"""Ag27 — FastAPI Web Server for Table Extraction Pipeline.

Wraps the existing 5-phase pipeline (TD → TSR → OCR → Cell Assignment)
behind a REST API and serves the React frontend.
"""

from __future__ import annotations

import io
import json
import logging
import mimetypes
import os
import shutil
import threading
import time
import uuid
import zipfile
from collections import deque
from enum import Enum
from pathlib import Path
from typing import Any

from fastapi.concurrency import run_in_threadpool
from fastapi import FastAPI, File, HTTPException, Query, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse, StreamingResponse
from pydantic import BaseModel, Field
from fastapi.staticfiles import StaticFiles
from starlette.responses import HTMLResponse

logging.basicConfig(
    level=logging.INFO, format="%(asctime)s  %(levelname)-8s  %(message)s"
)
logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# App & Config
# ---------------------------------------------------------------------------
app = FastAPI(
    title="Ag27 — Table Extractor",
    description="AI-powered table detection, structure recognition, and OCR from document images.",
    version="2.0.0",
)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

UPLOAD_DIR = Path(os.environ.get("UPLOAD_DIR", "./uploads"))
UPLOAD_DIR.mkdir(parents=True, exist_ok=True)

# ---------------------------------------------------------------------------
# Health Metrics (in-memory)
# ---------------------------------------------------------------------------
_server_start_time = time.time()
_health_lock = threading.Lock()
_health_metrics: dict[str, Any] = {
    "total_jobs": 0,
    "successful_jobs": 0,
    "failed_jobs": 0,
    "latencies": deque(maxlen=500),
    "recent_errors": deque(maxlen=50),
}


def _record_job_success(job_id: str, duration: float):
    with _health_lock:
        _health_metrics["total_jobs"] += 1
        _health_metrics["successful_jobs"] += 1
        _health_metrics["latencies"].append(
            {"job_id": job_id, "duration": duration, "timestamp": time.time()}
        )


def _record_job_failure(job_id: str, error: str):
    with _health_lock:
        _health_metrics["total_jobs"] += 1
        _health_metrics["failed_jobs"] += 1
        _health_metrics["recent_errors"].append(
            {"job_id": job_id, "error": error, "timestamp": time.time()}
        )


# ---------------------------------------------------------------------------
# Job store (in-memory for single-process deployment)
# ---------------------------------------------------------------------------


class JobStatus(str, Enum):
    QUEUED = "queued"
    PROCESSING = "processing"
    DONE = "done"
    ERROR = "error"


jobs: dict[str, dict[str, Any]] = {}


class CellEditRequest(BaseModel):
    table_id: int = Field(..., ge=0)
    row: int = Field(..., ge=0)
    col: int = Field(..., ge=0)
    text: str = ""


class TSRRequest(BaseModel):
    page_index: int = Field(0, ge=0)
    bbox: list[float] = Field(..., min_length=4, max_length=4)


class MarqueeOCRRequest(BaseModel):
    page_index: int = Field(0, ge=0)
    bbox: list[float] = Field(..., min_length=4, max_length=4)


class MergeCellsRequest(BaseModel):
    table_id: int = Field(..., ge=0)
    start_row: int = Field(..., ge=0)
    start_col: int = Field(..., ge=0)
    end_row: int = Field(..., ge=0)
    end_col: int = Field(..., ge=0)


class UnmergeCellRequest(BaseModel):
    table_id: int = Field(..., ge=0)
    row: int = Field(..., ge=0)
    col: int = Field(..., ge=0)


class ReplaceTableCellsRequest(BaseModel):
    table_id: int = Field(..., ge=0)
    cells: list[dict[str, Any]] = Field(default_factory=list)


class AddCellRequest(BaseModel):
    table_id: int = Field(..., ge=0)
    row: int = Field(..., ge=0)
    col: int = Field(..., ge=0)
    row_span: int = Field(1, ge=1)
    col_span: int = Field(1, ge=1)
    text: str = ""


class DeleteCellRequest(BaseModel):
    table_id: int = Field(..., ge=0)
    row: int = Field(..., ge=0)
    col: int = Field(..., ge=0)


MAX_EDIT_HISTORY = 500


def _record_cell_edit(
    job: dict[str, Any],
    *,
    table_id: int,
    row: int,
    col: int,
    previous: str,
    current: str,
):
    history = job.setdefault("edit_history", [])
    pointer = int(job.get("edit_pointer", len(history) - 1))
    if pointer < len(history) - 1:
        del history[pointer + 1 :]

    history.append(
        {
            "table_id": table_id,
            "row": row,
            "col": col,
            "previous": previous,
            "current": current,
            "timestamp": time.time(),
        }
    )
    if len(history) > MAX_EDIT_HISTORY:
        del history[: len(history) - MAX_EDIT_HISTORY]
    job["edit_pointer"] = len(history) - 1


def _cell_sort_key(cell: dict[str, Any]) -> tuple[int, int]:
    return int(cell.get("row", 0)), int(cell.get("col", 0))


def _sort_table_cells(table: dict[str, Any]) -> None:
    table["cells"] = sorted(table.get("cells", []), key=_cell_sort_key)


def _find_table(annotation: dict[str, Any], table_id: int) -> dict[str, Any] | None:
    for table in annotation.get("tables", []):
        if int(table.get("table_id", -1)) == table_id:
            return table
    return None


def _next_table_id(annotation: dict[str, Any]) -> int:
    table_ids = [
        int(table.get("table_id", -1)) for table in annotation.get("tables", [])
    ]
    return (max(table_ids) + 1) if table_ids else 0


def _normalize_bbox(bbox: list[float], image_size: tuple[int, int]) -> list[float]:
    if len(bbox) != 4:
        raise ValueError("bbox must contain four values")

    try:
        x1, y1, x2, y2 = [float(value) for value in bbox]
    except (TypeError, ValueError) as exc:
        raise ValueError("bbox values must be numeric") from exc

    left, right = sorted((x1, x2))
    top, bottom = sorted((y1, y2))
    width, height = image_size

    left = max(0.0, min(float(width), left))
    right = max(0.0, min(float(width), right))
    top = max(0.0, min(float(height), top))
    bottom = max(0.0, min(float(height), bottom))

    if right - left < 1.0 or bottom - top < 1.0:
        raise ValueError("bbox is too small after clamping")

    return [left, top, right, bottom]


def _coerce_cell_span(cell: dict[str, Any]) -> tuple[int, int, int, int]:
    row = max(0, int(cell.get("row", 0)))
    col = max(0, int(cell.get("col", 0)))
    row_span = max(1, int(cell.get("row_span", 1)))
    col_span = max(1, int(cell.get("col_span", 1)))
    cell["row"] = row
    cell["col"] = col
    cell["row_span"] = row_span
    cell["col_span"] = col_span
    return row, col, row_span, col_span


def _build_occupancy_map(
    table: dict[str, Any],
) -> dict[tuple[int, int], dict[str, Any]]:
    occupancy: dict[tuple[int, int], dict[str, Any]] = {}
    for cell in table.get("cells", []):
        row, col, row_span, col_span = _coerce_cell_span(cell)
        for row_index in range(row, row + row_span):
            for col_index in range(col, col + col_span):
                key = (row_index, col_index)
                existing = occupancy.get(key)
                if existing is not None and existing is not cell:
                    raise ValueError("Invalid table geometry: overlapping cells")
                occupancy[key] = cell
    return occupancy


def _normalize_range(
    start_row: int, start_col: int, end_row: int, end_col: int
) -> tuple[int, int, int, int]:
    top = min(start_row, end_row)
    left = min(start_col, end_col)
    bottom = max(start_row, end_row)
    right = max(start_col, end_col)
    return top, left, bottom, right


def _cell_within_range(
    cell: dict[str, Any], start_row: int, start_col: int, end_row: int, end_col: int
) -> bool:
    row, col, row_span, col_span = _coerce_cell_span(cell)
    cell_end_row = row + row_span - 1
    cell_end_col = col + col_span - 1
    return (
        row >= start_row
        and col >= start_col
        and cell_end_row <= end_row
        and cell_end_col <= end_col
    )


def _union_cell_bbox(cells: list[dict[str, Any]]) -> list[float]:
    boxes: list[tuple[float, float, float, float]] = []
    for cell in cells:
        bbox = cell.get("bbox")
        if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
            continue
        try:
            x1, y1, x2, y2 = [float(value) for value in bbox]
        except (TypeError, ValueError):
            continue
        boxes.append((min(x1, x2), min(y1, y2), max(x1, x2), max(y1, y2)))

    if not boxes:
        return [0.0, 0.0, 0.0, 0.0]

    return [
        round(min(box[0] for box in boxes), 1),
        round(min(box[1] for box in boxes), 1),
        round(max(box[2] for box in boxes), 1),
        round(max(box[3] for box in boxes), 1),
    ]


def _merge_cell_texts(cells: list[dict[str, Any]]) -> str:
    lines: list[str] = []
    by_row: dict[int, list[str]] = {}
    for cell in sorted(cells, key=_cell_sort_key):
        row = int(cell.get("row", 0))
        text = str(cell.get("text") or "").strip()
        if text:
            by_row.setdefault(row, []).append(text)

    for row in sorted(by_row):
        joined = " ".join(by_row[row]).strip()
        if joined:
            lines.append(joined)

    return "\n".join(lines)


def _average_ocr_score(cells: list[dict[str, Any]]) -> float | None:
    scores: list[float] = []
    for cell in cells:
        value = cell.get("ocr_score")
        if isinstance(value, (int, float)):
            scores.append(float(value))
    if not scores:
        return None
    return round(sum(scores) / len(scores), 4)


def _merge_table_cells(
    table: dict[str, Any], start_row: int, start_col: int, end_row: int, end_col: int
) -> dict[str, Any]:
    start_row, start_col, end_row, end_col = _normalize_range(
        start_row, start_col, end_row, end_col
    )

    occupancy = _build_occupancy_map(table)
    selected_cells: list[dict[str, Any]] = []
    seen_ids: set[int] = set()

    for row in range(start_row, end_row + 1):
        for col in range(start_col, end_col + 1):
            cell = occupancy.get((row, col))
            if cell is None:
                raise ValueError(f"Cannot merge with gaps at ({row}, {col})")
            marker = id(cell)
            if marker not in seen_ids:
                seen_ids.add(marker)
                selected_cells.append(cell)

    if len(selected_cells) < 2:
        raise ValueError("Select at least two cells to merge")

    for cell in selected_cells:
        if not _cell_within_range(cell, start_row, start_col, end_row, end_col):
            raise ValueError("Selection partially overlaps an existing merged cell")

    merged_cell = {
        "bbox": _union_cell_bbox(selected_cells),
        "row": start_row,
        "col": start_col,
        "row_span": end_row - start_row + 1,
        "col_span": end_col - start_col + 1,
        "text": _merge_cell_texts(selected_cells),
        "ocr_score": _average_ocr_score(selected_cells),
    }

    selected_markers = {id(cell) for cell in selected_cells}
    table["cells"] = [
        cell for cell in table.get("cells", []) if id(cell) not in selected_markers
    ]
    table["cells"].append(merged_cell)
    _sort_table_cells(table)
    return merged_cell


def _unmerge_table_cell(table: dict[str, Any], row: int, col: int) -> dict[str, Any]:
    target = None
    for cell in table.get("cells", []):
        cell_row, cell_col, _, _ = _coerce_cell_span(cell)
        if cell_row == row and cell_col == col:
            target = cell
            break

    if target is None:
        raise ValueError(f"Cell ({row}, {col}) not found")

    _, _, row_span, col_span = _coerce_cell_span(target)
    if row_span == 1 and col_span == 1:
        raise ValueError("Selected cell is not merged")

    bbox = target.get("bbox")
    if isinstance(bbox, (list, tuple)) and len(bbox) == 4:
        try:
            x1, y1, x2, y2 = [float(value) for value in bbox]
        except (TypeError, ValueError):
            x1 = y1 = x2 = y2 = 0.0
    else:
        x1 = y1 = x2 = y2 = 0.0

    width = (x2 - x1) / col_span if col_span else 0.0
    height = (y2 - y1) / row_span if row_span else 0.0

    replacement: list[dict[str, Any]] = []
    for row_offset in range(row_span):
        for col_offset in range(col_span):
            left = x1 + (col_offset * width)
            right = x1 + ((col_offset + 1) * width)
            top = y1 + (row_offset * height)
            bottom = y1 + ((row_offset + 1) * height)
            replacement.append(
                {
                    "bbox": [
                        round(left, 1),
                        round(top, 1),
                        round(right, 1),
                        round(bottom, 1),
                    ],
                    "row": row + row_offset,
                    "col": col + col_offset,
                    "row_span": 1,
                    "col_span": 1,
                    "text": str(target.get("text") or "")
                    if row_offset == 0 and col_offset == 0
                    else "",
                    "ocr_score": target.get("ocr_score"),
                }
            )

    table["cells"] = [cell for cell in table.get("cells", []) if cell is not target]
    table["cells"].extend(replacement)
    _sort_table_cells(table)
    return replacement[0]


def _table_shape(table: dict[str, Any]) -> tuple[int, int]:
    cells = table.get("cells", [])
    if not cells:
        return 1, 1
    max_row = 1
    max_col = 1
    for cell in cells:
        row, col, row_span, col_span = _coerce_cell_span(cell)
        max_row = max(max_row, row + row_span)
        max_col = max(max_col, col + col_span)
    return max_row, max_col


def _default_cell_bbox(
    table: dict[str, Any], row: int, col: int, row_span: int, col_span: int
) -> list[float]:
    bbox = table.get("bbox")
    if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
        return [0.0, 0.0, 0.0, 0.0]
    try:
        x1, y1, x2, y2 = [float(value) for value in bbox]
    except (TypeError, ValueError):
        return [0.0, 0.0, 0.0, 0.0]

    rows, cols = _table_shape(table)
    cell_w = (x2 - x1) / max(cols, 1)
    cell_h = (y2 - y1) / max(rows, 1)
    left = x1 + (col * cell_w)
    top = y1 + (row * cell_h)
    right = x1 + ((col + col_span) * cell_w)
    bottom = y1 + ((row + row_span) * cell_h)
    return [round(left, 1), round(top, 1), round(right, 1), round(bottom, 1)]


def _add_table_cell(
    table: dict[str, Any], row: int, col: int, row_span: int, col_span: int, text: str
) -> dict[str, Any]:
    occupancy = _build_occupancy_map(table)
    for row_index in range(row, row + row_span):
        for col_index in range(col, col + col_span):
            if occupancy.get((row_index, col_index)) is not None:
                raise ValueError(
                    f"Cell slot ({row_index}, {col_index}) is already occupied"
                )

    cell = {
        "row": row,
        "col": col,
        "row_span": row_span,
        "col_span": col_span,
        "text": str(text or ""),
        "bbox": _default_cell_bbox(table, row, col, row_span, col_span),
        "ocr_score": None,
        "font_family": "",
        "font_weight": "",
        "background_class": "",
    }
    table.setdefault("cells", []).append(cell)
    _sort_table_cells(table)
    return cell


def _delete_table_cell(table: dict[str, Any], row: int, col: int) -> dict[str, Any]:
    occupancy = _build_occupancy_map(table)
    target = occupancy.get((row, col))
    if target is None:
        raise ValueError(f"Cell ({row}, {col}) not found")
    table["cells"] = [cell for cell in table.get("cells", []) if cell is not target]
    _sort_table_cells(table)
    return target


# ---------------------------------------------------------------------------
# PDF Conversion (pymupdf/fitz)
# ---------------------------------------------------------------------------


def _convert_pdf_to_images(
    pdf_path: Path, output_dir: Path, dpi: int = 200
) -> list[Path]:
    """Convert a PDF file to a list of PNG images using pymupdf."""
    import fitz  # pymupdf

    doc = fitz.open(str(pdf_path))
    image_paths: list[Path] = []
    zoom = dpi / 72.0
    mat = fitz.Matrix(zoom, zoom)

    for page_idx in range(len(doc)):
        page = doc[page_idx]
        pix = page.get_pixmap(matrix=mat)
        img_path = output_dir / f"page_{page_idx:03d}.png"
        pix.save(str(img_path))
        image_paths.append(img_path)

    doc.close()
    return image_paths


# ---------------------------------------------------------------------------
# Pipeline runner (background thread)
# ---------------------------------------------------------------------------

# Protects the *first* model load only — acquired once then released forever.
_model_init_lock = threading.Lock()
_models_ready = False

# Limit concurrent heavy pipeline runs to avoid OOM on HF free-tier CPU.
# Increase this if you have more RAM (e.g. 3–4 on a 16 GB machine).
_pipeline_semaphore = threading.Semaphore(2)


@app.on_event("startup")
async def _maybe_prewarm_pipeline():
    if os.environ.get("PREWARM_PIPELINE_ON_STARTUP", "").strip().lower() not in {
        "1",
        "true",
        "yes",
        "on",
    }:
        return
    logger.info("Prewarming pipeline runtimes at server startup")
    try:
        from fastapi.concurrency import run_in_threadpool

        await run_in_threadpool(_ensure_models_loaded)
    except Exception:
        logger.exception("Pipeline prewarm failed during startup")


def _ensure_models_loaded():
    """Load pipeline models exactly once, even if called from multiple threads."""
    global _models_ready
    if _models_ready:
        return
    with _model_init_lock:
        if _models_ready:  # double-checked locking
            return
        from pipeline import prewarm_pipeline_runtimes

        prewarm_pipeline_runtimes()
        _models_ready = True
        logger.info("Pipeline models loaded and ready")


def _run_pipeline_thread(job_id: str):
    """Run the heavy pipeline in a background thread (concurrent-safe)."""
    job = jobs[job_id]
    job["status"] = JobStatus.PROCESSING
    job["started_at"] = time.time()

    logger.info("Starting pipeline for job %s", job_id)

    try:
        # Ensure models are loaded once (non-blocking for subsequent calls).
        _ensure_models_loaded()

        # Cap concurrency — each job still runs independently in its own thread.
        with _pipeline_semaphore:
            from pipeline import run_pipeline
            from PIL import Image

            image_paths = job.get("image_paths", [job["image_path"]])
            all_annotations: list[dict] = []
            page_count = len(image_paths)

            for page_idx, img_path in enumerate(image_paths):
                logger.info(
                    "Job %s: processing page %d/%d (%s)",
                    job_id,
                    page_idx + 1,
                    page_count,
                    img_path,
                )
                annotation = run_pipeline(img_path)
                for table in annotation.get("tables", []):
                    table["page"] = page_idx
                all_annotations.append(annotation)

            # Merge annotations across pages
            merged_tables = []
            global_table_id = 0
            for page_idx, ann in enumerate(all_annotations):
                for table in ann.get("tables", []):
                    table["table_id"] = global_table_id
                    merged_tables.append(table)
                    global_table_id += 1

            first_ann = all_annotations[0] if all_annotations else {}
            merged_annotation = {
                "source_image": job["image_name"],
                "image_size": first_ann.get("image_size", [0, 0]),
                "page_count": page_count,
                "pages": [
                    {
                        "page_index": page_idx,
                        "image_size": ann.get("image_size", [0, 0]),
                    }
                    for page_idx, ann in enumerate(all_annotations)
                ],
                "tables": merged_tables,
            }

            # Save table crops
            job_dir = Path(job["image_path"]).parent
            for table in merged_tables:
                page_idx = table.get("page", 0)
                page_img_path = (
                    image_paths[page_idx]
                    if page_idx < len(image_paths)
                    else image_paths[0]
                )
                pil = Image.open(page_img_path).convert("RGB")
                bbox = table["bbox"]
                x1, y1, x2, y2 = [int(v) for v in bbox]
                w, h = pil.size
                x1, y1 = max(0, x1), max(0, y1)
                x2, y2 = min(w, x2), min(h, y2)
                if x2 > x1 and y2 > y1:
                    crop = pil.crop((x1, y1, x2, y2))
                    crop_path = job_dir / f"table_{table['table_id']}.png"
                    crop.save(str(crop_path))

        job["annotation"] = merged_annotation
        job["edit_history"] = []
        job["edit_pointer"] = -1
        job["status"] = JobStatus.DONE
        job["finished_at"] = time.time()
        job["duration"] = round(job["finished_at"] - job["started_at"], 2)
        logger.info("Job %s done in %.1fs", job_id, job["duration"])
        _record_job_success(job_id, job["duration"])

    except Exception as e:
        logger.exception("Job %s failed", job_id)
        job["status"] = JobStatus.ERROR
        job["error"] = str(e)
        job["finished_at"] = time.time()
        _record_job_failure(job_id, str(e))


def _start_pipeline(job_id: str):
    """Start the pipeline in a background thread."""
    t = threading.Thread(target=_run_pipeline_thread, args=(job_id,), daemon=True)
    t.start()


# ---------------------------------------------------------------------------
# API Endpoints
# ---------------------------------------------------------------------------

IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif"}
PDF_EXTS = {".pdf"}
ACCEPTED_EXTS = IMAGE_EXTS | PDF_EXTS


@app.post("/api/upload")
async def upload_image(file: UploadFile = File(...)):
    """Upload an image or PDF and get a job_id back."""
    ext = Path(file.filename or "image.jpg").suffix.lower()
    if ext not in ACCEPTED_EXTS:
        raise HTTPException(
            400,
            f"Unsupported file type: {ext}. Accepted: {', '.join(sorted(ACCEPTED_EXTS))}",
        )

    job_id = str(uuid.uuid4())[:12]
    job_dir = UPLOAD_DIR / job_id
    job_dir.mkdir(parents=True, exist_ok=True)

    def _prepare_upload_artifacts() -> tuple[str, list[str], bool, str]:
        safe_name = (file.filename or f"image{ext}").replace(" ", "_")
        dest = job_dir / safe_name
        with open(dest, "wb") as f:
            shutil.copyfileobj(file.file, f)

        is_pdf = ext in PDF_EXTS
        if is_pdf:
            pages = _convert_pdf_to_images(dest, job_dir)
            image_paths = [str(p) for p in pages]
            primary_image = str(pages[0]) if pages else str(dest)
        else:
            primary_image = str(dest)
            image_paths = [str(dest)]
        return primary_image, image_paths, is_pdf, safe_name

    try:
        primary_image, image_paths, is_pdf, safe_name = await run_in_threadpool(
            _prepare_upload_artifacts
        )
    except Exception as e:
        raise HTTPException(400, f"Failed to persist upload: {e}") from e

    jobs[job_id] = {
        "id": job_id,
        "status": JobStatus.QUEUED,
        "image_path": primary_image,
        "image_paths": image_paths,
        "image_name": safe_name,
        "is_pdf": is_pdf,
        "annotation": None,
        "error": None,
        "created_at": time.time(),
        "started_at": None,
        "finished_at": None,
        "duration": None,
        "edit_history": [],
        "edit_pointer": -1,
    }

    return {
        "job_id": job_id,
        "filename": safe_name,
        "is_pdf": is_pdf,
        "page_count": len(image_paths),
    }


@app.post("/api/process/{job_id}")
async def process_image(job_id: str):
    """Trigger pipeline processing for an uploaded image."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] not in (JobStatus.QUEUED, JobStatus.ERROR):
        return {
            "job_id": job_id,
            "status": job["status"],
            "message": "Already processing or done",
        }

    job["status"] = JobStatus.QUEUED
    _start_pipeline(job_id)
    return {"job_id": job_id, "status": "queued"}


@app.post("/api/upload-and-process")
async def upload_and_process(file: UploadFile = File(...)):
    """Upload an image/PDF and immediately start processing it."""
    result = await upload_image(file)
    job_id = result["job_id"]
    _start_pipeline(job_id)
    return {
        "job_id": job_id,
        "filename": result["filename"],
        "status": "queued",
        "page_count": result.get("page_count", 1),
    }


@app.get("/api/status/{job_id}")
async def get_status(job_id: str):
    """Get the processing status of a job."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    return {
        "job_id": job_id,
        "status": job["status"],
        "image_name": job["image_name"],
        "duration": job.get("duration"),
        "error": job.get("error"),
    }


@app.get("/api/results/{job_id}")
async def get_results(job_id: str):
    """Get the full annotation results for a completed job."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, f"Job status is {job['status']}, not done")
    return {
        "job_id": job_id,
        "image_name": job["image_name"],
        "duration": job["duration"],
        "annotation": job["annotation"],
    }


@app.patch("/api/results/{job_id}/cells")
async def edit_cell(job_id: str, payload: CellEditRequest):
    """Edit a cell's text in the annotation (preview/edit feature)."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job["annotation"]
    for table in annotation.get("tables", []):
        if table["table_id"] == payload.table_id:
            for cell in table.get("cells", []):
                if cell["row"] == payload.row and cell["col"] == payload.col:
                    previous_text = str(cell.get("text") or "")
                    next_text = str(payload.text or "")
                    if previous_text != next_text:
                        _record_cell_edit(
                            job,
                            table_id=payload.table_id,
                            row=payload.row,
                            col=payload.col,
                            previous=previous_text,
                            current=next_text,
                        )
                    cell["text"] = next_text
                    return {
                        "ok": True,
                        "table_id": payload.table_id,
                        "row": payload.row,
                        "col": payload.col,
                        "text": next_text,
                    }
            raise HTTPException(
                404,
                f"Cell ({payload.row}, {payload.col}) not found in table {payload.table_id}",
            )
    raise HTTPException(404, f"Table {payload.table_id} not found")


@app.post("/api/results/{job_id}/cells/merge")
async def merge_cells(job_id: str, payload: MergeCellsRequest):
    """Merge a rectangular range of table cells into one spanning cell."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job.get("annotation") or {}
    table = _find_table(annotation, payload.table_id)
    if table is None:
        raise HTTPException(404, f"Table {payload.table_id} not found")

    start_row, start_col, end_row, end_col = _normalize_range(
        payload.start_row,
        payload.start_col,
        payload.end_row,
        payload.end_col,
    )

    try:
        merged_cell = _merge_table_cells(table, start_row, start_col, end_row, end_col)
    except ValueError as exc:
        raise HTTPException(400, str(exc)) from exc

    return {
        "ok": True,
        "annotation": annotation,
        "table_id": payload.table_id,
        "merged_cell": merged_cell,
    }


@app.post("/api/results/{job_id}/cells/unmerge")
async def unmerge_cell(job_id: str, payload: UnmergeCellRequest):
    """Split a merged anchor cell back into individual unit cells."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job.get("annotation") or {}
    table = _find_table(annotation, payload.table_id)
    if table is None:
        raise HTTPException(404, f"Table {payload.table_id} not found")

    try:
        anchor = _unmerge_table_cell(table, payload.row, payload.col)
    except ValueError as exc:
        raise HTTPException(400, str(exc)) from exc

    return {
        "ok": True,
        "annotation": annotation,
        "table_id": payload.table_id,
        "anchor": anchor,
    }


@app.put("/api/results/{job_id}/tables/{table_id}/cells")
@app.post("/api/results/{job_id}/tables/{table_id}/cells")
@app.patch("/api/results/{job_id}/tables/{table_id}/cells")
async def replace_table_cells(
    job_id: str, table_id: int, payload: ReplaceTableCellsRequest
):
    """Replace all cells for a table after advanced editor operations."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")
    if payload.table_id != table_id:
        raise HTTPException(400, "Payload table_id does not match path table_id")

    annotation = job.get("annotation") or {}
    table = _find_table(annotation, table_id)
    if table is None:
        raise HTTPException(404, f"Table {table_id} not found")

    normalized_cells: list[dict[str, Any]] = []
    for raw_cell in payload.cells:
        row = max(0, int(raw_cell.get("row", 0)))
        col = max(0, int(raw_cell.get("col", 0)))
        row_span = max(1, int(raw_cell.get("row_span", 1)))
        col_span = max(1, int(raw_cell.get("col_span", 1)))
        text = str(raw_cell.get("text") or "")
        score = raw_cell.get("ocr_score")
        if isinstance(score, (int, float)):
            ocr_score: float | None = float(score)
        else:
            ocr_score = None

        raw_bbox = raw_cell.get("bbox")
        if isinstance(raw_bbox, (list, tuple)) and len(raw_bbox) == 4:
            try:
                bbox = [float(value) for value in raw_bbox]
            except (TypeError, ValueError):
                bbox = [0.0, 0.0, 0.0, 0.0]
        else:
            bbox = [0.0, 0.0, 0.0, 0.0]

        normalized_cells.append(
            {
                "row": row,
                "col": col,
                "row_span": row_span,
                "col_span": col_span,
                "text": text,
                "bbox": bbox,
                "ocr_score": ocr_score,
                "font_family": str(raw_cell.get("font_family") or ""),
                "font_weight": str(raw_cell.get("font_weight") or ""),
                "background_class": str(raw_cell.get("background_class") or ""),
            }
        )

    try:
        _build_occupancy_map({"cells": normalized_cells})
    except ValueError as exc:
        raise HTTPException(400, str(exc)) from exc

    table["cells"] = normalized_cells
    _sort_table_cells(table)
    return {
        "ok": True,
        "annotation": annotation,
        "table_id": table_id,
        "cell_count": len(normalized_cells),
    }


@app.delete("/api/results/{job_id}/tables/{table_id}")
@app.post("/api/results/{job_id}/tables/{table_id}/delete")
async def delete_table(job_id: str, table_id: int):
    """Delete a table from annotation state."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job.get("annotation") or {}
    tables = annotation.get("tables", [])
    table_index = next(
        (
            idx
            for idx, table in enumerate(tables)
            if int(table.get("table_id", -1)) == table_id
        ),
        -1,
    )
    if table_index < 0:
        raise HTTPException(404, f"Table {table_id} not found")

    tables.pop(table_index)
    return {
        "ok": True,
        "annotation": annotation,
        "deleted_table_id": table_id,
        "remaining_tables": len(tables),
    }


@app.post("/api/results/{job_id}/cells/add")
async def add_cell(job_id: str, payload: AddCellRequest):
    """Add a new cell to an existing table."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job.get("annotation") or {}
    table = _find_table(annotation, payload.table_id)
    if table is None:
        raise HTTPException(404, f"Table {payload.table_id} not found")

    try:
        new_cell = _add_table_cell(
            table,
            row=payload.row,
            col=payload.col,
            row_span=payload.row_span,
            col_span=payload.col_span,
            text=payload.text,
        )
    except ValueError as exc:
        raise HTTPException(400, str(exc)) from exc

    return {
        "ok": True,
        "annotation": annotation,
        "table_id": payload.table_id,
        "cell": new_cell,
    }


@app.post("/api/results/{job_id}/cells/delete")
async def delete_cell(job_id: str, payload: DeleteCellRequest):
    """Delete a cell (anchor or covered position) from a table."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job.get("annotation") or {}
    table = _find_table(annotation, payload.table_id)
    if table is None:
        raise HTTPException(404, f"Table {payload.table_id} not found")

    try:
        deleted = _delete_table_cell(table, payload.row, payload.col)
    except ValueError as exc:
        raise HTTPException(400, str(exc)) from exc

    return {
        "ok": True,
        "annotation": annotation,
        "table_id": payload.table_id,
        "deleted_cell": {
            "row": int(deleted.get("row", 0)),
            "col": int(deleted.get("col", 0)),
        },
    }


@app.post("/api/process/{job_id}/tsr")
async def process_tsr_at_bbox(job_id: str, payload: TSRRequest):
    """Re-run TSR on a specific bounding box for a page."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]

    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    try:
        from pipeline import run_tsr_on_bbox

        image_paths = job.get("image_paths") or [job["image_path"]]
        if payload.page_index >= len(image_paths):
            raise HTTPException(400, f"Invalid page_index {payload.page_index}")
        img_path = image_paths[payload.page_index]

        def _run_tsr() -> dict[str, Any]:
            from PIL import Image

            pil = Image.open(img_path).convert("RGB")
            normalized_bbox = _normalize_bbox(payload.bbox, pil.size)
            return run_tsr_on_bbox(pil, normalized_bbox)

        result = await run_in_threadpool(_run_tsr)

        annotation = job.get("annotation") or {}
        next_id = _next_table_id(annotation)
        result["table_id"] = next_id
        result["page"] = payload.page_index
        for cell in result.get("cells", []):
            cell["table_id"] = next_id

        tables = annotation.setdefault("tables", [])
        tables.append(result)
        tables.sort(key=lambda table: int(table.get("table_id", 0)))

        return {
            "ok": True,
            "table": result,
            "annotation": annotation,
        }
    except HTTPException:
        raise
    except Exception as e:
        logger.exception("Custom TSR failed")
        raise HTTPException(500, str(e))


@app.post("/api/process/{job_id}/ocr")
async def process_marquee_ocr(job_id: str, payload: MarqueeOCRRequest):
    """Re-run OCR on a specific marquee area."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]

    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    try:
        from pipeline import run_ocr_on_bbox

        image_paths = job.get("image_paths") or [job["image_path"]]
        if payload.page_index >= len(image_paths):
            raise HTTPException(400, f"Invalid page_index {payload.page_index}")
        img_path = image_paths[payload.page_index]

        def _run_ocr() -> dict[str, Any]:
            from PIL import Image

            pil = Image.open(img_path).convert("RGB")
            normalized_bbox = _normalize_bbox(payload.bbox, pil.size)
            return run_ocr_on_bbox(pil, normalized_bbox)

        result = await run_in_threadpool(_run_ocr)

        return {
            "ok": True,
            "text": result.get("text", ""),
            "cells": result.get("cells", []),
        }
    except HTTPException:
        raise
    except Exception as e:
        logger.exception("Marquee OCR failed")
        raise HTTPException(500, str(e))


@app.get("/api/image/{job_id}")
async def get_image(job_id: str, page: int = Query(0, ge=0)):
    """Serve the original uploaded image or a specific converted PDF page."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    image_paths = jobs[job_id].get("image_paths") or [jobs[job_id]["image_path"]]
    if page >= len(image_paths):
        raise HTTPException(404, f"Page {page} not found")
    image_path = Path(image_paths[page])
    if not image_path.exists():
        raise HTTPException(404, "Image file not found")
    media_type = mimetypes.guess_type(str(image_path))[0] or "application/octet-stream"
    return FileResponse(image_path, media_type=media_type)


@app.get("/api/table-crop/{job_id}/{table_id}")
async def get_table_crop(job_id: str, table_id: int):
    """Serve a cropped table image."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job_dir = Path(jobs[job_id]["image_path"]).parent
    crop_path = job_dir / f"table_{table_id}.png"
    if not crop_path.exists():
        raise HTTPException(404, f"Table crop {table_id} not found")
    return FileResponse(
        crop_path, media_type="image/png", filename=f"table_{table_id}.png"
    )


@app.get("/api/table-crops/{job_id}")
async def get_table_crops_zip(job_id: str):
    """Download all table crops as a ZIP file."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    job_dir = Path(job["image_path"]).parent

    buf = io.BytesIO()
    with zipfile.ZipFile(buf, "w", zipfile.ZIP_DEFLATED) as zf:
        for crop_file in sorted(job_dir.glob("table_*.png")):
            zf.write(crop_file, crop_file.name)
    buf.seek(0)

    stem = Path(job["image_name"]).stem
    return StreamingResponse(
        buf,
        media_type="application/zip",
        headers={
            "Content-Disposition": f'attachment; filename="{stem}_table_crops.zip"'
        },
    )


@app.get("/api/export/{job_id}")
async def export_results(job_id: str, format: str = "json"):
    """Export results in various formats."""
    if job_id not in jobs:
        raise HTTPException(404, "Job not found")
    job = jobs[job_id]
    if job["status"] != JobStatus.DONE:
        raise HTTPException(400, "Job not done yet")

    annotation = job["annotation"]
    stem = Path(job["image_name"]).stem

    from export import export_csv_all, export_excel, export_html, export_json

    if format == "json":
        content = export_json(annotation)
        return StreamingResponse(
            io.BytesIO(content.encode("utf-8")),
            media_type="application/json",
            headers={"Content-Disposition": f'attachment; filename="{stem}.json"'},
        )
    elif format == "html":
        content = export_html(annotation)
        return StreamingResponse(
            io.BytesIO(content.encode("utf-8")),
            media_type="text/html",
            headers={"Content-Disposition": f'attachment; filename="{stem}.html"'},
        )
    elif format == "csv":
        content = export_csv_all(annotation)
        if not content:
            raise HTTPException(400, "No tables in annotation")
        return StreamingResponse(
            io.BytesIO(content.encode("utf-8")),
            media_type="text/csv",
            headers={"Content-Disposition": f'attachment; filename="{stem}.csv"'},
        )
    elif format == "xlsx":
        tmp_path = UPLOAD_DIR / f"{job_id}_{stem}.xlsx"
        export_excel(annotation, str(tmp_path))
        return FileResponse(
            tmp_path,
            media_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
            filename=f"{stem}.xlsx",
        )
    else:
        raise HTTPException(400, f"Unsupported format: {format}")


@app.get("/api/jobs")
async def list_jobs():
    """List all jobs with their status."""
    return [
        {
            "job_id": j["id"],
            "status": j["status"],
            "image_name": j["image_name"],
            "duration": j.get("duration"),
        }
        for j in sorted(jobs.values(), key=lambda x: x["created_at"], reverse=True)
    ]


# ---------------------------------------------------------------------------
# Health Monitoring API
# ---------------------------------------------------------------------------


@app.get("/api/health")
async def get_health():
    """Return aggregated health metrics for the monitoring dashboard."""
    import statistics

    with _health_lock:
        total = _health_metrics["total_jobs"]
        success = _health_metrics["successful_jobs"]
        failed = _health_metrics["failed_jobs"]
        latencies = [entry["duration"] for entry in _health_metrics["latencies"]]
        recent_errors = list(_health_metrics["recent_errors"])

    uptime = time.time() - _server_start_time
    success_rate = round(success / total * 100, 1) if total > 0 else 0.0
    failure_rate = round(failed / total * 100, 1) if total > 0 else 0.0

    lat_stats = {}
    if latencies:
        sorted_lat = sorted(latencies)
        lat_stats = {
            "avg": round(statistics.mean(sorted_lat), 2),
            "min": round(sorted_lat[0], 2),
            "max": round(sorted_lat[-1], 2),
            "p50": round(sorted_lat[len(sorted_lat) // 2], 2),
            "p95": round(sorted_lat[int(len(sorted_lat) * 0.95)], 2)
            if len(sorted_lat) >= 2
            else round(sorted_lat[-1], 2),
        }

    return {
        "uptime_seconds": round(uptime, 0),
        "total_jobs": total,
        "successful_jobs": success,
        "failed_jobs": failed,
        "success_rate": success_rate,
        "failure_rate": failure_rate,
        "latency": lat_stats,
        "recent_errors": recent_errors[-10:],
        "active_jobs": sum(
            1
            for j in jobs.values()
            if j["status"] in (JobStatus.QUEUED, JobStatus.PROCESSING)
        ),
    }


# ---------------------------------------------------------------------------
# Serve React frontend (production build)
# ---------------------------------------------------------------------------

_WEB_DIST = Path(__file__).parent / "web" / "dist"
if _WEB_DIST.exists():
    app.mount(
        "/assets", StaticFiles(directory=str(_WEB_DIST / "assets")), name="assets"
    )

    @app.get("/{full_path:path}")
    async def serve_spa(full_path: str):
        """Serve the React SPA — all non-API routes go to index.html."""
        file_path = _WEB_DIST / full_path
        if full_path and file_path.exists() and file_path.is_file():
            return FileResponse(file_path)
        return FileResponse(_WEB_DIST / "index.html")
else:

    @app.get("/")
    async def root():
        return HTMLResponse(
            "<h1>Ag27 — Table Extractor API</h1>"
            "<p>Frontend not built yet. Run <code>cd web && npm run build</code></p>"
            "<p><a href='/docs'>API Docs</a></p>"
        )


if __name__ == "__main__":
    import uvicorn

    uvicorn.run("server:app", host="0.0.0.0", port=8001, reload=True)