from __future__ import annotations

import imghdr
import json
import os
import re
import shutil
import tempfile
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))   # force single-core by default
BATCH_SIZE   = int(os.getenv("QC_BATCH",    4))   # small batches
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,
}

_model_cache: dict[str, YOLO] = {}

@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

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

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:
        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]

# ───────── Functions for I/O-bound concurrency ─────────────────────────────
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:
        return True

# ───────────────── Quality Checks ──────────────────────────────────────────
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(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 the grouped-DataFrame API first:
            try:
                cc = fd.connected_components_grouped(sort_by="comp_size", ascending=False)
                if "files" in cc.columns:
                    clusters = cc["files"].tolist()
                else:
                    # fallback: group by component ID, collect filenames
                    clusters = (
                        cc.groupby("component")["filename"]
                          .apply(list)
                          .tolist()
                    )
            except Exception:
                # final fallback to the old list-based API
                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 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 _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 aggregate(results: List[Dict]) -> float:
    return sum(DEFAULT_W[r["name"]]*r["score"] for r in results)

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

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

with gr.Blocks(title="YOLO Dataset Quality Evaluator v3") as demo:
    gr.Markdown("""
# YOLOv8 Dataset Quality Evaluator v3

* Configurable blur, IOU & confidence thresholds  
* Optional duplicates (fastdup)  
* Optional Model QA & cleanlab label-issue detection  
* Model caching for speed  
""")
    with gr.Row():
        api_in     = gr.Textbox(label="Roboflow API key", type="password")
        url_txt    = gr.File(label=".txt of RF dataset URLs", file_types=['.txt'])
    with gr.Row():
        zip_in     = gr.File(label="Dataset ZIP")
        path_in    = gr.Textbox(label="Server path")
    with gr.Row():
        yaml_in    = gr.File(label="Custom YAML", file_types=['.yaml'])
        weights_in = gr.File(label="YOLO weights (.pt)")
    with gr.Row():
        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")
    with gr.Row():
        run_dup     = gr.Checkbox(label="Check duplicates (fastdup)", value=False)
        run_modelqa = gr.Checkbox(label="Run Model QA & cleanlab",   value=False)
    run_btn = gr.Button("Evaluate")
    out_md   = gr.Markdown()
    out_df   = gr.Dataframe()

    def evaluate(
        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

        # Roboflow URLs
        if url_txt:
            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}")

        # ZIP upload
        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)

        # Server path
        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)
        combined = pd.concat(dfs).groupby(level=0).sum() if dfs else pd.DataFrame()
        return summary, combined

    run_btn.click(
        evaluate,
        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]
    )

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