Lumina-Image-PLUS

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ginipick commited on 8 days ago

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9ac4609

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1 Parent(s): 4b12e77

Update app.py

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@@ -1,484 +1,562 @@
-import subprocess
-import os
-# 필수 패키지 설치 (이미 설치되어 있다면 무시됩니다)
-subprocess.run(
-    "pip install flash-attn --no-build-isolation",
-    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
-    shell=True,
-)
-subprocess.run("pip install huggingface_hub==0.25.0", shell=True)
-subprocess.run("pip install numpy==1.26.4 sentencepiece sacremoses transformers gradio safetensors torchvision diffusers", shell=True)
-# 체크포인트 폴더 생성 및 모델 스냅샷 다운로드
-os.makedirs("/home/user/app/checkpoints", exist_ok=True)
-from huggingface_hub import snapshot_download
-snapshot_download(repo_id="Alpha-VLLM/Lumina-Image-2.0", local_dir="/home/user/app/checkpoints")
-hf_token = os.environ["HF_TOKEN"]
-# ★ 중요: CUDA 초기화 전에 spaces 패키지를 임포트합니다.
-import spaces
-# 이제 CUDA와 관련된 라이브러리들을 임포트합니다.
-import argparse
-import builtins
-import json
-import math
-import multiprocessing as mp
-import random
-import socket
-import traceback
-import torch
-import gradio as gr
-import numpy as np
-from safetensors.torch import load_file
-from torchvision.transforms.functional import to_pil_image
-# 번역 파이프라인 (한글 프롬프트를 영어로 번역)
-from transformers import pipeline
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
-from imgproc import generate_crop_size_list
-import models
-from transport import Sampler, create_transport
-from multiprocessing import Process, Queue, set_start_method, get_context
-class ModelFailure:
-    pass
-# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
-def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
-    captions = []
-    for caption in prompt_batch:
-        if random.random() < proportion_empty_prompts:
-            captions.append("")
-        elif isinstance(caption, str):
-            captions.append(caption)
-        elif isinstance(caption, (list, np.ndarray)):
-            captions.append(random.choice(caption) if is_train else caption[0])
-    with torch.no_grad():
-        text_inputs = tokenizer(
-            captions,
-            padding=True,
-            pad_to_multiple_of=8,
-            max_length=256,
-            truncation=True,
-            return_tensors="pt",
-        )
-        print(f"Text Encoder Device: {text_encoder.device}")
-        text_input_ids = text_inputs.input_ids.cuda()
-        prompt_masks = text_inputs.attention_mask.cuda()
-        print(f"Text Input Ids Device: {text_input_ids.device}")
-        print(f"Prompt Masks Device: {prompt_masks.device}")
-        prompt_embeds = text_encoder(
-            input_ids=text_input_ids,
-            attention_mask=prompt_masks,
-            output_hidden_states=True,
-        ).hidden_states[-2]
-        text_encoder.cpu()
-    return prompt_embeds, prompt_masks
-@torch.no_grad()
-def model_main(args, master_port, rank):
-    # diffusers, transformers 등의 내부 임포트를 위해 함수 내부에서 임포트합니다.
-    from diffusers.models import AutoencoderKL
-    from transformers import AutoModel, AutoTokenizer
-    # 기본 print 함수를 오버라이드하여 출력 지연을 최소화합니다.
-    original_print = builtins.print
-    def print(*args, **kwargs):
-        kwargs.setdefault("flush", True)
-        original_print(*args, **kwargs)
-    builtins.print = print
-    train_args = torch.load(os.path.join(args.ckpt, "model_args.pth"))
-    print("Loaded model arguments:", json.dumps(train_args.__dict__, indent=2))
-    print(f"Creating lm: Gemma-2-2B")
-    dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[args.precision]
-    text_encoder = AutoModel.from_pretrained("google/gemma-2-2b", torch_dtype=dtype, token=hf_token).eval().to("cuda")
-    cap_feat_dim = text_encoder.config.hidden_size
-    if args.num_gpus > 1:
-        raise NotImplementedError("Inference with >1 GPUs not yet supported")
-    tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b", token=hf_token)
-    tokenizer.padding_side = "right"
-    vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", token=hf_token).cuda()
-    print(f"Creating DiT: {train_args.model}")
-    model = models.__dict__[train_args.model](
-        in_channels=16,
-        qk_norm=train_args.qk_norm,
-        cap_feat_dim=cap_feat_dim,
-    )
-    model.eval().to("cuda", dtype=dtype)
-    assert train_args.model_parallel_size == args.num_gpus
-    if args.ema:
-        print("Loading EMA model.")
-    print('Loading model weights...')
-    ckpt_path = os.path.join(
-        args.ckpt,
-        f"consolidated{'_ema' if args.ema else ''}.{rank:02d}-of-{args.num_gpus:02d}.safetensors",
-    )
-    if os.path.exists(ckpt_path):
-        ckpt = load_file(ckpt_path)
-    else:
-        ckpt_path = os.path.join(
-            args.ckpt,
-            f"consolidated{'_ema' if args.ema else ''}.{rank:02d}-of-{args.num_gpus:02d}.pth",
-        )
-        assert os.path.exists(ckpt_path)
-        ckpt = torch.load(ckpt_path, map_location="cuda")
-    model.load_state_dict(ckpt, strict=True)
-    print('Model weights loaded.')
-    return text_encoder, tokenizer, vae, model
-@torch.no_grad()
-def inference(args, infer_args, text_encoder, tokenizer, vae, model):
-    dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[args.precision]
-    train_args = torch.load(os.path.join(args.ckpt, "model_args.pth"))
-    torch.cuda.set_device(0)
-    with torch.autocast("cuda", dtype):
-        (
-            cap,
-            neg_cap,
-            system_type,
-            resolution,
-            num_sampling_steps,
-            cfg_scale,
-            cfg_trunc,
-            renorm_cfg,
-            solver,
-            t_shift,
-            seed,
-            scaling_method,
-            scaling_watershed,
-            proportional_attn,
-        ) = infer_args
-        system_prompt = system_type
-        cap = system_prompt + cap
-        if neg_cap != "":
-            neg_cap = system_prompt + neg_cap
-        metadata = dict(
-            real_cap=cap,
-            real_neg_cap=neg_cap,
-            system_type=system_type,
-            resolution=resolution,
-            num_sampling_steps=num_sampling_steps,
-            cfg_scale=cfg_scale,
-            cfg_trunc=cfg_trunc,
-            renorm_cfg=renorm_cfg,
-            solver=solver,
-            t_shift=t_shift,
-            seed=seed,
-            scaling_method=scaling_method,
-            scaling_watershed=scaling_watershed,
-            proportional_attn=proportional_attn,
-        )
-        print("> Parameters:", json.dumps(metadata, indent=2))
-        try:
-            # 샘플러 설정
-            if solver == "dpm":
-                transport = create_transport("Linear", "velocity")
-                sampler = Sampler(transport)
-                sample_fn = sampler.sample_dpm(
-                    model.forward_with_cfg,
-                    model_kwargs=model_kwargs,
-                )
-            else:
-                transport = create_transport(
-                    args.path_type,
-                    args.prediction,
-                    args.loss_weight,
-                    args.train_eps,
-                    args.sample_eps,
-                )
-                sampler = Sampler(transport)
-                sample_fn = sampler.sample_ode(
-                    sampling_method=solver,
-                    num_steps=num_sampling_steps,
-                    atol=args.atol,
-                    rtol=args.rtol,
-                    reverse=args.reverse,
-                    time_shifting_factor=t_shift,
-                )
-            # 해상도 및 latent 공간 크기 계산
-            resolution = resolution.split(" ")[-1]
-            w, h = resolution.split("x")
-            w, h = int(w), int(h)
-            latent_w, latent_h = w // 8, h // 8
-            if int(seed) != 0:
-                torch.random.manual_seed(int(seed))
-            z = torch.randn([1, 16, latent_h, latent_w], device="cuda").to(dtype)
-            z = z.repeat(2, 1, 1, 1)
-            with torch.no_grad():
-                if neg_cap != "":
-                    cap_feats, cap_mask = encode_prompt([cap] + [neg_cap], text_encoder, tokenizer, 0.0)
-                else:
-                    cap_feats, cap_mask = encode_prompt([cap] + [""], text_encoder, tokenizer, 0.0)
-            cap_mask = cap_mask.to(cap_feats.device)
-            model_kwargs = dict(
-                cap_feats=cap_feats,
-                cap_mask=cap_mask,
-                cfg_scale=cfg_scale,
-                cfg_trunc=1 - cfg_trunc,
-                renorm_cfg=renorm_cfg,
-            )
-            print(f"> Caption: {cap}")
-            print(f"> Number of sampling steps: {num_sampling_steps}")
-            print(f"> CFG scale: {cfg_scale}")
-            print("> Starting sampling...")
-            if solver == "dpm":
-                samples = sample_fn(z, steps=num_sampling_steps, order=2, skip_type="time_uniform_flow", method="multistep", flow_shift=t_shift)
-            else:
-                samples = sample_fn(z, model.forward_with_cfg, **model_kwargs)[-1]
-            samples = samples[:1]
-            print("Sample dtype:", samples.dtype)
-            vae_scale = {
-                "sdxl": 0.13025,
-                "sd3": 1.5305,
-                "ema": 0.18215,
-                "mse": 0.18215,
-                "cogvideox": 1.15258426,
-                "flux": 0.3611,
-            }["flux"]
-            vae_shift = {
-                "sdxl": 0.0,
-                "sd3": 0.0609,
-                "ema": 0.0,
-                "mse": 0.0,
-                "cogvideox": 0.0,
-                "flux": 0.1159,
-            }["flux"]
-            print(f"> VAE scale: {vae_scale}, shift: {vae_shift}")
-            print("Samples shape:", samples.shape)
-            samples = vae.decode(samples / vae_scale + vae_shift).sample
-            samples = (samples + 1.0) / 2.0
-            samples.clamp_(0.0, 1.0)
-            img = to_pil_image(samples[0].float())
-            print("> Generated image successfully.")
-            return img, metadata
-        except Exception:
-            print(traceback.format_exc())
-            return ModelFailure()
-def none_or_str(value):
-    if value == "None":
-        return None
-    return value
-def parse_transport_args(parser):
-    group = parser.add_argument_group("Transport arguments")
-    group.add_argument(
-        "--path-type",
-        type=str,
-        default="Linear",
-        choices=["Linear", "GVP", "VP"],
-        help="Type of path for transport: 'Linear', 'GVP' (Geodesic Vector Pursuit), or 'VP' (Vector Pursuit).",
-    )
-    group.add_argument(
-        "--prediction",
-        type=str,
-        default="velocity",
-        choices=["velocity", "score", "noise"],
-        help="Prediction model for the transport dynamics.",
-    )
-    group.add_argument(
-        "--loss-weight",
-        type=none_or_str,
-        default=None,
-        choices=[None, "velocity", "likelihood"],
-        help="Weighting of different loss components: 'velocity', 'likelihood', or None.",
-    )
-    group.add_argument("--sample-eps", type=float, help="Sampling parameter in the transport model.")
-    group.add_argument("--train-eps", type=float, help="Training epsilon to stabilize learning.")
-def parse_ode_args(parser):
-    group = parser.add_argument_group("ODE arguments")
-    group.add_argument(
-        "--atol",
-        type=float,
-        default=1e-6,
-        help="Absolute tolerance for the ODE solver.",
-    )
-    group.add_argument(
-        "--rtol",
-        type=float,
-        default=1e-3,
-        help="Relative tolerance for the ODE solver.",
-    )
-    group.add_argument("--reverse", action="store_true", help="Run the ODE solver in reverse.")
-    group.add_argument(
-        "--likelihood",
-        action="store_true",
-        help="Enable likelihood calculation during the ODE solving process.",
-    )
-def find_free_port() -> int:
-    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-    sock.bind(("", 0))
-    port = sock.getsockname()[1]
-    sock.close()
-    return port
-# 한글 프롬프트가 감지되면 영어로 번역하는 함수
-def translate_if_korean(text: str) -> str:
-    import re
-    if re.search(r"[ㄱ-ㅎㅏ-ㅣ가-힣]", text):
-        print("Translating Korean prompt to English...")
-        translation = translator(text)
-        return translation[0]["translation_text"]
-    return text
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--num_gpus", type=int, default=1)
-    parser.add_argument("--ckpt", type=str, default='/home/user/app/checkpoints', required=False)
-    parser.add_argument("--ema", action="store_true")
-    parser.add_argument("--precision", default="bf16", choices=["bf16", "fp32"])
-    parser.add_argument("--hf_token", type=str, default=None, help="Hugging Face read token for accessing gated repo.")
-    parser.add_argument("--res", type=int, default=1024, choices=[256, 512, 1024])
-    parser.add_argument("--port", type=int, default=12123)
-    parse_transport_args(parser)
-    parse_ode_args(parser)
-    args = parser.parse_known_args()[0]
-    if args.num_gpus != 1:
-        raise NotImplementedError("Multi-GPU Inference is not yet supported")
-    master_port = find_free_port()
-    text_encoder, tokenizer, vae, model = model_main(args, master_port, 0)
-    description = "Lumina-Image 2.0 ([Github](https://github.com/Alpha-VLLM/Lumina-Image-2.0/tree/main))"
-    # 커스텀 CSS: 메뉴 컨테이너의 너비를 줄이고, 배경이 잘 보이도록 반투명 배경을 적용합니다.
-    custom_css = """
-    body {
-        background: linear-gradient(135deg, #1a2a6c, #b21f1f, #fdbb2d);
-        font-family: 'Helvetica', sans-serif;
-        color: #333;
-    }
-    .gradio-container {
-        background: rgba(255, 255, 255, 0.85); /* 반투명 배경 */
-        max-width: 800px;               /* 컨테이너 최대 너비 조정 */
-        margin: 20px auto;              /* 중앙 정렬 */
-        border-radius: 15px;
-        box-shadow: 0 8px 16px rgba(0, 0, 0, 0.25);
-        padding: 20px;
     }
-    .gradio-title {
-        font-weight: bold;
-        font-size: 1.5em;
-        text-align: center;
-        margin-bottom: 10px;
     }
-    """
-    with gr.Blocks(css=custom_css) as demo:
-        with gr.Row():
-            gr.Markdown(f"<div class='gradio-title'>{description}</div>")
-        with gr.Row():
-            with gr.Column():
-                cap = gr.Textbox(
-                    lines=2,
-                    label="Caption",
-                    interactive=True,
-                    value="Majestic landscape photograph of snow-capped mountains under a dramatic sky at sunset. The mountains dominate the lower half of the image, with rugged peaks and deep crevasses visible. A glacier flows down the right side, partially illuminated by the warm light. The sky is filled with fiery orange and golden clouds, contrasting with the cool tones of the snow. The central peak is partially obscured by clouds, adding a sense of mystery. The foreground features dark, shadowed forested areas, enhancing the depth. High contrast, natural lighting, warm color palette, photorealistic, expansive, awe-inspiring, serene, visually balanced, dynamic composition.",
-                    placeholder="Enter a caption."
-                )
-                neg_cap = gr.Textbox(
-                    lines=2,
-                    label="Negative Caption",
-                    interactive=True,
-                    value="",
-                    placeholder="Enter a negative caption."
-                )
-                default_value = "You are an assistant designed to generate high-quality images with the highest degree of image-text alignment based on textual prompts."
-                system_type = gr.Dropdown(
-                    value=default_value,
-                    choices=[
-                        "You are an assistant designed to generate high-quality images with the highest degree of image-text alignment based on textual prompts.",
-                        "You are an assistant designed to generate superior images with the superior degree of image-text alignment based on textual prompts or user prompts.",
-                        ""
-                    ],
-                    label="System Type"
-                )
-                with gr.Row():
-                    res_choices = [f"{w}x{h}" for w, h in generate_crop_size_list((args.res // 64) ** 2, 64)]
-                    default_value = "1024x1024"
-                    resolution = gr.Dropdown(value=default_value, choices=res_choices, label="Resolution")
-                with gr.Row():
-                    num_sampling_steps = gr.Slider(minimum=1, maximum=70, value=40, step=1, interactive=True, label="Sampling Steps")
-                    seed = gr.Slider(minimum=0, maximum=int(1e5), value=0, step=1, interactive=True, label="Seed (0 for random)")
-                    cfg_trunc = gr.Slider(minimum=0, maximum=1, value=0, step=0.01, interactive=True, label="CFG Truncation")
-                with gr.Row():
-                    solver = gr.Dropdown(value="euler", choices=["euler", "midpoint", "rk4"], label="Solver")
-                    t_shift = gr.Slider(minimum=1, maximum=20, value=6, step=1, interactive=True, label="Time Shift")
-                    cfg_scale = gr.Slider(minimum=1.0, maximum=20.0, value=4.0, interactive=True, label="CFG Scale")
-                with gr.Row():
-                    renorm_cfg = gr.Dropdown(value=True, choices=[True, False, 2.0], label="CFG Renorm")
-                with gr.Accordion("Advanced Settings for Resolution Extrapolation", open=False):
-                    with gr.Row():
-                        scaling_method = gr.Dropdown(value="Time-aware", choices=["Time-aware", "None"], label="RoPE Scaling Method")
-                        scaling_watershed = gr.Slider(minimum=0.0, maximum=1.0, value=0.3, interactive=True, label="Linear/NTK Watershed")
-                    with gr.Row():
-                        proportional_attn = gr.Checkbox(value=True, interactive=True, label="Proportional Attention")
-                with gr.Row():
-                    submit_btn = gr.Button("Submit", variant="primary")
-            with gr.Column():
-                output_img = gr.Image(label="Generated Image", interactive=False)
-                with gr.Accordion(label="Generation Parameters", open=True):
-                    gr_metadata = gr.JSON(label="Metadata", show_label=False)
-        with gr.Row():
-            prompts = [
-                "Close-up portrait of a young woman with light brown hair, looking to the right, illuminated by warm, golden sunlight. Her hair is gently tousled, catching the light and creating a halo effect around her head. She wears a white garment with a V-neck, visible in the lower left of the frame. The background is dark and out of focus, enhancing the contrast between her illuminated face and the shadows. Soft, ethereal lighting, high contrast, warm color palette, shallow depth of field, natural backlighting, serene and contemplative mood, cinematic quality, intimate and visually striking composition.",
-                "하늘을 나는 용, 신비로운 분위기, 구름 위를 날며 빛나는 비늘을 가진, 전설 속의 존재, 강렬한 색채와 디테일한 묘사.",
-                "Aesthetic photograph of a bouquet of pink and white ranunculus flowers in a clear glass vase, centrally positioned on a wooden surface. The flowers are in full bloom, displaying intricate layers of petals with a soft gradient from pale pink to white. The vase is filled with water, visible through the clear glass, and the stems are submerged. In the background, a blurred vase with green stems is partially visible, adding depth to the composition. The lighting is warm and natural, casting soft shadows and highlighting the delicate textures of the petals. The scene is serene and intimate, with a focus on the organic beauty of the flowers. Photorealistic, shallow depth of field, soft natural lighting, warm color palette, high contrast, glossy texture, tranquil, visually balanced.",
-                "한只优雅的白猫穿着一件紫色的旗袍，旗袍上绣有精致的牡丹花图案，显得高贵典雅。它头上戴着一朵金色的发饰，嘴里叼着一根象征好运的红色丝带。周围环绕着许多飘动的纸鹤和金色的光点，营造出一种祥瑞和梦幻的氛围。超写实风格。"
-            ]
-            prompts = [[p] for p in prompts]
-            gr.Examples(prompts, [cap], label="Examples")
-        @spaces.GPU(duration=200)
-        def on_submit(cap, neg_cap, system_type, resolution, num_sampling_steps, cfg_scale, cfg_trunc, renorm_cfg, solver, t_shift, seed, scaling_method, scaling_watershed, proportional_attn, progress=gr.Progress(track_tqdm=True)):
-            # 한글 프롬프트가 감지되면 영어로 번역
-            cap = translate_if_korean(cap)
-            if neg_cap and neg_cap.strip():
-                neg_cap = translate_if_korean(neg_cap)
-            infer_args = (cap, neg_cap, system_type, resolution, num_sampling_steps, cfg_scale, cfg_trunc, renorm_cfg, solver, t_shift, seed, scaling_method, scaling_watershed, proportional_attn)
-            result = inference(args, infer_args, text_encoder, tokenizer, vae, model)
-            if isinstance(result, ModelFailure):
-                raise RuntimeError("Model failed to generate the image.")
-            return result
-        submit_btn.click(
-            on_submit,
-            [cap, neg_cap, system_type, resolution, num_sampling_steps, cfg_scale, cfg_trunc, renorm_cfg, solver, t_shift, seed, scaling_method, scaling_watershed, proportional_attn],
-            [output_img, gr_metadata],
-        )
-        def show_scaling_watershed(scaling_m):
-            return gr.update(visible=scaling_m == "Time-aware")
-        scaling_method.change(show_scaling_watershed, scaling_method, scaling_watershed)
-    demo.queue().launch(server_name="0.0.0.0")
-if __name__ == "__main__":
-    main()

+from flask import Flask, render_template, request, jsonify
+import os, re, json
+app = Flask(__name__)
+# ────────────────────────── 1. CONFIGURATION ──────────────────────────
+# Domains that commonly block iframes
+BLOCKED_DOMAINS = [
+    "naver.com", "daum.net", "google.com",
+    "facebook.com", "instagram.com", "kakao.com",
+    "ycombinator.com"
+]
+# ────────────────────────── 2. CURATED CATEGORIES ──────────────────────────
+CATEGORIES = {
+    "Popular": [
+        "https://huggingface.co/spaces/openfree/AGI-Screenplay",
+        "https://huggingface.co/spaces/openfree/AGI-WebNovel",
+        "https://huggingface.co/spaces/openfree/AGI-NOVEL",
+        "https://huggingface.co/spaces/fantaxy/AGI-LEADERBOARD",
+        "https://cutechicken-3d-airforce-simulator.static.hf.space",
+        "https://huggingface.co/spaces/ginipick/Private-AI",
+        "https://huggingface.co/spaces/fantaxy/ofai-flx-logo",
+        "https://huggingface.co/spaces/aiqtech/FLUX-Ghibli-Studio-LoRA",
+        "https://huggingface.co/spaces/seawolf2357/REALVISXL-V5",
+        "https://huggingface.co/spaces/fantos/flx8lora",
+        "https://huggingface.co/spaces/ginipick/Realtime-FLUX",
+        "https://huggingface.co/spaces/fantaxy/flx-pulid",
+        "https://huggingface.co/spaces/ginipick/FLUX-Prompt-Generator",
+        "https://huggingface.co/spaces/aiqtech/kofaceid",
+        "https://huggingface.co/spaces/aiqtech/flxgif",
+        "https://huggingface.co/spaces/fantos/flxfashmodel",
+        "https://huggingface.co/spaces/fantos/flxcontrol",
+        "https://huggingface.co/spaces/fantos/textcutobject",
+        "https://huggingface.co/spaces/seawolf2357/flxloraexp",
+        "https://huggingface.co/spaces/fantaxy/flxloraexp",
+        "https://huggingface.co/spaces/aiqtech/imaginpaint",
+        "https://huggingface.co/spaces/ginipick/FLUXllama",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored",
+        "https://huggingface.co/spaces/fantaxy/flx-upscale",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video",
+        "https://huggingface.co/spaces/fantos/VoiceClone",
+        "https://huggingface.co/spaces/fantaxy/Rolls-Royce",
+        "https://huggingface.co/spaces/aiqtech/FLUX-military",
+        "https://huggingface.co/spaces/fantaxy/FLUX-Animations",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-photo",
+        "https://huggingface.co/spaces/ginipick/Time-Stream",
+        "https://huggingface.co/spaces/seawolf2357/sd-prompt-gen",
+        "https://huggingface.co/spaces/openfree/MagicFace-V3",
+        "https://huggingface.co/spaces/Heartsync/adult",
+        "https://huggingface.co/spaces/Heartsync/wan2-1-fast-security",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-REAL",
+        "https://huggingface.co/spaces/seawolf2357/img2vid",
+        "https://huggingface.co/spaces/openfree/image-to-vector",
+        "https://huggingface.co/spaces/openfree/DreamO-video",
+        "https://huggingface.co/spaces/VIDraft/FramePack_rotate_landscape",
+        "https://huggingface.co/spaces/fantaxy/Sound-AI-SFX",
+        "https://huggingface.co/spaces/ginigen/VoiceClone-TTS",
+        "https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB",
+        "https://huggingface.co/spaces/Heartsync/NSFW-image",
+        "https://huggingface.co/spaces/Heartsync/NSFW-detection",
+        "https://huggingface.co/spaces/Heartsync/VEO3-RealTime",
+        "https://huggingface.co/spaces/ginigen/VEO3-Free",
+        "https://huggingface.co/spaces/ginigen/FLUX-Text-Tree-Image",
+        "https://huggingface.co/spaces/ginigen/text3d-r1",
+        "https://huggingface.co/spaces/VIDraft/stable-diffusion-3.5-large-turboX",
+    ],
+    "BEST": [
+        "https://huggingface.co/spaces/MaziyarPanahi/FACTS-Leaderboard",
+        "https://huggingface.co/spaces/ginigen/Flux-Kontext-Style",
+        "https://huggingface.co/spaces/openfree/Cycle-Navigator",
+        "https://huggingface.co/spaces/ginigen/Flux-Kontext-FaceLORA",
+        "https://huggingface.co/spaces/ginigen/Seedance-Free",
+        "https://huggingface.co/spaces/VIDraft/SOMA-AGI",
+        "https://huggingface.co/spaces/aiqtech/Heatmap-Leaderboard",
+        "https://huggingface.co/spaces/VIDraft/DNA-CASINO",
+        "https://huggingface.co/spaces/aiqtech/SOMA-Oriental",
+        "https://huggingface.co/spaces/fantaxy/YTB-TEST",
+        "https://huggingface.co/spaces/aiqtech/Contributors-Leaderboard",
+        "https://huggingface.co/spaces/ginigen/text3d-r1",
+        "https://huggingface.co/spaces/VIDraft/stable-diffusion-3.5-large-turboX",
+        "https://huggingface.co/spaces/openfree/Korean-Leaderboard",
+        "https://huggingface.co/spaces/fantos/flxcontrol",
+        "https://huggingface.co/spaces/aiqtech/FLUX-Ghibli-Studio-LoRA",
+        "https://huggingface.co/spaces/openfree/AI-Podcast",
+        "https://huggingface.co/spaces/ginigen/Workflow-Canvas",
+        "https://huggingface.co/spaces/ginigen/3D-LLAMA",
+        "https://huggingface.co/spaces/ginigen/VoiceClone-TTS",
+        "https://huggingface.co/spaces/VIDraft/ACE-Singer",
+        "https://huggingface.co/spaces/ginipick/AI-BOOK",
+        "https://huggingface.co/spaces/immunobiotech/drug-discovery",
+        "https://huggingface.co/spaces/VIDraft/Robo-Beam",
+        "https://huggingface.co/spaces/fantaxy/fantasy-novel",
+        "https://huggingface.co/spaces/immunobiotech/Gemini-MICHELIN",
+        "https://huggingface.co/spaces/openfree/Chart-GPT",
+        "https://huggingface.co/spaces/ginipick/NH-Korea",
+        "https://huggingface.co/spaces/VIDraft/Voice-Clone-Podcast",
+        "https://huggingface.co/spaces/ginipick/Private-AI",
+        "https://huggingface.co/spaces/ginigen/Flux-VIDEO",
+        "https://huggingface.co/spaces/openfree/open-GAMMA",
+        "https://huggingface.co/spaces/ginipick/PharmAI-Korea",
+        "https://huggingface.co/spaces/ginipick/Pharmacy",
+        "https://huggingface.co/spaces/ginipick/PDF-EXAM",
+        "https://huggingface.co/spaces/ginipick/IDEA-DESIGN",
+        "https://huggingface.co/spaces/openfree/DreamO-video",
+        "https://huggingface.co/spaces/ginipick/10m-marketing",
+        "https://huggingface.co/spaces/VIDraft/voice-trans",
+        "https://huggingface.co/spaces/VIDraft/NH-Prediction",
+        "https://huggingface.co/spaces/fantos/flx8lora",
+        "https://huggingface.co/spaces/ginigen/MagicFace-V3",
+        "https://huggingface.co/spaces/openfree/Live-Podcast",
+        "https://huggingface.co/spaces/seawolf2357/ocrlatex",
+        "https://huggingface.co/spaces/Heartsync/VEO3-RealTime",
+        "https://huggingface.co/spaces/ginigen/VEO3-Free",
+        "https://huggingface.co/spaces/openfree/MagicFace-V3",
+        "https://huggingface.co/spaces/aiqtech/FLUX-military",
+        "https://huggingface.co/spaces/fantaxy/flxloraexp",
+        "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX",
+        "https://huggingface.co/spaces/ginigen/FLUXllama-Multilingual",
+        "https://huggingface.co/spaces/Heartsync/wan2-1-fast-security",
+        "https://huggingface.co/spaces/fantaxy/Rolls-Royce",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-REAL",
+        "https://huggingface.co/spaces/ginipick/Realtime-FLUX",
+        "https://huggingface.co/spaces/aiqtech/imaginpaint",
+        "https://huggingface.co/spaces/aiqtech/flxgif",
+        "https://huggingface.co/spaces/fantos/flxfashmodel",
+        "https://huggingface.co/spaces/aiqtech/kofaceid",
+        "https://huggingface.co/spaces/ginipick/FLUX-Prompt-Generator",
+        "https://huggingface.co/spaces/seawolf2357/REALVISXL-V5",
+        "https://huggingface.co/spaces/fantaxy/FLUX-Animations",
+        "https://huggingface.co/spaces/fantaxy/flx-pulid",
+        "https://huggingface.co/spaces/fantaxy/ofai-flx-logo",
+        "https://huggingface.co/spaces/openfree/image-to-vector",
+        "https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB",
+        "https://huggingface.co/spaces/seawolf2357/sd-prompt-gen",
+        "https://huggingface.co/spaces/VIDraft/FramePack_rotate_landscape",
+        "https://huggingface.co/spaces/ginipick/FLUXllama",
+        "https://huggingface.co/spaces/Heartsync/NSFW-image",
+        "https://huggingface.co/spaces/seawolf2357/img2vid",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2",
+        "https://huggingface.co/spaces/Heartsync/NSFW-detection",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video",
+        "https://huggingface.co/spaces/Heartsync/adult",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored",
+        "https://huggingface.co/spaces/fantos/VoiceClone",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-photo",
+        "https://huggingface.co/spaces/fantaxy/flx-upscale",
+        "https://huggingface.co/spaces/seawolf2357/flxloraexp",
+        "https://huggingface.co/spaces/ginipick/Time-Stream",
+        "https://huggingface.co/spaces/fantos/textcutobject",
+    ],
+    "NEW": [
+        "https://huggingface.co/spaces/ginigen/Flux-Kontext-Style",
+        "https://cutechicken-3d-airforce-simulator.static.hf.space",
+        "https://huggingface.co/spaces/ginipick/Private-AI",
+        "https://huggingface.co/spaces/VIDraft/ACE-Singer",
+        "https://huggingface.co/spaces/ginipick/AI-BOOK",
+        "https://huggingface.co/spaces/openfree/Best-AI",
+        "https://huggingface.co/spaces/aiqtech/Heatmap-Leaderboard",
+        "https://huggingface.co/spaces/VIDraft/DNA-CASINO",
+        "https://huggingface.co/spaces/openfree/AGI-Screenplay",
+        "https://huggingface.co/spaces/openfree/AGI-WebNovel",
+        "https://huggingface.co/spaces/openfree/AGI-NOVEL",
+        "https://huggingface.co/spaces/fantaxy/AGI-LEADERBOARD",
+        "https://huggingface.co/spaces/ginigen/Seedance-Free",
+        "https://huggingface.co/spaces/aiqtech/SOMA-Oriental",
+        "https://huggingface.co/spaces/ginigen/Flux-Kontext-FaceLORA",
+        "https://huggingface.co/spaces/VIDraft/SOMA-AGI",
+        "https://huggingface.co/spaces/Heartsync/VEO3-RealTime",
+        "https://huggingface.co/spaces/openfree/Open-GAMMA",
+        "https://huggingface.co/spaces/ginigen/VEO3-Free",
+        "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX",
+        "https://huggingface.co/spaces/VIDraft/voice-trans",
+        "https://huggingface.co/spaces/VIDraft/Robo-Beam",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-REAL",
+        "https://huggingface.co/spaces/fantaxy/fantasy-novel",
+        "https://huggingface.co/spaces/openfree/Chart-GPT",
+        "https://huggingface.co/spaces/Heartsync/Novel-NSFW",
+        "https://huggingface.co/spaces/ginigen/FLUX-Ghibli-LoRA2",
+        "https://huggingface.co/spaces/Heartsync/WAN-VIDEO-AUDIO",
+        "https://huggingface.co/spaces/Heartsync/wan2-1-fast-security",
+        "https://huggingface.co/spaces/ginigen/Flux-VIDEO",
+        "https://huggingface.co/spaces/aiqcamp/REMOVAL-TEXT-IMAGE",
+        "https://huggingface.co/spaces/VIDraft/Mistral-RAG-BitSix",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video",
+        "https://huggingface.co/spaces/fantaxy/YTB-TEST",
+        "https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB",
+        "https://huggingface.co/spaces/Heartsync/adult",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-photo",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored",
+        "https://huggingface.co/spaces/openfree/Live-Podcast",
+        "https://huggingface.co/spaces/openfree/AI-Podcast",
+        "https://huggingface.co/spaces/ginipick/NH-Korea",
+        "https://huggingface.co/spaces/VIDraft/NH-Prediction",
+        "https://huggingface.co/spaces/VIDraft/Voice-Clone-Podcast",
+        "https://huggingface.co/spaces/ginipick/PDF-EXAM",
+        "https://huggingface.co/spaces/openfree/Game-Gallery",
+        "https://huggingface.co/spaces/openfree/Vibe-Game",
+        "https://huggingface.co/spaces/ginipick/IDEA-DESIGN",
+        "https://huggingface.co/spaces/openfree/Cycle-Navigator",
+        "https://huggingface.co/spaces/openfree/DreamO-video",
+        "https://huggingface.co/spaces/Heartsync/NSFW-detection",
+    ],
+    "Productivity": [
+        "https://huggingface.co/spaces/aiqtech/Heatmap-Leaderboard",
+        "https://huggingface.co/spaces/VIDraft/DNA-CASINO",
+        "https://huggingface.co/spaces/openfree/Open-GAMMA",
+        "https://huggingface.co/spaces/VIDraft/Robo-Beam",
+        "https://huggingface.co/spaces/VIDraft/voice-trans",
+        "https://huggingface.co/spaces/Heartsync/FREE-NSFW-HUB",
+        "https://huggingface.co/spaces/openfree/Chart-GPT",
+        "https://huggingface.co/spaces/ginipick/AI-BOOK",
+        "https://huggingface.co/spaces/VIDraft/Voice-Clone-Podcast",
+        "https://huggingface.co/spaces/ginipick/PDF-EXAM",
+        "https://huggingface.co/spaces/ginigen/perflexity-clone",
+        "https://huggingface.co/spaces/ginipick/IDEA-DESIGN",
+        "https://huggingface.co/spaces/ginipick/10m-marketing",
+        "https://huggingface.co/spaces/openfree/Live-Podcast",
+        "https://huggingface.co/spaces/openfree/AI-Podcast",
+        "https://huggingface.co/spaces/ginipick/QR-Canvas-plus",
+        "https://huggingface.co/spaces/openfree/Badge",
+        "https://huggingface.co/spaces/VIDraft/mouse-webgen",
+        "https://huggingface.co/spaces/openfree/Vibe-Game",
+        "https://huggingface.co/spaces/VIDraft/NH-Prediction",
+        "https://huggingface.co/spaces/ginipick/NH-Korea",
+        "https://huggingface.co/spaces/openfree/Naming",
+        "https://huggingface.co/spaces/ginipick/Change-Hair",
+    ],
+    "Multimodal": [
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-photo",
+        "https://huggingface.co/spaces/fantaxy/YTB-TEST",
+        "https://huggingface.co/spaces/ginigen/Seedance-Free",
+        "https://huggingface.co/spaces/Heartsync/VEO3-RealTime",
+        "https://huggingface.co/spaces/ginigen/VEO3-Free",
+        "https://huggingface.co/spaces/ginigen/VEO3-Directors",
+        "https://huggingface.co/spaces/Heartsync/WAN2-1-fast-T2V-FusioniX",
+        "https://huggingface.co/spaces/Heartsync/adult",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video2",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-video",
+        "https://huggingface.co/spaces/Heartsync/WAN-VIDEO-AUDIO",
+        "https://huggingface.co/spaces/Heartsync/wan2-1-fast-security",
+        "https://huggingface.co/spaces/ginigen/Flux-VIDEO",
+        "https://huggingface.co/spaces/ginigen/3D-LLAMA-V1",
+        "https://huggingface.co/spaces/ginigen/Flux-VIDEO",
+        "https://huggingface.co/spaces/openfree/Multilingual-TTS",
+        "https://huggingface.co/spaces/VIDraft/ACE-Singer",
+        "https://huggingface.co/spaces/openfree/DreamO-video",
+        "https://huggingface.co/spaces/fantaxy/Sound-AI-SFX",
+        "https://huggingface.co/spaces/ginigen/SFX-Sound-magic",
+        "https://huggingface.co/spaces/ginigen/VoiceClone-TTS",
+        "https://huggingface.co/spaces/aiqcamp/ENGLISH-Speaking-Scoring",
+        "https://huggingface.co/spaces/fantaxy/Remove-Video-Background",
+    ],
+    "Professional": [
+        "https://huggingface.co/spaces/Heartsync/NSFW-novels",
+        "https://huggingface.co/spaces/aiqtech/SOMA-Oriental",
+        "https://huggingface.co/spaces/VIDraft/SOMA-AGI",
+        "https://huggingface.co/spaces/Heartsync/Novel-NSFW",
+        "https://huggingface.co/spaces/fantaxy/fantasy-novel",
+        "https://huggingface.co/spaces/VIDraft/money-radar",
+        "https://huggingface.co/spaces/immunobiotech/drug-discovery",
+        "https://huggingface.co/spaces/immunobiotech/Gemini-MICHELIN",
+        "https://huggingface.co/spaces/openfree/Cycle-Navigator",
+        "https://huggingface.co/spaces/VIDraft/Fashion-Fit",
+        "https://huggingface.co/spaces/openfree/Stock-Trading-Analysis",
+        "https://huggingface.co/spaces/ginipick/AgentX-Papers",
+        "https://huggingface.co/spaces/Heartsync/Papers-Leaderboard",
+        "https://huggingface.co/spaces/VIDraft/PapersImpact",
+        "https://huggingface.co/spaces/ginigen/multimodal-chat-mbti-korea",
+    ],
+    "Image": [
+        "https://huggingface.co/spaces/ginigen/Flux-Kontext-FaceLORA",
+        "https://huggingface.co/spaces/Heartsync/NSFW-Uncensored-REAL",
+        "https://huggingface.co/spaces/ginigen/FLUX-Ghibli-LoRA2",
+        "https://huggingface.co/spaces/aiqcamp/REMOVAL-TEXT-IMAGE",
+        "https://huggingface.co/spaces/VIDraft/BAGEL-Websearch",
+        "https://huggingface.co/spaces/ginigen/Every-Text",
+        "https://huggingface.co/spaces/ginigen/text3d-r1",
+        "https://huggingface.co/spaces/ginipick/FLUXllama",
+        "https://huggingface.co/spaces/ginigen/Workflow-Canvas",
+        "https://huggingface.co/spaces/ginigen/canvas-studio",
+        "https://huggingface.co/spaces/VIDraft/ReSize-Image-Outpainting",
+        "https://huggingface.co/spaces/Heartsync/FLUX-Vision",
+        "https://huggingface.co/spaces/fantos/textcutobject",
+        "https://huggingface.co/spaces/aiqtech/imaginpaint",
+        "https://huggingface.co/spaces/openfree/ColorRevive",
+        "https://huggingface.co/spaces/openfree/ultpixgen",
+        "https://huggingface.co/spaces/VIDraft/Polaroid-Style",
+        "https://huggingface.co/spaces/ginigen/VisualCloze",
+        "https://huggingface.co/spaces/fantaxy/ofai-flx-logo",
+        "https://huggingface.co/spaces/ginigen/interior-design",
+        "https://huggingface.co/spaces/ginigen/MagicFace-V3",
+        "https://huggingface.co/spaces/fantaxy/flx-pulid",
+        "https://huggingface.co/spaces/seawolf2357/Ghibli-Multilingual-Text-rendering",
+        "https://huggingface.co/spaces/VIDraft/Open-Meme-Studio",
+        "https://huggingface.co/spaces/VIDraft/stable-diffusion-3.5-large-turboX",
+        "https://huggingface.co/spaces/aiqtech/flxgif",
+        "https://huggingface.co/spaces/openfree/VectorFlow",
+        "https://huggingface.co/spaces/ginigen/3D-LLAMA",
+        "https://huggingface.co/spaces/ginigen/Multi-LoRAgen",
+    ],
+    "LLM / VLM": [
+        "https://huggingface.co/spaces/fantaxy/fantasy-novel",
+        "https://huggingface.co/spaces/ginigen/deepseek-r1-0528-API",
+        "https://huggingface.co/spaces/aiqcamp/Mistral-Devstral-API",
+        "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528",
+        "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528-qwen3-8b",
+        "https://huggingface.co/spaces/aiqcamp/deepseek-r1-0528",
+        "https://huggingface.co/spaces/aiqcamp/Mistral-Devstral-API",
+        "https://huggingface.co/spaces/VIDraft/Mistral-RAG-BitSix",
+        "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-4B",
+        "https://huggingface.co/spaces/VIDraft/Gemma-3-R1984-12B",
+        "https://huggingface.co/spaces/ginigen/Mistral-Perflexity",
+        "https://huggingface.co/spaces/aiqcamp/gemini-2.5-flash-preview",
+        "https://huggingface.co/spaces/openfree/qwen3-30b-a3b-research",
+        "https://huggingface.co/spaces/openfree/qwen3-235b-a22b-research",
+        "https://huggingface.co/spaces/openfree/Llama-4-Maverick-17B-Research",
+    ],
+}
+# ────────────────────────── 3. URL HELPERS ──────────────────────────
+def direct_url(hf_url):
+    m = re.match(r"https?://huggingface\.co/spaces/([^/]+)/([^/?#]+)", hf_url)
+    if not m:
+        return hf_url
+    owner, name = m.groups()
+    owner = owner.lower()
+    name = name.replace('.', '-').replace('_', '-').lower()
+    return f"https://{owner}-{name}.hf.space"
+def screenshot_url(url):
+    return f"https://image.thum.io/get/fullpage/{url}"
+def process_url_for_preview(url):
+    """Returns (preview_url, mode)"""
+    # Handle blocked domains first
+    if any(d for d in BLOCKED_DOMAINS if d in url):
+        return screenshot_url(url), "snapshot"
+    # Special case handling for problematic URLs
+    if "vibe-coding-tetris" in url or "World-of-Tank-GAME" in url or "Minesweeper-Game" in url:
+        return screenshot_url(url), "snapshot"
+    # General HF space handling
+    try:
+        if "huggingface.co/spaces" in url:
+            parts = url.rstrip("/").split("/")
+            if len(parts) >= 5:
+                owner = parts[-2]
+                name = parts[-1]
+                embed_url = f"https://huggingface.co/spaces/{owner}/{name}/embed"
+                return embed_url, "iframe"
+    except Exception:
+        return screenshot_url(url), "snapshot"
+    # Default handling
+    return url, "iframe"
+# ────────────────────────── 4. API ROUTES ──────────────────────────
+@app.route('/api/category')
+def api_category():
+    cat = request.args.get('name', '')
+    urls = CATEGORIES.get(cat, [])
+    # Add pagination for categories
+    page = int(request.args.get('page', 1))
+    per_page = int(request.args.get('per_page', 4))
+    total_pages = max(1, (len(urls) + per_page - 1) // per_page)
+    start = (page - 1) * per_page
+    end = min(start + per_page, len(urls))
+    urls_page = urls[start:end]
+    items = [
+        {
+            "title": url.split('/')[-1],
+            "owner": url.split('/')[-2] if '/spaces/' in url else '',
+            "iframe": direct_url(url),
+            "shot": screenshot_url(url),
+            "hf": url
+        } for url in urls_page
+    ]
+    return jsonify({
+        "items": items,
+        "page": page,
+        "total_pages": total_pages
+    })
+# ────────────────────────── 5. MAIN ROUTES ──────────────────────────
+@app.route('/')
+def home():
+    os.makedirs('templates', exist_ok=True)
+    with open('templates/index.html', 'w', encoding='utf-8') as fp:
+        fp.write(r'''<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Web Gallery</title>
+<style>
+@import url('https://fonts.googleapis.com/css2?family=Nunito:wght@300;600&display=swap');
+body{margin:0;font-family:Nunito,sans-serif;background:#f6f8fb;}
+.tabs{display:flex;flex-wrap:wrap;gap:8px;padding:16px;}
+.tab{padding:6px 14px;border:none;border-radius:18px;background:#e2e8f0;font-weight:600;cursor:pointer;}
+.tab.active{background:#a78bfa;color:#1a202c;}
+.tab.popular{background:#ff6b6b;color:white;}
+.tab.popular.active{background:#fa5252;color:white;}
+.tab.best{background:#4ecdc4;color:white;}
+.tab.best.active{background:#38d9a9;color:white;}
+.tab.new{background:#ffe066;color:#1a202c;}
+.tab.new.active{background:#ffd43b;color:#1a202c;}
+/* Updated grid to show 2x2 layout */
+.grid{display:grid;grid-template-columns:repeat(2,1fr);gap:20px;padding:0 16px 60px;max-width:1200px;margin:0 auto;}
+@media(max-width:800px){.grid{grid-template-columns:1fr;}}
+/* Increased card height for larger display */
+.card{background:#fff;border-radius:12px;box-shadow:0 2px 8px rgba(0,0,0,.08);overflow:hidden;height:540px;display:flex;flex-direction:column;position:relative;}
+.frame{flex:1;position:relative;overflow:hidden;}
+.frame iframe{position:absolute;width:166.667%;height:166.667%;transform:scale(.6);transform-origin:top left;border:0;}
+.frame img{width:100%;height:100%;object-fit:cover;}
+.card-label{position:absolute;top:10px;left:10px;padding:4px 8px;border-radius:4px;font-size:11px;font-weight:bold;z-index:100;text-transform:uppercase;letter-spacing:0.5px;box-shadow:0 2px 4px rgba(0,0,0,0.2);}
+.label-live{background:linear-gradient(135deg, #00c6ff, #0072ff);color:white;}
+.label-static{background:linear-gradient(135deg, #ff9a9e, #fad0c4);color:#333;}
+.foot{height:44px;background:#fafafa;display:flex;align-items:center;justify-content:center;border-top:1px solid #eee;}
+.foot a{font-size:.82rem;font-weight:700;color:#4a6dd8;text-decoration:none;}
+.pagination{display:flex;justify-content:center;margin:20px 0;gap:10px;}
+.pagination button{padding:5px 15px;border:none;border-radius:20px;background:#e2e8f0;cursor:pointer;}
+.pagination button:disabled{opacity:0.5;cursor:not-allowed;}
+</style>
+</head>
+<body>
+<header style="text-align: center; padding: 20px; background: linear-gradient(135deg, #f6f8fb, #e2e8f0); border-bottom: 1px solid #ddd;">
+  <h1 style="margin-bottom: 10px;">🌟OPEN & Free: BEST AI Playground</h1>
+  <p>
+    <a href="https://huggingface.co/OpenFreeAI" target="_blank"><img src="https://img.shields.io/static/v1?label=Community&message=OpenFree_AI&color=%23800080&labelColor=%23000080&logo=HUGGINGFACE&logoColor=%23ffa500&style=for-the-badge" alt="badge"></a>
+    <a href="https://discord.gg/openfreeai" target="_blank"><img src="https://img.shields.io/static/v1?label=Discord&message=Openfree%20AI&color=%230000ff&labelColor=%23800080&logo=discord&logoColor=white&style=for-the-badge" alt="badge"></a>
+    <a href="https://huggingface.co/spaces/openfree/Best-AI" target="_blank"><img src="https://img.shields.io/static/v1?label=OpenFree&message=BEST%20AI%20Services&color=%230000ff&labelColor=%23000080&logo=huggingface&logoColor=%23ffa500&style=for-the-badge" alt="badge"></a>
+  </p>
+</header>
+<div class="tabs" id="tabs"></div>
+<div id="content"></div>
+<script>
+// Basic configuration
+const cats = {{cats|tojson}};
+const tabs = document.getElementById('tabs');
+const content = document.getElementById('content');
+let active = "";
+let currentPage = 1;
+// Simple utility functions
+function makeRequest(url, method, data, callback) {
+  const xhr = new XMLHttpRequest();
+  xhr.open(method, url, true);
+  xhr.onreadystatechange = function() {
+    if (xhr.readyState === 4 && xhr.status === 200) {
+      callback(JSON.parse(xhr.responseText));
     }
+  };
+  if (method === 'POST') {
+    xhr.send(data);
+  } else {
+    xhr.send();
+  }
+}
+function updateTabs() {
+  Array.from(tabs.children).forEach(b => {
+    b.classList.toggle('active', b.dataset.c === active);
+  });
+}
+// Tab handlers
+function loadCategory(cat, page) {
+  if(cat === active && currentPage === page) return;
+  active = cat;
+  currentPage = page || 1;
+  updateTabs();
+  content.innerHTML = '<p style="text-align:center;padding:40px">Loading…</p>';
+  makeRequest('/api/category?name=' + encodeURIComponent(cat) + '&page=' + currentPage + '&per_page=4', 'GET', null, function(data) {
+    let html = '<div class="grid">';
+    if(data.items.length === 0) {
+      html += '<p style="grid-column:1/-1;text-align:center;padding:40px">No items in this category.</p>';
+    } else {
+      data.items.forEach(item => {
+        html += `
+          <div class="card">
+            <div class="card-label label-live">LIVE</div>
+            <div class="frame">
+              <iframe src="${item.iframe}" loading="lazy" sandbox="allow-forms allow-modals allow-popups allow-same-origin allow-scripts allow-downloads"></iframe>
+            </div>
+            <div class="foot">
+              <a href="${item.hf}" target="_blank">${item.title}</a>
+            </div>
+          </div>
+        `;
+      });
     }
+    html += '</div>';
+    // Add pagination
+    html += `
+      <div class="pagination">
+        <button ${currentPage <= 1 ? 'disabled' : ''} onclick="loadCategory('${cat}', ${currentPage-1})">« Previous</button>
+        <span>Page ${currentPage} of ${data.total_pages}</span>
+        <button ${currentPage >= data.total_pages ? 'disabled' : ''} onclick="loadCategory('${cat}', ${currentPage+1})">Next »</button>
+      </div>
+    `;
+    content.innerHTML = html;
+  });
+}
+// Create tabs
+// Special tabs first (Popular, BEST, NEW)
+['Popular', 'BEST', 'NEW'].forEach(specialCat => {
+  const b = document.createElement('button');
+  b.className = 'tab ' + specialCat.toLowerCase();
+  b.textContent = specialCat;
+  b.dataset.c = specialCat;
+  b.onclick = function() { loadCategory(specialCat, 1); };
+  tabs.appendChild(b);
+});
+// Regular category tabs
+cats.forEach(c => {
+  if (!['Popular', 'BEST', 'NEW'].includes(c)) {
+    const b = document.createElement('button');
+    b.className = 'tab';
+    b.textContent = c;
+    b.dataset.c = c;
+    b.onclick = function() { loadCategory(c, 1); };
+    tabs.appendChild(b);
+  }
+});
+// Start with Popular tab
+loadCategory('Popular', 1);
+</script>
+</body>
+</html>''')
+    # Return the rendered template
+    return render_template('index.html', cats=list(CATEGORIES.keys()))
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=7860)