from pathlib import Path
from datetime import datetime
from uuid import uuid4
import json
import os
import urllib.request
import urllib.error
import urllib.parse

from flask import Flask, send_from_directory, request, jsonify
from flask_cors import CORS
from PIL import Image
import ollama
import anthropic as _anthropic_sdk

ANTHROPIC_API_KEY = os.environ.get("ANTHROPIC_API_KEY", "")

DIRECTUS_URL = "https://db.hejyou.com"

BASE_DIR = Path(__file__).resolve().parent
PICTURES_DIR = BASE_DIR / "pictures"
OBJECTS_DIR = BASE_DIR / "objects_image"
SENTENCE_DIR = BASE_DIR / "sentence_object"
PROMPTS_DIR = BASE_DIR / "prompts"

app = Flask(
    __name__,
    template_folder=str(BASE_DIR / "templates"),
    static_folder=str(BASE_DIR / "static"),
)
CORS(app)


def read_prompt(filepath: Path, fallback: str) -> str:
    """
    Hilfsfunktion, um Prompt-Dateien zu lesen.
    """
    try:
        if filepath.exists():
            return filepath.read_text(encoding="utf-8").strip()
    except Exception:
        pass
    return fallback.strip()


def _directus(method, path, token, body=None):
    """Hilfsfunktion: Directus-API-Aufruf via urllib."""
    headers = {"Content-Type": "application/json"}
    if token:
        headers["Authorization"] = token
    req = urllib.request.Request(
        f"{DIRECTUS_URL}{path}",
        data=json.dumps(body).encode() if body is not None else None,
        headers=headers,
        method=method,
    )
    try:
        with urllib.request.urlopen(req) as resp:
            raw = resp.read().decode("utf-8")
            return json.loads(raw) if raw else {}, resp.status
    except urllib.error.HTTPError as e:
        raw = e.read().decode("utf-8")
        return json.loads(raw) if raw else {}, e.code


@app.route("/api/directus/auth/login", methods=["POST"])
def directus_auth_login():
    """Proxy: Directus-Login ohne CORS-Probleme."""
    data, status = _directus("POST", "/auth/login", token=None, body=request.get_json())
    return jsonify(data), status


@app.route("/api/directus/pictures", methods=["GET"])
def directus_pictures():
    """Proxy: Directus-Bilder nach Status filtern."""
    token = request.headers.get("Authorization", "")
    pic_status = request.args.get("status", "new")
    data, status = _directus("GET", f"/items/pictures?filter[status][_eq]={pic_status}&fields=id,media,status&sort=date_created", token)
    return jsonify(data), status


@app.route("/api/directus/pictures/<pic_id>", methods=["PATCH"])
def directus_picture(pic_id):
    """Proxy: Bild-Status aktualisieren."""
    token = request.headers.get("Authorization", "")
    data, status = _directus("PATCH", f"/items/pictures/{pic_id}", token, body=request.get_json())
    return jsonify(data), status


@app.route("/api/directus/objects", methods=["GET", "POST"])
def directus_objects():
    """Proxy: Objekte laden (GET) oder anlegen (POST)."""
    token = request.headers.get("Authorization", "")
    if request.method == "GET":
        picture_id = request.args.get("picture_id", "")
        fields = "id,selections,user_notes,parent,status,picture"
        path = f"/items/objects?filter[picture][_eq]={picture_id}&fields={fields}&sort=date_created"
        data, status = _directus("GET", path, token)
        return jsonify(data), status
    else:
        data, status = _directus("POST", "/items/objects", token, body=request.get_json())
        return jsonify(data), status


@app.route("/api/directus/objects/<obj_id>", methods=["PATCH", "DELETE"])
def directus_object(obj_id):
    """Proxy: Objekt aktualisieren (PATCH) oder löschen (DELETE)."""
    token = request.headers.get("Authorization", "")
    if request.method == "PATCH":
        data, status = _directus("PATCH", f"/items/objects/{obj_id}", token, body=request.get_json())
    else:
        data, status = _directus("DELETE", f"/items/objects/{obj_id}", token)
    return jsonify(data), status


@app.route("/api/images", methods=["GET"])
def list_images():
    """
    Gibt alle Bilder im pictures-Ordner zurück.
    Query-Parameter: ?mode=draw (Standard) oder ?mode=generate
    draw     → Bilder ohne _saved-Suffix
    generate → Bilder mit _saved-Suffix
    """
    mode = request.args.get("mode", "draw")
    PICTURES_DIR.mkdir(parents=True, exist_ok=True)
    if mode == "generate":
        image_paths = sorted(
            [f for f in PICTURES_DIR.iterdir() if f.is_file() and f.stem.endswith("_saved")],
            key=lambda p: p.stat().st_mtime,
        )
    else:
        image_paths = sorted(
            [f for f in PICTURES_DIR.iterdir() if f.is_file() and not f.stem.endswith("_saved")],
            key=lambda p: p.stat().st_mtime,
        )
    return jsonify({"images": [p.name for p in image_paths]})


REACT_BUILD_DIR = BASE_DIR / "static" / "react"


def _serve_spa():
    """Liefert die React SPA index.html für alle Frontend-Routen."""
    return send_from_directory(REACT_BUILD_DIR, "index.html")


@app.route("/")
@app.route("/draw")
@app.route("/generate")
def serve_spa():
    return _serve_spa()


@app.route("/assets/<path:filename>")
def serve_react_assets(filename: str):
    """Liefert JS/CSS-Assets aus dem Vite-Build."""
    return send_from_directory(REACT_BUILD_DIR / "assets", filename)


@app.route("/pictures/<path:filename>")
def serve_picture(filename: str):
    # Statisches Ausliefern der Originalbilder
    return send_from_directory(PICTURES_DIR, filename)


@app.route("/objects_image/<path:filename>")
def serve_object_image(filename: str):
    # Ausliefern der gespeicherten Ausschnitt-Bilder
    return send_from_directory(OBJECTS_DIR, filename)


@app.route("/api/objects", methods=["GET"])
def list_objects_for_image():
    """
    Gibt alle gespeicherten Objekte (Ausschnitte) zu einem Quellbild zurück.

    Query-Parameter:
      ?filename=testbild.png
    """
    filename = request.args.get("filename")
    if not filename:
        return jsonify({"error": "Missing filename query parameter"}), 400

    # Für *_saved-Bilder auch Objekte berücksichtigen, deren source_filename
    # noch auf den ursprünglichen Namen ohne "_saved" zeigt (Kompatibilität)
    base_filename = filename
    legacy_filename = None
    if base_filename.endswith(".png") or base_filename.endswith(".jpg") or base_filename.endswith(".jpeg") or base_filename.endswith(".webp"):
        stem = base_filename.rsplit(".", 1)[0]
        suffix = base_filename.rsplit(".", 1)[1]
        if stem.endswith("_saved"):
            legacy_filename = f"{stem[:-6]}.{suffix}"

    OBJECTS_DIR.mkdir(parents=True, exist_ok=True)
    objects = []
    for meta_file in OBJECTS_DIR.glob("*.txt"):
        try:
            meta = json.loads(meta_file.read_text(encoding="utf-8"))
        except Exception:
            continue

        src_name = meta.get("source_filename")
        if src_name != filename and (legacy_filename is None or src_name != legacy_filename):
            continue

        objects.append(
            {
                "id": meta.get("id"),
                "image_file": meta.get("image_file"),
                "title_de": meta.get("title_de", ""),
                "position_de": meta.get("position_de", ""),
                "action_de": meta.get("action_de", ""),
                "condition_de": meta.get("condition_de", ""),
                # Von KI erzeugte Details (optional)
                "label_en": meta.get("label_en"),
                "label_de": meta.get("label_de"),
                "label_se": meta.get("label_se"),
                "color_en": meta.get("color_en"),
                "adjective_en": meta.get("adjective_en"),
                "action_verb_en": meta.get("action_verb_en"),
                "preposition_en": meta.get("preposition_en"),
                "relative_position_en": meta.get("relative_position_en"),
                "season_en": meta.get("season_en"),
                "mode": meta.get("mode"),
                "bbox": meta.get("bbox"),
                "polygon": meta.get("polygon"),
                "hierarchy": meta.get("hierarchy", 1),
                "parent_id": meta.get("parent_id"),
                "created_at": meta.get("created_at"),
            }
        )

    # Älteste zuerst sortieren (1 = ältestes Objekt)
    objects.sort(key=lambda o: o.get("created_at") or "")

    # Laufende Nummer je Bild (1..n) nach obiger Sortierung
    for idx, obj in enumerate(objects, start=1):
        obj["index"] = idx

    return jsonify({"objects": objects})


@app.route("/api/object/<obj_id>/hierarchy", methods=["POST"])
def update_object_hierarchy(obj_id: str):
    """
    Aktualisiert die Hierarchie (1, 2 oder 3) eines gespeicherten Objekts.
    """
    data = request.get_json(force=True, silent=True) or {}
    try:
        hierarchy = int(data.get("hierarchy"))
    except (TypeError, ValueError):
        return jsonify({"error": "Invalid hierarchy"}), 400

    if hierarchy not in (1, 2, 3):
        return jsonify({"error": "Hierarchy must be 1, 2 or 3"}), 400

    meta_path = OBJECTS_DIR / f"{obj_id}.txt"
    if not meta_path.exists():
        return jsonify({"error": "Object not found"}), 404

    try:
        meta = json.loads(meta_path.read_text(encoding="utf-8"))
    except Exception:
        return jsonify({"error": "Could not read meta file"}), 500

    meta["hierarchy"] = hierarchy
    meta_path.write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")

    return jsonify({"id": obj_id, "hierarchy": hierarchy})


@app.route("/api/object/<obj_id>", methods=["POST"])
def update_object_meta(obj_id: str):
    """
    Aktualisiert Text-Metadaten (title_de, position_de, action_de, condition_de)
    eines gespeicherten Objekts.
    """
    data = request.get_json(force=True, silent=True) or {}

    meta_path = OBJECTS_DIR / f"{obj_id}.txt"
    if not meta_path.exists():
        return jsonify({"error": "Object not found"}), 404

    try:
        meta = json.loads(meta_path.read_text(encoding="utf-8"))
    except Exception:
        return jsonify({"error": "Could not read meta file"}), 500

    for key in ("title_de", "position_de", "action_de", "condition_de"):
        if key in data:
            meta[key] = data.get(key, "")

    meta_path.write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")

    return jsonify({k: meta.get(k) for k in ("id", "title_de", "position_de", "action_de", "condition_de")})


@app.route("/api/object/<obj_id>/parent", methods=["POST"])
def update_object_parent(obj_id: str):
    """
    Aktualisiert die Parent-Relation eines Objekts.
    Erwartet JSON: { "parent_id": "<uuid>" | null }
    """
    data = request.get_json(force=True, silent=True) or {}
    parent_id = data.get("parent_id")

    meta_path = OBJECTS_DIR / f"{obj_id}.txt"
    if not meta_path.exists():
        return jsonify({"error": "Object not found"}), 404

    try:
        meta = json.loads(meta_path.read_text(encoding="utf-8"))
    except Exception:
        return jsonify({"error": "Could not read meta file"}), 500

    # Optional: prüfen, ob parent_id existiert und zum selben Bild gehört
    if parent_id:
        parent_meta_path = OBJECTS_DIR / f"{parent_id}.txt"
        if not parent_meta_path.exists():
            return jsonify({"error": "Parent object not found"}), 400
        try:
            parent_meta = json.loads(parent_meta_path.read_text(encoding="utf-8"))
        except Exception:
            return jsonify({"error": "Could not read parent meta file"}), 500

        if parent_meta.get("source_filename") != meta.get("source_filename"):
            return jsonify({"error": "Parent must belong to same source image"}), 400

    meta["parent_id"] = parent_id or None
    meta_path.write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")

    return jsonify({"id": obj_id, "parent_id": meta.get("parent_id")})


@app.route("/api/object/<obj_id>/generate_details", methods=["POST"])
def generate_object_details(obj_id: str):
    """
    Erzeugt mit Llama 3.2 Vision zusätzliche englische Metadaten zu einem Objekt
    auf Basis des ausgeschnittenen Objektbildes + vorhandener deutscher Felder.
    """
    meta_path = OBJECTS_DIR / f"{obj_id}.txt"
    if not meta_path.exists():
        return jsonify({"error": "Object not found"}), 404

    try:
        meta = json.loads(meta_path.read_text(encoding="utf-8"))
    except Exception:
        return jsonify({"error": "Could not read meta file"}), 500

    image_file = meta.get("image_file")
    if not image_file:
        return jsonify({"error": "Object has no image_file"}), 400

    image_path = OBJECTS_DIR / image_file
    if not image_path.exists():
        return jsonify({"error": "Object image not found"}), 404

    # Prompt laden
    prompt_template = read_prompt(
        PROMPTS_DIR / "create_details.txt",
        "Analyze this object and return one JSON object with the requested fields.",
    )

    title_de = meta.get("title_de", "") or ""
    action_de = meta.get("action_de", "") or ""
    condition_de = meta.get("condition_de", "") or ""

    # Platzhalter im Prompt ersetzen (einfache .format-Verwendung)
    try:
        prompt = prompt_template.format(
            title_de=title_de,
            action_de=action_de,
            condition_de=condition_de,
        )
    except Exception:
        # Falls das Format fehlschlägt, einfach Rohprompt + Kontext anhängen
        prompt = (
            f"{prompt_template}\n\n"
            f"Titel (Deutsch): {title_de}\n"
            f"Status (Deutsch): {action_de}\n"
            f"Zustand (Deutsch): {condition_de}\n"
        )

    # Bild als Bytes laden
    try:
        img_bytes = image_path.read_bytes()
    except Exception:
        return jsonify({"error": "Could not read object image"}), 500

    try:
        response = ollama.chat(
            model="llama3.2-vision",
            messages=[{"role": "user", "content": prompt, "images": [img_bytes]}],
            format="json",
            options={"temperature": 0},
        )
    except Exception as e:
        return jsonify({"error": f"LLM request failed: {e}"}), 500

    content = response.get("message", {}).get("content")
    if not content:
        return jsonify({"error": "Empty response from LLM"}), 500

    try:
        ai_data = json.loads(content)
    except Exception:
        # Fallback: versuchen, das erste JSON-Objekt zu extrahieren
        try:
            start = content.index("{")
            end = content.rindex("}") + 1
            ai_data = json.loads(content[start:end])
        except Exception:
            return jsonify({"error": "Could not parse JSON from LLM response"}), 500

    # Debug-Ausgabe des rohen AI-Outputs
    try:
        print(f"[generate_details] Raw AI data for {obj_id}: {json.dumps(ai_data, ensure_ascii=False)}")
    except Exception:
        print(f"[generate_details] Raw AI data for {obj_id} (non-serializable): {ai_data!r}")

    # Erwartete Felder übernehmen (gemäß neuem Prompt)
    fields = [
        "label_en",
        "label_de",
        "label_se",
        "color_en",
        "adjective_en",
        "action_verb_en",
        "preposition_en",
        "relative_position_en",
        "season_en",
    ]
    for key in fields:
        if key in ai_data:
            meta[key] = ai_data.get(key)

    # Metadatei zurückschreiben
    meta_path.write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")

    # Nur die neuen Felder zurückgeben (None als leeren String konvertieren)
    result = {key: (meta.get(key) or "") for key in fields}

    try:
        print(f"[generate_details] Stored meta for {obj_id}: {json.dumps(result, ensure_ascii=False)}")
    except Exception:
        print(f"[generate_details] Stored meta for {obj_id}: {result!r}")

    return jsonify(result)


def load_sentences_for_object(obj_id: str):
    """
    Lädt alle gespeicherten Sätze für ein Objekt aus sentence_object/<obj_id>.txt.
    Rückgabe: Liste von Dicts mit mindestens question_en, answer_en, created_at.
    """
    SENTENCE_DIR.mkdir(parents=True, exist_ok=True)
    sentence_path = SENTENCE_DIR / f"{obj_id}.txt"
    if not sentence_path.exists():
        return []
    try:
        data = json.loads(sentence_path.read_text(encoding="utf-8"))
        if isinstance(data, list):
            return data
        if isinstance(data, dict):
            # Fallback: einzelnes Objekt in Liste wrappen
            return [data]
        return []
    except Exception:
        return []


@app.route("/api/object/<obj_id>/sentences", methods=["GET"])
def get_object_sentences(obj_id: str):
    """
    Gibt alle bisher erzeugten KI-Sätze zu einem Objekt zurück.
    """
    sentences = load_sentences_for_object(obj_id)
    return jsonify({"object_id": obj_id, "sentences": sentences})


@app.route("/api/object/<obj_id>/generate_sentence", methods=["POST"])
def generate_object_sentence(obj_id: str):
    """
    Erzeugt mit Llama 3.1 einen neuen englischen Frage-Antwort-Satz
    zu einem Objekt basierend auf den KI-Details und bisherigen Sätzen.
    Speichert alle Sätze in sentence_object/<obj_id>.txt.
    """
    # Objekt-Metadaten laden (inkl. KI-Details)
    meta_path = OBJECTS_DIR / f"{obj_id}.txt"
    if not meta_path.exists():
        return jsonify({"error": "Object not found"}), 404

    try:
        meta = json.loads(meta_path.read_text(encoding="utf-8"))
    except Exception:
        return jsonify({"error": "Could not read meta file"}), 500

    # Relevante Felder für den Satz (KI-Details)
    details = {
        "label_en": meta.get("label_en"),
        "label_de": meta.get("label_de"),
        "label_se": meta.get("label_se"),
        "color_en": meta.get("color_en"),
        "adjective_en": meta.get("adjective_en"),
        "action_verb_en": meta.get("action_verb_en"),
        "preposition_en": meta.get("preposition_en"),
        "relative_position_en": meta.get("relative_position_en"),
        "season_en": meta.get("season_en"),
        "title_de": meta.get("title_de"),
        "position_de": meta.get("position_de"),
        "action_de": meta.get("action_de"),
        "condition_de": meta.get("condition_de"),
    }

    # Bisherige Sätze laden
    previous_sentences = load_sentences_for_object(obj_id)

    # Prompt laden
    prompt_template = read_prompt(
        PROMPTS_DIR / "create_sentence.txt",
        "Create one new English question_en and answer_en as JSON, based on the object details and avoiding previous sentences.",
    )

    # Vollständigen Prompt zusammensetzen
    try:
        details_json = json.dumps(details, ensure_ascii=False, indent=2)
        prev_json = json.dumps(previous_sentences, ensure_ascii=False, indent=2)
    except Exception:
        details_json = str(details)
        prev_json = str(previous_sentences)

    prompt = (
        f"{prompt_template}\n\n"
        f"OBJECT_DETAILS_JSON:\n{details_json}\n\n"
        f"PREVIOUS_SENTENCES_JSON:\n{prev_json}\n"
    )

    # LLM-Aufruf (nur Text, kein Bild nötig)
    try:
        response = ollama.chat(
            model="llama3.1",
            messages=[{"role": "user", "content": prompt}],
            format="json",
            options={"temperature": 0.2},
        )
    except Exception as e:
        return jsonify({"error": f"LLM request failed: {e}"}), 500

    content = response.get("message", {}).get("content")
    if not content:
        return jsonify({"error": "Empty response from LLM"}), 500

    try:
        ai_data = json.loads(content)
    except Exception:
        # Fallback: erstes JSON-Objekt extrahieren
        try:
            start = content.index("{")
            end = content.rindex("}") + 1
            ai_data = json.loads(content[start:end])
        except Exception:
            return jsonify({"error": "Could not parse JSON from LLM response"}), 500

    question_simple = (ai_data.get("question_simple_en") or "").strip()
    answer_simple = (ai_data.get("answer_simple_en") or "").strip()
    question_advanced = (ai_data.get("question_advanced_en") or "").strip()
    answer_advanced = (ai_data.get("answer_advanced_en") or "").strip()
    
    if not question_simple or not answer_simple or not question_advanced or not answer_advanced:
        return jsonify({"error": "LLM response missing required fields (question_simple_en, answer_simple_en, question_advanced_en, answer_advanced_en)"}), 500

    # Neuen Satz mit Timestamp anreichern
    entry = {
        "object_id": obj_id,
        "created_at": datetime.now().isoformat(),
        "question_simple_en": question_simple,
        "answer_simple_en": answer_simple,
        "question_advanced_en": question_advanced,
        "answer_advanced_en": answer_advanced,
    }

    sentences = previous_sentences + [entry]

    # In Datei speichern
    SENTENCE_DIR.mkdir(parents=True, exist_ok=True)
    sentence_path = SENTENCE_DIR / f"{obj_id}.txt"
    sentence_path.write_text(json.dumps(sentences, ensure_ascii=False, indent=2), encoding="utf-8")

    return jsonify({"object_id": obj_id, "sentence": entry, "count": len(sentences)})


@app.route("/api/image/save", methods=["POST"])
def save_image():
    """
    Markiert ein Bild und alle zugehörigen Objekte als 'gespeichert',
    indem der Dateiname um '_saved' erweitert wird und die Metadaten
    der Objekte angepasst werden.
    """
    data = request.get_json(force=True, silent=True) or {}
    filename = data.get("filename")
    if not filename:
        return jsonify({"error": "Missing filename"}), 400

    src_path = PICTURES_DIR / filename
    if not src_path.exists():
        return jsonify({"error": "Image not found"}), 404

    if src_path.stem.endswith("_saved"):
        return jsonify({"error": "Image already saved"}), 400

    new_name = f"{src_path.stem}_saved{src_path.suffix}"
    dst_path = PICTURES_DIR / new_name

    # Bild umbenennen
    src_path.rename(dst_path)

    # Zugehörige Objekte aktualisieren
    OBJECTS_DIR.mkdir(parents=True, exist_ok=True)
    for meta_file in OBJECTS_DIR.glob("*.txt"):
        try:
            meta = json.loads(meta_file.read_text(encoding="utf-8"))
        except Exception:
            continue

        if meta.get("source_filename") != filename:
            continue

        meta["source_filename"] = new_name
        meta_file.write_text(
            json.dumps(meta, ensure_ascii=False, indent=2),
            encoding="utf-8",
        )

    return jsonify({"old_name": filename, "new_name": new_name})


@app.route("/api/crop", methods=["POST"])
def crop_image():
    """
    Erwartet JSON:
    {
      "filename": "bild.jpg",
      "selections": [
        {
          "number": 1,
          "mode": "rect" | "polygon",
          "bbox": {"x": 10, "y": 20, "width": 200, "height": 150},  // für rect
          "polygon": [{"x": 10, "y": 20}, ...]  // für polygon
        },
        ...
      ],
      "title_de": "...",
      "position_de": "...",
      "action_de": "...",
      "condition_de": "..."
    }
    
    ODER (Legacy-Format für Kompatibilität):
    {
      "filename": "bild.jpg",
      "mode": "rect" | "polygon",
      "x": 10, "y": 20, "width": 200, "height": 150,  // für rect
      "polygon": [{"x": 10, "y": 20}, ...]  // für polygon
    }
    """
    data = request.get_json(force=True, silent=True) or {}

    if "filename" not in data:
        return jsonify({"error": "Missing filename"}), 400

    src_path = PICTURES_DIR / data["filename"]
    if src_path.stem.endswith("_saved"):
        return jsonify({"error": "Cannot crop on saved image"}), 400
    if not src_path.exists():
        return jsonify({"error": "Source image not found"}), 404

    OBJECTS_DIR.mkdir(parents=True, exist_ok=True)

    # UUID für Bild + Metadaten
    obj_id = uuid4().hex
    timestamp = datetime.now().isoformat()
    out_image_name = f"{obj_id}{src_path.suffix}"
    out_image_path = OBJECTS_DIR / out_image_name
    out_meta_path = OBJECTS_DIR / f"{obj_id}.txt"

    # Prüfen, ob neues Format (selections) oder Legacy-Format
    selections = data.get("selections")
    
    if selections and isinstance(selections, list) and len(selections) > 0:
        # Neues Format: mehrere Auswahlen
        processed_selections = []
        
        with Image.open(src_path) as img:
            img_w, img_h = img.size
            
            for sel in selections:
                sel_mode = sel.get("mode")
                if sel_mode == "rect":
                    bbox = sel.get("bbox")
                    if not bbox:
                        continue
                    try:
                        x = int(bbox.get("x", 0))
                        y = int(bbox.get("y", 0))
                        w = int(bbox.get("width", 0))
                        h = int(bbox.get("height", 0))
                    except (ValueError, TypeError):
                        continue
                    
                    if w <= 0 or h <= 0:
                        continue
                    
                    x2 = min(x + w, img_w)
                    y2 = min(y + h, img_h)
                    x1 = max(0, x)
                    y1 = max(0, y)
                    
                    if x1 >= x2 or y1 >= y2:
                        continue
                    
                    processed_selections.append({
                        "number": sel.get("number", len(processed_selections) + 1),
                        "mode": "rect",
                        "bbox": {"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1},
                    })
                    
                elif sel_mode == "polygon":
                    polygon = sel.get("polygon")
                    if not isinstance(polygon, list) or len(polygon) < 3:
                        continue
                    
                    try:
                        xs = [int(p.get("x", 0)) for p in polygon]
                        ys = [int(p.get("y", 0)) for p in polygon]
                    except (KeyError, TypeError, ValueError):
                        continue
                    
                    min_x = max(min(xs), 0)
                    min_y = max(min(ys), 0)
                    max_x = min(max(xs), img_w)
                    max_y = min(max(ys), img_h)
                    
                    if min_x >= max_x or min_y >= max_y:
                        continue
                    
                    processed_selections.append({
                        "number": sel.get("number", len(processed_selections) + 1),
                        "mode": "polygon",
                        "polygon": polygon,
                        "bbox": {"x": min_x, "y": min_y, "width": max_x - min_x, "height": max_y - min_y},
                    })
        
        if not processed_selections:
            return jsonify({"error": "No valid selections provided"}), 400
        
        # Erstes Bild aus erster Auswahl erstellen
        first_sel = processed_selections[0]
        try:
            with Image.open(src_path) as img:
                if first_sel["mode"] == "rect":
                    bbox = first_sel["bbox"]
                    x1 = bbox["x"]
                    y1 = bbox["y"]
                    x2 = x1 + bbox["width"]
                    y2 = y1 + bbox["height"]
                    cropped = img.crop((x1, y1, x2, y2))
                else:  # polygon - verwende Bounding-Box für das Bild
                    bbox = first_sel["bbox"]
                    x1 = bbox["x"]
                    y1 = bbox["y"]
                    x2 = x1 + bbox["width"]
                    y2 = y1 + bbox["height"]
                    cropped = img.crop((x1, y1, x2, y2))
                
                # Bild speichern (Format beibehalten)
                img_format = img.format or "PNG"
                cropped.save(out_image_path, format=img_format)
                print(f"[crop_image] Bild gespeichert: {out_image_path} (Größe: {cropped.size}, Format: {img_format})")
        except Exception as e:
            print(f"[crop_image] Fehler beim Erstellen des Bildes: {e}")
            return jsonify({"error": f"Failed to create image: {e}"}), 500
        
        # Metadaten mit allen Auswahlen speichern
        meta = {
            "id": obj_id,
            "created_at": timestamp,
            "source_filename": data["filename"],
            "image_file": out_image_name,
            "hierarchy": 1,
            "parent_id": None,
            "title_de": data.get("title_de", ""),
            "position_de": data.get("position_de", ""),
            "action_de": data.get("action_de", ""),
            "condition_de": data.get("condition_de", ""),
            "selections": processed_selections,
        }
        
    else:
        # Legacy-Format: einzelne Auswahl (für Rückwärtskompatibilität)
        mode = data.get("mode", "rect")
        if mode not in {"rect", "polygon"}:
            return jsonify({"error": "Invalid mode"}), 400

        with Image.open(src_path) as img:
            img_w, img_h = img.size
            if mode == "rect":
                try:
                    x = int(data["x"])
                    y = int(data["y"])
                    w = int(data["width"])
                    h = int(data["height"])
                except (ValueError, TypeError, KeyError):
                    return jsonify({"error": "Invalid rectangle coordinates"}), 400

                if w <= 0 or h <= 0:
                    return jsonify({"error": "Width and height must be positive"}), 400

                x2 = min(x + w, img_w)
                y2 = min(y + h, img_h)
                x1 = max(0, x)
                y1 = max(0, y)

                if x1 >= x2 or y1 >= y2:
                    return jsonify({"error": "Crop area outside of image"}), 400

                cropped = img.crop((x1, y1, x2, y2))
                bbox = {"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1}
            else:  # polygon
                polygon = data.get("polygon") or []
                if not isinstance(polygon, list) or len(polygon) < 3:
                    return jsonify({"error": "Polygon must have at least 3 points"}), 400

                try:
                    xs = [int(p["x"]) for p in polygon]
                    ys = [int(p["y"]) for p in polygon]
                except (KeyError, TypeError, ValueError):
                    return jsonify({"error": "Invalid polygon coordinates"}), 400

                min_x = max(min(xs), 0)
                min_y = max(min(ys), 0)
                max_x = min(max(xs), img_w)
                max_y = min(max(ys), img_h)

                if min_x >= max_x or min_y >= max_y:
                    return jsonify({"error": "Polygon bounding box outside of image"}), 400

                cropped = img.crop((min_x, min_y, max_x, max_y))
                bbox = {
                    "x": min_x,
                    "y": min_y,
                    "width": max_x - min_x,
                    "height": max_y - min_y,
                }

            cropped.save(out_image_path)

        # Metadaten als JSON in .txt speichern
        meta = {
            "id": obj_id,
            "created_at": timestamp,
            "source_filename": data["filename"],
            "mode": mode,
            "bbox": bbox,
            "image_file": out_image_name,
            "hierarchy": 1,
            "parent_id": None,
            "title_de": data.get("title_de", ""),
            "position_de": data.get("position_de", ""),
            "action_de": data.get("action_de", ""),
            "condition_de": data.get("condition_de", ""),
        }
        if mode == "polygon":
            meta["polygon"] = data.get("polygon")

    out_meta_path.write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")

    return jsonify(
        {
            "id": obj_id,
            "image_file": out_image_name,
            "meta_file": out_meta_path.name,
        }
    )


# ── Claude / Directus Helpers ─────────────────────────────────────────────────

def _ensure_junction(collection: str, field1: str, field2: str, token: str):
    """Create a simple M2M junction collection in Directus if it doesn't exist."""
    _, status = _directus("GET", f"/collections/{collection}", token)
    if status == 200:
        return
    body = {
        "collection": collection,
        "schema": {},
        "meta": {"hidden": True, "icon": "import_export"},
        "fields": [
            {
                "field": "id",
                "type": "integer",
                "schema": {"is_primary_key": True, "has_auto_increment": True},
                "meta": {"hidden": True},
            },
            {"field": field1, "type": "uuid", "schema": {}, "meta": {"hidden": True}},
            {"field": field2, "type": "uuid", "schema": {}, "meta": {"hidden": True}},
        ],
    }
    _directus("POST", "/collections", token, body)


def _ensure_link(collection: str, match: dict, payload: dict, token: str):
    """Insert a junction row only if it doesn't already exist."""
    qs = "&".join(
        f"filter[{k}][_eq]={urllib.parse.quote(str(v), safe='')}"
        for k, v in match.items()
    )
    data, status = _directus("GET", f"/items/{collection}?{qs}&limit=1", token)
    if status == 200 and data.get("data"):
        return  # already linked
    _directus("POST", f"/items/{collection}", token, payload)


def _find_or_create_word(title_de: str, level: int, token: str):
    """Return (word_id, is_new). Creates word with status=draft if missing."""
    enc = urllib.parse.quote(title_de, safe="")
    data, status = _directus(
        "GET", f"/items/words?filter[title_de][_eq]={enc}&fields=id&limit=1", token
    )
    if status == 200 and data.get("data"):
        return data["data"][0]["id"], False

    body = {"status": "draft", "title_de": title_de, "level": level}
    data, status = _directus("POST", "/items/words", token, body)
    if status in (200, 201):
        return data["data"]["id"], True
    raise RuntimeError(f"Word creation failed ({status}): {data}")


def _find_or_create_question(question_de: str, answer_de: str, level: int,
                              short_answer_id, obj_id: str, token: str):
    """Return (question_id, is_new). Creates question with status=draft if missing."""
    enc = urllib.parse.quote(question_de, safe="")
    data, status = _directus(
        "GET",
        f"/items/questions?filter[question_de][_eq]={enc}&fields=id&limit=1",
        token,
    )
    if status == 200 and data.get("data"):
        return data["data"][0]["id"], False

    body = {
        "status": "draft",
        "question_de": question_de,
        "answer_de": answer_de,
        "level": level,
        "object": obj_id,
    }
    if short_answer_id:
        body["short_answer"] = short_answer_id

    data, status = _directus("POST", "/items/questions", token, body)
    if status in (200, 201):
        return data["data"]["id"], True
    raise RuntimeError(f"Question creation failed ({status}): {data}")


# ── Generate & Publish endpoints ──────────────────────────────────────────────

@app.route("/api/object/<obj_id>/generate_questions", methods=["POST"])
def generate_questions(obj_id: str):
    """
    1. Holt Objekt + Elternobjekt aus Directus
    2. Füllt Prompt-Platzhalter
    3. Ruft Claude Haiku auf
    4. Speichert Wörter + Fragen als Entwurf in Directus
    """
    token = request.headers.get("Authorization", "")
    body = request.get_json(force=True, silent=True) or {}
    prompt_template = (body.get("prompt") or "").strip()

    if not prompt_template:
        return jsonify({"error": "Missing prompt"}), 400
    if not ANTHROPIC_API_KEY:
        return jsonify({"error": "ANTHROPIC_API_KEY not configured on server"}), 500

    # Objekt laden
    obj_resp, s = _directus("GET", f"/items/objects/{obj_id}?fields=id,user_notes,parent", token)
    if s != 200:
        return jsonify({"error": "Object not found"}), 404
    obj = obj_resp.get("data") or {}

    # Elternobjekt laden
    parent_notes = ""
    if obj.get("parent"):
        p_resp, _ = _directus("GET", f"/items/objects/{obj['parent']}?fields=id,user_notes", token)
        parent_notes = ((p_resp.get("data") or {}).get("user_notes") or "")

    # Platzhalter ersetzen
    prompt = (
        prompt_template
        .replace("{user-notes_object}", obj.get("user_notes") or "")
        .replace("{user-notes_parentobject}", parent_notes)
    )

    # Claude Haiku aufrufen
    try:
        client = _anthropic_sdk.Anthropic(api_key=ANTHROPIC_API_KEY)
        msg = client.messages.create(
            model="claude-haiku-4-5-20251001",
            max_tokens=4096,
            messages=[{"role": "user", "content": prompt}],
        )
        raw = msg.content[0].text
    except Exception as e:
        return jsonify({"error": f"Claude API error: {e}"}), 500

    # JSON parsen
    try:
        ai = json.loads(raw)
    except Exception:
        try:
            start = raw.index("{")
            end = raw.rindex("}") + 1
            ai = json.loads(raw[start:end])
        except Exception:
            return jsonify({"error": "Invalid JSON from AI", "raw": raw[:400]}), 500

    levels = ai.get("levels", [])
    if not levels:
        return jsonify({"error": "No levels in AI response"}), 500

    # Junction-Collections sicherstellen (einmalig)
    for col, f1, f2 in [
        ("words_objects", "words_id", "objects_id"),
        ("questions_objects", "questions_id", "objects_id"),
        ("questions_distractor_words", "questions_id", "words_id"),
    ]:
        _ensure_junction(col, f1, f2, token)

    stats = {
        "words_created": 0,
        "words_linked": 0,
        "questions_created": 0,
        "questions_linked": 0,
    }

    # Alle eindeutigen Wörter aus allen Leveln vorab sammeln und einmalig laden
    all_words_by_level: dict[str, int] = {}  # title_de → first level seen
    for lvl in levels:
        level = int(lvl.get("level") or 1)
        for w in lvl.get("words", []) + lvl.get("distractor_words", []) + [lvl.get("short_answer", "")]:
            w = str(w).strip()
            if w and w not in all_words_by_level:
                all_words_by_level[w] = level

    # Wörter einmalig anlegen / finden (globaler Cache über alle Level)
    global_word_map: dict[str, str] = {}  # title_de → id
    for w, lvl_num in all_words_by_level.items():
        try:
            wid, is_new = _find_or_create_word(w, lvl_num, token)
            global_word_map[w] = wid
            _ensure_link(
                "words_objects",
                {"words_id": wid, "objects_id": obj_id},
                {"words_id": wid, "objects_id": obj_id},
                token,
            )
            stats["words_created" if is_new else "words_linked"] += 1
        except Exception as e:
            print(f"[generate_questions] word error '{w}': {e}")

    for lvl in levels:
        level = int(lvl.get("level") or 1)
        q_de = (lvl.get("question") or "").strip()
        a_de = (lvl.get("answer") or "").strip()
        short_text = (lvl.get("short_answer") or "").strip()
        words_list = [str(w).strip() for w in lvl.get("words", []) if str(w).strip()]
        distractor_list = [str(w).strip() for w in lvl.get("distractor_words", []) if str(w).strip()]

        if not q_de:
            continue

        short_answer_id = global_word_map.get(short_text) if short_text else None

        # Frage anlegen / verknüpfen
        try:
            q_id, q_is_new = _find_or_create_question(q_de, a_de, level, short_answer_id, obj_id, token)
        except Exception as e:
            print(f"[generate_questions] question error level {level}: {e}")
            continue

        stats["questions_created" if q_is_new else "questions_linked"] += 1

        # Frage ↔ Objekt
        _ensure_link(
            "questions_objects",
            {"questions_id": q_id, "objects_id": obj_id},
            {"questions_id": q_id, "objects_id": obj_id},
            token,
        )

        # related_words
        for w in words_list:
            if w in global_word_map:
                _ensure_link(
                    "questions_words",
                    {"questions_id": q_id, "words_id": global_word_map[w]},
                    {"questions_id": q_id, "words_id": global_word_map[w]},
                    token,
                )

        # distractor_words
        for w in distractor_list:
            if w in global_word_map:
                _ensure_link(
                    "questions_distractor_words",
                    {"questions_id": q_id, "words_id": global_word_map[w]},
                    {"questions_id": q_id, "words_id": global_word_map[w]},
                    token,
                )

    print(f"[generate_questions] obj={obj_id} stats={stats}")
    return jsonify({"ok": True, "object_id": obj_id, "stats": stats})


@app.route("/api/object/<obj_id>/publish_questions", methods=["POST"])
def publish_questions(obj_id: str):
    """Ändert Status aller verknüpften Entwurfs-Fragen und -Wörter auf 'published'."""
    token = request.headers.get("Authorization", "")

    # Verknüpfte Fragen
    q_resp, _ = _directus(
        "GET",
        f"/items/questions_objects?filter[objects_id][_eq]={obj_id}&fields=questions_id&limit=200",
        token,
    )
    q_ids = [e["questions_id"] for e in (q_resp.get("data") or [])]

    # Verknüpfte Wörter
    w_resp, _ = _directus(
        "GET",
        f"/items/words_objects?filter[objects_id][_eq]={obj_id}&fields=words_id&limit=2000",
        token,
    )
    w_ids = [e["words_id"] for e in (w_resp.get("data") or [])]

    published_q = 0
    published_w = 0

    for q_id in q_ids:
        _, s = _directus("PATCH", f"/items/questions/{q_id}", token, {"status": "published"})
        if s == 200:
            published_q += 1

    for w_id in w_ids:
        _, s = _directus("PATCH", f"/items/words/{w_id}", token, {"status": "published"})
        if s == 200:
            published_w += 1

    return jsonify({
        "ok": True,
        "published_questions": published_q,
        "published_words": published_w,
    })


@app.route("/api/object/<obj_id>/questions", methods=["GET"])
def get_object_questions_list(obj_id: str):
    """Gibt alle verknüpften Fragen eines Objekts zurück (2-Schritt-Query)."""
    token = request.headers.get("Authorization", "")
    junc, _ = _directus("GET",
        f"/items/questions_objects?filter[objects_id][_eq]={obj_id}&fields=questions_id&limit=200", token)
    q_ids = [e["questions_id"] for e in (junc.get("data") or []) if e.get("questions_id")]
    if not q_ids:
        return jsonify({"data": []})
    ids_param = urllib.parse.quote(",".join(q_ids), safe="")
    q_data, _ = _directus("GET",
        f"/items/questions?filter[id][_in]={ids_param}&fields=id,question_de,answer_de,level,status&limit=200", token)
    items = sorted(q_data.get("data") or [], key=lambda x: x.get("level") or 0)
    return jsonify({"data": items})


@app.route("/api/object/<obj_id>/words", methods=["GET"])
def get_object_words_list(obj_id: str):
    """Gibt alle verknüpften Wörter eines Objekts zurück (2-Schritt-Query)."""
    token = request.headers.get("Authorization", "")
    junc, _ = _directus("GET",
        f"/items/words_objects?filter[objects_id][_eq]={obj_id}&fields=words_id&limit=2000", token)
    w_ids = [e["words_id"] for e in (junc.get("data") or []) if e.get("words_id")]
    if not w_ids:
        return jsonify({"data": []})
    ids_param = urllib.parse.quote(",".join(w_ids), safe="")
    w_data, _ = _directus("GET",
        f"/items/words?filter[id][_in]={ids_param}&fields=id,title_de,level,status&limit=2000", token)
    items = sorted(w_data.get("data") or [], key=lambda x: x.get("title_de") or "")
    return jsonify({"data": items})


@app.route("/api/question/<q_id>", methods=["DELETE"])
def delete_question_item(q_id: str):
    """Löscht eine Frage aus Directus."""
    token = request.headers.get("Authorization", "")
    _, status = _directus("DELETE", f"/items/questions/{q_id}", token)
    if status in (200, 204):
        return jsonify({"ok": True})
    return jsonify({"error": "Delete failed"}), status


@app.route("/api/word/<w_id>", methods=["DELETE"])
def delete_word_item(w_id: str):
    """Löscht ein Wort aus Directus."""
    token = request.headers.get("Authorization", "")
    _, status = _directus("DELETE", f"/items/words/{w_id}", token)
    if status in (200, 204):
        return jsonify({"ok": True})
    return jsonify({"error": "Delete failed"}), status


@app.route("/api/setup-schema", methods=["POST"])
def setup_directus_schema():
    """
    Einmalig ausführen: Konfiguriert alle M2M-Relationen in Directus.
    Idempotent – bereits vorhandene Felder/Relationen werden übersprungen.
    """
    token = request.headers.get("Authorization", "")
    results = []

    def do(label, method, path, body=None):
        data, status = _directus(method, path, token, body)
        ok = status in (200, 201, 204)
        results.append({"label": label, "status": status, "ok": ok,
                         "err": None if ok else (data.get("errors") or data)})
        return ok

    # ── Fix M2O: questions.object → objects ──────────────────────────────────
    do("relation questions.object→objects", "POST", "/relations", {
        "collection": "questions", "field": "object",
        "related_collection": "objects",
        "schema": {"on_delete": "SET NULL"},
        "meta": {"one_deselect_action": "nullify"},
    })
    # Update display template so the object label shows
    do("update field questions.object template", "PATCH", "/fields/questions/object", {
        "meta": {"options": {"template": "{{user_notes}}"}}
    })

    # ── Fix M2O: questions.short_answer → words ───────────────────────────────
    do("relation questions.short_answer→words", "POST", "/relations", {
        "collection": "questions", "field": "short_answer",
        "related_collection": "words",
        "schema": {"on_delete": "SET NULL"},
        "meta": {"one_deselect_action": "nullify"},
    })

    # ── M2M: questions ↔ objects via questions_objects ────────────────────────
    do("field questions.linked_objects", "POST", "/fields/questions", {
        "field": "linked_objects", "type": "alias",
        "meta": {"special": ["m2m"], "interface": "list-m2m",
                 "options": {"template": "{{objects_id.user_notes}}"},
                 "note": "Verknüpfte Objekte"},
    })
    do("field objects.linked_questions", "POST", "/fields/objects", {
        "field": "linked_questions", "type": "alias",
        "meta": {"special": ["m2m"], "interface": "list-m2m",
                 "options": {"template": "{{questions_id.question_de}}"},
                 "note": "Verknüpfte Fragen"},
    })
    do("relation questions_objects.questions_id→questions", "POST", "/relations", {
        "collection": "questions_objects", "field": "questions_id",
        "related_collection": "questions",
        "schema": {"on_delete": "CASCADE"},
        "meta": {"junction_field": "objects_id", "one_field": "linked_objects",
                 "one_deselect_action": "nullify"},
    })
    do("relation questions_objects.objects_id→objects", "POST", "/relations", {
        "collection": "questions_objects", "field": "objects_id",
        "related_collection": "objects",
        "schema": {"on_delete": "CASCADE"},
        "meta": {"junction_field": "questions_id", "one_field": "linked_questions",
                 "one_deselect_action": "nullify"},
    })

    # ── M2M: words ↔ objects via words_objects ────────────────────────────────
    do("field words.linked_objects", "POST", "/fields/words", {
        "field": "linked_objects", "type": "alias",
        "meta": {"special": ["m2m"], "interface": "list-m2m",
                 "options": {"template": "{{objects_id.user_notes}}"},
                 "note": "Verknüpfte Objekte"},
    })
    do("field objects.linked_words", "POST", "/fields/objects", {
        "field": "linked_words", "type": "alias",
        "meta": {"special": ["m2m"], "interface": "list-m2m",
                 "options": {"template": "{{words_id.title_de}}"},
                 "note": "Verknüpfte Wörter"},
    })
    do("relation words_objects.words_id→words", "POST", "/relations", {
        "collection": "words_objects", "field": "words_id",
        "related_collection": "words",
        "schema": {"on_delete": "CASCADE"},
        "meta": {"junction_field": "objects_id", "one_field": "linked_objects",
                 "one_deselect_action": "nullify"},
    })
    do("relation words_objects.objects_id→objects", "POST", "/relations", {
        "collection": "words_objects", "field": "objects_id",
        "related_collection": "objects",
        "schema": {"on_delete": "CASCADE"},
        "meta": {"junction_field": "words_id", "one_field": "linked_words",
                 "one_deselect_action": "nullify"},
    })

    # ── M2M: questions ↔ words (distractor) via questions_distractor_words ────
    do("field questions.distractor_words", "POST", "/fields/questions", {
        "field": "distractor_words", "type": "alias",
        "meta": {"special": ["m2m"], "interface": "list-m2m",
                 "options": {"template": "{{words_id.title_de}}"},
                 "note": "Ablenker-Wörter (nicht in Frage/Antwort)"},
    })
    do("relation questions_distractor_words.questions_id→questions", "POST", "/relations", {
        "collection": "questions_distractor_words", "field": "questions_id",
        "related_collection": "questions",
        "schema": {"on_delete": "CASCADE"},
        "meta": {"junction_field": "words_id", "one_field": "distractor_words",
                 "one_deselect_action": "nullify"},
    })
    do("relation questions_distractor_words.words_id→words", "POST", "/relations", {
        "collection": "questions_distractor_words", "field": "words_id",
        "related_collection": "words",
        "schema": {"on_delete": "CASCADE"},
        "meta": {"junction_field": "questions_id", "one_deselect_action": "nullify"},
    })

    # ── Backref: words → questions via existing questions_words junction ───────
    do("field words.linked_questions", "POST", "/fields/words", {
        "field": "linked_questions", "type": "alias",
        "meta": {"special": ["m2m"], "interface": "list-m2m",
                 "options": {"template": "{{questions_id.question_de}}"},
                 "note": "Fragen in denen dieses Wort vorkommt"},
    })
    # Patch the existing questions_words relation to add the one_field backref on words
    do("patch relation questions_words.words_id one_field", "PATCH",
       "/relations/questions_words/words_id", {
           "meta": {"one_field": "linked_questions", "one_deselect_action": "nullify"}
       })

    failed = [r for r in results if not r["ok"]]
    return jsonify({"ok": len(failed) == 0, "total": len(results),
                    "failed": len(failed), "results": results})


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8000, debug=True)