Create app.py

app.py

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+import gradio as gr
+import torch
+import os
+import gc
+import shutil
+import requests
+import json
+import struct
+import numpy as np
+import yaml
+import subprocess
+import sys
+import tempfile
+import re
+from pathlib import Path
+from typing import Dict, Any, Optional, List, Iterable
+from huggingface_hub import HfApi, hf_hub_download, list_repo_files, login
+from safetensors.torch import load_file, save_file
+from tqdm import tqdm
+
+# --- Import MergeKit Config ---
+try:
+    from mergekit.config import MergeConfiguration
+except ImportError:
+    # Fallback if installation fails temporarily
+    class MergeConfiguration:
+        @staticmethod
+        def model_validate(config): pass
+
+# --- Constants & Setup ---
+try:
+    TempDir = Path("/tmp/temp_tool")
+    os.makedirs(TempDir, exist_ok=True)
+except:
+    TempDir = Path("./temp_tool")
+    os.makedirs(TempDir, exist_ok=True)
+
+api = HfApi()
+
+def cleanup_temp():
+    if TempDir.exists():
+        shutil.rmtree(TempDir)
+    os.makedirs(TempDir, exist_ok=True)
+    gc.collect()
+
+# =================================================================================
+# SHARED HELPERS (Tabs 1-4 & 10)
+# =================================================================================
+
+def parse_hf_url(url):
+    if "huggingface.co" in url and "resolve" in url:
+        try:
+            parts = url.split("huggingface.co/")[-1].split("/")
+            repo_id = f"{parts[0]}/{parts[1]}"
+            filename = "/".join(parts[4:]).split("?")[0]
+            return repo_id, filename
+        except:
+            return None, None
+    return None, None
+
+def download_lora_smart(input_str, token):
+    local_path = TempDir / "adapter.safetensors"
+    if local_path.exists(): os.remove(local_path)
+    
+    repo_id, filename = parse_hf_url(input_str)
+    if repo_id and filename:
+        try:
+            hf_hub_download(repo_id=repo_id, filename=filename, token=token, local_dir=TempDir)
+            found = list(TempDir.rglob(filename.split("/")[-1]))[0]
+            if found != local_path: shutil.move(found, local_path)
+            return local_path
+        except: pass
+    try:
+        if ".safetensors" in input_str and input_str.count("/") >= 2:
+            parts = input_str.split("/")
+            repo_id = f"{parts[0]}/{parts[1]}"
+            filename = "/".join(parts[2:])
+            hf_hub_download(repo_id=repo_id, filename=filename, token=token, local_dir=TempDir)
+            found = list(TempDir.rglob(filename.split("/")[-1]))[0]
+            if found != local_path: shutil.move(found, local_path)
+            return local_path
+        candidates = ["adapter_model.safetensors", "model.safetensors"]
+        files = list_repo_files(repo_id=input_str, token=token)
+        target = next((f for f in files if f in candidates), None)
+        if not target:
+            safes = [f for f in files if f.endswith(".safetensors")]
+            if safes: target = safes[0]
+        if not target: raise ValueError("No safetensors found")
+        hf_hub_download(repo_id=input_str, filename=target, token=token, local_dir=TempDir)
+        found = list(TempDir.rglob(target.split("/")[-1]))[0]
+        if found != local_path: shutil.move(found, local_path)
+        return local_path
+    except Exception as e:
+        if input_str.startswith("http"):
+            try:
+                headers = {"Authorization": f"Bearer {token}"} if token else {}
+                r = requests.get(input_str, stream=True, headers=headers, timeout=60)
+                r.raise_for_status()
+                with open(local_path, 'wb') as f:
+                    for chunk in r.iter_content(chunk_size=8192): f.write(chunk)
+                return local_path
+            except: pass
+        raise e
+
+def load_lora_to_memory(lora_path, precision_dtype=torch.bfloat16):
+    state_dict = load_file(lora_path, device="cpu")
+    pairs = {} 
+    alphas = {}
+    for k, v in state_dict.items():
+        stem = get_key_stem(k)
+        if "alpha" in k:
+            alphas[stem] = v.item() if isinstance(v, torch.Tensor) else v
+        else:
+            if stem not in pairs: pairs[stem] = {}
+            if "lora_down" in k or "lora_A" in k:
+                pairs[stem]["down"] = v.to(dtype=precision_dtype)
+                pairs[stem]["rank"] = v.shape[0]
+            elif "lora_up" in k or "lora_B" in k:
+                pairs[stem]["up"] = v.to(dtype=precision_dtype)
+    for stem in pairs:
+        pairs[stem]["alpha"] = alphas.get(stem, float(pairs[stem].get("rank", 1.0)))
+    return pairs
+
+def get_key_stem(key):
+    key = key.replace(".weight", "").replace(".bias", "")
+    key = key.replace(".lora_down", "").replace(".lora_up", "")
+    key = key.replace(".lora_A", "").replace(".lora_B", "")
+    key = key.replace(".alpha", "")
+    prefixes = [
+        "model.diffusion_model.", "diffusion_model.", "model.", 
+        "transformer.", "text_encoder.", "lora_unet_", "lora_te_", "base_model.model."
+    ]
+    changed = True
+    while changed:
+        changed = False
+        for p in prefixes:
+            if key.startswith(p):
+                key = key[len(p):]
+                changed = True
+    return key
+
+# =================================================================================
+# TABS 1-4 LOGIC (RESTORED)
+# =================================================================================
+
+class MemoryEfficientSafeOpen:
+    def __init__(self, filename):
+        self.filename = filename
+        self.file = open(filename, "rb")
+        self.header, self.header_size = self._read_header()
+    def __enter__(self): return self
+    def __exit__(self, exc_type, exc_val, exc_tb): self.file.close()
+    def keys(self) -> list[str]: return [k for k in self.header.keys() if k != "__metadata__"]
+    def metadata(self) -> Dict[str, str]: return self.header.get("__metadata__", {})
+    def get_tensor(self, key):
+        if key not in self.header: raise KeyError(f"Tensor '{key}' not found")
+        metadata = self.header[key]
+        start, end = metadata["data_offsets"]
+        self.file.seek(self.header_size + 8 + start)
+        return self._deserialize_tensor(self.file.read(end - start), metadata)
+    def _read_header(self):
+        header_size = struct.unpack("<Q", self.file.read(8))[0]
+        return json.loads(self.file.read(header_size).decode("utf-8")), header_size
+    def _deserialize_tensor(self, tensor_bytes, metadata):
+        dtype_map = {"F32": torch.float32, "F16": torch.float16, "BF16": torch.bfloat16, "I64": torch.int64, "I32": torch.int32, "I16": torch.int16, "I8": torch.int8, "U8": torch.uint8, "BOOL": torch.bool}
+        dtype = dtype_map[metadata["dtype"]]
+        shape = metadata["shape"]
+        return torch.frombuffer(tensor_bytes, dtype=torch.uint8).view(dtype).reshape(shape)
+
+class ShardBuffer:
+    def __init__(self, max_size_gb, output_dir, output_repo, subfolder, hf_token, filename_prefix="model"):
+        self.max_bytes = int(max_size_gb * 1024**3)
+        self.output_dir, self.output_repo, self.subfolder, self.hf_token, self.filename_prefix = output_dir, output_repo, subfolder, hf_token, filename_prefix
+        self.buffer, self.current_bytes, self.shard_count, self.index_map, self.total_size = [], 0, 0, {}, 0
+    def add_tensor(self, key, tensor):
+        if tensor.dtype == torch.bfloat16: raw, dt = tensor.view(torch.int16).numpy().tobytes(), "BF16"
+        elif tensor.dtype == torch.float16: raw, dt = tensor.numpy().tobytes(), "F16"
+        else: raw, dt = tensor.numpy().tobytes(), "F32"
+        self.buffer.append({"key": key, "data": raw, "dtype": dt, "shape": tensor.shape})
+        self.current_bytes += len(raw)
+        self.total_size += len(raw)
+        if self.current_bytes >= self.max_bytes: self.flush()
+    def flush(self):
+        if not self.buffer: return
+        self.shard_count += 1
+        fname = f"{self.filename_prefix}-{self.shard_count:05d}.safetensors"
+        header = {"__metadata__": {"format": "pt"}}
+        curr_off = 0
+        for i in self.buffer:
+            header[i["key"]] = {"dtype": i["dtype"], "shape": i["shape"], "data_offsets": [curr_off, curr_off + len(i["data"])]}
+            curr_off += len(i["data"])
+            self.index_map[i["key"]] = fname
+        out = self.output_dir / fname
+        header_json = json.dumps(header).encode('utf-8')
+        with open(out, 'wb') as f:
+            f.write(struct.pack('<Q', len(header_json)))
+            f.write(header_json)
+            for i in self.buffer: f.write(i["data"])
+        api.upload_file(path_or_fileobj=out, path_in_repo=f"{self.subfolder}/{fname}" if self.subfolder else fname, repo_id=self.output_repo, token=self.hf_token)
+        os.remove(out)
+        self.buffer, self.current_bytes = [], 0
+        gc.collect()
+
+def task_merge_legacy(hf_token, base, sub, lora, scale, prec, shard, out, struct_s, priv, progress=gr.Progress()):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    try: api.create_repo(repo_id=out, private=priv, exist_ok=True, token=hf_token)
+    except Exception as e: return f"Error: {e}"
+    if struct_s:
+        try:
+            files = api.list_repo_files(repo_id=struct_s, token=hf_token)
+            for f in tqdm(files, desc="Copying Structure"):
+                if sub and f.startswith(sub): continue 
+                if not sub and any(f.endswith(x) for x in ['.safetensors', '.bin', '.pt', '.pth']): continue
+                l = hf_hub_download(repo_id=struct_s, filename=f, token=hf_token, local_dir=TempDir)
+                api.upload_file(path_or_fileobj=l, path_in_repo=f, repo_id=out, token=hf_token)
+        except: pass
+    
+    files = [f for f in list_repo_files(repo_id=base, token=hf_token) if f.endswith(".safetensors")]
+    if sub: files = [f for f in files if f.startswith(sub)]
+    if not files: return "No safetensors found"
+    
+    prefix = "diffusion_pytorch_model" if (sub in ["transformer", "unet"] or "diffusion_pytorch_model" in os.path.basename(files[0])) else "model"
+    dtype = torch.bfloat16 if prec == "bf16" else torch.float16 if prec == "fp16" else torch.float32
+    try: lora_pairs = load_lora_to_memory(download_lora_smart(lora, hf_token), dtype)
+    except Exception as e: return f"LoRA Error: {e}"
+
+    buf = ShardBuffer(shard, TempDir, out, sub, hf_token, prefix)
+    for i, fpath in enumerate(files):
+        local = hf_hub_download(repo_id=base, filename=fpath, token=hf_token, local_dir=TempDir)
+        with MemoryEfficientSafeOpen(local) as f:
+            for k in f.keys():
+                v = f.get_tensor(k)
+                stem = get_key_stem(k)
+                match = lora_pairs.get(stem) or lora_pairs.get(stem.replace("to_q", "qkv")) or lora_pairs.get(stem.replace("to_k", "qkv")) or lora_pairs.get(stem.replace("to_v", "qkv"))
+                if match:
+                    d, u = match["down"], match["up"]
+                    s = scale * (match["alpha"] / match["rank"])
+                    if len(v.shape)==4 and len(d.shape)==2: d, u = d.unsqueeze(-1).unsqueeze(-1), u.unsqueeze(-1).unsqueeze(-1)
+                    delta = (up.squeeze() @ down.squeeze()).reshape(up.shape[0], down.shape[1], 1, 1) if len(up.shape)==4 else u @ d
+                    v = v.to(dtype).add_((delta * s).to(dtype))
+                buf.add_tensor(k, v.to(dtype))
+        os.remove(local)
+    buf.flush()
+    idx = {"metadata": {"total_size": buf.total_size}, "weight_map": buf.index_map}
+    idx_n = f"{prefix}.safetensors.index.json"
+    with open(TempDir/idx_n, "w") as f: json.dump(idx, f, indent=4)
+    api.upload_file(path_or_fileobj=TempDir/idx_n, path_in_repo=f"{sub}/{idx_n}" if sub else idx_n, repo_id=out, token=hf_token)
+    return "Done"
+
+def task_extract(hf_token, org, tun, rank, out):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    try:
+        p1 = download_lora_smart(org, hf_token)
+        p2 = download_lora_smart(tun, hf_token)
+        org_f, tun_f = MemoryEfficientSafeOpen(p1), MemoryEfficientSafeOpen(p2)
+        lora_sd = {}
+        common = set(org_f.keys()) & set(tun_f.keys())
+        for k in tqdm(common, desc="Extracting"):
+            if "num_batches_tracked" in k or "running_mean" in k or "running_var" in k: continue
+            m1, m2 = org_f.get_tensor(k).float(), tun_f.get_tensor(k).float()
+            if m1.shape != m2.shape: continue
+            diff = m2 - m1
+            if torch.max(torch.abs(diff)) < 1e-4: continue
+            out_d, in_d = diff.shape[0], diff.shape[1] if len(diff.shape) > 1 else 1
+            r = min(int(rank), in_d, out_d)
+            if len(diff.shape)==4: diff = diff.flatten(1)
+            elif len(diff.shape)==1: diff = diff.unsqueeze(1)
+            U, S, V = torch.svd_lowrank(diff, q=r+4, niter=4)
+            Vh = V.t()
+            U, S, Vh = U[:, :r], S[:r], Vh[:r, :]
+            U = U @ torch.diag(S)
+            dist = torch.cat([U.flatten(), Vh.flatten()])
+            hi_val = torch.quantile(torch.abs(dist), 0.99)
+            if hi_val > 0: U, Vh = U.clamp(-hi_val, hi_val), Vh.clamp(-hi_val, hi_val)
+            if len(m1.shape)==4:
+                U = U.reshape(out_d, r, 1, 1)
+                Vh = Vh.reshape(r, in_d, m1.shape[2], m1.shape[3])
+            else:
+                U, Vh = U.reshape(out_d, r), Vh.reshape(r, in_d)
+            stem = k.replace(".weight", "")
+            lora_sd[f"{stem}.lora_up.weight"] = U.contiguous()
+            lora_sd[f"{stem}.lora_down.weight"] = Vh.contiguous()
+            lora_sd[f"{stem}.alpha"] = torch.tensor(r).float()
+        out_f = TempDir/"extracted.safetensors"
+        save_file(lora_sd, out_f)
+        api.create_repo(repo_id=out, exist_ok=True, token=hf_token)
+        api.upload_file(path_or_fileobj=out_f, path_in_repo="extracted_lora.safetensors", repo_id=out, token=hf_token)
+        return "Done"
+    except Exception as e: return f"Error: {e}"
+
+def load_full_state_dict(path):
+    raw = load_file(path, device="cpu")
+    cleaned = {}
+    for k, v in raw.items():
+        if "lora_A" in k: new_k = k.replace("lora_A", "lora_down")
+        elif "lora_B" in k: new_k = k.replace("lora_B", "lora_up")
+        else: new_k = k
+        cleaned[new_k] = v.float()
+    return cleaned
+
+def task_merge_adapters_advanced(hf_token, inputs_text, method, weight_str, beta, sigma_rel, target_rank, out_repo, private):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    urls = [line.strip() for line in inputs_text.replace(" ", "\n").split('\n') if line.strip()]
+    if len(urls) < 2: return "Error: Provide at least 2 adapters."
+    try: weights = [float(w.strip()) for w in weight_str.split(',')] if weight_str.strip() else [1.0] * len(urls)
+    except: return "Error parsing weights."
+    if len(weights) < len(urls): weights += [1.0] * (len(urls) - len(weights))
+    
+    paths = []
+    for url in tqdm(urls, desc="Downloading"): paths.append(download_lora_smart(url, hf_token))
+    
+    merged = {}
+    if "Iterative EMA" in method:
+        base_sd = load_file(paths[0], device="cpu")
+        gamma = None
+        if sigma_rel > 0:
+            t_val = sigma_rel**-2
+            roots = np.roots([1, 7, 16 - t_val, 12 - t_val])
+            gamma = roots[np.isreal(roots) & (roots.real >= 0)].real.max()
+        for i, path in enumerate(paths[1:]):
+            current_beta = (1 - 1 / (i + 1)) ** (gamma + 1) if gamma is not None else beta
+            curr = load_file(path, device="cpu")
+            for k in base_sd:
+                if k in curr and "alpha" not in k:
+                    base_sd[k] = base_sd[k].float() * current_beta + curr[k].float() * (1 - current_beta)
+        merged = base_sd
+    else:
+        states = [load_full_state_dict(p) for p in paths]
+        all_stems = set()
+        for s in states:
+            for k in s: 
+                if "lora_" in k: all_stems.add(k.split(".lora_")[0])
+        for stem in tqdm(all_stems):
+            down_list, up_list = [], []
+            alpha_sum, total_delta = 0.0, None
+            for i, state in enumerate(states):
+                w = weights[i]
+                dk, uk, ak = f"{stem}.lora_down.weight", f"{stem}.lora_up.weight", f"{stem}.alpha"
+                if dk in state and uk in state:
+                    d, u = state[dk], state[uk]
+                    alpha_sum += state[ak].item() if ak in state else d.shape[0]
+                    if "Concatenation" in method:
+                        down_list.append(d); up_list.append(u * w)
+                    elif "SVD" in method:
+                        rank = d.shape[0]
+                        alpha = state[ak].item() if ak in state else rank
+                        scale = (alpha / rank) * w
+                        delta = ((u.flatten(1) @ d.flatten(1)).reshape(u.shape[0], d.shape[1], d.shape[2], d.shape[3]) if len(d.shape)==4 else u @ d) * scale
+                        total_delta = delta if total_delta is None else total_delta + delta
+            if "Concatenation" in method and down_list:
+                merged[f"{stem}.lora_down.weight"] = torch.cat(down_list, dim=0).contiguous()
+                merged[f"{stem}.lora_up.weight"] = torch.cat(up_list, dim=1).contiguous()
+                merged[f"{stem}.alpha"] = torch.tensor(alpha_sum)
+            elif "SVD" in method and total_delta is not None:
+                tr = int(target_rank)
+                flat = total_delta.flatten(1) if len(total_delta.shape)==4 else total_delta
+                try:
+                    U, S, V = torch.svd_lowrank(flat, q=tr + 4, niter=4)
+                    Vh = V.t()
+                    U, S, Vh = U[:, :tr], S[:tr], Vh[:tr, :]
+                    U = U @ torch.diag(S)
+                    if len(total_delta.shape) == 4:
+                        U = U.reshape(total_delta.shape[0], tr, 1, 1)
+                        Vh = Vh.reshape(tr, total_delta.shape[1], total_delta.shape[2], total_delta.shape[3])
+                    else:
+                        U, Vh = U.reshape(total_delta.shape[0], tr), Vh.reshape(tr, total_delta.shape[1])
+                    merged[f"{stem}.lora_down.weight"] = Vh.contiguous()
+                    merged[f"{stem}.lora_up.weight"] = U.contiguous()
+                    merged[f"{stem}.alpha"] = torch.tensor(tr).float()
+                except: pass
+    
+    out = TempDir / "merged_adapters.safetensors"
+    if merged: save_file(merged, out)
+    api.create_repo(repo_id=out_repo, private=private, exist_ok=True, token=hf_token)
+    api.upload_file(path_or_fileobj=out, path_in_repo="merged_adapters.safetensors", repo_id=out_repo, token=hf_token)
+    return f"Success! Merged to {out_repo}"
+
+def task_resize(hf_token, lora_input, new_rank, dynamic_method, dynamic_param, out_repo):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    path = download_lora_smart(lora_input, hf_token)
+    state = load_file(path, device="cpu")
+    new_state = {}
+    groups = {}
+    for k in state:
+        simple = k.split(".lora_")[0]
+        if simple not in groups: groups[simple] = {}
+        if "lora_down" in k or "lora_A" in k: groups[simple]["down"] = state[k]
+        if "lora_up" in k or "lora_B" in k: groups[simple]["up"] = state[k]
+        if "alpha" in k: groups[simple]["alpha"] = state[k]
+    
+    target_rank_limit = int(new_rank)
+    for stem, g in tqdm(groups.items()):
+        if "down" in g and "up" in g:
+            down, up = g["down"].float(), g["up"].float()
+            merged = (up.squeeze() @ down.squeeze()).reshape(up.shape[0], down.shape[1], down.shape[2], down.shape[3]) if len(down.shape)==4 else up @ down
+            flat = merged.flatten(1)
+            U, S, V = torch.svd_lowrank(flat, q=target_rank_limit + 32)
+            Vh = V.t()
+            calc_rank = target_rank_limit
+            if dynamic_method == "sv_ratio":
+                calc_rank = int(torch.sum(S > (S[0] / dynamic_param)).item())
+            elif dynamic_method == "sv_cumulative":
+                calc_rank = int(torch.searchsorted(torch.cumsum(S, 0) / torch.sum(S), dynamic_param)) + 1
+            elif dynamic_method == "sv_fro":
+                calc_rank = int(torch.searchsorted(torch.cumsum(S.pow(2), 0) / torch.sum(S.pow(2)), dynamic_param**2)) + 1
+            final_rank = max(1, min(calc_rank, target_rank_limit, S.shape[0]))
+            U = U[:, :final_rank] @ torch.diag(S[:final_rank])
+            Vh = Vh[:final_rank, :]
+            if len(down.shape) == 4:
+                U = U.reshape(up.shape[0], final_rank, 1, 1)
+                Vh = Vh.reshape(final_rank, down.shape[1], down.shape[2], down.shape[3])
+            new_state[f"{stem}.lora_down.weight"] = Vh.contiguous()
+            new_state[f"{stem}.lora_up.weight"] = U.contiguous()
+            new_state[f"{stem}.alpha"] = torch.tensor(final_rank).float()
+    out = TempDir / "shrunken.safetensors"
+    save_file(new_state, out)
+    api.create_repo(repo_id=out_repo, exist_ok=True, token=hf_token)
+    api.upload_file(path_or_fileobj=out, path_in_repo="shrunken.safetensors", repo_id=out_repo, token=hf_token)
+    return "Done"
+
+# =================================================================================
+# MERGEKIT & STREAMING LOGS (TABS 5-9)
+# =================================================================================
+
+def parse_weight(w_str):
+    if not w_str.strip(): return 1.0
+    try:
+        if "[" in w_str: return yaml.safe_load(w_str)
+        return float(w_str)
+    except: return 1.0
+
+def run_mergekit_logic(config_dict, token, out_repo, private, shard_size, output_precision, tokenizer_source, chat_template, program="mergekit-yaml"):
+    # Using generator for streaming logs directly to a Textbox, bypassing component issues
+    logs = []
+    def log(msg):
+        logs.append(msg)
+        return "\n".join(logs)
+
+    yield log("Starting MergeKit Process...")
+    
+    cleanup_temp()
+    
+    if chat_template and chat_template.strip():
+        config_dict["chat_template"] = chat_template.strip()
+    
+    # Validation
+    try:
+        if program != "mergekit-moe":
+            MergeConfiguration.model_validate(config_dict)
+            yield log("Configuration Validated Successfully.")
+    except Exception as e:
+        yield log(f"Invalid Config: {e}")
+        return
+
+    if token: 
+        login(token.strip())
+        os.environ["HF_TOKEN"] = token.strip()
+    
+    if "dtype" not in config_dict: config_dict["dtype"] = output_precision
+    if "tokenizer_source" not in config_dict and tokenizer_source != "base":
+        config_dict["tokenizer_source"] = tokenizer_source
+
+    config_path = TempDir / "config.yaml"
+    with open(config_path, "w") as f: yaml.dump(config_dict, f, sort_keys=False)
+    
+    yield log(f"Config saved to {config_path}")
+    yield log(f"YAML:\n{yaml.dump(config_dict, sort_keys=False)}")
+
+    try:
+        api.create_repo(repo_id=out_repo, private=private, exist_ok=True, token=token)
+        yield log(f"Repo {out_repo} ready.")
+    except Exception as e:
+        yield log(f"Repo Creation Error (might exist): {e}")
+
+    out_path = TempDir / "merge_output"
+    shard_arg = f"{int(float(shard_size) * 1024)}M"
+    
+    cmd = [
+        program, 
+        str(config_path), 
+        str(out_path), 
+        "--allow-crimes", 
+        "--copy-tokenizer", 
+        "--out-shard-size", shard_arg,
+        "--lazy-unpickle"
+    ]
+    
+    if torch.cuda.is_available():
+        cmd.extend(["--cuda", "--low-cpu-memory"])
+    
+    yield log(f"Executing: {' '.join(cmd)}")
+    
+    env = os.environ.copy()
+    env["HF_HOME"] = str(TempDir / ".cache")
+    
+    # Run process
+    process = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True)
+    
+    for line in iter(process.stdout.readline, ""):
+        yield log(line.strip())
+    
+    process.wait()
+    
+    if process.returncode != 0:
+        yield log("Merge failed with exit code " + str(process.returncode))
+        return
+
+    yield log(f"Uploading to {out_repo}...")
+    try:
+        api.upload_folder(repo_id=out_repo, folder_path=out_path)
+        yield log("Upload Complete!")
+    except Exception as e:
+        yield log(f"Upload failed: {e}")
+
+# --- UI Wrappers for Tabs 5-9 ---
+
+def wrapper_amphinterpolative(token, method, base, t, norm, i8, flat, row, eps, m_iter, tol, m1, w1, m2, w2, m3, w3, m4, w4, m5, w5, out, priv, shard, prec, tok_src, chat_t):
+    params = {"normalize": norm, "int8_mask": i8}
+    if method in ["slerp", "nuslerp"]: params["t"] = float(t)
+    if method == "nuslerp": params.update({"flatten": flat, "row_wise": row})
+    if method == "multislerp": params["eps"] = float(eps)
+    if method == "karcher": params.update({"max_iter": int(m_iter), "tol": float(tol)})
+    
+    config = {"merge_method": method}
+    
+    if method in ["slerp", "nuslerp"]:
+        if not base.strip(): yield "Error: Base model required"; return
+        config["base_model"] = base.strip()
+        sources = [{"model": m, "parameters": {"weight": parse_weight(w)}} for m, w in [(m1,w1), (m2,w2)] if m.strip()]
+        config["slices"] = [{"sources": sources, "parameters": params}]
+    else:
+        if base.strip() and method == "multislerp": config["base_model"] = base.strip()
+        models = [{"model": m, "parameters": {"weight": parse_weight(w)}} for m, w in [(m1,w1), (m2,w2), (m3,w3), (m4,w4), (m5,w5)] if m.strip()]
+        config["models"] = models
+        config["parameters"] = params
+
+    yield from run_mergekit_logic(config, token, out, priv, shard, prec, tok_src, chat_t, program="mergekit-yaml")
+
+def wrapper_stirtie(token, method, base, norm, i8, lamb, resc, topk, m1, w1, d1, g1, e1, m2, w2, d2, g2, e2, m3, w3, d3, g3, e3, m4, w4, d4, g4, e4, out, priv, shard, prec, tok_src, chat_t):
+    models = []
+    # Explicit loop over the 4 sets of model inputs
+    for m, w, d, g, e in [
+        (m1, w1, d1, g1, e1),
+        (m2, w2, d2, g2, e2),
+        (m3, w3, d3, g3, e3),
+        (m4, w4, d4, g4, e4)
+    ]:
+        if not m.strip(): continue
+        p = {"weight": parse_weight(w)}
+        if method in ["ties", "dare_ties", "dare_linear", "breadcrumbs_ties"]: p["density"] = parse_weight(d)
+        if "breadcrumbs" in method: p["gamma"] = float(g)
+        if "della" in method: p["epsilon"] = float(e)
+        models.append({"model": m, "parameters": p})
+    
+    g_params = {"normalize": norm, "int8_mask": i8}
+    if method != "sce": g_params["lambda"] = float(lamb)
+    if method == "dare_linear": g_params["rescale"] = resc
+    if method == "sce": g_params["select_topk"] = float(topk)
+
+    config = {
+        "merge_method": method,
+        "base_model": base.strip() if base.strip() else models[0]["model"],
+        "parameters": g_params,
+        "models": models
+    }
+    yield from run_mergekit_logic(config, token, out, priv, shard, prec, tok_src, chat_t, program="mergekit-yaml")
+
+def wrapper_specious(token, method, base, norm, i8, t, filt_w, m1, w1, f1, m2, w2, m3, w3, m4, w4, m5, w5, out, priv, shard, prec, tok_src, chat_t):
+    models = []
+    if method == "passthrough":
+        p = {"weight": parse_weight(w1)}
+        if f1.strip(): p["filter"] = f1.strip()
+        models.append({"model": m1, "parameters": p})
+    else:
+        models = [{"model": m, "parameters": {"weight": parse_weight(w)}} for m, w in [(m1,w1), (m2,w2), (m3,w3), (m4,w4), (m5,w5)] if m.strip()]
+
+    config = {"merge_method": method, "parameters": {"normalize": norm, "int8_mask": i8}}
+    if base.strip(): config["base_model"] = base.strip()
+    if method == "nearswap": config["parameters"]["t"] = float(t)
+    if method == "model_stock": config["parameters"]["filter_wise"] = filt_w
+    config["models"] = models
+
+    yield from run_mergekit_logic(config, token, out, priv, shard, prec, tok_src, chat_t, program="mergekit-yaml")
+
+def wrapper_moer(token, base, experts, gate, dtype, out, priv, shard, prec, tok_src, chat_t):
+    formatted = [{"source_model": e.strip(), "positive_prompts": ["chat", "assist"]} for e in experts.split('\n') if e.strip()]
+    config = {
+        "base_model": base.strip() if base.strip() else formatted[0]["source_model"],
+        "gate_mode": gate,
+        "dtype": dtype, 
+        "experts": formatted
+    }
+    # Uses mergekit-moe CLI
+    yield from run_mergekit_logic(config, token, out, priv, shard, prec, tok_src, chat_t, program="mergekit-moe")
+
+def wrapper_rawer(token, models, method, dtype, out, priv, shard, prec, tok_src, chat_t):
+    models_list = [{"model": m.strip(), "parameters": {"weight": 1.0}} for m in models.split('\n') if m.strip()]
+    config = {
+        "models": models_list,
+        "merge_method": method,
+        "dtype": dtype
+    }
+    yield from run_mergekit_logic(config, token, out, priv, shard, prec, tok_src, chat_t, program="mergekit-yaml")
+
+# --- TAB 10 (Custom DARE) Logic ---
+def task_dare_custom(token, base, ft, ratio, mask, out, priv):
+    cleanup_temp()
+    if token: login(token.strip())
+    try:
+        b_path = download_lora_smart(base, token) 
+        f_path = download_lora_smart(ft, token)
+        b_sd = load_file(b_path, device="cpu")
+        f_sd = load_file(f_path, device="cpu")
+        merged = {}
+        common = set(b_sd.keys()) & set(f_sd.keys())
+        for k in tqdm(common, desc="Merging"):
+            tb, tf = b_sd[k], f_sd[k]
+            if tb.shape != tf.shape: 
+                merged[k] = tf
+                continue
+            delta = tf.float() - tb.float()
+            if mask > 0:
+                m = torch.bernoulli(torch.full_like(delta, 1.0 - mask))
+                delta = (delta * m) / (1.0 - mask)
+            merged[k] = (tb.float() + ratio * delta).to(tb.dtype)
+        
+        out_f = TempDir / "model.safetensors"
+        save_file(merged, out_f)
+        api.create_repo(repo_id=out, private=priv, exist_ok=True, token=token)
+        api.upload_file(path_or_fileobj=out_f, path_in_repo="model.safetensors", repo_id=out, token=token)
+        return f"Done! {out}"
+    except Exception as e: return str(e)
+
+# =================================================================================
+# UI GENERATION
+# =================================================================================
+
+css = ".container { max-width: 1100px; margin: auto; }"
+
+with gr.Blocks() as demo:
+    gr.HTML("""<h1><img src="https://huggingface.co/spaces/AlekseyCalvin/Soon_Merger/resolve/main/SMerger3.png" alt="SOONmerge®"> Transform Transformers for FREE!</h1>""")
+    gr.Markdown("# 🧰Training-Free CPU-run Model Creation Toolkit")
+    
+    with gr.Tabs():
+        # --- TAB 1: RESTORED ---
+        with gr.Tab("Merge to Base Model + Reshard Output"):
+            t1_token = gr.Textbox(label="Token", type="password")
+            t1_base = gr.Textbox(label="Base Repo", value="name/repo")
+            t1_sub = gr.Textbox(label="Subfolder (Optional)", value="")
+            t1_lora = gr.Textbox(label="LoRA Direct Link or Repo", value="https://huggingface.co/GuangyuanSD/Z-Image-Re-Turbo-LoRA/resolve/main/Z-image_re_turbo_lora_8steps_rank_32_v1_fp16.safetensors")
+            with gr.Row():
+                t1_scale = gr.Slider(label="Scale", value=1.0, minimum=0, maximum=3.0, step=0.1)
+                t1_prec = gr.Radio(["bf16", "fp16", "float32"], value="bf16", label="Precision")
+                t1_shard = gr.Slider(label="Max Shard Size (GB)", value=2.0, minimum=0.1, maximum=10.0, step=0.1)
+            t1_out = gr.Textbox(label="Output Repo")
+            t1_struct = gr.Textbox(label="Extras Source (copies configs/components/etc)", value="name/repo")
+            t1_priv = gr.Checkbox(label="Private", value=True)
+            gr.Button("Merge").click(task_merge_legacy, [t1_token, t1_base, t1_sub, t1_lora, t1_scale, t1_prec, t1_shard, t1_out, t1_struct, t1_priv], gr.Textbox(label="Result"))
+
+        # --- TAB 2: RESTORED ---
+        with gr.Tab("Extract Adapter"):
+            t2_token = gr.Textbox(label="Token", type="password")
+            t2_org = gr.Textbox(label="Original Model")
+            t2_tun = gr.Textbox(label="Tuned or Homologous Model")
+            t2_rank = gr.Number(label="Extract At Rank", value=32, minimum=1, maximum=1024, step=1)
+            t2_out = gr.Textbox(label="Output Repo")
+            gr.Button("Extract").click(task_extract, [t2_token, t2_org, t2_tun, t2_rank, t2_out], gr.Textbox(label="Result"))
+
+        # --- TAB 3: RESTORED ---
+        with gr.Tab("Merge Adapters"):
+            gr.Markdown("### Batch Adapter Merging")
+            t3_token = gr.Textbox(label="Token", type="password")
+            t3_urls = gr.TextArea(label="Adapter URLs/Repos (one per line, or space-separated)")
+            t3_method = gr.Dropdown(["Iterative EMA (Linear w/ Beta/Sigma coefficient)", "Concatenation (MOE-like weights-stack)", "SVD Fusion (Task Arithmetic/Compressed)"], value="Iterative EMA (Linear w/ Beta/Sigma coefficient)", label="Merge Method")
+            with gr.Row():
+                t3_weights = gr.Textbox(label="Weights (comma-separated) – for Concat/SVD")
+                t3_rank = gr.Number(label="Target Rank – For SVD only", value=128)
+            with gr.Row():
+                t3_beta = gr.Slider(label="Beta – for linear/post-hoc EMA", value=0.95, minimum=0.01, maximum=1.00)
+                t3_sigma = gr.Slider(label="Sigma Rel – for linear/post-hoc EMA", value=0.21, minimum=0.01, maximum=1.00)
+            t3_out = gr.Textbox(label="Output Repo")
+            t3_priv = gr.Checkbox(label="Private Output", value=True)
+            gr.Button("Merge").click(task_merge_adapters_advanced, [t3_token, t3_urls, t3_method, t3_weights, t3_beta, t3_sigma, t3_rank, t3_out, t3_priv], gr.Textbox(label="Result"))
+
+        # --- TAB 4: RESTORED ---
+        with gr.Tab("Resize Adapter"):
+            t4_token = gr.Textbox(label="Token", type="password")
+            t4_in = gr.Textbox(label="LoRA")
+            with gr.Row():
+                t4_rank = gr.Number(label="To Rank (Safety Ceiling)", value=8)
+                t4_method = gr.Dropdown(["None", "sv_ratio", "sv_fro", "sv_cumulative"], value="None", label="Dynamic Method")
+                t4_param = gr.Number(label="Dynamic Param", value=0.9)
+            gr.Markdown("### 📉 Dynamic Resizing Guide\nThese methods intelligently determine the best rank per layer.\n- **sv_ratio (Relative Strength):** Keeps features that are at least `1/Param` as strong as the main feature. **Param must be >= 2**.\n- **sv_fro (Visual Information Density):** Preserves `Param%` of total information content. **Param between 0.0 and 1.0**.\n- **sv_cumulative (Cumulative Sum):** Preserves weights that sum up to `Param%` of total strength. **Param between 0.0 and 1.0**.\n- **⚠️ Safety Ceiling:** The **'To Rank'** slider acts as a hard limit.")
+            t4_out = gr.Textbox(label="Output")
+            gr.Button("Resize").click(task_resize, [t4_token, t4_in, t4_rank, t4_method, t4_param, t4_out], gr.Textbox(label="Result"))
+
+        # --- TAB 5 ---
+        with gr.Tab("Amphinterpolative"):
+            gr.Markdown("### Spherical Interpolation Family")
+            t5_token = gr.Textbox(label="HF Token", type="password")
+            t5_method = gr.Dropdown(["slerp", "nuslerp", "multislerp", "karcher"], value="slerp", label="Method")
+            with gr.Row():
+                t5_shard = gr.Slider(label="Max Shard Size (GB)", value=5.0, minimum=1.0, maximum=20.0)
+                t5_prec = gr.Dropdown(["float16", "bfloat16", "float32"], value="bfloat16", label="Output Precision")
+                t5_tok = gr.Dropdown(["base", "union", "model:path"], value="base", label="Tokenizer Source")
+                t5_chat = gr.Textbox(label="Chat Template (write-in, default: auto)", placeholder="auto")
+            with gr.Row():
+                t5_base = gr.Textbox(label="Base Model")
+                t5_t = gr.Slider(0, 1, 0.5, label="t")
+            with gr.Row():
+                t5_norm = gr.Checkbox(label="Normalize", value=True); t5_i8 = gr.Checkbox(label="Int8 Mask", value=False); t5_flat = gr.Checkbox(label="NuSlerp Flatten", value=False); t5_row = gr.Checkbox(label="NuSlerp Row Wise", value=False)
+            with gr.Row():
+                t5_eps = gr.Textbox(label="eps", value="1e-8"); t5_iter = gr.Number(label="max_iter", value=10); t5_tol = gr.Textbox(label="tol", value="1e-5")
+            m1, w1 = gr.Textbox(label="Model 1"), gr.Textbox(label="Weight 1", value="1.0"); m2, w2 = gr.Textbox(label="Model 2"), gr.Textbox(label="Weight 2", value="1.0")
+            with gr.Accordion("More", open=False):
+                m3, w3 = gr.Textbox(label="Model 3"), gr.Textbox(label="Weight 3", value="1.0"); m4, w4 = gr.Textbox(label="Model 4"), gr.Textbox(label="Weight 4", value="1.0"); m5, w5 = gr.Textbox(label="Model 5"), gr.Textbox(label="Weight 5", value="1.0")
+            t5_out = gr.Textbox(label="Output Repo"); t5_priv = gr.Checkbox(label="Private", value=True)
+            t5_btn = gr.Button("Execute")
+            t5_res = gr.Textbox(label="Result", lines=10)
+            t5_btn.click(wrapper_amphinterpolative, [t5_token, t5_method, t5_base, t5_t, t5_norm, t5_i8, t5_flat, t5_row, t5_eps, t5_iter, t5_tol, m1, w1, m2, w2, m3, w3, m4, w4, m5, w5, t5_out, t5_priv, t5_shard, t5_prec, t5_tok, t5_chat], t5_res)
+
+        # --- TAB 6 ---
+        with gr.Tab("Stir/Tie Bases"):
+            gr.Markdown("### Task Vector Family")
+            t6_token = gr.Textbox(label="Token", type="password")
+            t6_method = gr.Dropdown(["task_arithmetic", "ties", "dare_ties", "dare_linear", "della", "della_linear", "breadcrumbs", "breadcrumbs_ties", "sce"], value="ties", label="Method")
+            with gr.Row():
+                t6_shard = gr.Slider(label="Max Shard Size (GB)", value=5.0, minimum=1.0, maximum=20.0); t6_prec = gr.Dropdown(["float16", "bfloat16", "float32"], value="bfloat16", label="Output Precision"); t6_tok = gr.Dropdown(["base", "union", "model:path"], value="base", label="Tokenizer Source"); t6_chat = gr.Textbox(label="Chat Template", placeholder="auto")
+            t6_base = gr.Textbox(label="Base Model")
+            with gr.Row():
+                t6_norm = gr.Checkbox(label="Normalize", value=True); t6_i8 = gr.Checkbox(label="Int8 Mask", value=False); t6_resc = gr.Checkbox(label="Rescale", value=True); t6_lamb = gr.Number(label="Lambda", value=1.0); t6_topk = gr.Slider(0, 1, 1.0, label="Select TopK")
+            m1_6, w1_6 = gr.Textbox(label="Model 1"), gr.Textbox(label="Weight 1", value="1.0"); d1_6, g1_6, e1_6 = gr.Textbox(label="Density", value="1.0"), gr.Number(label="Gamma", value=0.01), gr.Number(label="Epsilon", value=0.15)
+            with gr.Accordion("More", open=False):
+                m2_6, w2_6 = gr.Textbox(label="Model 2"), gr.Textbox(label="Weight 2", value="1.0"); d2_6, g2_6, e2_6 = gr.Textbox(label="Density", value="1.0"), gr.Number(label="Gamma", value=0.01), gr.Number(label="Epsilon", value=0.15)
+                m3_6, w3_6 = gr.Textbox(label="Model 3"), gr.Textbox(label="Weight 3", value="1.0"); d3_6, g3_6, e3_6 = gr.Textbox(label="Density", value="1.0"), gr.Number(label="Gamma", value=0.01), gr.Number(label="Epsilon", value=0.15)
+                m4_6, w4_6 = gr.Textbox(label="Model 4"), gr.Textbox(label="Weight 4", value="1.0"); d4_6, g4_6, e4_6 = gr.Textbox(label="Density", value="1.0"), gr.Number(label="Gamma", value=0.01), gr.Number(label="Epsilon", value=0.15)
+            t6_out = gr.Textbox(label="Output Repo"); t6_priv = gr.Checkbox(label="Private", value=True)
+            t6_btn = gr.Button("Execute")
+            t6_res = gr.Textbox(label="Result", lines=10)
+            t6_btn.click(wrapper_stirtie, [t6_token, t6_method, t6_base, t6_norm, t6_i8, t6_lamb, t6_resc, t6_topk, m1_6, w1_6, d1_6, g1_6, e1_6, m2_6, w2_6, d2_6, g2_6, e2_6, m3_6, w3_6, d3_6, g3_6, e3_6, m4_6, w4_6, d4_6, g4_6, e4_6, t6_out, t6_priv, t6_shard, t6_prec, t6_tok, t6_chat], t6_res)
+
+        # --- TAB 7 ---
+        with gr.Tab("Specious"):
+            gr.Markdown("### Specialized Methods")
+            t7_token = gr.Textbox(label="Token", type="password")
+            t7_method = gr.Dropdown(["model_stock", "nearswap", "arcee_fusion", "passthrough", "linear"], value="model_stock", label="Method")
+            with gr.Row():
+                t7_shard = gr.Slider(label="Max Shard Size (GB)", value=5.0, minimum=1.0, maximum=20.0); t7_prec = gr.Dropdown(["float16", "bfloat16", "float32"], value="bfloat16", label="Output Precision"); t7_tok = gr.Dropdown(["base", "union", "model:path"], value="base", label="Tokenizer Source"); t7_chat = gr.Textbox(label="Chat Template", placeholder="auto")
+            t7_base = gr.Textbox(label="Base Model")
+            with gr.Row():
+                t7_norm = gr.Checkbox(label="Normalize", value=True); t7_i8 = gr.Checkbox(label="Int8 Mask", value=False); t7_t = gr.Slider(0, 1, 0.5, label="t"); t7_filt_w = gr.Checkbox(label="Filter Wise", value=False)
+            m1_7, w1_7, f1_7 = gr.Textbox(label="Model 1"), gr.Textbox(label="Weight 1", value="1.0"), gr.Textbox(label="Filter (Passthrough)")
+            m2_7, w2_7 = gr.Textbox(label="Model 2"), gr.Textbox(label="Weight 2", value="1.0")
+            with gr.Accordion("More", open=False):
+                m3_7, w3_7 = gr.Textbox(label="Model 3"), gr.Textbox(label="Weight 3", value="1.0"); m4_7, w4_7 = gr.Textbox(label="Model 4"), gr.Textbox(label="Weight 4", value="1.0"); m5_7, w5_7 = gr.Textbox(label="Model 5"), gr.Textbox(label="Weight 5", value="1.0")
+            t7_out = gr.Textbox(label="Output Repo"); t7_priv = gr.Checkbox(label="Private", value=True)
+            t7_btn = gr.Button("Execute")
+            t7_res = gr.Textbox(label="Result", lines=10)
+            t7_btn.click(wrapper_specious, [t7_token, t7_method, t7_base, t7_norm, t7_i8, t7_t, t7_filt_w, m1_7, w1_7, f1_7, m2_7, w2_7, m3_7, w3_7, m4_7, w4_7, m5_7, w5_7, t7_out, t7_priv, t7_shard, t7_prec, t7_tok, t7_chat], t7_res)
+
+        # --- TAB 8 (MoEr) ---
+        with gr.Tab("MoEr"):
+            gr.Markdown("### Mixture of Experts")
+            t8_token = gr.Textbox(label="Token", type="password")
+            with gr.Row():
+                t8_shard = gr.Slider(label="Max Shard Size (GB)", value=5.0, minimum=1.0, maximum=20.0); t8_prec = gr.Dropdown(["float16", "bfloat16", "float32"], value="bfloat16", label="Output Precision"); t8_tok = gr.Dropdown(["base", "union", "model:path"], value="base", label="Tokenizer Source"); t8_chat = gr.Textbox(label="Chat Template", placeholder="auto")
+            t8_base = gr.Textbox(label="Base Model"); t8_experts = gr.TextArea(label="Experts List"); t8_gate = gr.Dropdown(["cheap_embed", "random", "hidden"], value="cheap_embed", label="Gate Mode"); t8_dtype = gr.Dropdown(["float16", "bfloat16"], value="bfloat16", label="Internal Dtype")
+            t8_out = gr.Textbox(label="Output Repo"); t8_priv = gr.Checkbox(label="Private", value=True)
+            t8_btn = gr.Button("Build MoE")
+            t8_res = gr.Textbox(label="Result", lines=10)
+            t8_btn.click(wrapper_moer, [t8_token, t8_base, t8_experts, t8_gate, t8_dtype, t8_out, t8_priv, t8_shard, t8_prec, t8_tok, t8_chat], t8_res)
+
+        # --- TAB 9 (Rawer) ---
+        with gr.Tab("Rawer"):
+            gr.Markdown("### Raw PyTorch / Non-Transformer")
+            t9_token = gr.Textbox(label="Token", type="password"); t9_models = gr.TextArea(label="Models (one per line)")
+            with gr.Row():
+                t9_shard = gr.Slider(label="Max Shard Size (GB)", value=5.0, minimum=1.0, maximum=20.0); t9_prec = gr.Dropdown(["float16", "bfloat16", "float32"], value="bfloat16", label="Output Precision"); t9_tok = gr.Dropdown(["base", "union", "model:path"], value="base", label="Tokenizer Source"); t9_chat = gr.Textbox(label="Chat Template", placeholder="auto")
+            t9_method = gr.Dropdown(["linear", "passthrough"], value="linear", label="Method"); t9_dtype = gr.Dropdown(["float32", "float16", "bfloat16"], value="float32", label="Config Dtype")
+            t9_out = gr.Textbox(label="Output Repo"); t9_priv = gr.Checkbox(label="Private", value=True)
+            t9_btn = gr.Button("Merge Raw")
+            t9_res = gr.Textbox(label="Result", lines=10)
+            t9_btn.click(wrapper_rawer, [t9_token, t9_models, t9_method, t9_dtype, t9_out, t9_priv, t9_shard, t9_prec, t9_tok, t9_chat], t9_res)
+
+        # --- TAB 10 ---
+        with gr.Tab("Mario,DARE!"):
+            t10_token = gr.Textbox(label="Token", type="password")
+            with gr.Row():
+                t10_base = gr.Textbox(label="Base Model"); t10_ft = gr.Textbox(label="Fine-Tuned Model")
+            with gr.Row():
+                t10_ratio = gr.Slider(0, 5, 1.0, label="Ratio"); t10_mask = gr.Slider(0, 0.99, 0.5, label="Mask Rate")
+            t10_out = gr.Textbox(label="Output Repo"); t10_priv = gr.Checkbox(label="Private", value=True)
+            gr.Button("Run").click(task_dare_custom, [t10_token, t10_base, t10_ft, t10_ratio, t10_mask, t10_out, t10_priv], gr.Textbox(label="Result"))
+
+if __name__ == "__main__":
+    demo.queue().launch(css=css, ssr_mode=False)