PROMPT), elem_classes=["ea-hint"])
            mode = gr.Dropdown(
                [MODE_PROMPT, MODE_MOTION, MODE_REF],
                value=MODE_PROMPT,
                label="Workflow",
                info="Prompt for text edits. Motion uses a first frame.",
            )
            video_in = gr.Video(label="Source video", height=260)
            with gr.Group(visible=False) as motion_anchor:
                edited_frame = gr.Image(
                    label="Edited first frame",
                    type="pil",
                    image_mode="RGB",
                    height=230,
                )
            edit_type = gr.Radio(
                ["Add", "Remove", "Replace", "Style"],
                value="Replace",
                label="Edit task",
                elem_classes=["edit-type-selector"],
            )
            edit_hint = gr.Markdown(_edit_type_hint("Replace"), elem_classes=["ea-task-hint"])
            prompt = gr.Textbox(
                label="Edit prompt",
                lines=4,
                placeholder=EDIT_PLACEHOLDERS["Replace"],
            )
            style_name = gr.Textbox(
                label="Style name",
                placeholder="Watercolor Painting",
                visible=False,
            )
            with gr.Accordion("Settings", open=False):
                preset = gr.Dropdown(list(RES_PRESETS), value="Fast (768x448)", label="Resolution")
                num_frames = gr.Dropdown(FRAME_CHOICES, value=73, label="Frames at 24 fps")
                randomize_seed = gr.Checkbox(True, label="Randomize seed")
                seed = gr.Slider(0, MAX_SEED, value=42, step=1, label="Seed")
                lora_scale = gr.Slider(0.2, 1.4, value=1.0, step=0.05, label="LoRA scale")
                guide_strength = gr.Slider(0.2, 1.0, value=1.0, step=0.05, label="Guide video strength")
                source_attention = gr.Slider(
                    0.2,
                    1.0,
                    value=1.0,
                    step=0.05,
                    label="Source/reference attention",
                )
            run = gr.Button("Generate edited video", variant="primary", elem_classes=["ea-generate"])
        with gr.Column(scale=1, min_width=0, elem_classes=["ea-output-column"]):
            gr.Markdown("## Output", elem_classes=["ea-column-title"])
            video_out = gr.Video(label="Edited video", height=300)
            with gr.Accordion("Generation details", open=False):
                details = gr.Textbox(label="Run details", lines=5, show_label=False)
            with gr.Accordion("Examples", open=True, elem_classes=["ea-examples"]):
                with gr.Tabs():
                    with gr.Tab("Prompt"):
                        gr.Examples(
                            examples=PROMPT_EDIT_EXAMPLES,
                            inputs=[mode, video_in, edit_type, prompt, style_name],
                            outputs=[video_out, seed, details],
                            fn=_run_prompt_example,
                            cache_examples=True,
                            cache_mode="lazy",
                            examples_per_page=4,
                            label="Prompt Edit examples",
                        )
                    with gr.Tab("Motion"):
                        gr.Examples(
                            examples=MOTION_TRANSFER_EXAMPLES,
                            inputs=[mode, video_in, edited_frame, prompt, style_name],
                            outputs=[video_out, seed, details],
                            fn=_run_motion_example,
                            cache_examples=True,
                            cache_mode="lazy",
                            examples_per_page=1,
                            label="Motion Transfer example",
                        )

    generate_inputs = [
        mode,
        video_in,
        edited_frame,
        edit_type,
        prompt,
        style_name,
        preset,
        num_frames,
        seed,
        randomize_seed,
        lora_scale,
        guide_strength,
        source_attention,
    ]
    generate_outputs = [video_out, seed, details]

    mode.change(_mode_updates, inputs=mode, outputs=[mode_hint, motion_anchor])
    edit_type.change(_edit_type_updates, inputs=edit_type, outputs=[edit_hint, prompt, style_name])
    run.click(
        edit_anything,
        inputs=generate_inputs,
        outputs=generate_outputs,
        api_name="generate",
    )

if __name__ == "__main__":
    demo.launch(show_error=True)