auto-git:

[add] node/server/spirits/Neuer Ordner mit Objekten/generate_json.py

node/server/spirits/Neuer Ordner mit Objekten/generate_json.py

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+import json
+import os
+import re
+from difflib import get_close_matches
+
+# --- Paste hier die Model-Dateinamen rein (oder lies sie aus dem Ordner) ---
+MODEL_FILES = [
+    "Ebisu.glb", "Enenra.glb", "Enenra2.glb", "Oboroguruma.glb", "Oiwa.glb", "Okiku.glb", "Okomeki.001.glb",
+    "Okuninushi.glb", "Oni.glb", "Onryo.glb", "Oyamatsumi.001.glb", "Raijin.glb", "Rokurokubi.glb", "Ryujin.glb",
+    "Sarutahiko_Okami.glb", "Shinigami.001.glb", "Shuten_Doji.glb", "Sojobo.glb", "Sojobo2.glb", "Susanoo.glb",
+    "Takeminakata.glb", "Takeminakata2.001.glb", "Tanuki.glb", "Tengu.glb", "Tenjin.glb", "Tsukumogami.glb",
+    "Tsukuyomi_No_Mikoto.glb", "Tsurube_Otoshi.glb", "Tsurube_Otoshi2.glb", "Tsurube_Otoshi3.glb", "Tsurube_Otoshi4.glb",
+    "Ubume.glb", "Yama_Uba.glb", "Yama_Uba2.glb", "Yamata_No_Orichi.glb", "Yamawaro.glb", "Yatagarasu2.glb",
+    "Yuki_Onna.glb", "Yurei.glb", "Abe_No_Seimei.glb", "Abura_Akago.glb", "Abura_Sumashi.glb", "Abura_Sumashi2.glb",
+    "Aka_Manto.glb", "Akaname.glb", "Akateko2.glb", "Akkorokamui.glb", "Akuchu.glb", "Amabie2.glb", "Amanojaku.glb",
+    "Amaterasu.glb", "Ame_No_Uzume.001.glb", "Amenominakanushi.glb", "Aoandon.001.glb", "Aoandon2.001.glb",
+    "Ashiari_Yashiki.glb", "Ashinaga_Tenaga2.glb", "Azukiarai.glb", "Azukibabaa.glb", "Azukihakari.glb",
+    "Bake_Kujira.glb", "Bake_Kujira2.glb", "Bake_Kujira3.glb", "Bakezori.glb", "Baku.glb", "Basan.glb",
+    "Benzaiten.glb", "Betobeto_San.glb", "Bishamonten.glb", "Biwa_Bokuboku.glb", "Chochin_Obake.glb", "Daidarabotchi.glb",
+    "Daikokuten+Text.glb", "Daikokuten.glb", "Fujin.glb", "Funayurei.glb", "Furaribi.glb", "Futakuchi_Onna.glb",
+    "Gaki.glb", "Gashadokuro.glb", "Hachiman.glb", "Hiderigami.001.glb", "Hitotsume_Kozo.glb", "Hoko.glb",
+    "Inari_Okami.glb", "Ittan_Momen2.glb", "Izanagi_No_Mikoto.glb", "Izanami_No_Mikoto.glb", "Jikininki.glb",
+    "Jorogumo3.glb", "Kamaitachi.glb", "Kamikiri.glb", "Kappa.glb", "Karakasa_Obake.glb", "Karakasa_Obake2.glb",
+    "Kitsune.glb", "Kodama.glb", "Kudan.glb", "Mizushi.glb", "Mokumokuren.glb", "Mujina.glb", "Nekomata.glb",
+    "Noppera_Bo.glb", "Nue.glb", "Nuppeppo2.glb", "Nurarihyon.glb", "Nure_Onna.glb", "Nurikabe.glb", "Nurikabe2.glb"
+]
+
+def normalize(s):
+    s = s.lower()
+    s = re.sub(r"[^a-z0-9]", "", s)
+    s = s.replace("ou", "o") # YamatanoOrOchi vs Yamata_No_Orichi etc.
+    return s
+
+def generate_candidates(spirit_name):
+    # z.B. "Tsukuyomi (月読命)" => ["Tsukuyomi", "TsukuyomiNoMikoto", ...]
+    # Extrahiere lateinische Namen und alle Wörter, splitte auf Sonderzeichen
+    base = spirit_name.split()[0]
+    latin = re.split(r"\s|\(|（", spirit_name)[0]
+    # Alternativen, z.B. alles ohne Klammern, nur erstes Wort etc.
+    candidates = [latin]
+    candidates += [latin.replace("-", "_"), latin.replace("-", ""), latin.replace("_", ""), latin.title(), latin.upper()]
+    # Häufig bei Kami: _No_Mikoto-Suffix
+    if not latin.endswith("NoMikoto"):
+        candidates.append(latin + "NoMikoto")
+        candidates.append(latin + "_No_Mikoto")
+    # Auch mal alles Klein, Snake, Camel
+    return list(set([normalize(c) for c in candidates]))
+
+def find_best_model(spirit_name):
+    candidates = generate_candidates(spirit_name)
+    model_names = [f[:-4] for f in MODEL_FILES] # .glb weg
+    normalized_models = [normalize(n) for n in model_names]
+    # Alle Matches (ab Distanz <= 2 oder exaktes Substring-Match)
+    results = []
+    for c in candidates:
+        for i, n in enumerate(normalized_models):
+            dist = levenshtein(c, n)
+            if dist <= 2 or c in n or n in c:
+                results.append(MODEL_FILES[i])
+    # Unique!
+    results = sorted(list(set(results)))
+    if not results:
+        # Fuzzy best 3
+        matches = get_close_matches(candidates[0], normalized_models, n=3, cutoff=0.6)
+        models = [MODEL_FILES[normalized_models.index(m)] for m in matches]
+        return models
+    return results
+
+# Levenshtein-Distanz
+def levenshtein(a, b):
+    if a == b: return 0
+    if not a: return len(b)
+    if not b: return len(a)
+    v0 = list(range(len(b) + 1))
+    v1 = [0] * (len(b) + 1)
+    for i in range(len(a)):
+        v1[0] = i + 1
+        for j in range(len(b)):
+            cost = 0 if a[i] == b[j] else 1
+            v1[j + 1] = min(v1[j] + 1, v0[j + 1] + 1, v0[j] + cost)
+        v0, v1 = v1, v0
+    return v0[len(b)]
+
+# ---- Hauptlogik ----
+def main():
+    with open("spirit_list.json", encoding="utf-8") as f:
+        spirits = json.load(f)
+    for spirit in spirits:
+        if "Model URL" in spirit and spirit["Model URL"]:
+            continue # already done
+        name = spirit.get("Name", "")
+        matches = find_best_model(name)
+        if len(matches) == 1:
+            spirit["Model URL"] = "/assets/models/spirits/" + matches[0]
+        elif len(matches) > 1:
+            print(f"\n[?] Mehrere mögliche Modelle für '{name}': {matches}")
+            spirit["Model URL"] = "/assets/models/spirits/" + matches[0] # Default das erste, Handcheck empfohlen!
+        else:
+            print(f"[!] Kein Modell gefunden für '{name}'!")
+            spirit["Model URL"] = ""
+    with open("spirit_list_out.json", "w", encoding="utf-8") as f:
+        json.dump(spirits, f, ensure_ascii=False, indent=2)
+    print("\nFERTIG. Ergebnis: spirit_list_out.json")
+
+if __name__ == "__main__":
+    main()