from __future__ import annotations

import base64
import imghdr
import io
import json
import logging
import os
import random
import re
import shutil
import stat
import tempfile
import zipfile
from collections import Counter
from concurrent.futures import ThreadPoolExecutor, as_completed
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Tuple

import gradio as gr
import numpy as np
import pandas as pd
import yaml
from PIL import Image

# Optional heavy deps -------------------------------------------------------
try:
    import cv2
except ImportError:
    cv2 = None
try:
    import imagehash
except ImportError:
    imagehash = None
try:
    import fastdup
except ImportError:
    fastdup = None
try:
    from ultralytics import YOLO
except ImportError:
    YOLO = None
try:
    from roboflow import Roboflow
except ImportError:
    Roboflow = None
try:
    from cleanlab.pruning import get_noise_indices
except ImportError:
    get_noise_indices = None

# ───────────────── Config & Constants ───────────────────────────────────────
TMP_ROOT = Path(tempfile.gettempdir()) / "rf_datasets"
TMP_ROOT.mkdir(parents=True, exist_ok=True)
CPU_COUNT    = int(os.getenv("QC_CPU",      1))
BATCH_SIZE   = int(os.getenv("QC_BATCH",    4))
SAMPLE_LIMIT = int(os.getenv("QC_SAMPLE", 200))

DEFAULT_W = {
    "Integrity":     0.25,
    "Class balance": 0.10,
    "Image quality": 0.15,
    "Duplicates":    0.10,
    "Model QA":      0.30,
    "Label issues":  0.10,
}

logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s")

_model_cache: dict[str, YOLO] = {}
autoinc = 0  # helper for tmp‑dir names

# ────────────────────────────────────────────────────────────────────────────
#  Data‑class & basic helpers
# ────────────────────────────────────────────────────────────────────────────
@dataclass
class QCConfig:
    blur_thr:    float
    iou_thr:     float
    conf_thr:    float
    weights:     str | None
    cpu_count:   int = CPU_COUNT
    batch_size:  int = BATCH_SIZE
    sample_limit:int = SAMPLE_LIMIT

def load_yaml(path: Path) -> Dict:
    with path.open('r', encoding='utf-8') as f:
        return yaml.safe_load(f)

def load_class_names(yaml_path: Path) -> List[str]:
    data = load_yaml(yaml_path)
    names = data.get("names", [])
    if isinstance(names, dict):
        return [names[k] for k in sorted(names, key=lambda x: int(x))]
    return list(names)

def parse_label_file(path: Path) -> list[tuple[int, float, float, float, float]]:
    if not path or not path.exists() or path.stat().st_size == 0:
        return []
    try:
        arr = np.loadtxt(path, dtype=float)
        if arr.ndim == 1:
            arr = arr.reshape(1, -1)
        return [tuple(row) for row in arr]
    except Exception:
        return []

def guess_image_dirs(root: Path) -> List[Path]:
    candidates = [
        root/'images',
        root/'train'/'images',
        root/'valid'/'images',
        root/'val'  /'images',
        root/'test' /'images',
    ]
    return [d for d in candidates if d.exists()]

def gather_dataset(root: Path, yaml_path: Path | None):
    if yaml_path is None:
        yamls = list(root.glob('*.yaml'))
        if not yamls:
            raise FileNotFoundError("Dataset YAML not found")
        yaml_path = yamls[0]
    meta = load_yaml(yaml_path)
    img_dirs = guess_image_dirs(root)
    if not img_dirs:
        raise FileNotFoundError("images/ directory missing")
    imgs = [p for d in img_dirs for p in d.rglob('*.*') if imghdr.what(p)]
    labels_roots = {d.parent/'labels' for d in img_dirs}
    lbls = [
        next((lr/f"{p.stem}.txt" for lr in labels_roots if (lr/f"{p.stem}.txt").exists()), None)
        for p in imgs
    ]
    return imgs, lbls, meta

def get_model(weights: str) -> YOLO | None:
    if not weights or YOLO is None:
        return None
    if weights not in _model_cache:
        _model_cache[weights] = YOLO(weights)
    return _model_cache[weights]

# ───────── Concurrency helpers & QC functions ───────────────────────────────
def _quality_stat_args(args: Tuple[Path, float]) -> Tuple[Path, bool, bool, bool]:
    path, thr = args
    if cv2 is None:
        return path, False, False, False
    im = cv2.imread(str(path))
    if im is None:
        return path, False, False, False
    gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
    lap = cv2.Laplacian(gray, cv2.CV_64F).var()
    mean = gray.mean()
    return path, lap < thr, mean < 25, mean > 230

def _is_corrupt(path: Path) -> bool:
    try:
        with Image.open(path) as im:
            im.verify()
        return False
    except Exception:
        return True

def qc_integrity(imgs: List[Path], lbls: List[Path], cfg: QCConfig) -> Dict:
    missing = [i for i, l in zip(imgs, lbls) if l is None]
    corrupt = []
    sample = imgs[:cfg.sample_limit]
    with ThreadPoolExecutor(max_workers=cfg.cpu_count) as ex:
        fut = {ex.submit(_is_corrupt, p): p for p in sample}
        for f in as_completed(fut):
            if f.result():
                corrupt.append(fut[f])
    score = 100 - (len(missing) + len(corrupt)) / max(len(imgs), 1) * 100
    return {
        "name": "Integrity",
        "score": max(score, 0),
        "details": {
            "missing_label_files": [str(p) for p in missing],
            "corrupt_images": [str(p) for p in corrupt],
        }
    }

def qc_class_balance(lbls: List[Path], cfg: QCConfig) -> Dict:
    counts, boxes = Counter(), []
    for l in lbls[:cfg.sample_limit]:
        bs = parse_label_file(l) if l else []
        boxes.append(len(bs))
        counts.update(int(b[0]) for b in bs)
    if not counts:
        return {"name": "Class balance", "score": 0, "details": "No labels"}
    bal = min(counts.values()) / max(counts.values()) * 100
    return {
        "name": "Class balance",
        "score": bal,
        "details": {
            "class_counts": dict(counts),
            "boxes_per_image": {
                "min": min(boxes),
                "max": max(boxes),
                "mean": float(np.mean(boxes))
            }
        }
    }

def qc_image_quality(imgs: List[Path], cfg: QCConfig) -> Dict:
    if cv2 is None:
        return {"name": "Image quality", "score": 100, "details": "cv2 missing"}
    blurry, dark, bright = [], [], []
    sample = imgs[:cfg.sample_limit]
    with ThreadPoolExecutor(max_workers=cfg.cpu_count) as ex:
        args = [(p, cfg.blur_thr) for p in sample]
        for p, isb, isd, isB in ex.map(_quality_stat_args, args):
            if isb: blurry.append(p)
            if isd: dark.append(p)
            if isB: bright.append(p)
    bad = len({*blurry, *dark, *bright})
    score = 100 - bad / max(len(sample), 1) * 100
    return {
        "name": "Image quality",
        "score": score,
        "details": {
            "blurry": [str(p) for p in blurry],
            "dark": [str(p) for p in dark],
            "bright": [str(p) for p in bright]
        }
    }

def qc_duplicates(imgs: List[Path], cfg: QCConfig) -> Dict:
    if fastdup is not None and len(imgs) > 50:
        try:
            fd = fastdup.create(
                input_dir=str(Path(imgs[0]).parent.parent),
                work_dir=str(TMP_ROOT / "fastdup")
            )
            fd.run()
            try:
                cc = fd.connected_components_grouped(sort_by="comp_size", ascending=False)
                clusters = cc["files"].tolist() if "files" in cc.columns else cc.groupby("component")["filename"].apply(list).tolist()
            except Exception:
                clusters = fd.connected_components()
            dup = sum(len(c) - 1 for c in clusters)
            score = max(0.0, 100 - dup / len(imgs) * 100)
            return {"name": "Duplicates", "score": score, "details": {"groups": clusters[:50]}}
        except Exception as e:
            return {"name": "Duplicates", "score": 100.0, "details": {"fastdup_error": str(e)}}
    return {"name": "Duplicates", "score": 100.0, "details": {"note": "skipped"}}

def _rel_iou(b1, b2):
    x1, y1, w1, h1 = b1
    x2, y2, w2, h2 = b2
    xa1, ya1 = x1 - w1/2, y1 - h1/2
    xa2, ya2 = x1 + w1/2, y1 + h1/2
    xb1, yb1 = x2 - w2/2, y2 - h2/2
    xb2, yb2 = x2 + w2/2, y2 + h2/2
    ix1 = max(xa1, xb1); iy1 = max(ya1, yb1)
    ix2 = min(xa2, xb2); iy2 = min(ya2, yb2)
    inter = max(ix2 - ix1, 0) * max(iy2 - iy1, 0)
    union = w1*h1 + w2*h2 - inter
    return inter/union if union else 0.0

def qc_model_qa(imgs: List[Path], lbls: List[Path], cfg: QCConfig) -> Dict:
    model = get_model(cfg.weights)
    if model is None:
        return {"name": "Model QA", "score": 100, "details": "skipped"}
    ious, mism = [], []
    sample = imgs[:cfg.sample_limit]
    for i in range(0, len(sample), cfg.batch_size):
        batch = sample[i:i+cfg.batch_size]
        results = model.predict(batch, verbose=False, half=True, dynamic=True)
        for p, res in zip(batch, results):
            gt = parse_label_file(Path(p).parent.parent/'labels'/f"{Path(p).stem}.txt")
            for cls, x, y, w, h in gt:
                best = 0.0
                for b, c, conf in zip(
                    res.boxes.xywh.cpu().numpy(),
                    res.boxes.cls.cpu().numpy(),
                    res.boxes.conf.cpu().numpy()
                ):
                    if conf < cfg.conf_thr or int(c) != cls:
                        continue
                    best = max(best, _rel_iou((x, y, w, h), tuple(b)))
                ious.append(best)
                if best < cfg.iou_thr:
                    mism.append(str(p))
    miou = float(np.mean(ious)) if ious else 1.0
    return {"name": "Model QA", "score": miou*100, "details": {"mean_iou": miou, "mismatches": mism[:50]}}

def qc_label_issues(imgs: List[Path], lbls: List[Path], cfg: QCConfig) -> Dict:
    if get_noise_indices is None:
        return {"name": "Label issues", "score": 100, "details": "skipped"}
    labels, idxs = [], []
    sample = imgs[:cfg.sample_limit]
    for i, p in enumerate(sample):
        bs = parse_label_file(lbls[i]) if lbls[i] else []
        for cls, *_ in bs:
            labels.append(int(cls)); idxs.append(i)
    if not labels:
        return {"name": "Label issues", "score": 100, "details": "no GT"}
    labels_arr = np.array(labels)
    uniq = sorted(set(labels_arr))
    probs = np.eye(len(uniq))[np.searchsorted(uniq, labels_arr)]
    noise = get_noise_indices(labels=labels_arr, probabilities=probs)
    flags = sorted({idxs[n] for n in noise})
    files = [str(sample[i]) for i in flags]
    score = 100 - len(flags)/len(labels)*100
    return {"name": "Label issues", "score": score, "details": {"files": files[:50]}}

def aggregate(results: List[Dict]) -> float:
    return sum(DEFAULT_W[r["name"]]*r["score"] for r in results)

# ───────── gathering actual per-class counts ────────────────────────────────
def gather_class_counts(
    dataset_info_list: List[Tuple[str, List[str], List[str], str]]
) -> Counter[str]:
    counts: Counter[str] = Counter()
    for dloc, class_names, splits, _ in dataset_info_list:
        for split in splits:
            labels_dir = Path(dloc) / split / "labels"
            if not labels_dir.exists():
                continue
            for lp in labels_dir.rglob("*.txt"):
                for cls_id_float, *_ in parse_label_file(lp):
                    idx = int(cls_id_float)
                    if 0 <= idx < len(class_names):
                        counts[class_names[idx]] += 1
    return counts

# ───────────────── Roboflow TXT‑loading logic ─────────────────────────────
RF_RE = re.compile(r"https?://universe\.roboflow\.com/([^/]+)/([^/]+)/dataset/(\d+)")

def download_rf_dataset(url: str, rf_api: Roboflow, dest: Path) -> Path:
    m = RF_RE.match(url.strip())
    if not m:
        raise ValueError(f"Bad RF URL: {url}")
    ws, proj, ver = m.groups()
    ds_dir = dest / f"{ws}_{proj}_v{ver}"
    if ds_dir.exists():
        return ds_dir
    pr = rf_api.workspace(ws).project(proj)
    pr.version(int(ver)).download("yolov8", location=str(ds_dir))
    return ds_dir

# ───────────────── run_quality & merge_datasets ────────────────────────────
def run_quality(
    root: Path,
    yaml_file: Path | None,
    weights: Path | None,
    cfg: QCConfig,
    run_dup: bool,
    run_modelqa: bool
) -> Tuple[str, pd.DataFrame]:
    imgs, lbls, meta = gather_dataset(root, yaml_file)
    results = [
        qc_integrity(imgs, lbls, cfg),
        qc_class_balance(lbls, cfg),
        qc_image_quality(imgs, cfg),
        qc_duplicates(imgs, cfg)      if run_dup     else {"name":"Duplicates","score":100,"details":"skipped"},
        qc_model_qa(imgs, lbls, cfg)  if run_modelqa else {"name":"Model QA","score":100,"details":"skipped"},
        qc_label_issues(imgs, lbls, cfg) if run_modelqa else {"name":"Label issues","score":100,"details":"skipped"},
    ]
    final = aggregate(results)
    md = [f"## **{meta.get('name', root.name)}** — Score {final:.1f}/100"]
    for r in results:
        md.append(f"### {r['name']}  {r['score']:.1f}")
        md.append("<details><summary>details</summary>\n```json")
        md.append(json.dumps(r["details"], indent=2))
        md.append("```\n</details>\n")
    df = pd.DataFrame.from_dict(
        next(r for r in results if r["name"] == "Class balance")["details"]["class_counts"],
        orient="index", columns=["count"]
    )
    df.index.name = "class"
    return "\n".join(md), df

def merge_datasets(
    dataset_info_list: List[Tuple[str, List[str], List[str], str]],
    class_map_df: pd.DataFrame,
    out_dir: Path = Path("merged_dataset"),
    seed: int = 1234,
) -> Path:
    random.seed(seed)
    if out_dir.exists():
        shutil.rmtree(out_dir, onerror=lambda f, p, _: (os.chmod(p, stat.S_IWRITE), f(p)))
    for sub in ("train/images","train/labels","valid/images","valid/labels"):
        (out_dir / sub).mkdir(parents=True, exist_ok=True)

    class_name_mapping = {
        row["original_class"]: row["new_name"] if not row["remove"] else "__REMOVED__"
        for _, row in class_map_df.iterrows()
    }
    limits_per_merged = {
        row["new_name"]: int(row["max_images"])
        for _, row in class_map_df.iterrows()
        if not row["remove"]
    }
    active_classes = [c for c in sorted(set(class_name_mapping.values())) if c != "__REMOVED__"]
    id_map = {cls: idx for idx, cls in enumerate(active_classes)}

    image_to_classes: dict[str, set[str]] = {}
    image_to_label: dict[str, Path] = {}
    class_to_images: dict[str, set[str]] = {c: set() for c in active_classes}

    for dloc, class_names_dataset, splits, _ in dataset_info_list:
        for split in splits:
            labels_root = Path(dloc) / split / "labels"
            if not labels_root.exists():
                continue
            for lp in labels_root.rglob("*.txt"):
                cls_set: set[str] = set()
                for cls_id_float, *rest in parse_label_file(lp):
                    idx = int(cls_id_float)
                    if 0 <= idx < len(class_names_dataset):
                        orig = class_names_dataset[idx]
                        new = class_name_mapping.get(orig, orig)
                        if new in active_classes:
                            cls_set.add(new)
                if not cls_set:
                    continue
                img_path = str(lp.parent.parent / "images" / f"{lp.stem}.jpg")
                image_to_classes[img_path] = cls_set
                image_to_label[img_path] = lp
                for c in cls_set:
                    class_to_images[c].add(img_path)

    selected_images: set[str] = set()
    counters = {c: 0 for c in active_classes}
    pool = [img for imgs in class_to_images.values() for img in imgs]
    random.shuffle(pool)

    for img in pool:
        cs = image_to_classes[img]
        if any(counters[c] >= limits_per_merged.get(c, 0) for c in cs):
            continue
        selected_images.add(img)
        for c in cs:
            counters[c] += 1

    for img in selected_images:
        split = "train" if random.random() < 0.9 else "valid"
        dst_img = out_dir / split / "images" / Path(img).name
        dst_img.parent.mkdir(parents=True, exist_ok=True)
        shutil.copy(img, dst_img)

        lp_src = image_to_label[img]
        dst_lbl = out_dir / split / "labels" / lp_src.name
        dst_lbl.parent.mkdir(parents=True, exist_ok=True)
        lines = lp_src.read_text().splitlines()
        new_lines: List[str] = []
        for line in lines:
            parts = line.split()
            cid = int(parts[0])
            orig = None
            # find which dataset tuple this lp_src belongs to, to get class_names_dataset
            for dloc, class_names_dataset, splits, _ in dataset_info_list:
                if str(lp_src).startswith(dloc):
                    orig = class_names_dataset[cid] if cid < len(class_names_dataset) else None
                    break
            merged = class_name_mapping.get(orig, orig) if orig else None
            if merged and merged in active_classes:
                new_id = id_map[merged]
                new_lines.append(" ".join([str(new_id)] + parts[1:]))
        if new_lines:
            dst_lbl.write_text("\n".join(new_lines))
        else:
            dst_img.unlink(missing_ok=True)

    data_yaml = {
        "path": str(out_dir.resolve()),
        "train": "train/images",
        "val": "valid/images",
        "nc": len(active_classes),
        "names": active_classes,
    }
    (out_dir / "data.yaml").write_text(yaml.safe_dump(data_yaml))
    return out_dir

# ════════════════════════════════════════════════════════════════════════════
#                                 UI LAYER
# ════════════════════════════════════════════════════════════════════════════
with gr.Blocks(css="#classdf td{min-width:120px}") as demo:
    gr.Markdown("""
    # 🏹 **YOLO Dataset Toolkit**  
    _Evaluate • Merge • Edit • Download_
    """)

    # Evaluate Tab ...
    with gr.Tab("Evaluate"):
        api_in     = gr.Textbox(label="Roboflow API key", type="password")
        url_txt    = gr.File(label=".txt of RF dataset URLs", file_types=['.txt'])
        zip_in     = gr.File(label="Dataset ZIP")
        path_in    = gr.Textbox(label="Server path")
        yaml_in    = gr.File(label="Custom YAML", file_types=['.yaml'])
        weights_in = gr.File(label="YOLO weights (.pt)")

        blur_sl    = gr.Slider(0.0, 500.0, value=100.0, label="Blur threshold")
        iou_sl     = gr.Slider(0.0, 1.0,   value=0.5,   label="IOU threshold")
        conf_sl    = gr.Slider(0.0, 1.0,   value=0.25,  label="Min detection confidence")

        run_dup    = gr.Checkbox(label="Check duplicates (fastdup)", value=False)
        run_modelqa= gr.Checkbox(label="Run Model QA & cleanlab", value=False)

        run_eval   = gr.Button("Run Evaluation")
        out_md     = gr.Markdown()
        out_df     = gr.Dataframe()

        def _evaluate_cb(
            api_key, url_txt, zip_file, server_path, yaml_file, weights,
            blur_thr, iou_thr, conf_thr, run_dup, run_modelqa
        ):
            reports, dfs = [], []
            cfg = QCConfig(blur_thr, iou_thr, conf_thr, weights.name if weights else None)
            rf = Roboflow(api_key) if api_key and Roboflow else None

            if url_txt and rf:
                for line in Path(url_txt.name).read_text().splitlines():
                    if not line.strip(): continue
                    try:
                        ds = download_rf_dataset(line, rf, TMP_ROOT)
                        md, df = run_quality(
                            ds, None,
                            Path(weights.name) if weights else None,
                            cfg, run_dup, run_modelqa
                        )
                        reports.append(md); dfs.append(df)
                    except Exception as e:
                        reports.append(f"### {line}\n⚠️ {e}")

            if zip_file:
                tmp = Path(tempfile.mkdtemp())
                shutil.unpack_archive(zip_file.name, tmp)
                md, df = run_quality(
                    tmp,
                    Path(yaml_file.name) if yaml_file else None,
                    Path(weights.name) if weights else None,
                    cfg, run_dup, run_modelqa
                )
                reports.append(md); dfs.append(df)
                shutil.rmtree(tmp, ignore_errors=True)

            if server_path:
                ds = Path(server_path)
                md, df = run_quality(
                    ds,
                    Path(yaml_file.name) if yaml_file else None,
                    Path(weights.name) if weights else None,
                    cfg, run_dup, run_modelqa
                )
                reports.append(md); dfs.append(df)

            summary  = "\n---\n".join(reports) if reports else ""
            combined = pd.concat(dfs).groupby(level=0).sum() if dfs else pd.DataFrame()
            return summary, combined

        run_eval.click(
            _evaluate_cb,
            inputs=[api_in, url_txt, zip_in, path_in, yaml_in, weights_in,
                    blur_sl, iou_sl, conf_sl, run_dup, run_modelqa],
            outputs=[out_md, out_df]
        )

    # Merge / Edit Tab
    with gr.Tab("Merge / Edit"):
        gr.Markdown("### 1️⃣ Load one or more datasets")
        rf_key   = gr.Textbox(label="Roboflow API key", type="password")
        rf_urls  = gr.File(label=".txt of RF URLs", file_types=['.txt'])
        zips_in  = gr.Files(label="One or more dataset ZIPs")
        load_btn = gr.Button("Load datasets")
        load_log = gr.Markdown()
        ds_state = gr.State([])

        def _load_cb(rf_key, rf_urls_file, zip_files):
            global autoinc
            info_list, log_lines = [], []
            rf = Roboflow(rf_key) if rf_key and Roboflow else None

            if rf_urls_file and rf:
                for url in Path(rf_urls_file.name).read_text().splitlines():
                    url = url.strip()
                    if not url: continue
                    try:
                        ds = download_rf_dataset(url, rf, TMP_ROOT)
                        names = load_class_names(ds/"data.yaml")
                        splits = [s for s in ("train","valid","test") if (ds/s).exists()]
                        info_list.append((str(ds), names, splits, Path(ds).name))
                        log_lines.append(f"✔️ RF dataset **{Path(ds).name}** loaded ({len(names)} classes)")
                    except Exception as e:
                        log_lines.append(f"⚠️ RF load failed for {url!r}: {e}")

            for f in zip_files or []:
                autoinc += 1
                tmp = TMP_ROOT / f"zip_{autoinc}"
                tmp.mkdir(parents=True, exist_ok=True)
                shutil.unpack_archive(f.name, tmp)
                yaml_p = next(tmp.rglob("*.yaml"), None)
                if yaml_p:
                    names = load_class_names(yaml_p)
                    splits = [s for s in ("train","valid","test") if (tmp/s).exists()]
                    info_list.append((str(tmp), names, splits, tmp.name))
                    log_lines.append(f"✔️ ZIP **{tmp.name}** loaded")

            return info_list, "\n".join(log_lines) or "No datasets loaded."

        load_btn.click(_load_cb, [rf_key, rf_urls, zips_in], [ds_state, load_log])

        gr.Markdown("### 2️⃣ Edit class mapping / limits / removal")
        class_df = gr.Dataframe(
            headers=["original_class","new_name","max_images","remove"],
            datatype=["str","str","number","bool"],
            interactive=True, elem_id="classdf"
        )
        refresh_btn = gr.Button("Build class table from loaded datasets")

        def _build_class_df(ds_info):
            counts = gather_class_counts(ds_info)
            all_names = sorted(counts.keys())
            return pd.DataFrame({
                "original_class": all_names,
                "new_name":       all_names,
                "max_images":     [counts[n] for n in all_names],
                "remove":         [False]*len(all_names),
            })

        refresh_btn.click(_build_class_df, [ds_state], [class_df])

        merge_btn = gr.Button("Merge datasets ✨")
        zip_out   = gr.File(label="Download merged ZIP")
        merge_log = gr.Markdown()

        def _merge_cb(ds_info, class_df):
            if not ds_info:
                return None, "⚠️ Load datasets first."
            out_dir = merge_datasets(ds_info, class_df)
            zip_path = shutil.make_archive(str(out_dir), "zip", out_dir)
            count = len(list(Path(out_dir).rglob("*.jpg")))
            return zip_path, f"✅ Merged dataset at **{out_dir}** with {count} images."

        merge_btn.click(_merge_cb, [ds_state, class_df], [zip_out, merge_log])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=int(os.getenv("PORT", 7860)))