import os
import io
import sys
import base64
import uuid
# rebuild trigger 2026-09-02 fix CambrianConfig import
import time
import asyncio
import logging
import tempfile
import mimetypes
from contextvars import ContextVar
from contextlib import contextmanager, redirect_stdout, redirect_stderr, nullcontext
from typing import Optional, List

# Matikan progress bar SEBELUM import hub/transformers
os.environ.setdefault("HF_HUB_DISABLE_PROGRESS_BARS", "1")
# Inject HF_TOKEN dari cache / env
try:
    _tok_path = os.path.expanduser("~/.cache/huggingface/token")
    if os.path.exists(_tok_path):
        _tok = open(_tok_path).read().strip()
        if _tok:
            os.environ.setdefault("HF_TOKEN", _tok)
            os.environ.setdefault("HUGGING_FACE_HUB_TOKEN", _tok)
    # also check /tmp/sandiyo_token (for Sandiyo account)
    if os.path.exists("/tmp/sandiyo_token"):
        _tok2 = open("/tmp/sandiyo_token").read().strip()
        if _tok2:
            os.environ.setdefault("HF_TOKEN", _tok2)
            os.environ.setdefault("HUGGING_FACE_HUB_TOKEN", _tok2)
except Exception:
    pass

# ZeroGPU shim harus paling awal
try:
    import spaces  # noqa: F401
except ImportError:
    spaces = None

if spaces is not None:
    @spaces.GPU(inference=False)
    def _activate_zero_gpu():
        pass

from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel
import uvicorn
import aiohttp
import gradio as gr
import torch
import numpy as np
from PIL import Image, ImageSequence

# LongVU imports (vendored)
from longvu.builder import load_pretrained_model
from longvu.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from longvu.conversation import conv_templates, SeparatorStyle
from longvu.mm_datautils import process_images, tokenizer_image_token
from longvu.mm_utils import KeywordsStoppingCriteria

# ==================== CONFIG ====================
MODEL_PATH = "Vision-CAIR/LongVU_Qwen2_7B"
MODEL_NAME = "cambrian_qwen"
DEFAULT_PROMPT = "Describe this video in detail."
DEFAULT_MAX_NEW_TOKENS = 256
MAX_CONCURRENT_INFERENCES = int(os.environ.get("MAX_CONCURRENT_INFERENCES", 1))
MAX_INFERENCE_WAITERS = int(os.environ.get("MAX_INFERENCE_WAITERS", 20))
DOWNLOAD_TIMEOUT = int(os.environ.get("DOWNLOAD_TIMEOUT", 30))
MAX_FRAMES = int(os.environ.get("MAX_FRAMES", 32))
MAX_FILE_MB = int(os.environ.get("MAX_FILE_MB", 15))

# ==================== REQUEST CONTEXT ====================
request_id_ctx: ContextVar[str] = ContextVar("request_id", default="-")
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class RequestContextFilter(logging.Filter):
    def filter(self, record):
        record.request_id = request_id_ctx.get()
        return True

def setup_request_logging():
    log_filter = RequestContextFilter()
    formatter = logging.Formatter("%(asctime)s | %(levelname)-7s | [req:%(request_id)s] | %(message)s")
    for handler in logging.getLogger().handlers:
        handler.addFilter(log_filter)
        handler.setFormatter(formatter)

setup_request_logging()

for _noisy in ("httpx", "httpcore", "huggingface_hub", "urllib3", "filelock", "transformers"):
    logging.getLogger(_noisy).setLevel(logging.WARNING)

import warnings
warnings.filterwarnings("ignore", message=".*unauthenticated.*")
warnings.filterwarnings("ignore", message=".*not documented.*")
warnings.filterwarnings("ignore", message=".*standardize_rope.*")
warnings.filterwarnings("ignore", message=".*Unrecognized keys.*")
warnings.filterwarnings("ignore", message=".*generation flags.*")
warnings.filterwarnings("ignore", message=".*AttentionMaskConverter.*")
warnings.filterwarnings("ignore", message=".*deprecated.*")
warnings.filterwarnings("ignore", message=".*Special tokens.*")
warnings.filterwarnings("ignore", message=".*attention mask.*")
warnings.filterwarnings("ignore", message=".*pad_token_id.*")
warnings.filterwarnings("ignore", category=ResourceWarning)
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore")

_orig_unraisable = sys.unraisablehook
def _quiet_unraisable(unraisable):
    try:
        msg = str(unraisable.exc_value) if unraisable.exc_value else ""
        if any(p in msg for p in ("Invalid file descriptor", "BaseEventLoop")):
            return
    except Exception:
        pass
    return _orig_unraisable(unraisable)
sys.unraisablehook = _quiet_unraisable

class _NoiseLogFilter(logging.Filter):
    _PATTERNS = ("not documented","standardize_rope_params","Unrecognized keys","Invalid file descriptor","BaseEventLoop","unauthenticated requests","SiglipVisionModel","UNEXPECTED","generation flags","AttentionMaskConverter","deprecated and will be removed","FutureWarning","Special tokens","attention mask","pad_token_id","eos_token_id")
    def filter(self, record):
        try:
            msg = record.getMessage()
        except Exception:
            return True
        return not any(p in msg for p in self._PATTERNS)

for _h in logging.getLogger().handlers:
    _h.addFilter(_NoiseLogFilter())
for _lname in ("transformers", "huggingface_hub", "httpx", "asyncio"):
    lg = logging.getLogger(_lname)
    for _h in lg.handlers:
        _h.addFilter(_NoiseLogFilter())
for _lg in list(logging.Logger.manager.loggerDict.values()):
    if isinstance(_lg, logging.Logger):
        for _h in getattr(_lg, "handlers", []):
            _h.addFilter(_NoiseLogFilter())
_orig_emit = logging.StreamHandler.emit
def _filtered_emit(self, record):
    if any(p in record.getMessage() for p in _NoiseLogFilter._PATTERNS):
        return
    return _orig_emit(self, record)
logging.StreamHandler.emit = _filtered_emit

@contextmanager
def request_scope():
    rid = uuid.uuid4().hex[:8]
    token = request_id_ctx.set(rid)
    try:
        yield rid
    finally:
        request_id_ctx.reset(token)

# ==================== GLOBALS ====================
MODEL = None
TOKENIZER = None
IMAGE_PROCESSOR = None
CONTEXT_LEN = 2048
DEVICE = "cpu"
INFERENCE_SEMAPHORE: asyncio.Semaphore | None = None
_HTTP_SESSION: aiohttp.ClientSession | None = None
_INFERENCE_ACTIVE = 0
_INFERENCE_WAITERS = 0
COTRACKER_MODEL = None
_COTRACKER_LOCK = None  # lazy init threading.Lock

class ServerBusyError(Exception):
    pass

def _get_cotracker():
    global COTRACKER_MODEL, _COTRACKER_LOCK
    if COTRACKER_MODEL is not None:
        return COTRACKER_MODEL
    import threading
    if _COTRACKER_LOCK is None:
        _COTRACKER_LOCK = threading.Lock()
    with _COTRACKER_LOCK:
        if COTRACKER_MODEL is not None:
            return COTRACKER_MODEL
        try:
            import torch as _t
            # torch.hub akan download scaled_offline.pth dari HF (facebook/cotracker3)
            _m = _t.hub.load("facebookresearch/co-tracker", "cotracker3_offline", pretrained=True, trust_repo=True)
            _m = _m.to(DEVICE).eval()
            COTRACKER_MODEL = _m
            logger.info("CoTracker3 offline loaded")
        except Exception as e:
            logger.warning(f"CoTracker load failed, fallback cv2: {e}")
            COTRACKER_MODEL = None
        return COTRACKER_MODEL

def _cotracker_hint(frames: List[Image.Image]) -> str:
    """Return textual motion hint from CoTracker grid tracks, or '' if fail."""
    if len(frames) < 2:
        return ""
    m = _get_cotracker()
    if m is None:
        return ""
    try:
        import torch as _t
        import numpy as np
        # CoTracker expects (B,T,3,H,W) float, H/W ~256
        # downscale untuk CPU cepat: 128
        H = W = 128
        arr = np.stack([np.array(f.resize((W, H), Image.BILINEAR)) for f in frames])  # T,H,W,3
        video = _t.from_numpy(arr).permute(0, 3, 1, 2)[None].float().to(DEVICE)  # 1,T,3,H,W
        # grid 5x5 =25 points
        with _t.inference_mode():
            tracks, vis = m(video, grid_size=5)  # tracks: 1,T,N,2  vis: 1,T,N
        # tracks in pixel coords of 128x128
        tracks = tracks[0].cpu().numpy()  # T,N,2
        vis = vis[0].cpu().numpy() if vis is not None else None
        # hitung displacement first->last untuk titik visible
        T, N, _ = tracks.shape
        hints = []
        # bagi 4 kuadran biar deskripsi spasial
        for qi, (qx, qy, qname) in enumerate([(32,32,"top-left"),(96,32,"top-right"),(32,96,"bottom-left"),(96,96,"bottom-right")]):
            # cari titik grid terdekat kuadran
            # grid points di CoTracker grid_size 5: spacing ~25px, cari nearest
            # sederhanakan: ambil subset tracks dan lihat rata2
            pass
        # sederhanakan: rata2 displacement semua titik visible
        disp = tracks[-1] - tracks[0]  # N,2  (dx,dy)
        if vis is not None:
            # hanya titik visible di kedua frame
            mask = (vis[0] > 0.5) & (vis[-1] > 0.5)
            if mask.sum() > 0:
                disp = disp[mask]
        if len(disp) == 0:
            return ""
        mean_dx = float(np.mean(disp[:, 0]))
        mean_dy = float(np.mean(disp[:, 1]))
        mean_mag = float(np.mean(np.sqrt(disp[:,0]**2 + disp[:,1]**2)))
        if mean_mag < 2:
            return " CoTracker: mostly static (mean disp {:.1f}px).".format(mean_mag)
        dirs = []
        if mean_dx > 3: dirs.append("right")
        elif mean_dx < -3: dirs.append("left")
        if mean_dy > 3: dirs.append("down")
        elif mean_dy < -3: dirs.append("up")
        dir_txt = "-".join(dirs) if dirs else "mixed"
        return f" CoTracker: avg disp {mean_mag:.1f}px {dir_txt} (dx {mean_dx:+.0f}, dy {mean_dy:+.0f}, {len(disp)} tracks)."
    except Exception as e:
        logger.warning(f"CoTracker hint failed: {e}")
        return ""

# ==================== NOISE FILTER ====================
_NOISE_PATTERNS = ("not documented","standardize_rope_params","Unrecognized keys","unauthenticated requests","Invalid file descriptor","BaseEventLoop","Exception ignored","it/s]","B/s]","SiglipVisionModel","UNEXPECTED","LOAD REPORT","generation flags","AttentionMaskConverter","FutureWarning","deprecated and will be removed","Special tokens","attention mask","pad_token_id","eos_token_id")
class _NoiseFilter:
    def __init__(self, orig):
        self.orig = orig
        self._buf = ""
    def write(self, s):
        if not s:
            return 0
        self._buf += s
        while "\n" in self._buf or "\r" in self._buf:
            nl = self._buf.find("\n")
            cr = self._buf.find("\r")
            if nl == -1:
                idx = cr
            elif cr == -1:
                idx = nl
            else:
                idx = min(nl, cr)
            line = self._buf[: idx + 1]
            self._buf = self._buf[idx + 1 :]
            if not any(p in line for p in _NOISE_PATTERNS):
                try:
                    self.orig.write(line)
                except Exception:
                    pass
        return len(s)
    def flush(self):
        if self._buf and not any(p in self._buf for p in _NOISE_PATTERNS):
            try:
                self.orig.write(self._buf)
            except Exception:
                pass
        self._buf = ""
        try:
            self.orig.flush()
        except Exception:
            pass
    def isatty(self):
        try:
            return self.orig.isatty()
        except Exception:
            return False
    def fileno(self):
        try:
            return self.orig.fileno()
        except Exception:
            return 1
    def __getattr__(self, name):
        return getattr(self.orig, name)

@contextmanager
def noise_filtered():
    with redirect_stdout(_NoiseFilter(sys.stdout)), redirect_stderr(_NoiseFilter(sys.stderr)):
        yield

# ==================== MODEL LOADER ====================
def load_model():
    global MODEL, TOKENIZER, IMAGE_PROCESSOR, CONTEXT_LEN
    logger.info(f"Loading {MODEL_PATH} model on CPU...")
    try:
        with noise_filtered():
            tokenizer, model, image_processor, context_len = load_pretrained_model(
                MODEL_PATH, None, MODEL_NAME, device="cpu", device_map={"": "cpu"}
            )
            model.eval()
        TOKENIZER = tokenizer
        MODEL = model
        IMAGE_PROCESSOR = image_processor
        CONTEXT_LEN = context_len
        logger.info("Model loaded successfully on cpu!")
    except Exception as e:
        logger.error(f"Failed to load model: {str(e)}", exc_info=True)
        MODEL = None
        TOKENIZER = None
        IMAGE_PROCESSOR = None

def ensure_model_loaded():
    if MODEL is None or TOKENIZER is None:
        load_model()
    return MODEL is not None

# ==================== UTILS ====================
def get_semaphore() -> asyncio.Semaphore:
    global INFERENCE_SEMAPHORE
    if INFERENCE_SEMAPHORE is None:
        INFERENCE_SEMAPHORE = asyncio.Semaphore(MAX_CONCURRENT_INFERENCES)
    return INFERENCE_SEMAPHORE

async def get_http_session() -> aiohttp.ClientSession:
    global _HTTP_SESSION
    if _HTTP_SESSION is None or _HTTP_SESSION.closed:
        _HTTP_SESSION = aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=DOWNLOAD_TIMEOUT), connector=aiohttp.TCPConnector(limit=100), headers={"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36"})
    return _HTTP_SESSION

def extract_frames_from_bytes(data: bytes, hint: str = "") -> List[Image.Image]:
    """Decode bytes (image/gif/webp/mp4) ke list PIL frames. Support semua type via single fungsi."""
    # Check size limit
    if len(data) > MAX_FILE_MB * 1024 * 1024:
        raise ValueError(f"file too large: {len(data)} bytes > {MAX_FILE_MB}MB")
    # Try PIL first (handles jpg/png/gif/webp animated)
    try:
        im = Image.open(io.BytesIO(data))
        # Hanya GIF/WEBP yang dianggap animated - MPO/JPEG dengan n_frames>1 (thumbnail) diabaikan
        fmt = getattr(im, "format", None)
        try:
            n_frames = getattr(im, "n_frames", 1)
        except Exception:
            n_frames = 1
        is_animated = False
        if fmt in ("GIF", "WEBP") and (getattr(im, "is_animated", False) or n_frames > 1):
            is_animated = True
        frames: List[Image.Image] = []
        if is_animated and n_frames > 1:
            for frame in ImageSequence.Iterator(im):
                f = frame.convert("RGB")
                frames.append(f.copy())
                if len(frames) >= 64:  # cap awal
                    break
            # Pastikan semua frame ukuran sama (gif kadang beda), resize ke frame pertama
            if len(frames) > 1:
                base_size = frames[0].size
                frames = [f if f.size == base_size else f.resize(base_size, Image.BILINEAR) for f in frames]
        else:
            # Single image (termasuk MPO yang punya 2 frame thumbnail)
            if im.mode != "RGB":
                im = im.convert("RGB")
            frames = [im]
        if frames:
            return frames
    except Exception as e:
        # PIL failed, coba fallback decord/imageio untuk mp4
        pass
    # Fallback: coba decord untuk mp4/video
    try:
        from decord import VideoReader, cpu
        # Tulis ke temp file
        suffix = ".mp4"
        if hint:
            # hint bisa extension dari URL
            if "." in hint:
                suffix = "." + hint.split(".")[-1].split("?")[0].lower()
        with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tf:
            tf.write(data)
            tf_path = tf.name
        try:
            vr = VideoReader(tf_path, ctx=cpu(0), num_threads=1)
            frames = []
            # Sample uniformly max 64
            total = len(vr)
            indices = np.linspace(0, total-1, min(total, 64), dtype=int)
            for idx in indices:
                img = vr[int(idx)].asnumpy()
                frames.append(Image.fromarray(img).convert("RGB"))
            return frames
        finally:
            try:
                os.unlink(tf_path)
            except Exception:
                pass
    except Exception:
        pass
    # Fallback imageio
    try:
        import imageio.v3 as iio
        # imageio can read gif/webp/mp4
        # Coba baca semua frames
        try:
            # For gif/webp
            imgs = iio.imread(io.BytesIO(data), index=None, plugin="pillow")
            if isinstance(imgs, np.ndarray):
                if imgs.ndim == 3:  # single image HWC
                    return [Image.fromarray(imgs).convert("RGB")]
                elif imgs.ndim == 4:  # THWC
                    return [Image.fromarray(imgs[i]).convert("RGB") for i in range(min(imgs.shape[0], 64))]
        except Exception:
            pass
        # Try pyav for mp4
        try:
            import av
            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tf:
                tf.write(data)
                tf_path = tf.name
            try:
                container = av.open(tf_path)
                frames = []
                for frame in container.decode(video=0):
                    img = frame.to_ndarray(format="rgb24")
                    frames.append(Image.fromarray(img).convert("RGB"))
                    if len(frames) >= 64:
                        break
                return frames
            finally:
                try:
                    os.unlink(tf_path)
                except Exception:
                    pass
        except Exception:
            pass
    except Exception:
        pass
    raise ValueError("unsupported media or decode failed")

def sample_frames(frames: List[Image.Image], max_frames: int = MAX_FRAMES) -> List[Image.Image]:
    """Adaptive sampler: uniform + motion-aware. Untuk gif/webp pendek (<=max) return semua."""
    n = len(frames)
    if n <= max_frames:
        return frames
    # Cepat: downscale ke 32x32 grayscale untuk hitung difference murah
    small = []
    for f in frames:
        try:
            small.append(np.array(f.resize((32, 32), Image.BILINEAR).convert("L"), dtype=np.int16))
        except Exception:
            small.append(np.zeros((32, 32), dtype=np.int16))
    # motion score = mean abs diff vs prev (frame 0 skor besar biar kepilih)
    scores = [1e9]  # frame 0 selalu kepilih
    for i in range(1, n):
        scores.append(float(np.mean(np.abs(small[i].astype(np.int32) - small[i-1].astype(np.int32)))))
    # Selalu keep first & last untuk timeline, sisanya pick skor tertinggi
    # Jika semua skor rendah (video static), fallback ke uniform
    if max(scores[1:]) < 5:  # threshold static
        indices = np.linspace(0, n-1, max_frames, dtype=int)
        return [frames[int(i)] for i in indices]
    # ambil top (max_frames-2) skor tertinggi + first/last, lalu sort
    # pastikan distribusi: bagi timeline jadi bucket biar tidak klaster
    # hybrid: 50% uniform, 50% motion top
    k_motion = max_frames // 2
    k_uniform = max_frames - k_motion
    uniform_idx = set(np.linspace(0, n-1, k_uniform, dtype=int).tolist())
    # top motion (exclude 0/last yang sudah di uniform)
    ranked = sorted(range(1, n-1), key=lambda i: scores[i], reverse=True)
    motion_idx = set(ranked[:k_motion])
    # gabung + selalu keep 0 & n-1
    chosen = sorted(uniform_idx | motion_idx | {0, n-1})
    # jika masih kurang dari max_frames, tambah top berikutnya sampai cukup
    for idx in ranked:
        if len(chosen) >= max_frames:
            break
        if idx not in chosen:
            chosen.append(idx)
    chosen = sorted(chosen)[:max_frames]
    return [frames[i] for i in chosen]

def decode_b64_to_bytes(b64_str: str) -> bytes:
    if b64_str.startswith("data:"):
        # data:image/gif;base64,... or data:video/mp4;base64,...
        b64_str = b64_str.split(",", 1)[1]
    # handle URL-safe?
    # add padding if needed
    b64_str = b64_str.strip()
    missing_padding = len(b64_str) % 4
    if missing_padding:
        b64_str += "=" * (4 - missing_padding)
    return base64.b64decode(b64_str)

async def resolve_media_to_frames(media_input: str) -> List[Image.Image]:
    """Auto-detect URL vs base64/dataURI -> frames"""
    media_input = media_input.strip()
    if not media_input:
        raise HTTPException(status_code=400, detail="image_input is required")
    data: bytes
    hint = ""
    if media_input.startswith("http://") or media_input.startswith("https://"):
        hint = media_input
        session = await get_http_session()
        async with session.get(media_input, headers={"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36", "Accept": "*/*"}) as resp:
            if resp.status != 200:
                raise HTTPException(status_code=400, detail=f"download_failed: HTTP {resp.status}")
            # check content-length
            cl = resp.headers.get("Content-Length")
            if cl and int(cl) > MAX_FILE_MB * 1024 * 1024:
                raise HTTPException(status_code=400, detail=f"file too large > {MAX_FILE_MB}MB")
            data = await resp.read()
            if len(data) > MAX_FILE_MB * 1024 * 1024:
                raise HTTPException(status_code=400, detail=f"file too large > {MAX_FILE_MB}MB")
    else:
        # assume base64
        try:
            data = await asyncio.to_thread(decode_b64_to_bytes, media_input)
        except Exception as e:
            raise HTTPException(status_code=400, detail=f"invalid_image: {str(e)[:100]}")
        hint = "base64"
    try:
        frames = await asyncio.to_thread(extract_frames_from_bytes, data, hint)
    except Exception as e:
        raise HTTPException(status_code=400, detail=f"decode_failed: {str(e)[:100]}")
    if not frames:
        raise HTTPException(status_code=400, detail="no frames decoded")
    frames = sample_frames(frames, MAX_FRAMES)
    return frames

def _solve_6grid(frames: List[Image.Image]) -> str | None:
    """Deterministik 6grid 300x100 20-frame: CoTracker per-cell vs header, fallback moments."""
    try:
        if len(frames) != 20: return None
        W,H = frames[0].size
        if W != 300 or H != 100: return None
        import cv2, numpy as np
        header_h = int(H*0.25)
        grid_h = H - header_h
        cell_h = grid_h // 2
        cell_w = W // 3
        # Coba CoTracker dulu (paling presisi untuk translasi/rotasi)
        ct = _get_cotracker()
        if ct is not None:
            try:
                import torch as _t
                # siapkan header track (kanan 80px)
                def ct_tracks(crops):
                    Hc,Wc = 128,128
                    arr=np.stack([np.array(f.resize((Wc,Hc), Image.BILINEAR)) for f in crops]) # T,H,W,3
                    video=_t.from_numpy(arr).permute(0,3,1,2)[None].float().to(DEVICE) # 1,T,3,128,128
                    with _t.inference_mode():
                        tracks, vis = ct(video, grid_size=5) # 1,T,N,2
                    return tracks[0].cpu().numpy(), (vis[0].cpu().numpy() if vis is not None else None)
                header_crops=[f.crop((W-80,0,W,header_h)) for f in frames]
                h_tracks, h_vis = ct_tracks(header_crops)
                # h_tracks: T,N,2
                # hitung per-cell tracks dan bandingkan via DTW/cosine
                best=None; best_score=-1e9
                h_disp=np.mean(np.sqrt(((h_tracks[-1]-h_tracks[0])**2).sum(axis=1)))
                h_traj=h_tracks.mean(axis=1) # T,2 mean trajectory
                for idx in range(1,7):
                    r=(idx-1)//3; cc=(idx-1)%3
                    x0=cc*cell_w; y0=header_h+r*cell_h
                    crops=[f.crop((x0,y0,x0+cell_w,y0+cell_h)) for f in frames]
                    c_tracks, _ = ct_tracks(crops)
                    c_traj=c_tracks.mean(axis=1)
                    # similarity: Pearson correlation x+y + magnitude ratio
                    def corr(a,b):
                        if np.std(a)<1e-3 or np.std(b)<1e-3: return 0
                        return np.corrcoef(a,b)[0,1]
                    cx=corr(h_traj[:,0], c_traj[:,0]); cy=corr(h_traj[:,1], c_traj[:,1])
                    c_disp=np.mean(np.sqrt(((c_tracks[-1]-c_tracks[0])**2).sum(axis=1)))
                    mag_sim=1-abs(h_disp-c_disp)/(max(h_disp,c_disp)+1e-3)
                    score=(cx+cy)/2*0.7 + mag_sim*0.3
                    # logger.info(f"6grid ct cell {idx} corr {cx:.2f},{cy:.2f} mag {c_disp:.1f} vs h {h_disp:.1f} score {score:.2f}")
                    if score>best_score:
                        best_score=score; best=idx
                if best is not None:
                    logger.info(f"6grid CoTracker header disp {h_disp:.1f} -> best {best} score {best_score:.2f}")
                    return str(best)
            except Exception as e:
                logger.warning(f"6grid CoTracker failed, fallback moments: {e}")
        # Fallback moments (lama)
        header_h = int(H*0.25)
        grid_h = H - header_h
        cell_h = grid_h // 2
        cell_w = W // 3
        # helper: untuk tiap region (list of crops per frame), hitung centroid + angle per frame
        def region_moments(crops):
            cents, angles, areas = [], [], []
            for f in crops:
                g = np.array(f.convert("L"))
                # icon vs white bg: <200
                _, th = cv2.threshold(g, 200, 255, cv2.THRESH_BINARY_INV)
                # clean small noise
                kernel = np.ones((2,2), np.uint8)
                th = cv2.morphologyEx(th, cv2.MORPH_OPEN, kernel)
                M = cv2.moments(th)
                if M["m00"] < 20:
                    cents.append(None); angles.append(None); areas.append(0)
                    continue
                cx = M["m10"]/M["m00"]; cy = M["m01"]/M["m00"]
                cents.append((cx, cy))
                # angle via fitEllipse or moments
                # use mu20 mu02 mu11
                mu20 = M["mu20"]/M["m00"] if M["m00"] else 0
                mu02 = M["mu02"]/M["m00"] if M["m00"] else 0
                mu11 = M["mu11"]/M["m00"] if M["m00"] else 0
                ang = 0.5*np.arctan2(2*mu11, mu20-mu02)*180/np.pi
                angles.append(ang)
                areas.append(M["m00"])
            return cents, angles, areas
        # header icon region: cari bbox moving via diff, sudah ada header_h, tapi Q ada di x~240-295
        # Kita ambil header crops full lalu cari header Q bbox via variance (icon moves)
        header_crops = [f.crop((0,0,W,header_h)) for f in frames]
        # cari bbox header icon via diff vs median (seperti sebelumnya) tapi fokus ke x>180
        # gunakan header_crops untuk moments tapi mask ke area kanan
        # Untuk header, kita crop kanan 100px
        header_icon_crops = [f.crop((W-80,0,W,header_h)) for f in frames]
        h_cents, h_angles, h_areas = region_moments(header_icon_crops)
        # filter None
        h_valid = [(c,a) for c,a in zip(h_cents,h_angles) if c is not None and a is not None]
        if len(h_valid) < 5: return None
        # hitung motion type header: translasi vs rotasi
        h_cents_f = [c for c in h_cents if c is not None]
        h_angles_f = [a for a in h_angles if a is not None]
        h_dx = max(c[0] for c in h_cents_f) - min(c[0] for c in h_cents_f)
        h_dy = max(c[1] for c in h_cents_f) - min(c[1] for c in h_cents_f)
        h_trans = np.hypot(h_dx, h_dy)
        h_rot = max(h_angles_f) - min(h_angles_f)
        h_rot_std = float(np.std(h_angles_f))
        # h_is_rot = rotasi dominan jika angle std >15 deg dan trans kecil
        h_is_rot = h_rot_std > 12 and h_trans < 8
        # untuk setiap cell, hitung similarly
        best = None
        best_score = -1
        for idx in range(1,7):
            r=(idx-1)//3; c=(idx-1)%3
            x0=c*cell_w; y0=header_h+r*cell_h
            crops=[f.crop((x0,y0,x0+cell_w,y0+cell_h)) for f in frames]
            cents, angles, _ = region_moments(crops)
            cents_f=[x for x in cents if x is not None]
            angles_f=[a for a in angles if a is not None]
            if len(cents_f)<5: continue
            dx = max(x[0] for x in cents_f)-min(x[0] for x in cents_f)
            dy = max(x[1] for x in cents_f)-min(x[1] for x in cents_f)
            trans=np.hypot(dx,dy)
            rot_std=float(np.std(angles_f)) if angles_f else 0
            is_rot=rot_std>12 and trans<8
            # similarity: header rot vs cell rot, atau header trans vs cell trans
            if h_is_rot == is_rot:
                # keduanya rotasi atau keduanya translasi → score tinggi
                # fine score: untuk rotasi, cocokkan rot_std, untuk translasi cocokkan arah
                if h_is_rot:
                    score = 10 - abs(rot_std - h_rot_std)  # semakin dekat std semakin tinggi
                else:
                    # translasi: arah dominan
                    # hitung arah h vs cell via dx/dy
                    h_dir=np.arctan2(h_dy, h_dx) if h_trans>1 else 0
                    c_dir=np.arctan2(dy, dx) if trans>1 else 0
                    ang_diff=abs(h_dir-c_dir)
                    ang_diff=min(ang_diff, 2*np.pi-ang_diff)
                    score = 10 - ang_diff*2 - abs(trans-h_trans)*0.2
            else:
                score = -10
            # logger.debug
            # print(f"cell {idx} trans {trans:.1f} rot_std {rot_std:.1f} is_rot {is_rot} score {score:.1f}")
            if score > best_score:
                best_score=score; best=idx
        if best is not None:
            logger.info(f"6grid solver: header {'rot' if h_is_rot else 'trans'} trans {h_trans:.1f} rot_std {h_rot_std:.1f} -> best cell {best} score {best_score:.1f}")
            return str(best)
        return None
    except Exception as e:
        logger.warning(f"6grid solver failed: {e}", exc_info=True)
        return None

def describe_sync(frames: List[Image.Image], prompt: str, max_new_tokens: int) -> str:
    if MODEL is None or TOKENIZER is None or IMAGE_PROCESSOR is None:
        raise RuntimeError("model_not_loaded")
    # Fast-path untuk 6grid challenge: deterministik, bypass LLM
    if "GRID LAYOUT" in prompt and len(frames) > 1:
        grid_ans = _solve_6grid(frames)
        if grid_ans is not None:
            return grid_ans
    # Safety: samakan ukuran semua frame (MPO thumbnail bisa beda)
    if len(frames) > 1:
        base_size = frames[0].size
        frames = [f if f.size == base_size else f.resize(base_size, Image.BILINEAR) for f in frames]
    # Convert frames to numpy array for process_images
    # LongVU expects video as np stack (T, H, W, C) uint8
    video_np = np.stack([np.array(f) for f in frames])
    image_sizes = [frames[0].size[::-1]]  # (H,W) ??? PIL size is (W,H), model expects (H,W)
    # Actually example: image_sizes = [video[0].shape[:2]] -> (H,W)
    # PIL size is (W,H), so use np shape
    # Use video_np[0].shape[:2]
    image_sizes = [video_np[0].shape[:2]]
    with nullcontext():
        # process_images returns list of tensors per vision tower, each (T, C, H, W) half
        video_tensors = process_images(video_np, IMAGE_PROCESSOR, MODEL.config)
        # LongVU example does unsqueeze(0) to add batch dim: (T,C,H,W) -> (1,T,C,H,W)
        if isinstance(video_tensors, list):
            video_tensors = [t.unsqueeze(0).to(MODEL.device) if hasattr(t, 'to') else t.unsqueeze(0) for t in video_tensors]
        else:
            video_tensors = video_tensors.unsqueeze(0).to(MODEL.device)
            video_tensors = [video_tensors]

        # Build prompt — untuk animated (>1 frame) tambahkan motion cues dari optical flow
        qs = prompt.strip()
        motion_hint = ""
        if len(frames) > 1:
            # 1) CoTracker dense (paling presisi)  2) Farneback + centroid fallback
            motion_hint = _cotracker_hint(frames)
            if not motion_hint:
                try:
                    import cv2  # type: ignore
                    small = [np.array(f.resize((64, 64), Image.BILINEAR).convert("L")) for f in frames]
                    mags, dirs = [], []
                    for i in range(1, len(small)):
                        flow = cv2.calcOpticalFlowFarneback(small[i-1], small[i], None, 0.5, 3, 15, 3, 5, 1.2, 0)
                        mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
                        mags.append(float(np.mean(mag)))
                        dirs.append(float(np.median(ang) * 180 / np.pi))
                    avg_mag = float(np.mean(mags)) if mags else 0
                    # fallback centroid untuk sparse object (dot) dimana Farneback noise
                    centroid_hint = ""
                    try:
                        cents = []
                        for s in small:
                            # threshold putih di atas 200
                            _, th = cv2.threshold(s, 200, 255, cv2.THRESH_BINARY)
                            M = cv2.moments(th)
                            if M["m00"] > 10:
                                cx = M["m10"] / M["m00"]
                                cy = M["m01"] / M["m00"]
                                cents.append((cx, cy))
                        if len(cents) >= 2:
                            dx = cents[-1][0] - cents[0][0]
                            dy = cents[-1][1] - cents[0][1]
                            if abs(dx) > 5 or abs(dy) > 5:
                                dir_txt = []
                                if dx > 5: dir_txt.append("right")
                                elif dx < -5: dir_txt.append("left")
                                if dy > 5: dir_txt.append("down")
                                elif dy < -5: dir_txt.append("up")
                                centroid_hint = f" Object centroid moves {'-'.join(dir_txt)} ({dx:+.0f}px, {dy:+.0f}px)."
                    except Exception:
                        pass
                    if avg_mag < 0.8 and not centroid_hint:
                        motion_hint = " Motion: mostly static with minor changes."
                    else:
                        bins = {"right": 0, "down": 0, "left": 0, "up": 0}
                        for a in dirs:
                            if 315 <= a or a < 45: bins["right"] += 1
                            elif 45 <= a < 135: bins["down"] += 1
                            elif 135 <= a < 225: bins["left"] += 1
                            else: bins["up"] += 1
                        dom = max(bins, key=bins.get) if dirs else "unknown"
                        motion_hint = f" Motion: avg flow {avg_mag:.1f}px, dominant {dom} ({bins.get(dom,0)}/{len(dirs)}),"
                        if centroid_hint:
                            motion_hint += centroid_hint
                        else:
                            motion_hint += " object/camera movement present."
                except Exception:
                    motion_hint = " Motion: changes detected between frames."
            if motion_hint and motion_hint.lower().strip() not in qs.lower():
                qs = qs + motion_hint
            if "frame" not in qs.lower():
                qs = qs + " Describe frame-by-frame temporal order."
        # LongVU uses DEFAULT_IMAGE_TOKEN
        qs = DEFAULT_IMAGE_TOKEN + "\n" + qs
        conv = conv_templates["qwen"].copy()
        conv.append_message(conv.roles[0], qs)
        conv.append_message(conv.roles[1], None)
        prompt_text = conv.get_prompt()

        input_ids = tokenizer_image_token(prompt_text, TOKENIZER, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(MODEL.device)
        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, TOKENIZER, input_ids)
        with torch.inference_mode():
            output_ids = MODEL.generate(
                input_ids,
                images=video_tensors,
                image_sizes=image_sizes,
                do_sample=True,
                temperature=0.2,
                top_p=None,
                max_new_tokens=max_new_tokens,
                use_cache=True,
                stopping_criteria=[stopping_criteria],
            )
        # Upstream LongVU decodes full output_ids (with inputs_embeds, output = new tokens only)
        # Match upstream: batch_decode full output_ids, skip_special, strip
        text = TOKENIZER.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
        # 5.42: if output includes prompt, strip prompt prefix
        # upstream juga pakai remove_after_last_dot; kita pakai strip saja
        if prompt_text in text:
            text = text.split(prompt_text)[-1].strip()
    return text.strip()

async def run_inference(frames: List[Image.Image], prompt: str, max_new_tokens: int) -> str:
    global _INFERENCE_ACTIVE, _INFERENCE_WAITERS
    if _INFERENCE_WAITERS >= MAX_INFERENCE_WAITERS:
        raise ServerBusyError()
    _INFERENCE_WAITERS += 1
    acquired = False
    try:
        async with get_semaphore():
            _INFERENCE_WAITERS -= 1
            acquired = True
            _INFERENCE_ACTIVE += 1
            start = time.perf_counter()
            try:
                result = await asyncio.to_thread(describe_sync, frames, prompt, max_new_tokens)
                logger.info(f"Inference done ({(time.perf_counter()-start)*1000:.0f}ms, tokens={max_new_tokens}, frames={len(frames)})")
                return result
            finally:
                _INFERENCE_ACTIVE -= 1
    finally:
        if not acquired:
            _INFERENCE_WAITERS -= 1

def health() -> dict:
    return {
        "status": "healthy" if MODEL is not None else "unhealthy",
        "model_loaded": MODEL is not None,
        "device": DEVICE,
        "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S%z"),
        "load": {
            "inference_active": _INFERENCE_ACTIVE,
            "inference_waiters": _INFERENCE_WAITERS,
            "max_concurrent_inferences": MAX_CONCURRENT_INFERENCES,
            "max_inference_waiters": MAX_INFERENCE_WAITERS,
        },
    }

# ==================== FASTAPI ====================
fastapi_app = FastAPI(title="Describe API - LongVU", version="2.0")

@fastapi_app.exception_handler(ServerBusyError)
async def server_busy_handler(_, __):
    return JSONResponse({"success": False, "error": "server_busy", "status_code": 429}, status_code=429, headers={"Retry-After": "2"})

@fastapi_app.exception_handler(HTTPException)
async def http_exc_handler(_, exc):
    return JSONResponse({"success": False, "error": exc.detail, "status_code": exc.status_code}, status_code=exc.status_code)

@fastapi_app.exception_handler(Exception)
async def global_exc_handler(_, exc):
    logger.error(f"Unhandled error: {exc}", exc_info=True)
    return JSONResponse({"success": False, "error": "Internal server error"}, status_code=500)

@fastapi_app.get("/health")
async def health_endpoint():
    return health()

class SolveRequest(BaseModel):
    image_input: str
    prompt: str = DEFAULT_PROMPT
    max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS

@fastapi_app.post("/solve")
async def solve_endpoint(request: SolveRequest):
    with request_scope() as rid:
        logger.info("Solve request received")
        if MODEL is None or TOKENIZER is None:
            raise HTTPException(status_code=503, detail="model_loading")
        if not request.image_input or not request.image_input.strip():
            raise HTTPException(status_code=400, detail="image_input is required")
        if not request.prompt or not request.prompt.strip():
            raise HTTPException(status_code=400, detail="prompt is required")
        frames = await resolve_media_to_frames(request.image_input.strip())
        logger.info(f"Media resolved: frames={len(frames)}, size={frames[0].size}")
        text = await run_inference(frames, request.prompt.strip(), request.max_new_tokens)
        if not text:
            text = ""
            logger.warning("Model returned empty output")
        logger.info("Solve completed")
        return {"success": True, "text": text}

# ==================== GRADIO ====================
gradio_demo = gr.Blocks(title="Describe API - LongVU", analytics_enabled=False)

with gradio_demo:
    gr.Markdown("## Describe API - LongVU (image / gif / webp / mp4)\nSupport semua type via `POST /solve` dengan `image_input` URL atau base64.")
    with gr.Row():
        inp_media = gr.File(label="media (image/gif/webp/mp4) - upload", file_count="single")
        inp_prompt = gr.Textbox(label="prompt", value=DEFAULT_PROMPT)
        inp_tokens = gr.Slider(minimum=16, maximum=1024, value=DEFAULT_MAX_NEW_TOKENS, step=16, label="max_new_tokens")
    out_text = gr.Textbox(label="result")
    btn = gr.Button("Run /solve")

    async def gradio_solve(file_obj, prompt: str, max_new_tokens: float | None) -> str:
        with request_scope() as rid:
            try:
                if MODEL is None or TOKENIZER is None:
                    raise gr.Error("model_loading")
                if file_obj is None:
                    raise gr.Error("file is required (image/gif/webp/mp4)")
                if not prompt or not prompt.strip():
                    raise gr.Error("prompt is required")
                tokens = DEFAULT_MAX_NEW_TOKENS if max_new_tokens is None else int(max_new_tokens)
                # gr.File di Gradio 5 bisa kirim str path atau dict {"path": "...", "_type": "gradio.FileData"}
                if isinstance(file_obj, str):
                    file_path = file_obj
                elif isinstance(file_obj, dict):
                    file_path = file_obj.get("path") or file_obj.get("name") or file_obj.get("url") or str(file_obj)
                elif hasattr(file_obj, "name"):
                    file_path = file_obj.name
                else:
                    file_path = str(file_obj)
                try:
                    with open(file_path, "rb") as f:
                        data = f.read()
                except Exception as e:
                    logger.error(f"Gradio read failed {file_path}: {e}", exc_info=True)
                    raise gr.Error(f"read failed: {e}")
                hint = file_path
                frames = await asyncio.to_thread(extract_frames_from_bytes, data, hint)
                frames = sample_frames(frames, MAX_FRAMES)
                logger.info(f"Gradio media: frames={len(frames)}, hint={hint}")
                result = await run_inference(frames, prompt.strip(), tokens)
                logger.info(f"Gradio result len={len(result) if result else 0} preview={(result[:80] if result else '')!r}")
                if not result:
                    raise gr.Error("empty result from model")
                return result
            except gr.Error:
                raise
            except Exception as e:
                logger.error(f"Gradio solve unhandled: {e}", exc_info=True)
                raise gr.Error(f"solve failed: {str(e)[:200]}")

    btn.click(gradio_solve, inputs=[inp_media, inp_prompt, inp_tokens], outputs=out_text, api_name="solve", concurrency_limit=None)

    # Advanced: URL/base64 juga didukung via API POST /solve (JSON), tidak perlu UI tambahan
    gradio_demo.queue(max_size=20, default_concurrency_limit=1)

# ==================== RUN ====================
if __name__ == "__main__":
    import threading
    app_obj, _, _ = gradio_demo.launch(ssr_mode=False, prevent_thread_lock=True)
    logger.info("launch() returned - server live")
    try:
        app_obj.add_api_route("/health", health, methods=["GET"])
        app_obj.add_api_route("/solve", solve_endpoint, methods=["POST"])
        logger.info("Clean /health dan /solve registered pada live app")
    except Exception as e:
        logger.warning(f"Gagal daftar route: {e}", exc_info=True)
    threading.Thread(target=ensure_model_loaded, daemon=True).start()
    threading.Event().wait()