"""
OCR Pipeline Dewarp Endpoints

Auto dewarp (with VLM/CV ensemble), manual dewarp, combined
rotation+shear adjustment, and ground truth.
Extracted from ocr_pipeline_geometry.py for file-size compliance.
"""

import json
import logging
import os
import re
import time
from datetime import datetime
from typing import Any, Dict

import cv2
from fastapi import APIRouter, HTTPException, Query

from cv_vocab_pipeline import (
    _apply_shear,
    create_ocr_image,
    dewarp_image,
    dewarp_image_manual,
)
from ocr_pipeline_session_store import (
    get_session_db,
    update_session_db,
)
from ocr_pipeline_common import (
    _cache,
    _load_session_to_cache,
    _get_cached,
    _append_pipeline_log,
    ManualDewarpRequest,
    CombinedAdjustRequest,
    DewarpGroundTruthRequest,
)

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["ocr-pipeline"])


async def _detect_shear_with_vlm(image_bytes: bytes) -> Dict[str, Any]:
    """Ask qwen2.5vl:32b to estimate the vertical shear angle of a scanned page.

    The VLM is shown the image and asked: are the column/table borders tilted?
    If yes, by how many degrees? Returns a dict with shear_degrees and confidence.
    Confidence is 0.0 if Ollama is unavailable or parsing fails.
    """
    import httpx
    import base64

    ollama_base = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
    model = os.getenv("OLLAMA_HTR_MODEL", "qwen2.5vl:32b")

    prompt = (
        "This is a scanned vocabulary worksheet. Look at the vertical borders of the table columns. "
        "Are they perfectly vertical, or do they tilt slightly? "
        "If they tilt, estimate the tilt angle in degrees (positive = top tilts right, negative = top tilts left). "
        "Reply with ONLY a JSON object like: {\"shear_degrees\": 1.2, \"confidence\": 0.8} "
        "Use confidence 0.0-1.0 based on how clearly you can see the tilt. "
        "If the columns look straight, return {\"shear_degrees\": 0.0, \"confidence\": 0.9}"
    )

    img_b64 = base64.b64encode(image_bytes).decode("utf-8")
    payload = {
        "model": model,
        "prompt": prompt,
        "images": [img_b64],
        "stream": False,
    }

    try:
        async with httpx.AsyncClient(timeout=60.0) as client:
            resp = await client.post(f"{ollama_base}/api/generate", json=payload)
            resp.raise_for_status()
            text = resp.json().get("response", "")

        # Parse JSON from response (may have surrounding text)
        match = re.search(r'\{[^}]+\}', text)
        if match:
            data = json.loads(match.group(0))
            shear = float(data.get("shear_degrees", 0.0))
            conf = float(data.get("confidence", 0.0))
            # Clamp to reasonable range
            shear = max(-3.0, min(3.0, shear))
            conf = max(0.0, min(1.0, conf))
            return {"method": "vlm_qwen2.5vl", "shear_degrees": round(shear, 3), "confidence": round(conf, 2)}
    except Exception as e:
        logger.warning(f"VLM dewarp failed: {e}")

    return {"method": "vlm_qwen2.5vl", "shear_degrees": 0.0, "confidence": 0.0}


@router.post("/sessions/{session_id}/dewarp")
async def auto_dewarp(
    session_id: str,
    method: str = Query("ensemble", description="Detection method: ensemble | vlm | cv"),
):
    """Detect and correct vertical shear on the deskewed image.

    Methods:
    - **ensemble** (default): 3-method CV ensemble (vertical edges + projection + Hough)
    - **cv**: CV ensemble only (same as ensemble)
    - **vlm**: Ask qwen2.5vl:32b to estimate the shear angle visually
    """
    if method not in ("ensemble", "cv", "vlm"):
        raise HTTPException(status_code=400, detail="method must be one of: ensemble, cv, vlm")

    if session_id not in _cache:
        await _load_session_to_cache(session_id)
    cached = _get_cached(session_id)

    deskewed_bgr = cached.get("deskewed_bgr")
    if deskewed_bgr is None:
        raise HTTPException(status_code=400, detail="Deskew must be completed before dewarp")

    t0 = time.time()

    if method == "vlm":
        # Encode deskewed image to PNG for VLM
        success, png_buf = cv2.imencode(".png", deskewed_bgr)
        img_bytes = png_buf.tobytes() if success else b""
        vlm_det = await _detect_shear_with_vlm(img_bytes)
        shear_deg = vlm_det["shear_degrees"]
        if abs(shear_deg) >= 0.05 and vlm_det["confidence"] >= 0.3:
            dewarped_bgr = _apply_shear(deskewed_bgr, -shear_deg)
        else:
            dewarped_bgr = deskewed_bgr
        dewarp_info = {
            "method": vlm_det["method"],
            "shear_degrees": shear_deg,
            "confidence": vlm_det["confidence"],
            "detections": [vlm_det],
        }
    else:
        dewarped_bgr, dewarp_info = dewarp_image(deskewed_bgr)

    duration = time.time() - t0

    # Encode as PNG
    success, png_buf = cv2.imencode(".png", dewarped_bgr)
    dewarped_png = png_buf.tobytes() if success else b""

    dewarp_result = {
        "method_used": dewarp_info["method"],
        "shear_degrees": dewarp_info["shear_degrees"],
        "confidence": dewarp_info["confidence"],
        "duration_seconds": round(duration, 2),
        "detections": dewarp_info.get("detections", []),
    }

    # Update cache
    cached["dewarped_bgr"] = dewarped_bgr
    cached["dewarp_result"] = dewarp_result

    # Persist to DB
    await update_session_db(
        session_id,
        dewarped_png=dewarped_png,
        dewarp_result=dewarp_result,
        auto_shear_degrees=dewarp_info.get("shear_degrees", 0.0),
        current_step=4,
    )

    logger.info(f"OCR Pipeline: dewarp session {session_id}: "
                f"method={dewarp_info['method']} shear={dewarp_info['shear_degrees']:.3f} "
                f"conf={dewarp_info['confidence']:.2f} ({duration:.2f}s)")

    await _append_pipeline_log(session_id, "dewarp", {
        "shear_degrees": dewarp_info["shear_degrees"],
        "confidence": dewarp_info["confidence"],
        "method": dewarp_info["method"],
        "ensemble_methods": [d.get("method", "") for d in dewarp_info.get("detections", [])],
    }, duration_ms=int(duration * 1000))

    return {
        "session_id": session_id,
        **dewarp_result,
        "dewarped_image_url": f"/api/v1/ocr-pipeline/sessions/{session_id}/image/dewarped",
    }


@router.post("/sessions/{session_id}/dewarp/manual")
async def manual_dewarp(session_id: str, req: ManualDewarpRequest):
    """Apply shear correction with a manual angle."""
    if session_id not in _cache:
        await _load_session_to_cache(session_id)
    cached = _get_cached(session_id)

    deskewed_bgr = cached.get("deskewed_bgr")
    if deskewed_bgr is None:
        raise HTTPException(status_code=400, detail="Deskew must be completed before dewarp")

    shear_deg = max(-2.0, min(2.0, req.shear_degrees))

    if abs(shear_deg) < 0.001:
        dewarped_bgr = deskewed_bgr
    else:
        dewarped_bgr = dewarp_image_manual(deskewed_bgr, shear_deg)

    success, png_buf = cv2.imencode(".png", dewarped_bgr)
    dewarped_png = png_buf.tobytes() if success else b""

    dewarp_result = {
        **(cached.get("dewarp_result") or {}),
        "method_used": "manual",
        "shear_degrees": round(shear_deg, 3),
    }

    # Update cache
    cached["dewarped_bgr"] = dewarped_bgr
    cached["dewarp_result"] = dewarp_result

    # Persist to DB
    await update_session_db(
        session_id,
        dewarped_png=dewarped_png,
        dewarp_result=dewarp_result,
    )

    logger.info(f"OCR Pipeline: manual dewarp session {session_id}: shear={shear_deg:.3f}")

    return {
        "session_id": session_id,
        "shear_degrees": round(shear_deg, 3),
        "method_used": "manual",
        "dewarped_image_url": f"/api/v1/ocr-pipeline/sessions/{session_id}/image/dewarped",
    }


@router.post("/sessions/{session_id}/adjust-combined")
async def adjust_combined(session_id: str, req: CombinedAdjustRequest):
    """Apply rotation + shear combined to the original image.

    Used by the fine-tuning sliders to preview arbitrary rotation/shear
    combinations without re-running the full deskew/dewarp pipeline.
    """
    if session_id not in _cache:
        await _load_session_to_cache(session_id)
    cached = _get_cached(session_id)

    img_bgr = cached.get("original_bgr")
    if img_bgr is None:
        raise HTTPException(status_code=400, detail="Original image not available")

    rotation = max(-15.0, min(15.0, req.rotation_degrees))
    shear_deg = max(-5.0, min(5.0, req.shear_degrees))

    h, w = img_bgr.shape[:2]
    result_bgr = img_bgr

    # Step 1: Apply rotation
    if abs(rotation) >= 0.001:
        center = (w // 2, h // 2)
        M = cv2.getRotationMatrix2D(center, rotation, 1.0)
        result_bgr = cv2.warpAffine(result_bgr, M, (w, h),
                                     flags=cv2.INTER_LINEAR,
                                     borderMode=cv2.BORDER_REPLICATE)

    # Step 2: Apply shear
    if abs(shear_deg) >= 0.001:
        result_bgr = dewarp_image_manual(result_bgr, shear_deg)

    # Encode
    success, png_buf = cv2.imencode(".png", result_bgr)
    dewarped_png = png_buf.tobytes() if success else b""

    # Binarize
    binarized_png = None
    try:
        binarized = create_ocr_image(result_bgr)
        success_bin, bin_buf = cv2.imencode(".png", binarized)
        binarized_png = bin_buf.tobytes() if success_bin else None
    except Exception:
        pass

    # Build combined result dicts
    deskew_result = {
        **(cached.get("deskew_result") or {}),
        "angle_applied": round(rotation, 3),
        "method_used": "manual_combined",
    }
    dewarp_result = {
        **(cached.get("dewarp_result") or {}),
        "method_used": "manual_combined",
        "shear_degrees": round(shear_deg, 3),
    }

    # Update cache
    cached["deskewed_bgr"] = result_bgr
    cached["dewarped_bgr"] = result_bgr
    cached["deskew_result"] = deskew_result
    cached["dewarp_result"] = dewarp_result

    # Persist to DB
    db_update = {
        "dewarped_png": dewarped_png,
        "deskew_result": deskew_result,
        "dewarp_result": dewarp_result,
    }
    if binarized_png:
        db_update["binarized_png"] = binarized_png
        db_update["deskewed_png"] = dewarped_png
    await update_session_db(session_id, **db_update)

    logger.info(f"OCR Pipeline: combined adjust session {session_id}: "
                f"rotation={rotation:.3f} shear={shear_deg:.3f}")

    return {
        "session_id": session_id,
        "rotation_degrees": round(rotation, 3),
        "shear_degrees": round(shear_deg, 3),
        "method_used": "manual_combined",
        "dewarped_image_url": f"/api/v1/ocr-pipeline/sessions/{session_id}/image/dewarped",
    }


@router.post("/sessions/{session_id}/ground-truth/dewarp")
async def save_dewarp_ground_truth(session_id: str, req: DewarpGroundTruthRequest):
    """Save ground truth feedback for the dewarp step."""
    session = await get_session_db(session_id)
    if not session:
        raise HTTPException(status_code=404, detail=f"Session {session_id} not found")

    ground_truth = session.get("ground_truth") or {}
    gt = {
        "is_correct": req.is_correct,
        "corrected_shear": req.corrected_shear,
        "notes": req.notes,
        "saved_at": datetime.utcnow().isoformat(),
        "dewarp_result": session.get("dewarp_result"),
    }
    ground_truth["dewarp"] = gt

    await update_session_db(session_id, ground_truth=ground_truth)

    if session_id in _cache:
        _cache[session_id]["ground_truth"] = ground_truth

    logger.info(f"OCR Pipeline: ground truth dewarp session {session_id}: "
                f"correct={req.is_correct}, corrected_shear={req.corrected_shear}")

    return {"session_id": session_id, "ground_truth": gt}