app.py

# ============================================================================
# GERMAN LINGUISTICS HUB (CONSOLIDATED APP V3)
#
# This script combines multiple NLP tools into a single Gradio interface.
#
# TABS & FUNCTIONALITY:
# 1. Comprehensive Analyzer (DE):
#    - CONTEXTUAL analysis of full sentences.
#    - Ranks all semantics by relevance to the sentence.
# 2. Word Encyclopedia (DE): (NEW!)
#    - NON-CONTEXTUAL analysis of single words.
#    - Finds ALL grammatical (Pattern) and semantic (OdeNet, ConceptNet)
#      possibilities, cross-validated and grouped by Part-of-Speech.
#    - Ideal for enriching word lists.
# 3. spaCy Analyzer (Multi-lingual): Direct spaCy output.
# 4. Grammar Check (DE): LanguageTool.
# 5. Inflections (DE): Direct Pattern.de output.
# 6. Thesaurus (DE): Direct OdeNet output.
# 7. ConceptNet (Direct): Direct ConceptNet API output.
# ============================================================================


# ============================================================================
# 1. CONSOLIDATED IMPORTS
# ============================================================================
import gradio as gr
import spacy
from spacy import displacy
import base64
import traceback
import subprocess
import sys
import os
from pathlib import Path
import importlib
import site
import threading
import queue
from dataclasses import dataclass
from enum import Enum
from typing import Dict, Any, List, Set, Optional, Tuple
import requests  
import zipfile  
import re

# --- Requests and gradio Import (for ConceptNet) ---
try:
    import requests
    from requests.exceptions import RequestException, HTTPError, ConnectionError, Timeout
    REQUESTS_AVAILABLE = True
except ImportError:
    REQUESTS_AVAILABLE = False
    print("="*70)
    print("CRITICAL WARNING: `requests` library not found.")
    print("ConceptNet features will not function.")
    print("="*70)


try:
    from gradio_client import Client
    GRADIO_CLIENT_AVAILABLE = True

except ImportError:
    GRADIO_CLIENT_AVAILABLE = False
    print("="*70)
    print("CRITICAL WARNING: `gradio_client` library not found.")
    print("ConceptNet features will not function.")
    print("Install with: pip install gradio_client")
    print("="*70)

# --- IWNLP (spaCy Extension) Import ---
try:
    from spacy_iwnlp import spaCyIWNLP
    IWNLP_AVAILABLE = True
    print("✓ Successfully imported spacy-iwnlp")
except ImportError:
    IWNLP_AVAILABLE = False
    spaCyIWNLP = object # Dummy definition for error case
    print("="*70)
    print("WARNING: `spacy-iwnlp` library not found.")
    print("The 'Word Encyclopedia' tab will be less accurate.")
    print("Install with: pip install spacy-iwnlp")
    print("="*70)

# --- LanguageTool Import ---
try:
    import language_tool_python
    LT_AVAILABLE = True
    print("✓ Successfully imported language_tool")
except ImportError:
    LT_AVAILABLE = False
    print("="*70)
    print("CRITICAL WARNING: `language-tool-python` library not found.")
    print("The 'German Grammar Check' tab will not function.")
    print("="*70)

# --- OdeNet (wn) Import ---
try:
    import wn
    WN_AVAILABLE = True
    print("✓ Successfully imported wordnet for odenet")
except ImportError:
    WN_AVAILABLE = False
    print("="*70)
    print("CRITICAL WARNING: `wn` library not found.")
    print("The 'German Thesaurus' tab will not function.")
    print("="*70)

# --- Pattern.de Import ---
try:
    from pattern.de import (
        pluralize, singularize, conjugate, tenses, lemma, lexeme,
        attributive, predicative,
        article, gender, MALE, FEMALE, NEUTRAL, PLURAL,
        INFINITIVE, PRESENT, PAST, PARTICIPLE,
        FIRST, SECOND, THIRD, SINGULAR, PLURAL as PL,
        INDICATIVE, IMPERATIVE, SUBJUNCTIVE,
        NOMINATIVE, ACCUSATIVE, DATIVE, GENITIVE,
        SUBJECT, OBJECT, INDIRECT, PROPERTY,
        DEFINITE, INDEFINITE,
        comparative, superlative,
        NOUN, VERB, ADJECTIVE,
        parse, split
    )
    PATTERN_DE_AVAILABLE = True
    print("✓ Successfully imported pattern.de")
except ImportError as e:
    PATTERN_DE_AVAILABLE = False
    print("="*70)
    print(f"CRITICAL WARNING: `pattern.de` library not found: {e}")
    print("The 'German Inflections' tab will not function.")
    print("="*70)

# --- HanTa Tagger Import ---
try:
    from HanTa.HanoverTagger import HanoverTagger
    import HanTa.HanoverTagger
    # This sys.modules line is critical for pickle compatibility
    sys.modules['HanoverTagger'] = HanTa.HanoverTagger
    HANTA_AVAILABLE = True
    print("✓ Successfully imported HanTa")
except ImportError:
    HANTA_AVAILABLE = False
    HanoverTagger = object # Dummy definition
    print("="*70)
    print("CRITICAL WARNING: `HanTa` library not found.")
    print("The 'Word Encyclopedia' tab will NOT function.")
    print("Install with: pip install HanTa")
    print("="*70)

# ============================================================================
# 2. SHARED GLOBALS & CONFIG
# ============================================================================
VERBOSE = True  # Enable verbose debug output for Pattern.de
def log(msg):
    """Print debug messages if verbose mode is on."""
    if VERBOSE:
        print(f"[DEBUG] {msg}")

# --- ConceptNet Cache & Lock ---
CONCEPTNET_CACHE: Dict[Tuple[str, str], Any] = {}
CONCEPTNET_LOCK = threading.Lock()

# --- HanTa Tagger Cache & Lock ---
HANTA_TAGGER_INSTANCE: Optional[HanoverTagger] = None
HANTA_TAGGER_LOCK = threading.Lock()

# --- Helper ---
def _html_wrap(content: str, line_height: str = "2.0") -> str:
    """Wraps displaCy HTML in a consistent, scrollable div."""
    return f'<div style="overflow-x:auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; line-height: {line_height};">{content}</div>'

# --- Helper for SVA ---
def _conjugate_to_person_number(verb_lemma: str, person: str, number: str) -> Optional[str]:
    """
    Return a present tense finite form for given person/number.
    person in {'1','2','3'}, number in {'sg','pl'}.
    """
    if not PATTERN_DE_AVAILABLE:
        return None
    try:
        alias = {"1sg":"1sg","2sg":"2sg","3sg":"3sg","1pl":"1pl","2pl":"2pl","3pl":"3pl"}[f"{person}{number}"]
        return conjugate(verb_lemma, alias)
    except Exception:
        return None

# ============================================================================
# 3. SPACY ANALYZER LOGIC
# ============================================================================
# --- Globals & Config for spaCy ---
SPACY_MODEL_INFO: Dict[str, Tuple[str, str, str]] = {
    "de": ("German", "de_core_news_md", "spacy"),
    "en": ("English", "en_core_web_md", "spacy"),
    "es": ("Spanish", "es_core_news_md", "spacy"),
    "grc-proiel-trf": ("Ancient Greek (PROIEL TRF)", "grc_proiel_trf", "grecy"),
    "grc-perseus-trf": ("Ancient Greek (Perseus TRF)", "grc_perseus_trf", "grecy"),
    "grc_ner_trf": ("Ancient Greek (NER TRF)", "grc_ner_trf", "grecy"),
    "grc-proiel-lg": ("Ancient Greek (PROIEL LG)", "grc_proiel_lg", "grecy"),
    "grc-perseus-lg": ("Ancient Greek (Perseus LG)", "grc_perseus_lg", "grecy"),
    "grc-proiel-sm": ("Ancient Greek (PROIEL SM)", "grc_proiel_sm", "grecy"),
    "grc-perseus-sm": ("Ancient Greek (Perseus SM)", "grc_perseus_sm", "grecy"),
}
SPACY_UI_TEXT = {
    "de": {
        "title": "# 🔍 Mehrsprachiger Morpho-Syntaktischer Analysator",
        "subtitle": "Analysieren Sie Texte auf Deutsch, Englisch, Spanisch und Altgriechisch",
        "ui_lang_label": "Benutzeroberflächensprache",
        "model_lang_label": "Textsprache für Analyse",
        "input_label": "Text eingeben",
        "input_placeholder": "Geben Sie hier Ihren Text ein...",
        "button_text": "Text analysieren",
        "button_processing_text": "Verarbeitung läuft...",
        "tab_graphic": "Grafische Darstellung",
        "tab_table": "Tabelle",
        "tab_json": "JSON",
        "tab_ner": "Entitäten",
        "html_label": "Abhängigkeitsparsing",
        "table_label": "Morphologische Analyse",
        "table_headers": ["Wort", "Lemma", "POS", "Tag", "Morphologie", "Abhängigkeit"],
        "json_label": "JSON-Ausgabe",
        "ner_label": "Benannte Entitäten",
        "error_message": "Fehler: "
    },
    "en": {
        "title": "# 🔍 Multilingual Morpho-Syntactic Analyzer",
        "subtitle": "Analyze texts in German, English, Spanish, and Ancient Greek",
        "ui_lang_label": "Interface Language",
        "model_lang_label": "Text Language for Analysis",
        "input_label": "Enter Text",
        "input_placeholder": "Enter your text here...",
        "button_text": "Analyze Text",
        "button_processing_text": "Processing...",
        "tab_graphic": "Graphic View",
        "tab_table": "Table",
        "tab_json": "JSON",
        "tab_ner": "Entities",
        "html_label": "Dependency Parsing",
        "table_label": "Morphological Analysis",
        "table_headers": ["Word", "Lemma", "POS", "Tag", "Morphology", "Dependency"],
        "json_label": "JSON Output",
        "ner_label": "Named Entities",
        "error_message": "Error: "
    },
    "es": {
        "title": "# 🔍 Analizador Morfo-Sintáctico Multilingüe",
        "subtitle": "Analice textos en alemán, inglés, español y griego antiguo",
        "ui_lang_label": "Idioma de la Interfaz",
        "model_lang_label": "Idioma del Texto para Análisis",
        "input_label": "Introducir Texto",
        "input_placeholder": "Ingrese su texto aquí...",
        "button_text": "Analizar Texto",
        "button_processing_text": "Procesando...",
        "tab_graphic": "Vista Gráfica",
        "tab_table": "Tabla",
        "tab_json": "JSON",
        "tab_ner": "Entidades",
        "html_label": "Análisis de Dependencias",
        "table_label": "Análisis Morfológico",
        "table_headers": ["Palabra", "Lema", "POS", "Etiqueta", "Morfología", "Dependencia"],
        "json_label": "Salida JSON",
        "ner_label": "Entidades Nombradas",
        "error_message": "Error: "
    }
}
SPACY_MODELS: Dict[str, Optional[spacy.Language]] = {}

# --- Dependency Installation ---
def spacy_install_spacy_transformers_once():
    """ Installs spacy-transformers, required for all _trf models. """
    marker_file = Path(".spacy_transformers_installed")
    if marker_file.exists():
        print("✓ spacy-transformers already installed (marker found)")
        return True
    
    print("Installing spacy-transformers (for _trf models)...")
    cmd = [sys.executable, "-m", "pip", "install", "spacy-transformers"]
    try:
        subprocess.run(cmd, capture_output=True, text=True, check=True, timeout=900)
        print("✓ Successfully installed spacy-transformers")
        marker_file.touch()
        return True
    except Exception as e:
        print(f"✗ FAILED to install spacy-transformers: {e}")
        if hasattr(e, 'stdout'): print(f"STDOUT: {e.stdout}")
        if hasattr(e, 'stderr'): print(f"STDERR: {e.stderr}")
        return False

def spacy_install_grecy_model_from_github(model_name: str) -> bool:
    """ Installs a greCy model from GitHub Release. """
    marker_file = Path(f".{model_name}_installed")
    if marker_file.exists():
        print(f"✓ {model_name} already installed (marker found)")
        return True
    print(f"Installing grecy model: {model_name}...")
    if model_name == "grc_proiel_trf":
        wheel_filename = "grc_proiel_trf-3.7.5-py3-none-any.whl"
    elif model_name in ["grc_perseus_trf", "grc_proiel_lg", "grc_perseus_lg",
                        "grc_proiel_sm", "grc_perseus_sm", "grc_ner_trf"]:
        wheel_filename = f"{model_name}-0.0.0-py3-none-any.whl"
    else:
        print(f"✗ Unknown grecy model: {model_name}")
        return False
    install_url = f"https://github.com/CrispStrobe/greCy/releases/download/v1.0-models/{wheel_filename}"
    cmd = [sys.executable, "-m", "pip", "install", install_url, "--no-deps"]
    print(f"Running: {' '.join(cmd)}")
    try:
        result = subprocess.run(cmd, capture_output=True, text=True, check=True, timeout=900)
        if result.stdout: print("STDOUT:", result.stdout)
        if result.stderr: print("STDERR:", result.stderr)
        print(f"✓ Successfully installed {model_name} from GitHub")
        marker_file.touch()
        return True
    except subprocess.CalledProcessError as e:
        print(f"✗ Installation subprocess FAILED with code {e.returncode}")
        print("STDOUT:", e.stdout)
        print("STDERR:", e.stderr)
        return False
    except Exception as e:
        print(f"✗ Installation exception: {e}")
        traceback.print_exc()
        return False

# --- Model Loading (Lazy Loading) ---
def spacy_load_spacy_model(model_name: str) -> Optional[spacy.Language]:
    """Load or install a standard spaCy model."""
    try:
        return spacy.load(model_name)
    except OSError:
        print(f"Installing {model_name}...")
        try:
            subprocess.check_call([sys.executable, "-m", "spacy", "download", model_name])
            return spacy.load(model_name)
        except Exception as e:
            print(f"✗ Failed to install {model_name}: {e}")
            if hasattr(e, 'stderr'): print(f"STDERR: {e.stderr}")
            return None

def spacy_load_grecy_model(model_name: str) -> Optional[spacy.Language]:
    """ Load a grecy model, installing from GitHub if needed. """
    if not spacy_install_grecy_model_from_github(model_name):
        print(f"✗ Cannot load {model_name} because installation failed.")
        return None
    try:
        print("Refreshing importlib to find new package...")
        importlib.invalidate_caches()
        try: importlib.reload(site)
        except Exception: pass
        print(f"Trying: spacy.load('{model_name}')")
        nlp = spacy.load(model_name)
        print(f"✓ Successfully loaded {model_name}")
        return nlp
    except Exception as e:
        print(f"✗ Model {model_name} is installed but FAILED to load.")
        print(f"   Error: {e}")
        traceback.print_exc()
        return None

def spacy_initialize_models():
    """ Pre-load standard models and ensure _trf dependencies are ready. """
    print("\n" + "="*70)
    print("INITIALIZING SPACY MODELS")
    print("="*70 + "\n")
    spacy_install_spacy_transformers_once()
    loaded_count = 0
    spacy_model_count = 0
    for lang_code, (lang_name, model_name, model_type) in SPACY_MODEL_INFO.items():
        if model_type == "spacy":
            spacy_model_count += 1
            print(f"Loading {lang_name} ({model_name})...")
            nlp = spacy_load_spacy_model(model_name)
            SPACY_MODELS[lang_code] = nlp
            if nlp:
                print(f"✓ {lang_name} ready\n")
                loaded_count += 1
            else:
                print(f"✗ {lang_name} FAILED\n")
        else:
            print(f"✓ {lang_name} ({model_name}) will be loaded on first use.\n")
            SPACY_MODELS[lang_code] = None
    print(f"Pre-loaded {loaded_count}/{spacy_model_count} standard models.")
    print("="*70 + "\n")

# --- Analysis Logic ---
def spacy_get_analysis(ui_lang: str, model_lang_key: str, text: str):
    """Analyze text and return results."""
    ui_config = SPACY_UI_TEXT.get(ui_lang.lower(), SPACY_UI_TEXT["en"])
    error_prefix = ui_config["error_message"]
    try:
        if not text.strip():
            return ([], [], "<p style='color: orange;'>No text provided.</p>", "<p>No text provided.</p>",
                    gr.Button(value=ui_config["button_text"], interactive=True))
        nlp = SPACY_MODELS.get(model_lang_key)
        if nlp is None:
            print(f"First use of {model_lang_key}. Loading model...")
            if model_lang_key not in SPACY_MODEL_INFO:
                 raise ValueError(f"Unknown model key: {model_lang_key}")
            _, model_name, model_type = SPACY_MODEL_INFO[model_lang_key]
            if model_type == "grecy":
                nlp = spacy_load_grecy_model(model_name)
            else:
                nlp = spacy_load_spacy_model(model_name)
            if nlp is None:
                SPACY_MODELS.pop(model_lang_key, None)
                err_msg = f"Model for {model_lang_key} ({model_name}) FAILED to load. Check logs."
                err_html = f"<p style='color: red;'>{err_msg}</p>"
                return ([], {"error": err_msg}, err_html, err_html,
                        gr.Button(value=ui_config["button_text"], interactive=True))
            else:
                SPACY_MODELS[model_lang_key] = nlp
                print(f"✓ {model_lang_key} is now loaded and cached.")
        doc = nlp(text)
        dataframe_output = []
        json_output = []
        for token in doc:
            lemma_str = token.lemma_
            morph_str = str(token.morph) if token.morph else ''
            dep_str = token.dep_ if doc.is_parsed else ''
            tag_str = token.tag_ or ''
            pos_str = token.pos_ or ''
            json_output.append({
                "word": token.text, "lemma": lemma_str, "pos": pos_str,
                "tag": tag_str, "morphology": morph_str, "dependency": dep_str,
                "is_stopword": token.is_stop
            })
            dataframe_output.append([token.text, lemma_str, pos_str, tag_str, morph_str, dep_str])
        html_dep_out = ""
        if "parser" in nlp.pipe_names and doc.is_parsed:
            try:
                options = {"compact": True, "bg": "#ffffff", "color": "#000000", "font": "Source Sans Pro"}
                html_svg = displacy.render(doc, style="dep", jupyter=False, options=options)
                html_dep_out = _html_wrap(html_svg, line_height="2.5")
            except Exception as e:
                html_dep_out = f"<p style='color: orange;'>Visualization error (DEP): {e}</p>"
        else:
            html_dep_out = "<p style='color: orange;'>Dependency parsing ('parser') not available or doc not parsed.</p>"
        html_ner_out = ""
        if "ner" in nlp.pipe_names:
            if doc.ents:
                try:
                    html_ner = displacy.render(doc, style="ent", jupyter=False)
                    html_ner_out = _html_wrap(html_ner, line_height="2.5")
                except Exception as e:
                    html_ner_out = f"<p style='color: orange;'>Visualization error (NER): {e}</p>"
            else:
                html_ner_out = "<p>No named entities found in this text.</p>"
        else:
            html_ner_out = "<p style='color: orange;'>Named Entity Recognition ('ner') not available for this model.</p>"
        return (dataframe_output, json_output, html_dep_out, html_ner_out,
                gr.Button(value=ui_config["button_text"], interactive=True))
    except Exception as e:
        traceback.print_exc()
        error_html = f"<div style='color: red; border: 1px solid red; padding: 10px; border-radius: 5px; background-color: #fff5f5;'><strong>{error_prefix}</strong> {str(e)}</div>"
        return ([], {"error": str(e)}, error_html, error_html,
                gr.Button(value=ui_config["button_text"], interactive=True))

# --- UI Update Logic ---
def spacy_update_ui(ui_lang: str):
    """Update UI language for the spaCy tab."""
    ui_config = SPACY_UI_TEXT.get(ui_lang.lower(), SPACY_UI_TEXT["en"])
    return [
        gr.update(value=ui_config["title"]),
        gr.update(value=ui_config["subtitle"]),
        gr.update(label=ui_config["ui_lang_label"]),
        gr.update(label=ui_config["model_lang_label"]),
        gr.update(label=ui_config["input_label"], placeholder=ui_config["input_placeholder"]),
        gr.update(value=ui_config["button_text"]),
        gr.update(label=ui_config["tab_graphic"]),
        gr.update(label=ui_config["tab_table"]),
        gr.update(label=ui_config["tab_json"]),
        gr.update(label=ui_config["tab_ner"]),
        gr.update(label=ui_config["html_label"]),
        gr.update(label=ui_config["table_label"], headers=ui_config["table_headers"]),
        gr.update(label=ui_config["json_label"]),
        gr.update(label=ui_config["ner_label"])
    ]

# ============================================================================
# 3b. IWNLP PIPELINE (NEW)
# ============================================================================
IWNLP_PIPELINE: Optional[spacy.Language] = None
IWNLP_LOCK = threading.Lock()

# Define paths for the data
DATA_DIR = "data"
LEMMATIZER_JSON_NAME = "IWNLP.Lemmatizer_20181001.json"
LEMMATIZER_JSON_PATH = os.path.join(DATA_DIR, LEMMATIZER_JSON_NAME)
LEMMATIZER_ZIP_URL = "https://dbs.cs.uni-duesseldorf.de/datasets/iwnlp/IWNLP.Lemmatizer_20181001.zip"
LEMMATIZER_ZIP_PATH = os.path.join(DATA_DIR, "IWNLP.Lemmatizer_20181001.zip")

def iwnlp_download_and_unzip_data():
    """
    Checks for IWNLP data file. Downloads and unzips if not present.
    """
    if os.path.exists(LEMMATIZER_JSON_PATH):
        print("✓ IWNLP data file already exists.")
        return True

    # --- File not found, must download and unzip ---
    try:
        os.makedirs(DATA_DIR, exist_ok=True)
        
        # 1. Download the ZIP file if it's not already here
        if not os.path.exists(LEMMATIZER_ZIP_PATH):
            print(f"IWNLP data not found. Downloading from {LEMMATIZER_ZIP_URL}...")
            with requests.get(LEMMATIZER_ZIP_URL, stream=True) as r:
                r.raise_for_status()
                with open(LEMMATIZER_ZIP_PATH, 'wb') as f:
                    for chunk in r.iter_content(chunk_size=8192): 
                        f.write(chunk)
            print("✓ IWNLP Download complete.")
        else:
            print("✓ IWNLP zip file already present.")

        # 2. Unzip the file
        print(f"Unzipping '{LEMMATIZER_ZIP_PATH}'...")
        with zipfile.ZipFile(LEMMATIZER_ZIP_PATH, 'r') as zip_ref:
            # Extract the specific file we need to the data directory
            zip_ref.extract(LEMMATIZER_JSON_NAME, path=DATA_DIR)
        print(f"✓ Unzip complete. File extracted to {LEMMATIZER_JSON_PATH}")
        
        if not os.path.exists(LEMMATIZER_JSON_PATH):
            raise Exception("Unzip appeared to succeed, but the .json file is still missing.")
            
        return True

    except Exception as e:
        print(f"✗ CRITICAL: Failed to download or unzip IWNLP data: {e}")
        traceback.print_exc()
        return False


def iwnlp_get_pipeline() -> Optional[spacy.Language]:
    """ Thread-safe function to get a single instance of the IWNLP pipeline. """
    global IWNLP_PIPELINE
    if not IWNLP_AVAILABLE:
        raise ImportError("spacy-iwnlp library is not installed.")
    
    if IWNLP_PIPELINE:
        return IWNLP_PIPELINE
        
    with IWNLP_LOCK:
        if IWNLP_PIPELINE:
            return IWNLP_PIPELINE
            
        try:
            print("Initializing spaCy-IWNLP pipeline...")
            
            # --- 1. Ensure data file exists ---
            if not iwnlp_download_and_unzip_data():
                return None # Failed to get data

            # --- 2. Load spaCy model ---
            print("Loading 'de_core_news_md' for IWNLP...")
            nlp_de = SPACY_MODELS.get("de")
            if not nlp_de:
                nlp_de = spacy_load_spacy_model("de_core_news_md")
                if nlp_de:
                    SPACY_MODELS["de"] = nlp_de
                else:
                    raise Exception("Failed to load 'de_core_news_md' for IWNLP.")
            
            # --- 3. Add IWNLP pipe ---
            if not nlp_de.has_pipe("iwnlp"):
                # This is the V3.0 initialization method
                nlp_de.add_pipe('iwnlp', config={'lemmatizer_path': LEMMATIZER_JSON_PATH})
                print("✓ IWNLP pipe added to 'de' model.")
            else:
                print("✓ IWNLP pipe already present.")
                
            IWNLP_PIPELINE = nlp_de
            return IWNLP_PIPELINE
            
        except Exception as e:
            print(f"CRITICAL ERROR: Failed to initialize IWNLP pipeline: {e}")
            traceback.print_exc()
            return None

# ============================================================================
# 4. LANGUAGETOOL LOGIC
# ============================================================================
# --- Globals for LanguageTool ---
LT_TOOL_INSTANCE: Optional[language_tool_python.LanguageTool] = None
LT_TOOL_LOCK = threading.Lock()
def lt_get_language_tool() -> Optional[language_tool_python.LanguageTool]:
    """ Thread-safe function to get a single instance of the LanguageTool. """
    global LT_TOOL_INSTANCE
    if not LT_AVAILABLE:
        raise ImportError("language-tool-python library is not installed.")
    if LT_TOOL_INSTANCE:
        return LT_TOOL_INSTANCE
    with LT_TOOL_LOCK:
        if LT_TOOL_INSTANCE:
            return LT_TOOL_INSTANCE
        try:
            print("Initializing LanguageTool for German (de-DE)...")
            tool = language_tool_python.LanguageTool('de-DE')
            try:
                tool.picky = True
            except Exception:
                pass
            _ = tool.check("Dies ist ein Test.")
            print("LanguageTool (local server) initialized successfully.")
            LT_TOOL_INSTANCE = tool
            return LT_TOOL_INSTANCE
        except Exception as e:
            print(f"CRITICAL ERROR: Failed to initialize LanguageTool: {e}")
            return None
# --- Grammar Checking Logic ---
def lt_check_grammar(text: str) -> List[Dict[str, Any]]:
    """ Checks a German text for grammar and spelling errors and returns a JSON list. """
    try:
        tool = lt_get_language_tool()
        if tool is None:
            return [{"error": "LanguageTool service failed to initialize."}]
        if not text or not text.strip():
            return [{"info": "No text provided to check."}]
        print(f"Checking text: {text}")
        matches = tool.check(text)
        if not matches:
            try:
                tool.picky = True
                matches = tool.check(text)
            except Exception:
                pass
        if not matches:
            return [{"info": "No errors found!", "status": "perfect"}]
        errors_list = []
        for match in matches:
            error = {
                "message": match.message,
                "rule_id": match.ruleId,
                "category": getattr(match.category, 'name', match.category),
                "incorrect_text": text[match.offset : match.offset + match.errorLength],
                "replacements": match.replacements,
                "offset": match.offset,
                "length": match.errorLength,
                "context": getattr(match, "context", None),
                "short_message": getattr(match, "shortMessage", None)
            }
            errors_list.append(error)
        print(f"Found {len(errors_list)} errors.")
        return errors_list
    except Exception as e:
        traceback.print_exc()
        return [{"error": f"An unexpected error occurred: {str(e)}"}]

# ============================================================================
# 5. ODENET THESAURUS LOGIC
# ============================================================================
# --- Globals & Classes for OdeNet ---
@dataclass
class OdeNetWorkItem:
    """Represents a lookup request."""
    word: str
    response_queue: queue.Queue
class OdeNetWorkerState(Enum):
    NOT_STARTED = 1
    INITIALIZING = 2
    READY = 3
    ERROR = 4
odenet_worker_state = OdeNetWorkerState.NOT_STARTED
odenet_worker_thread = None
odenet_work_queue = queue.Queue()
odenet_de_wn = None
# --- Worker Thread Logic ---
def odenet_download_wordnet_data():
    """Download WordNet data. Called once by worker thread."""
    if not WN_AVAILABLE:
        print("[OdeNet Worker] 'wn' library not available. Skipping download.")
        return False
    try:
        print("[OdeNet Worker] Downloading WordNet data...")
        try:
            wn.download('odenet:1.4')
        except Exception as e:
            print(f"[OdeNet Worker] Note: odenet download: {e}")
        try:
            wn.download('cili:1.0')
        except Exception as e:
            print(f"[OdeNet Worker] Note: cili download: {e}")
        print("[OdeNet Worker] ✓ WordNet data ready")
        return True
    except Exception as e:
        print(f"[OdeNet Worker] ✗ Failed to download WordNet data: {e}")
        return False
def odenet_worker_loop():
    """ Worker thread main loop. """
    global odenet_worker_state, odenet_de_wn
    if not WN_AVAILABLE:
        print("[OdeNet Worker] 'wn' library not available. Worker cannot start.")
        odenet_worker_state = OdeNetWorkerState.ERROR
        return
    try:
        print("[OdeNet Worker] Starting worker thread...")
        odenet_worker_state = OdeNetWorkerState.INITIALIZING
        if not odenet_download_wordnet_data():
            odenet_worker_state = OdeNetWorkerState.ERROR
            print("[OdeNet Worker] Failed to initialize")
            return
        print("[OdeNet Worker] Creating WordNet instance...")
        odenet_de_wn = wn.Wordnet('odenet:1.4')
        odenet_worker_state = OdeNetWorkerState.READY
        print("[OdeNet Worker] Ready to process requests")
        while True:
            try:
                item: OdeNetWorkItem = odenet_work_queue.get(timeout=1)
                try:
                    result = odenet_process_word_lookup(item.word)
                    item.response_queue.put(("success", result))
                except Exception as e:
                    traceback.print_exc()
                    item.response_queue.put(("error", str(e)))
                finally:
                    odenet_work_queue.task_done()
            except queue.Empty:
                continue
    except Exception as e:
        print(f"[OdeNet Worker] Fatal error: {e}")
        traceback.print_exc()
        odenet_worker_state = OdeNetWorkerState.ERROR
def odenet_process_word_lookup(word: str) -> Dict[str, Any]:
    """ Process a single word lookup. Runs in the worker thread. """
    global odenet_de_wn
    if not word or not word.strip():
        return {"info": "No word provided to check."}
    word = word.strip().lower()
    senses = odenet_de_wn.senses(word)
    if not senses:
        return {"info": f"The word '{word}' was not found in the thesaurus."}
    results: Dict[str, Any] = {"input_word": word, "senses": []}
    for sense in senses:
        synset = sense.synset()
        def get_lemmas(synsets, remove_self=False):
            lemmas: Set[str] = set()
            for s in synsets:
                for lemma in s.lemmas():
                    if not (remove_self and lemma == word):
                        lemmas.add(lemma)
            return sorted(list(lemmas))
        antonym_words: Set[str] = set()
        try:
            for ant_sense in sense.get_related('antonym'):
                antonym_words.add(ant_sense.word().lemma())
        except Exception:
            pass
        sense_info = {
            "pos": synset.pos,
            "definition": synset.definition() or "No definition available.",
            "synonyms": get_lemmas([synset], remove_self=True),
            "antonyms": sorted(list(antonym_words)),
            "hypernyms (is a type of)": get_lemmas(synset.hypernyms()),
            "hyponyms (examples are)": get_lemmas(synset.hyponyms()),
            "holonyms (is part of)": get_lemmas(synset.holonyms()),
            "meronyms (has parts)": get_lemmas(synset.meronyms()),
        }
        results["senses"].append(sense_info)
    print(f"[OdeNet Worker] Found {len(results['senses'])} senses for '{word}'")
    return results
def odenet_start_worker():
    """Start the worker thread if not already started."""
    global odenet_worker_thread, odenet_worker_state
    if odenet_worker_state != OdeNetWorkerState.NOT_STARTED:
        return
    if not WN_AVAILABLE:
        print("[OdeNet] 'wn' library not available. Worker will not be started.")
        odenet_worker_state = OdeNetWorkerState.ERROR
        return
    odenet_worker_thread = threading.Thread(target=odenet_worker_loop, daemon=True, name="OdeNetWorker")
    odenet_worker_thread.start()
    timeout = 30
    for _ in range(timeout * 10):
        if odenet_worker_state in (OdeNetWorkerState.READY, OdeNetWorkerState.ERROR):
            break
        threading.Event().wait(0.1)
    if odenet_worker_state != OdeNetWorkerState.READY:
        raise Exception("OdeNet Worker failed to initialize")
# --- Public API (Called by Gradio) ---
def odenet_get_thesaurus_info(word: str) -> Dict[str, Any]:
    """ Public API: Finds thesaurus info for a German word. Thread-safe. """
    if not WN_AVAILABLE:
        return {"error": "WordNet (wn) library is not available."}
    if odenet_worker_state != OdeNetWorkerState.READY:
        return {"error": "WordNet service is not ready yet. Please try again in a moment."}
    try:
        response_queue = queue.Queue()
        item = OdeNetWorkItem(word=word, response_queue=response_queue)
        odenet_work_queue.put(item)
        try:
            status, result = response_queue.get(timeout=30)
            if status == "success":
                return result
            else:
                return {"error": f"Lookup failed: {result}"}
        except queue.Empty:
            return {"error": "Request timed out"}
    except Exception as e:
        traceback.print_exc()
        return {"error": f"An unexpected error occurred: {str(e)}"}

# ============================================================================
# 6. PATTERN INFLECTION LOGIC
# ============================================================================
# --- Word Type Detection ---
def pattern_detect_word_type(word: str) -> Dict[str, Any]:
    """ Use pattern.de's parser as a hint. """
    if not PATTERN_DE_AVAILABLE:
        return {'pos': None, 'lemma': word, 'type': 'unknown'}
    if not word or not word.strip() or all(ch in ".,;:!?()[]{}-–—'.../\|" for ch in word):
        return {'pos': None, 'lemma': word, 'type': 'unknown'}
    word_norm = word.strip()
    log(f"Detecting type for: {word_norm}")
    parser_result = {'pos': None, 'lemma': word_norm, 'type': None}
    try:
        parsed = parse(word_norm, lemmata=True)
        for sentence in split(parsed):
            if hasattr(sentence, "words") and sentence.words:
                w = sentence.words[0]
                w_type = getattr(w, "type", None) or getattr(w, "pos", None)
                w_lemma = (getattr(w, "lemma", None) or word_norm)
                non_content_prefixes = ("DT","ART","IN","APPR","APPRART","APPO","APZR","PTK","PRP","PPER","PPOS","PDS","PIS","KOUI","KON","$,","$.")
                if w_type and any(w_type.startswith(p) for p in non_content_prefixes):
                    return {'pos': w_type, 'lemma': w_lemma, 'type': None}
                parser_result['pos'] = w_type or ""
                parser_result['lemma'] = w_lemma
                if w_type and w_type.startswith('NN'):
                    parser_result['type'] = 'noun'
                elif w_type and w_type.startswith('VB'):
                    parser_result['type'] = 'verb'
                elif w_type and w_type.startswith('JJ'):
                    parser_result['type'] = 'adjective'
                log(f"  Parser says: POS={w_type}, lemma={w_lemma}, type={parser_result['type']}")
    except Exception as e:
        log(f"  Parser failed: {e}")
    return parser_result

def pattern_is_good_analysis(analysis, analysis_type):
    """Check if an analysis has meaningful data."""
    if not analysis: return False
    
    if analysis_type == 'noun':
        # Check for declensions, either in the simple or ambiguous map
        return len(analysis.get('declension', {})) >= 4 or len(analysis.get('declension_by_gender', {})) > 0
        
    elif analysis_type == 'verb':
        present = analysis.get('conjugation', {}).get('Präsens', {})
        if len(present) < 4: return False
        unique_forms = set(present.values())
        if len(unique_forms) < 2: return False
        return True
        
    elif analysis_type == 'adjective':
        # **FIX: Better adjective validation**
        # Must have attributive forms
        if len(analysis.get('attributive', {})) == 0:
            log("    ✗ Not a good adjective: No attributive forms.")
            return False
        
        pred = analysis.get('predicative', '')
        comp = analysis.get('comparative', '')
        sup = analysis.get('superlative', '')
        
        if not pred:
            log("    ✗ Not a good adjective: No predicative form.")
            return False

        # Filter out nonsense: "lauf" -> "laufer", "laufst"
        # Real comparatives end in -er. Real superlatives end in -st or -est.
        # This allows "rasch" (rascher, raschst) but rejects "lauf" (laufer, laufst)
        if comp and not comp.endswith("er"):
            log(f"    ✗ Not a good adjective: Comparative '{comp}' doesn't end in -er.")
            return False
        if sup and not (sup.endswith("st") or sup.endswith("est")):
            log(f"    ✗ Not a good adjective: Superlative '{sup}' doesn't end in -st/-est.")
            return False
            
        return True
        
    return False


# --- Inflection Generators ---
def pattern_analyze_as_noun(word: str, hint_lemma: str = None) -> Dict[str, Any]:
    """Comprehensive noun inflection analysis."""
    log(f"  Analyzing as noun (hint_lemma={hint_lemma})")
    analysis = {}
    singular = singularize(word)
    plural = pluralize(word)
    log(f"    singularize({word}) = {singular}")
    log(f"    pluralize({word}) = {plural}")
    if plural != word and singular != word:
        base = word
        log(f"    Word changes when pluralized => base = {base}")
    elif singular != word:
        base = singular
        log(f"    Word changes when singularized => base = {base}")
    elif hint_lemma and hint_lemma != word:
        base = hint_lemma
        log(f"    Using hint lemma => base = {base}")
    else:
        # This is a valid case, e.g. "Lauf" (singular)
        base = word
        log(f"    Word is already base form => base = {base}")
    
    g = gender(base, pos=NOUN)
    log(f"    gender({base}) = {g}")
    
    # --- AMBIGUITY HANDLING for Nouns (e.g., der/das See) ---
    if isinstance(g, tuple):
        genders = list(g)
        log(f"    Detected ambiguous gender: {genders}")
    elif g is None:
        genders = [MALE] # Default
        log(f"    Gender unknown, defaulting to MALE")
    else:
        genders = [g]
        
    analysis["base_form"] = base
    analysis["plural"] = pluralize(base)
    analysis["singular"] = base
    analysis["declension_by_gender"] = {}

    for gen in genders:
        gender_str = {MALE: "Masculine", FEMALE: "Feminine", NEUTRAL: "Neuter"}.get(gen, "Unknown")
        gen_declension = {}
        for number, number_name in [(SINGULAR, "Singular"), (PLURAL, "Plural")]:
            word_form = base if number == SINGULAR else pluralize(base)
            word_form_cap = word_form.capitalize()
            gender_for_article = gen if number == SINGULAR else PLURAL
            for case, case_name in [(NOMINATIVE, "Nominativ"), (ACCUSATIVE, "Akkusativ"),
                                    (DATIVE, "Dativ"), (GENITIVE, "Genitiv")]:
                try:
                    def_art = article(word_form, DEFINITE, gender_for_article, case)
                    indef_art = article(word_form, INDEFINITE, gender_for_article, case)
                    indef_form = f"{indef_art} {word_form_cap}" if indef_art else word_form_cap
                    if number == PLURAL:
                        indef_form = "—"
                    gen_declension[f"{case_name} {number_name}"] = {
                        "definite": f"{def_art} {word_form_cap}" if def_art else word_form_cap,
                        "indefinite": indef_form,
                        "bare": word_form_cap
                    }
                except Exception as e:
                    log(f"    Failed to get article for {gender_str}/{case_name} {number_name}: {e}")
        analysis["declension_by_gender"][gender_str] = gen_declension
    
    log(f"    Generated declensions for {len(genders)} gender(s)")
    if len(genders) == 1:
        analysis["declension"] = analysis["declension_by_gender"][list(analysis["declension_by_gender"].keys())[0]]
        analysis["gender"] = list(analysis["declension_by_gender"].keys())[0]

    return analysis
def pattern_analyze_as_verb(word: str, hint_lemma: str = None) -> Dict[str, Any]:
    """Comprehensive verb conjugation analysis."""
    log(f"  Analyzing as verb (hint_lemma={hint_lemma})")
    verb_lemma = lemma(word)
    log(f"    lemma({word}) = {verb_lemma}")
    if not verb_lemma or verb_lemma == word:
        if hint_lemma and hint_lemma != word:
            verb_lemma = hint_lemma
            log(f"    Using hint lemma: {verb_lemma}")
        elif not verb_lemma:
            log(f"    No lemma found, trying base word")
            verb_lemma = word # e.g. "lauf"
    
    analysis = {"infinitive": verb_lemma}
    try:
        lex = lexeme(verb_lemma)
        if lex and len(lex) > 1:
            analysis["lexeme"] = lex
            log(f"    lexeme has {len(lex)} forms")
    except Exception as e:
        log(f"    Failed to get lexeme: {e}")
    analysis["conjugation"] = {}
    analysis["conjugation"]["Präsens"] = {}
    present_count = 0
    for alias, name in [("1sg", "ich"), ("2sg", "du"), ("3sg", "er/sie/es"),
                        ("1pl", "wir"), ("2pl", "ihr"), ("3pl", "sie/Sie")]:
        try:
            form = conjugate(verb_lemma, alias)
            if form:
                analysis["conjugation"]["Präsens"][name] = form
                present_count += 1
        except Exception as e:
            log(f"    Failed conjugate({verb_lemma}, {alias}): {e}")
    log(f"    Generated {present_count} present tense forms")
    if present_count < 4:
        # Try again with infinitive, e.g. if input was "lauf"
        try:
            verb_lemma = conjugate(word, INFINITIVE)
            log(f"    Retrying with infinitive '{verb_lemma}'")
            analysis["infinitive"] = verb_lemma
            present_count = 0
            for alias, name in [("1sg", "ich"), ("2sg", "du"), ("3sg", "er/sie/es"),
                                ("1pl", "wir"), ("2pl", "ihr"), ("3pl", "sie/Sie")]:
                form = conjugate(verb_lemma, alias)
                if form:
                    analysis["conjugation"]["Präsens"][name] = form
                    present_count += 1
            if present_count < 4:
                log(f"    Too few present forms, not a valid verb")
                return None
        except Exception as e:
             log(f"    Retry failed, not a valid verb: {e}")
             return None

    analysis["conjugation"]["Präteritum"] = {}
    for alias, name in [("1sgp", "ich"), ("2sgp", "du"), ("3sgp", "er/sie/es"),
                        ("1ppl", "wir"), ("2ppl", "ihr"), ("3ppl", "sie/Sie")]:
        try:
            form = conjugate(verb_lemma, alias)
            if form: analysis["conjugation"]["Präteritum"][name] = form
        except: pass
    analysis["participles"] = {}
    try:
        form = conjugate(verb_lemma, "part")
        if form: analysis["participles"]["Partizip Präsens"] = form
    except: pass
    try:
        form = conjugate(verb_lemma, "ppart")
        if form: analysis["participles"]["Partizip Perfekt"] = form
    except: pass
    analysis["conjugation"]["Imperativ"] = {}
    for alias, name in [("2sg!", "du"), ("2pl!", "ihr")]:
        try:
            form = conjugate(verb_lemma, alias)
            if form: analysis["conjugation"]["Imperativ"][name] = form
        except: pass
    analysis["conjugation"]["Konjunktiv I"] = {}
    for alias, name in [("1sg?", "ich"), ("2sg?", "du"), ("3sg?", "er/sie/es"),
                        ("1pl?", "wir"), ("2pl?", "ihr"), ("3pl?", "sie/Sie")]:
        try:
            form = conjugate(verb_lemma, alias)
            if form: analysis["conjugation"]["Konjunktiv I"][name] = form
        except: pass
    analysis["conjugation"]["Konjunktiv II"] = {}
    for alias, name in [("1sgp?", "ich"), ("2sgp?", "du"), ("3sgp?", "er/sie/es"),
                        ("1ppl?", "wir"), ("2ppl?", "ihr"), ("3ppl?", "sie/Sie")]:
        try:
            form = conjugate(verb_lemma, alias)
            if form: analysis["conjugation"]["Konjunktiv II"][name] = form
        except: pass
    return analysis
def pattern_analyze_as_adjective(word: str, hint_lemma: str = None) -> Dict[str, Any]:
    """Comprehensive adjective inflection analysis."""
    log(f"  Analyzing as adjective (hint_lemma={hint_lemma})")
    base = predicative(word)
    log(f"    predicative({word}) = {base}")
    if base == word.lower() and hint_lemma and hint_lemma != word:
        base = hint_lemma
        log(f"    Using hint lemma: {base}")

    analysis = {}
    analysis["predicative"] = base
    
    # *** FIX: Removed pos=ADJECTIVE, which was causing a crash ***
    try:
        analysis["comparative"] = comparative(base)
    except Exception as e:
        log(f"    Failed to get comparative: {e}")
        analysis["comparative"] = f"{base}er" # Fallback
        
    try:
        analysis["superlative"] = superlative(base)
    except Exception as e:
        log(f"    Failed to get superlative: {e}")
        analysis["superlative"] = f"{base}st" # Fallback
    
    log(f"    comparative = {analysis['comparative']}")
    log(f"    superlative = {analysis['superlative']}")
    
    analysis["attributive"] = {}
    attr_count = 0
    for article_type, article_name in [(None, "Strong"), (INDEFINITE, "Mixed"), (DEFINITE, "Weak")]:
        analysis["attributive"][article_name] = {}
        for gender, gender_name in [(MALE, "Masculine"), (FEMALE, "Feminine"),
                                    (NEUTRAL, "Neuter"), (PLURAL, "Plural")]:
            analysis["attributive"][article_name][gender_name] = {}
            for case, case_name in [(NOMINATIVE, "Nom"), (ACCUSATIVE, "Acc"),
                                    (DATIVE, "Dat"), (GENITIVE, "Gen")]:
                try:
                    attr_form = attributive(base, gender, case, article_type)
                    if article_type:
                        art = article("_", article_type, gender, case)
                        full_form = f"{art} {attr_form} [Noun]" if art else f"{attr_form} [Noun]"
                    else:
                        full_form = f"{attr_form} [Noun]"
                    analysis["attributive"][article_name][gender_name][case_name] = {
                        "form": attr_form, "example": full_form
                    }
                    attr_count += 1
                except Exception as e:
                    log(f"    Failed attributive for {article_name}/{gender_name}/{case_name}: {e}")
                    
    log(f"    Generated {attr_count} attributive forms")
    if attr_count == 0:
        return None
    return analysis
# --- Public API (Called by Gradio) ---
def pattern_get_all_inflections(word: str) -> Dict[str, Any]:
    """
    Generates ALL possible inflections for a German word.
    Analyzes the word as-is AND its lowercase version to catch
    ambiguities like "Lauf" (noun) vs "lauf" (verb).
    """
    if not PATTERN_DE_AVAILABLE:
        return {"error": "`PatternLite` library not available."}
    if not word or not word.strip():
        return {"info": "Please enter a word."}
    word = word.strip()
    word_lc = word.lower()
    log("="*70); log(f"ANALYZING: {word} (and {word_lc})"); log("="*70)
    
    # --- Analyze word as-is (e.g., "Lauf") ---
    detection_as_is = pattern_detect_word_type(word)
    analyses_as_is: Dict[str, Any] = {}
    try:
        log("\n--- Trying analysis for: " + word + " ---")
        noun_analysis_as_is = pattern_analyze_as_noun(word, detection_as_is['lemma'])
        if noun_analysis_as_is and pattern_is_good_analysis(noun_analysis_as_is, 'noun'):
            log("✓ Noun analysis is good")
            analyses_as_is["noun"] = noun_analysis_as_is
        verb_analysis_as_is = pattern_analyze_as_verb(word, detection_as_is['lemma'])
        if verb_analysis_as_is and pattern_is_good_analysis(verb_analysis_as_is, 'verb'):
            log("✓ Verb analysis is good")
            analyses_as_is["verb"] = verb_analysis_as_is
        adj_analysis_as_is = pattern_analyze_as_adjective(word, detection_as_is['lemma'])
        if adj_analysis_as_is and pattern_is_good_analysis(adj_analysis_as_is, 'adjective'):
            log("✓ Adjective analysis is good")
            analyses_as_is["adjective"] = adj_analysis_as_is
    except Exception as e:
        log(f"\nERROR during 'as-is' analysis: {e}")
        traceback.print_exc()
        return {"error": f"An unexpected error occurred during 'as-is' analysis: {str(e)}"}

    # --- Analyze lowercase version (e.g., "lauf") if different ---
    analyses_lc: Dict[str, Any] = {}
    if word != word_lc:
        detection_lc = pattern_detect_word_type(word_lc)
        try:
            log("\n--- Trying analysis for: " + word_lc + " ---")
            noun_analysis_lc = pattern_analyze_as_noun(word_lc, detection_lc['lemma'])
            if noun_analysis_lc and pattern_is_good_analysis(noun_analysis_lc, 'noun'):
                log("✓ Noun analysis (lc) is good")
                analyses_lc["noun"] = noun_analysis_lc
            verb_analysis_lc = pattern_analyze_as_verb(word_lc, detection_lc['lemma'])
            if verb_analysis_lc and pattern_is_good_analysis(verb_analysis_lc, 'verb'):
                log("✓ Verb analysis (lc) is good")
                analyses_lc["verb"] = verb_analysis_lc
            adj_analysis_lc = pattern_analyze_as_adjective(word_lc, detection_lc['lemma'])
            if adj_analysis_lc and pattern_is_good_analysis(adj_analysis_lc, 'adjective'):
                log("✓ Adjective analysis (lc) is good")
                analyses_lc["adjective"] = adj_analysis_lc
        except Exception as e:
            log(f"\nERROR during 'lowercase' analysis: {e}")
            traceback.print_exc()
            return {"error": f"An unexpected error occurred during 'lowercase' analysis: {str(e)}"}

    # --- Merge the results ---
    final_analyses = analyses_as_is.copy()
    for key, value in analyses_lc.items():
        if key not in final_analyses:
            final_analyses[key] = value
            
    results: Dict[str, Any] = {
        "input_word": word,
        "analyses": final_analyses
    }
    if not results["analyses"]:
        results["info"] = "Word could not be analyzed as noun, verb, or adjective."
    log(f"\nFinal merged result: {len(results['analyses'])} analysis/analyses")
    return results

def word_appears_in_inflections(word: str, inflections: Dict[str, Any], pos_type: str) -> bool:
    """
    Check if the input word appears in the inflection forms AND
    cross-validate the POS with OdeNet to reject artifacts.
    """
    import re
    word_lower = word.lower()
    word_cap = word.capitalize()

    # 1. Extract all actual inflection forms (not metadata)
    actual_forms = []
    if pos_type == 'noun':
        declension = inflections.get('declension', {})
        declension_by_gender = inflections.get('declension_by_gender', {})
        for case_data in declension.values():
            if isinstance(case_data, dict): actual_forms.append(case_data.get('bare', ''))
        for gender_data in declension_by_gender.values():
            if isinstance(gender_data, dict):
                for case_data in gender_data.values():
                    if isinstance(case_data, dict): actual_forms.append(case_data.get('bare', ''))
                    
    elif pos_type == 'verb':
        conjugation = inflections.get('conjugation', {})
        for tense_data in conjugation.values():
            if isinstance(tense_data, dict): actual_forms.extend(tense_data.values())
        participles = inflections.get('participles', {})
        actual_forms.extend(participles.values())
        actual_forms.extend(inflections.get('lexeme', []))
        actual_forms.append(inflections.get('infinitive', ''))

    elif pos_type == 'adjective':
        actual_forms.append(inflections.get('predicative', ''))
        actual_forms.append(inflections.get('comparative', ''))
        actual_forms.append(inflections.get('superlative', ''))
        attributive = inflections.get('attributive', {})
        for article_data in attributive.values():
            if isinstance(article_data, dict):
                for gender_data in article_data.values():
                    if isinstance(gender_data, dict):
                        for case_data in gender_data.values():
                            if isinstance(case_data, dict): actual_forms.append(case_data.get('form', ''))

    # 2. Clean forms and check for match
    cleaned_forms = set()
    for form in actual_forms:
        if not form or form == '—': continue
        # For simple forms (most verb forms, adjectives), use as-is
        # For complex forms (nouns with articles), extract words
        if ' ' in form or '[' in form:
            words = re.findall(r'\b[\wäöüÄÖÜß]+\b', form)
            cleaned_forms.update(w.lower() for w in words)
        else:
            cleaned_forms.add(form.lower())
            
    articles = {'der', 'die', 'das', 'den', 'dem', 'des', 'ein', 'eine', 'einen', 'einem', 'eines', 'einer'}
    cleaned_forms = {f for f in cleaned_forms if f not in articles}

    word_found_in_forms = False
    if pos_type == 'noun':
        # Nouns can be input as lowercase, but inflections are capitalized.
        # We check if the *lowercase* input word matches a *lowercase* form.
        if word_lower in cleaned_forms:
            word_found_in_forms = True
    else:
        # For verbs/adjectives, a lowercase match is sufficient
        if word_lower in cleaned_forms:
            word_found_in_forms = True
    
    if not word_found_in_forms:
        log(f"    ✗ Word '{word}' not found in any {pos_type} inflection forms.")
        return False

    log(f"    ✓ Word '{word}' was found in the {pos_type} inflection table.")
    
    # 3. Cross-validate POS with OdeNet to filter artifacts (e.g., 'heute' as 'heuen')
    if not WN_AVAILABLE:
        log("    ⚠️  OdeNet (WN_AVAILABLE=False) is not available to validate POS. Accepting pattern.de's analysis.")
        return True

    try:
        if pos_type == 'noun':
            pos_lemma = inflections.get("base_form", word_lower)
            expected_pos_tag = 'n'
        elif pos_type == 'verb':
            pos_lemma = inflections.get("infinitive", word_lower)
            expected_pos_tag = 'v'
        elif pos_type == 'adjective':
            pos_lemma = inflections.get("predicative", word_lower)
            expected_pos_tag = 'a'
        else:
            log(f"    ? Unknown pos_type '{pos_type}' for OdeNet check.")
            return True # Don't block unknown types
            
        log(f"    Validating {pos_type} (lemma: '{pos_lemma}') with OdeNet (expecting pos='{expected_pos_tag}')...")
        odenet_result = odenet_get_thesaurus_info(pos_lemma)
        senses = odenet_result.get('senses', [])
        pos_senses = [s for s in senses if s.get('pos') == expected_pos_tag]
        
        # If no senses for lemma, check input word as fallback
        if not pos_senses and pos_lemma.lower() != word.lower():
            log(f"    No '{expected_pos_tag}' senses for lemma '{pos_lemma}'. Checking input word '{word}'...")
            odenet_result = odenet_get_thesaurus_info(word)
            senses = odenet_result.get('senses', [])
            pos_senses = [s for s in senses if s.get('pos') == expected_pos_tag]

        if not pos_senses:
            log(f"    ✗ REJECTED: OdeNet has no '{expected_pos_tag}' senses for '{pos_lemma}' or '{word}'. This is likely a pattern.de artifact.")
            return False
        else:
            log(f"    ✓ VERIFIED: OdeNet found {len(pos_senses)} '{expected_pos_tag}' sense(s).")
            return True
            
    except Exception as e:
        log(f"    ⚠️  OdeNet validation check failed with error: {e}")
        return True # Fail open: If OdeNet fails, trust pattern.de

# ============================================================================
# 6b. CONCEPTNET HELPER LOGIC (V2 - ROBUST PARSER)
# ============================================================================
def conceptnet_get_relations(word: str, language: str = 'de') -> Dict[str, Any]:
    """
    Fetches relations from the cstr/conceptnet_normalized Gradio API.
    
    This V2 version uses a robust regex parser to correctly handle the
    Markdown output and filter self-referential junk.
    """
    if not GRADIO_CLIENT_AVAILABLE:
        return {"error": "`gradio_client` library is not installed. Install with: pip install gradio_client"}
        
    if not word or not word.strip():
        return {"info": "No word provided."}
        
    word_lower = word.strip().lower()
    cache_key = (word_lower, language)
    
    # --- 1. Check Cache ---
    with CONCEPTNET_LOCK:
        if cache_key in CONCEPTNET_CACHE:
            log(f"ConceptNet: Found '{word_lower}' in cache.")
            return CONCEPTNET_CACHE[cache_key]
            
    log(f"ConceptNet: Fetching '{word_lower}' from Gradio API...")
    
    try:
        # --- 2. Call Gradio API ---
        client = Client("cstr/conceptnet_normalized")
        
        selected_relations = [
            "RelatedTo", "IsA", "PartOf", "HasA", "UsedFor",
            "CapableOf", "AtLocation", "Synonym", "Antonym",
            "Causes", "HasProperty", "MadeOf", "HasSubevent",
            "DerivedFrom", "SimilarTo", "Desires", "CausesDesire"
        ]
        
        result_markdown = client.predict(
            word=word_lower,
            lang=language,
            selected_relations=selected_relations,
            api_name="/get_semantic_profile"
        )
        
        # --- 3. Parse the Markdown Result (Robustly) ---
        relations_list = []
        if not isinstance(result_markdown, str):
            raise TypeError(f"ConceptNet API returned type {type(result_markdown)}, expected str.")

        lines = result_markdown.split('\n')
        current_relation = None
        
        # Regex to capture: "- <NODE1> <RELATION> → <NODE2> `[WEIGHT]`"
        # Groups:           (1: Node1) (2: Relation) (3: Node2) (4: Weight)
        line_pattern = None

        for line in lines:
            line = line.strip()
            if not line:
                continue

            # Check for relation headers (e.g., "## IsA")
            if line.startswith('## '):
                current_relation = line[3:].strip()
                if current_relation:
                    # Pre-compile the regex for this specific relation
                    line_pattern = re.compile(
                        r"-\s*(.+?)\s+(%s)\s+→\s+(.+?)\s+\`\[([\d.]+)\]\`" % re.escape(current_relation)
                    )
                continue

            # Parse relation entries
            if line.startswith('- ') and current_relation and line_pattern:
                match = line_pattern.search(line)
                
                if not match:
                    log(f"ConceptNet Parser: No match for line '{line}' with relation '{current_relation}'")
                    continue
                    
                try:
                    # Extract parts
                    node1 = match.group(1).strip().strip('*')
                    relation = match.group(2) # This is current_relation
                    node2 = match.group(3).strip().strip('*')
                    weight = float(match.group(4))
                    
                    other_node = None
                    direction = None
                    
                    # Determine direction and filter self-references
                    if node1.lower() == word_lower and node2.lower() != word_lower:
                        other_node = node2
                        direction = "->"
                    elif node2.lower() == word_lower and node1.lower() != word_lower:
                        other_node = node1
                        direction = "<-"
                    else:
                        # This filters "schnell Synonym → schnell"
                        continue 
                        
                    relations_list.append({
                        "relation": relation,
                        "direction": direction,
                        "other_node": other_node,
                        "other_lang": language, # We assume the other node is also in the same lang
                        "weight": weight,
                        "surface": f"{node1} {relation} {node2}"
                    })

                except Exception as e:
                    log(f"ConceptNet Parser: Error parsing line '{line}': {e}")
                    continue
        
        # --- 4. Finalize and Cache Result ---
        if not relations_list:
            final_result = {"info": f"No valid (non-self-referential) relations found for '{word_lower}'."}
        else:
            # Sort by weight, descending
            relations_list.sort(key=lambda x: x.get('weight', 0.0), reverse=True)
            final_result = {"relations": relations_list}
            
        with CONCEPTNET_LOCK:
            CONCEPTNET_CACHE[cache_key] = final_result
            
        log(f"ConceptNet: Returning {len(relations_list)} relations for '{word_lower}'")
        return final_result
        
    except Exception as e:
        error_msg = f"ConceptNet Gradio API request failed: {type(e).__name__} - {e}"
        log(f"ConceptNet API error for '{word_lower}': {e}")
        traceback.print_exc()
        return {"error": error_msg, "traceback": traceback.format_exc()}

# ============================================================================
# 6c. NEW: HANTA INITIALIZER & HELPERS
# ============================================================================

def hanta_get_tagger() -> Optional[HanoverTagger]:
    """ Thread-safe function to get a single instance of the HanTa Tagger. """
    global HANTA_TAGGER_INSTANCE
    if not HANTA_AVAILABLE:
        raise ImportError("HanTa library is not installed.")
    
    if HANTA_TAGGER_INSTANCE:
        return HANTA_TAGGER_INSTANCE
    
    with HANTA_TAGGER_LOCK:
        if HANTA_TAGGER_INSTANCE:
            return HANTA_TAGGER_INSTANCE
        
        try:
            print("Initializing HanTa Tagger (loading model)...")
            PACKAGE_DIR = os.path.dirname(HanTa.HanoverTagger.__file__)
            MODEL_PATH = os.path.join(PACKAGE_DIR, 'morphmodel_ger.pgz')
            
            if not os.path.exists(MODEL_PATH):
                 print(f"CRITICAL: HanTa model file 'morphmodel_ger.pgz' not found at {MODEL_PATH}")
                 raise FileNotFoundError("HanTa model file missing. Please ensure HanTa is correctly installed.")
            
            tagger = HanoverTagger(MODEL_PATH)
            _ = tagger.analyze("Test") # Warm-up call
            print("✓ HanTa Tagger initialized successfully.")
            HANTA_TAGGER_INSTANCE = tagger
            return HANTA_TAGGER_INSTANCE
        except Exception as e:
            print(f"CRITICAL ERROR: Failed to initialize HanTa Tagger: {e}")
            traceback.print_exc()
            return None

def _get_odenet_senses_by_pos(word: str) -> Dict[str, List[Dict[str, Any]]]:
    """
    (Helper) Fetches OdeNet senses for a word and groups them by POS.
    
    *** V18 FIX: OdeNet uses 'a' for BOTH Adjective and Adverb. ***
    """
    senses_by_pos: Dict[str, List[Dict]] = {
        "noun": [], "verb": [], "adjective": [], "adverb": []
    }
    if not WN_AVAILABLE:
        log(f"OdeNet check skipped for '{word}': WN_AVAILABLE=False")
        # If OdeNet is down, we can't validate, so we must return
        # non-empty lists to avoid incorrectly rejecting a POS.
        # This is a "fail-open" strategy.
        return {"noun": [{"info": "OdeNet unavailable"}],
                "verb": [{"info": "OdeNet unavailable"}],
                "adjective": [{"info": "OdeNet unavailable"}],
                "adverb": [{"info": "OdeNet unavailable"}]}
        
    try:
        all_senses = odenet_get_thesaurus_info(word).get("senses", [])
        for sense in all_senses:
            if "error" in sense: continue
            pos_tag = sense.get("pos")
            
            if pos_tag == 'n':
                senses_by_pos["noun"].append(sense)
            elif pos_tag == 'v':
                senses_by_pos["verb"].append(sense)
            
            # --- THIS IS THE CRITICAL FIX ---
            elif pos_tag == 'a':
                log(f"Found OdeNet 'a' tag (Adj/Adv) for sense: {sense.get('definition', '...')[:30]}")
                senses_by_pos["adjective"].append(sense)
                senses_by_pos["adverb"].append(sense)
            # --- END OF FIX ---
                
    except Exception as e:
        log(f"OdeNet helper check failed for '{word}': {e}")
        
    log(f"OdeNet senses for '{word}': "
        f"{len(senses_by_pos['noun'])}N, "
        f"{len(senses_by_pos['verb'])}V, "
        f"{len(senses_by_pos['adjective'])}Adj, "
        f"{len(senses_by_pos['adverb'])}Adv")
    return senses_by_pos

def _hanta_get_candidates(word: str, hanta_tagger: "HanoverTagger") -> Set[str]:
    """
    (Helper) Gets all possible HanTa STTS tags for a word,
    checking both lowercase and capitalized versions.
    """
    all_tags = set()
    try:
        # Check lowercase (for verbs, adjs, advs)
        tags_lower = hanta_tagger.tag_word(word.lower(), cutoff=20)
        all_tags.update(tag[0] for tag in tags_lower)
    except Exception as e:
        log(f"HanTa tag_word (lower) failed for '{word}': {e}")
    
    try:
        # Check capitalized (for nouns)
        tags_upper = hanta_tagger.tag_word(word.capitalize(), cutoff=20)
        all_tags.update(tag[0] for tag in tags_upper)
    except Exception as e:
        log(f"HanTa tag_word (upper) failed for '{word}': {e}")
    
    log(f"HanTa candidates for '{word}': {all_tags}")
    return all_tags

def _hanta_map_tags_to_pos(hanta_tags: Set[str]) -> Dict[str, Set[str]]:
    """
    (Helper) Maps STTS tags to simplified POS groups and injects the
    ADJ(D) -> ADV heuristic.
    """
    pos_groups = {"noun": set(), "verb": set(), "adjective": set(), "adverb": set()}
    has_adjd = False

    for tag in hanta_tags:
        # Nouns (NN), Proper Nouns (NE), Nominalized Inf. (NNI), Nom. Adj. (NNA)
        if tag.startswith("NN") or tag == "NE":
            pos_groups["noun"].add(tag)
        # Verbs (VV...), Auxiliaries (VA...), Modals (VM...)
        elif tag.startswith("VV") or tag.startswith("VA") or tag.startswith("VM"):
            pos_groups["verb"].add(tag)
        # Adjectives (Attributive ADJ(A), Predicative ADJ(D))
        elif tag.startswith("ADJ"):
            pos_groups["adjective"].add(tag)
            if tag == "ADJ(D)":
                has_adjd = True
        # Adverbs
        elif tag == "ADV":
            pos_groups["adverb"].add(tag)
    
    # --- The Core Heuristic ---
    # If HanTa found a predicative adjective (ADJD), it can *also* be used
    # as an adverb (e..g, "er singt schön" [ADV] vs. "er ist schön" [ADJD]).
    if has_adjd:
        log("Injecting ADV possibility based on ADJ(D) tag.")
        pos_groups["adverb"].add("ADV (from ADJD)")

    # Filter out empty groups
    return {k: v for k, v in pos_groups.items() if v}

def _hanta_get_lemma_for_pos(word: str, pos_group: str, hanta_tagger: "HanoverTagger") -> str:
    """
    (Helper) Gets the correct lemma for a given word and POS group
    using case-sensitive analysis.
    """
    lemma = ""
    try:
        if pos_group == "noun":
            # Nouns must be lemmatized from their capitalized form
            lemma = hanta_tagger.analyze(word.capitalize(), casesensitive=True)[0]
        elif pos_group == "verb":
            # Verbs must be lemmatized from their lowercase form
            lemma = hanta_tagger.analyze(word.lower(), casesensitive=True)[0]
        elif pos_group == "adjective":
            # Adjectives are lemmatized from their lowercase form
            lemma = hanta_tagger.analyze(word.lower(), casesensitive=True)[0]
        elif pos_group == "adverb":
            # Adverbs are also lemmatized from lowercase
            lemma = hanta_tagger.analyze(word.lower(), casesensitive=True)[0]
            
    except Exception as e:
        log(f"HanTa analyze failed for {word}/{pos_group}: {e}. Falling back.")
        
    # Fallback logic
    if not lemma:
        if pos_group == "noun":
            return word.capitalize()
        return word.lower()
        
    return lemma

def _build_semantics(lemma: str, odenet_senses: List[Dict], top_n: int) -> Dict[str, Any]:
    """
    (Helper) Builds the semantics block with OdeNet and ConceptNet.
    """
    conceptnet_relations = []
    if REQUESTS_AVAILABLE:
        try:
            conceptnet_result = conceptnet_get_relations(lemma, language='de')
            conceptnet_relations = conceptnet_result.get("relations", [])
        except Exception as e:
            conceptnet_relations = [{"error": str(e)}]
    
    if top_n > 0:
        odenet_senses = odenet_senses[:top_n]
        conceptnet_relations.sort(key=lambda x: x.get('weight', 0.0), reverse=True)
        conceptnet_relations = conceptnet_relations[:top_n]
        
    return {
        "lemma": lemma,
        "odenet_senses": odenet_senses,
        "conceptnet_relations": conceptnet_relations
    }

# ============================================================================
# 7. CONSOLIDATED ANALYZER LOGIC
# ============================================================================

# --- 7a. Comprehensive (Contextual) Analyzer ---
def comprehensive_german_analysis(text: str, top_n_value: Optional[float] = 0) -> Dict[str, Any]:
    """
    (CONTEXTUAL) Combines NLP tools for a deep analysis of German text.
    
    **V17 UPDATE:** This function now calls the new HanTa-led
    `analyze_word_encyclopedia()` function as its morphological engine.
    This makes its analysis robust against artifacts.
    """
    
    try:
        if not text or not text.strip():
            return {"info": "Please enter text to analyze."}
        top_n = int(top_n_value) if top_n_value is not None else 0
        
        print(f"\n[Comprehensive Analysis] Starting analysis for: \"{text}\" (top_n={top_n})")
        results: Dict[str, Any] = {"input_text": text}
        nlp_de = None
        context_doc = None

        # --- 1. LanguageTool Grammar Check ---
        print("[Comprehensive Analysis] Running LanguageTool...")
        # (Grammar check logic remains unchanged)
        if LT_AVAILABLE:
            try:
                results["grammar_check"] = lt_check_grammar(text)
            except Exception as e:
                results["grammar_check"] = {"error": f"LanguageTool failed: {e}"}
        else:
            results["grammar_check"] = {"error": "LanguageTool not available."}

        # --- 2. spaCy Morpho-Syntactic Backbone ---
        print("[Comprehensive Analysis] Running spaCy...")
        # (spaCy analysis logic remains unchanged, it's needed for context)
        spacy_json_output = []
        try:
            _, spacy_json, _, _, _ = spacy_get_analysis("en", "de", text)
            if isinstance(spacy_json, list):
                spacy_json_output = spacy_json
                results["spacy_analysis"] = spacy_json_output
                nlp_de = SPACY_MODELS.get("de")
                if nlp_de:
                    context_doc = nlp_de(text)
                    if not context_doc.has_vector or context_doc.vector_norm == 0:
                        print("[Comprehensive Analysis] WARNING: Context sentence has no vector.")
                        context_doc = None
            else:
                 results["spacy_analysis"] = spacy_json
        except Exception as e:
            results["spacy_analysis"] = {"error": f"spaCy analysis failed: {e}"}

        # --- 2b. Heuristic SVA check ---
        # (SVA logic remains unchanged)
        # ... (your existing SVA code) ...

        # --- 3. Lemma-by-Lemma Deep Dive (NEW V17 LOGIC) ---
        print("[Comprehensive Analysis] Running Lemma Deep Dive...")
        FUNCTION_POS = {"DET","ADP","AUX","PUNCT","SCONJ","CCONJ","PART","PRON","NUM","SYM","X", "SPACE"}
        lemma_deep_dive: Dict[str, Any] = {}
        processed_lemmas: Set[str] = set()

        if not spacy_json_output:
            print("[Comprehensive Analysis] No spaCy tokens to analyze. Skipping deep dive.")
        else:
            for token in spacy_json_output:
                lemma = token.get("lemma")
                pos = (token.get("pos") or "").upper()
                
                if not lemma or lemma == "--" or pos in FUNCTION_POS or lemma in processed_lemmas:
                    continue
                processed_lemmas.add(lemma)
                print(f"[Deep Dive] Analyzing lemma: '{lemma}' (from token '{token.get('word')}')")
                lemma_report: Dict[str, Any] = {}

                # --- 3a. Get Validated Grammatical & Semantic Analysis ---
                # *** THIS IS THE KEY CHANGE ***
                # We call our new, HanTa-powered function.
                inflection_analysis = {}
                semantic_analysis = {}
                
                try:
                    # We pass top_n=0 to get ALL semantic possibilities
                    encyclopedia_data = analyze_word_encyclopedia(lemma, 0)
                    
                    # The "analysis" key contains {"noun": {...}, "verb": {...}, ...}
                    word_analysis = encyclopedia_data.get("analysis", {})
                    
                    # Re-structure this data to fit the Comprehensive Analyzer's format
                    for pos_key, data in word_analysis.items():
                        inflection_analysis[pos_key] = data.get("inflections")
                        # Add all semantic data to one big list
                        semantic_analysis[f"{pos_key}_senses"] = data.get("semantics", {}).get("odenet_senses", [])
                        
                        # Add ConceptNet relations, if any
                        if "conceptnet_relations" not in semantic_analysis:
                            semantic_analysis["conceptnet_relations"] = []
                        semantic_analysis["conceptnet_relations"].extend(
                            data.get("semantics", {}).get("conceptnet_relations", [])
                        )

                    lemma_report["inflection_analysis"] = inflection_analysis
                    
                except Exception as e:
                    lemma_report["inflection_analysis"] = {"error": f"V17 Analyzer failed: {e}", "traceback": traceback.format_exc()}


                # --- 3b. Contextual Re-ranking (Unchanged) ---
                # This logic is perfect and remains the same. It just re-ranks
                # the semantic data we gathered in step 3a.
                
                # OdeNet Senses
                for key in semantic_analysis:
                    if key.endswith("_senses") and nlp_de:
                        ranked_senses = []
                        for sense in semantic_analysis[key]:
                            # ... (your existing re-ranking code) ...
                            if "error" in sense: continue
                            definition = sense.get("definition", "")
                            relevance = 0.0
                            if definition and context_doc:
                                try:
                                    def_doc = nlp_de(definition)
                                    if def_doc.has_vector and def_doc.vector_norm > 0:
                                        relevance = context_doc.similarity(def_doc)
                                except Exception:
                                    relevance = 0.0
                            sense["relevance_score"] = float(relevance)
                            ranked_senses.append(sense)
                        
                        ranked_senses.sort(key=lambda x: x.get('relevance_score', 0.0), reverse=True)
                        if top_n > 0:
                            ranked_senses = ranked_senses[:top_n]
                        semantic_analysis[key] = ranked_senses

                # ConceptNet Relations
                if "conceptnet_relations" in semantic_analysis and nlp_de:
                    ranked_relations = []
                    # ... (your existing re-ranking code) ...
                    for rel in semantic_analysis["conceptnet_relations"]:
                        if "error" in rel: continue
                        text_to_score = rel.get('surface') or rel.get('other_node', '')
                        relevance = 0.0
                        if text_to_score and context_doc:
                             try:
                                rel_doc = nlp_de(text_to_score)
                                if rel_doc.has_vector and rel_doc.vector_norm > 0:
                                    relevance = context_doc.similarity(rel_doc)
                             except Exception:
                                relevance = 0.0
                        rel["relevance_score"] = float(relevance)
                        ranked_relations.append(rel)
                    
                    ranked_relations.sort(key=lambda x: x.get('relevance_score', 0.0), reverse=True)
                    if top_n > 0:
                        ranked_relations = ranked_relations[:top_n]
                    semantic_analysis["conceptnet_relations"] = ranked_relations

                lemma_report["semantic_analysis"] = semantic_analysis
                lemma_deep_dive[lemma] = lemma_report
                
        results["lemma_deep_dive"] = lemma_deep_dive
        print("[Comprehensive Analysis] Analysis complete.")
        return results
    
    except Exception as e:
        print(f"[Comprehensive Analysis] FATAL ERROR: {e}")
        traceback.print_exc()
        return {
            "error": f"Analysis failed: {str(e)}",
            "traceback": traceback.format_exc(),
            "input_text": text
        }

# --- 7b. NEW: Word Encyclopedia (Non-Contextual) Analyzer ---
# --- 7b. NEW: Word Encyclopedia (Non-Contextual) Analyzer ---
def analyze_word_encyclopedia(word: str, top_n_value: Optional[float] = 0) -> Dict[str, Any]:
    """
    (PUBLIC DISPATCHER) Analyzes a single word for all possible forms.
    
    This function intelligently selects the best available engine:
    1.  PRIMARY: Attempts to use the HanTa-led engine (V17) for maximum accuracy.
    2.  FALLBACK: If HanTa is not available, it uses the spaCy-IWNLP-led
        engine (V16 logic from 'analyze_word_comprehensively') as a robust fallback.
    """
    if not word or not word.strip():
        return {"info": "Please enter a word."}

    top_n = int(top_n_value) if top_n_value is not None else 0

    # --- PRIMARY ENGINE: HanTa-led (V17) ---
    if HANTA_AVAILABLE:
        print(f"\n[Word Encyclopedia] Starting V18 (HanTa) analysis for: \"{word}\"")
        final_result: Dict[str, Any] = {
            "input_word": word,
            "analysis": {}
        }
        
        try:
            hanta_tagger = hanta_get_tagger()
            if not hanta_tagger:
                raise Exception("HanTa Tagger failed to initialize.") # Will be caught and trigger fallback

            # --- 1. Get All Grammatical Candidates (HanTa) ---
            hanta_tags = _hanta_get_candidates(word, hanta_tagger)
            if not hanta_tags:
                return {"info": f"No grammatical analysis found for '{word}'."}
                
            # --- 2. Map Tags to POS Groups (with Adverb Heuristic) ---
            pos_groups_map = _hanta_map_tags_to_pos(hanta_tags)
            log(f"Found {len(pos_groups_map)} possible POS group(s): {list(pos_groups_map.keys())}")

            # --- 3. Validate and Build Report for each POS Group ---
            for pos_group, specific_tags in pos_groups_map.items():
                print(f"--- Analyzing as: {pos_group.upper()} ---")
                
                # --- 3a. Get Lemma (HanTa) ---
                lemma = _hanta_get_lemma_for_pos(word, pos_group, hanta_tagger)
                log(f"Lemma for {pos_group} is: '{lemma}'")
                
                # --- 3b. Get Semantics & VALIDATE (OdeNet) ---
                # We call the NEW, CORRECTED helper from Section 6c
                all_odenet_senses = _get_odenet_senses_by_pos(lemma)
                pos_odenet_senses = all_odenet_senses.get(pos_group, [])
                
                # We only reject if OdeNet is working and returns no senses.
                # If OdeNet is down, the list will contain a placeholder and we proceed.
                if not pos_odenet_senses:
                    log(f"✗ REJECTED {pos_group}: OdeNet is available but has no '{pos_group}' senses for lemma '{lemma}'.")
                    continue
                
                # Filter out the placeholder if OdeNet is down
                if pos_odenet_senses and "info" in pos_odenet_senses[0]:
                    log(f"✓ VERIFIED {pos_group}: OdeNet is unavailable, proceeding without validation.")
                    pos_odenet_senses = [] # Clear the placeholder
                else:
                    log(f"✓ VERIFIED {pos_group}: OdeNet found {len(pos_odenet_senses)} sense(s).")
                
                # --- 3c. Get Inflections (Pattern) ---
                inflection_report = {}
                if not PATTERN_DE_AVAILABLE:
                    inflection_report = {"info": "pattern.de library not available. No inflections generated."}
                else:
                    try:
                        if pos_group == "noun":
                            inflection_report = pattern_analyze_as_noun(lemma)
                        elif pos_group == "verb":
                            inflection_report = pattern_analyze_as_verb(lemma)
                        elif pos_group == "adjective":
                            inflection_report = pattern_analyze_as_adjective(lemma)
                        elif pos_group == "adverb":
                            inflection_report = {"base_form": lemma, "info": "Adverbs are non-inflecting."}
                        
                        if not pattern_is_good_analysis(inflection_report, pos_group) and pos_group != "adverb":
                            log(f"⚠️ Warning: pattern.de generated a poor inflection table for {lemma} ({pos_group}).")
                            inflection_report["warning"] = "Inflection table from pattern.de seems incomplete or invalid."
                    except Exception as e:
                        log(f"pattern.de inflection failed for {lemma} ({pos_group}): {e}")
                        inflection_report = {"error": f"pattern.de failed: {e}", "traceback": traceback.format_exc()}

                # --- 3d. Build Final Report Block ---
                final_result["analysis"][pos_group] = {
                    "hanta_analysis": {
                        "detected_tags": sorted(list(specific_tags)),
                        "lemma": lemma,
                        "morphemes": [
                             hanta_tagger.analyze(word.capitalize() if pos_group == 'noun' else word.lower(), taglevel=3)
                        ]
                    },
                    "inflections": inflection_report,
                    "semantics": _build_semantics(lemma, pos_odenet_senses, top_n)
                }

            if not final_result["analysis"]:
                return {
                    "input_word": word,
                    "info": f"No valid, semantically-verified analysis found for '{word}'. It may be a typo or a function word."
                }
            
            final_result["info"] = "Analysis performed by HanTa-led primary engine."
            return final_result

        except Exception as e:
            print(f"[Word Encyclopedia] HanTa PRIMARY Engine FAILED: {e}")
            traceback.print_exc()
            # If HanTa fails, fall through to the IWNLP fallback
            pass

    # --- FALLBACK ENGINE: spaCy-IWNLP-led (V16) ---
    if IWNLP_AVAILABLE:
        try:
            log("--- Dispatcher: HanTa not found or failed. Attempting IWNLP Fallback Engine ---")
            # We call your existing V16 function, which we just made robust in Step 2.
            result = analyze_word_comprehensively(word, top_n_value)
            result["info"] = result.get("info", "") + " (Analysis performed by IWNLP-based fallback engine)"
            return result
        except Exception as e:
            log(f"--- IWNLP Fallback Engine FAILED: {e} ---")
            traceback.print_exc()
            return {"error": f"IWNLP Fallback Engine failed: {e}"}

    # --- No engines available ---
    log("--- Dispatcher: No valid analysis engines found. ---")
    return {
        "input_word": word,
        "error": "Fatal Error: Neither HanTa nor spacy-iwnlp are available. "
                 "Please install at least one to use the Word Encyclopedia."
    }
    
def analyze_word_comprehensively(word: str, top_n_value: Optional[float] = 0) -> Dict[str, Any]:
    """
    (NON-CONTEXTUAL) Analyzes a single word for ALL its possible
    grammatical and semantic forms.
    
    ** Strategy: IWNLP Lemmas + spaCy POS + Pattern.de Validators**
    1. Get spaCy's primary POS (e.g., "ADV" for "heute").
    2. Get IWNLP's list of *lemmas* (e.g., "Lauf" -> ['Lauf', 'laufen']).
    3. Create a unique set of all possible lemmas from spaCy, IWNLP, and the word itself.
    4. Iterate this lemma set:
       - Try to analyze each lemma as NOUN (capitalized).
       - Try to analyze each lemma as VERB.
       - Try to analyze each lemma as ADJECTIVE.
       - Validate each with pattern_is_good_analysis AND by checking for OdeNet senses.
    5. After checking inflections, check if spaCy's POS was 'ADV'.
       If so, and OdeNet has 'r' senses, add an 'adverb' report.
    6. This finds all inflecting forms ("Lauf", "gut") AND non-inflecting
       forms ("heute") while rejecting artifacts ("klauf", "heutst").
    """
    if not word or not word.strip():
        return {"info": "Please enter a word."}

    if not IWNLP_AVAILABLE:
        return {"error": "`spacy-iwnlp` library not available. This tab requires it."}

    top_n = int(top_n_value) if top_n_value is not None else 0
        
    print(f"\n[Word Encyclopedia] Starting analysis for: \"{word}\" (top_n={top_n})")
    
    final_result: Dict[str, Any] = {
        "input_word": word,
        "analysis": {}
    }
    
    # --- Helper: Get OdeNet senses ---
    def _get_odenet_senses_by_pos(w):
        """
        (Internal helper for IWNLP fallback)
        
        *** V18 FIX: OdeNet uses 'a' for BOTH Adjective and Adverb. ***
        """
        senses_by_pos: Dict[str, List[Dict]] = {
            "noun": [], "verb": [], "adjective": [], "adverb": []
        }
        if not WN_AVAILABLE:
            log(f"[IWNLP Fallback] OdeNet check skipped for '{w}': WN_AVAILABLE=False")
            # Fail-open strategy
            return {"noun": [{"info": "OdeNet unavailable"}],
                    "verb": [{"info": "OdeNet unavailable"}],
                    "adjective": [{"info": "OdeNet unavailable"}],
                    "adverb": [{"info": "OdeNet unavailable"}]}
            
        try:
            all_senses = odenet_get_thesaurus_info(w).get("senses", [])
            for sense in all_senses:
                if "error" in sense: continue
                pos_tag = sense.get("pos")
                
                if pos_tag == 'n':
                    senses_by_pos["noun"].append(sense)
                elif pos_tag == 'v':
                    senses_by_pos["verb"].append(sense)
                    
                # --- THIS IS THE CRITICAL FIX ---
                elif pos_tag == 'a':
                    log(f"[IWNLP Fallback] Found OdeNet 'a' tag (Adj/Adv) for sense: {sense.get('definition', '...')[:30]}")
                    senses_by_pos["adjective"].append(sense)
                    senses_by_pos["adverb"].append(sense)
                # --- END OF FIX ---
                    
        except Exception as e:
            print(f"[Word Encyclopedia] OdeNet check failed: {e}")
        return senses_by_pos

    # --- Helper: Build semantics block ---
    def _build_semantics(lemma, odenet_senses, top_n):
        conceptnet_relations = []
        if REQUESTS_AVAILABLE:
            try:
                conceptnet_result = conceptnet_get_relations(lemma, language='de')
                conceptnet_relations = conceptnet_result.get("relations", [])
            except Exception as e:
                conceptnet_relations = [{"error": str(e)}]
        
        if top_n > 0:
            odenet_senses = odenet_senses[:top_n]
            conceptnet_relations.sort(key=lambda x: x.get('weight', 0.0), reverse=True)
            conceptnet_relations = conceptnet_relations[:top_n]
            
        return {
            "lemma": lemma,
            "odenet_senses": odenet_senses,
            "conceptnet_relations": conceptnet_relations
        }

    # --- 1. GET ALL LEMMA CANDIDATES & SPACY POS ---
    try:
        iwnlp = iwnlp_get_pipeline()
        if not iwnlp:
            return {"error": "IWNLP pipeline failed to initialize."}
            
        doc = iwnlp(word)
        token = doc[0]
        
        # Get spaCy's best POS guess
        spacy_pos = token.pos_ # e.g., "NOUN" for "Lauf", "ADV" for "heute"
        spacy_lemma = token.lemma_

        # *** THIS IS THE FIX ***
        # Get IWNLP's lemma list (it only registers 'iwnlp_lemmas')
        iwnlp_lemmas_list = token._.iwnlp_lemmas or []
        
        # Combine all possible lemmas
        all_lemmas = set(iwnlp_lemmas_list)
        all_lemmas.add(spacy_lemma)
        all_lemmas.add(word) # Add the word itself
        
        print(f"[Word Encyclopedia] spaCy POS: {spacy_pos}")
        print(f"[Word Encyclopedia] All lemmas to check: {all_lemmas}")
        
    except Exception as e:
        traceback.print_exc()
        return {"error": f"IWNLP analysis failed: {e}"}

    # --- 2. CHECK INFLECTING POSSIBILITIES FOR EACH LEMMA ---
    
    # This dict will hold the *best* analysis for each POS
    # e.g., "gut" -> { 'adjective': {...}, 'noun': {...} }
    valid_analyses: Dict[str, Dict[str, Any]] = {}

    for lemma in all_lemmas:
        if not lemma: continue
        
        odenet_senses_by_pos = _get_odenet_senses_by_pos(lemma)

        # --- Check NOUN ---
        if 'noun' not in valid_analyses:
            noun_inflections = {}
            is_good_noun = False
            
            if not PATTERN_DE_AVAILABLE:
                noun_inflections = {"info": "pattern.de not available."}
                is_good_noun = True 
            else:
                try:
                    noun_inflections = pattern_analyze_as_noun(lemma.capitalize())
                    if pattern_is_good_analysis(noun_inflections, "noun"):
                        is_good_noun = True
                except Exception as e:
                    noun_inflections = {"error": f"pattern.de failed: {e}"}

            if is_good_noun:
                odenet_senses = odenet_senses_by_pos.get('noun', [])
                if not odenet_senses and lemma.lower() == word.lower():
                     odenet_senses = _get_odenet_senses_by_pos(lemma.capitalize()).get('noun', [])
                
                # We accept if (senses exist) OR (OdeNet is down and we can't check)
                if odenet_senses:
                     # We must filter out the "unavailable" placeholder
                    if "info" not in odenet_senses[0]:
                        log(f"  ✓ [IWNLP Fallback] Valid NOUN found: {lemma}")
                        valid_analyses['noun'] = {
                            "lemma": noun_inflections.get("base_form", lemma),
                            "inflections": noun_inflections,
                            "odenet_senses": odenet_senses
                        }
                    elif not WN_AVAILABLE: # OdeNet is down
                        log(f"  ✓ [IWNLP Fallback] Accepting NOUN (OdeNet unavailable): {lemma}")
                        valid_analyses['noun'] = {
                            "lemma": noun_inflections.get("base_form", lemma),
                            "inflections": noun_inflections,
                            "odenet_senses": [] # No senses to show
                        }


        # --- Check VERB ---
        if 'verb' not in valid_analyses:
            verb_inflections = {}
            is_good_verb = False
            
            if not PATTERN_DE_AVAILABLE:
                verb_inflections = {"info": "pattern.de not available."}
                is_good_verb = True
            else:
                try:
                    verb_inflections = pattern_analyze_as_verb(lemma)
                    if pattern_is_good_analysis(verb_inflections, "verb"):
                        is_good_verb = True
                except Exception as e:
                    verb_inflections = {"error": f"pattern.de failed: {e}"}

            if is_good_verb:
                odenet_senses = odenet_senses_by_pos.get('verb', [])
                
                if odenet_senses:
                    if "info" not in odenet_senses[0]:
                        log(f"  ✓ [IWNLP Fallback] Valid VERB found: {lemma}")
                        valid_analyses['verb'] = {
                            "lemma": verb_inflections.get("infinitive", lemma),
                            "inflections": verb_inflections,
                            "odenet_senses": odenet_senses
                        }
                    elif not WN_AVAILABLE:
                         log(f"  ✓ [IWNLP Fallback] Accepting VERB (OdeNet unavailable): {lemma}")
                         valid_analyses['verb'] = {
                            "lemma": verb_inflections.get("infinitive", lemma),
                            "inflections": verb_inflections,
                            "odenet_senses": []
                        }

        # --- Check ADJECTIVE ---
        if 'adjective' not in valid_analyses:
            adj_inflections = {}
            is_good_adj = False
            
            if not PATTERN_DE_AVAILABLE:
                adj_inflections = {"info": "pattern.de not available."}
                is_good_adj = True
            else:
                try:
                    adj_inflections = pattern_analyze_as_adjective(lemma)
                    if pattern_is_good_analysis(adj_inflections, "adjective"):
                        is_good_adj = True
                except Exception as e:
                    adj_inflections = {"error": f"pattern.de failed: {e}"}
            
            if is_good_adj:
                odenet_senses = odenet_senses_by_pos.get('adjective', [])
                
                if odenet_senses:
                    if "info" not in odenet_senses[0]:
                        log(f"  ✓ [IWNLP Fallback] Valid ADJECTIVE found: {lemma}")
                        valid_analyses['adjective'] = {
                            "lemma": adj_inflections.get("predicative", lemma),
                            "inflections": adj_inflections,
                            "odenet_senses": odenet_senses
                        }
                    elif not WN_AVAILABLE:
                        log(f"  ✓ [IWNLP Fallback] Accepting ADJECTIVE (OdeNet unavailable): {lemma}")
                        valid_analyses['adjective'] = {
                            "lemma": adj_inflections.get("predicative", lemma),
                            "inflections": adj_inflections,
                            "odenet_senses": []
                        }

    # --- 3. CHECK NON-INFLECTING POS (ADVERB) ---
    if spacy_pos == "ADV":
        odenet_senses = _get_odenet_senses_by_pos(word).get('adverb', [])
        
        if odenet_senses:
            if "info" not in odenet_senses[0]:
                log(f"  ✓ [IWNLP Fallback] Valid ADVERB found: {word}")
                valid_analyses['adverb'] = {
                    "lemma": word,
                    "inflections": {"base_form": word},
                    "odenet_senses": odenet_senses
                }
            elif not WN_AVAILABLE:
                log(f"  ✓ [IWNLP Fallback] Accepting ADVERB (OdeNet unavailable): {word}")
                valid_analyses['adverb'] = {
                    "lemma": word,
                    "inflections": {"base_form": word},
                    "odenet_senses": []
                }
            
    # --- 4. CHECK OTHER FUNCTION WORDS (e.g. "mein" -> DET) ---
    # We add this if spaCy found a function word AND we haven't found any
    # content-word analyses (which are more informative).
    FUNCTION_POS = {"DET", "PRON", "ADP", "AUX", "CCONJ", "SCONJ", "PART", "PUNCT", "SYM"}
    if spacy_pos in FUNCTION_POS and not valid_analyses:
        pos_key = spacy_pos.lower()
        print(f"  ✓ Valid Function Word found: {word} (POS: {spacy_pos})")
        valid_analyses[pos_key] = {
            "lemma": spacy_lemma,
            "inflections": {"base_form": spacy_lemma},
            "odenet_senses": [], # Function words aren't in OdeNet
            "spacy_analysis": { # Add the spaCy info
                "word": token.text, "lemma": token.lemma_,
                "pos_UPOS": token.pos_, "pos_TAG": token.tag_,
                "morphology": str(token.morph)
            }
        }

    # --- 5. BUILD FINAL REPORT ---
    for pos_key, analysis_data in valid_analyses.items():
        pos_report = {
            "inflections": analysis_data["inflections"],
            "semantics": _build_semantics(
                analysis_data["lemma"],
                analysis_data["odenet_senses"],
                top_n
            )
        }
        # Add spaCy analysis if it was included
        if "spacy_analysis" in analysis_data:
             pos_report["spacy_analysis"] = analysis_data["spacy_analysis"]
             
        final_result["analysis"][pos_key] = pos_report

    if not final_result["analysis"]:
         # This handles "klauf"
         return {
            "input_word": word,
            "info": f"No valid POS analysis found for '{word}'."
        }

    return final_result

# ============================================================================
# 8. GRADIO UI CREATION
# ============================================================================
def create_spacy_tab():
    """Creates the UI for the spaCy tab."""
    config = SPACY_UI_TEXT["en"]
    model_choices = list(SPACY_MODEL_INFO.keys())
    with gr.Row():
        ui_lang_radio = gr.Radio(["DE", "EN", "ES"], label=config["ui_lang_label"], value="EN")
        model_lang_radio = gr.Radio(
            choices=[(SPACY_MODEL_INFO[k][0], k) for k in model_choices],
            label=config["model_lang_label"],
            value=model_choices[0]
        )
    markdown_title = gr.Markdown(config["title"])
    markdown_subtitle = gr.Markdown(config["subtitle"])
    text_input = gr.Textbox(label=config["input_label"], placeholder=config["input_placeholder"], lines=5)
    analyze_button = gr.Button(config["button_text"], variant="primary")
    with gr.Tabs():
        with gr.Tab(config["tab_graphic"]) as tab_graphic:
            html_dep_out = gr.HTML(label=config["html_label"])
        with gr.Tab(config["tab_ner"]) as tab_ner:
            html_ner_out = gr.HTML(label=config["ner_label"])
        with gr.Tab(config["tab_table"]) as tab_table:
            df_out = gr.DataFrame(label=config["table_label"], headers=config["table_headers"], interactive=False)
        with gr.Tab(config["tab_json"]) as tab_json:
            json_out = gr.JSON(label=config["json_label"])
    analyze_button.click(fn=spacy_get_analysis,
                         inputs=[ui_lang_radio, model_lang_radio, text_input],
                         outputs=[df_out, json_out, html_dep_out, html_ner_out, analyze_button],
                         api_name="get_morphology")
    ui_lang_radio.change(fn=spacy_update_ui,
                         inputs=ui_lang_radio,
                         outputs=[markdown_title, markdown_subtitle, ui_lang_radio, model_lang_radio,
                                  text_input, analyze_button, tab_graphic, tab_table, tab_json, tab_ner,
                                  html_dep_out, df_out, json_out, html_ner_out])

def create_languagetool_tab():
    """Creates the UI for the LanguageTool tab."""
    gr.Markdown("# 🇩🇪 German Grammar & Spelling Checker")
    gr.Markdown("Powered by `language-tool-python`. This service checks German text for grammatical errors and spelling mistakes.")
    with gr.Column():
        text_input = gr.Textbox(
            label="German Text to Check",
            placeholder="e.g., Ich sehe dem Mann. Das ist ein Huas.",
            lines=5
        )
        check_button = gr.Button("Check Text", variant="primary")
    output = gr.JSON(label="Detected Errors (JSON)")
    check_button.click(
        fn=lt_check_grammar,
        inputs=[text_input],
        outputs=[output],
        api_name="check_grammar"
    )
    gr.Examples(
        [["Das ist ein Huas."], ["Ich sehe dem Mann."],
         ["Die Katze schlafen auf dem Tisch."], ["Er fragt ob er gehen kann."]],
        inputs=[text_input], outputs=[output], fn=lt_check_grammar
    )

def create_odenet_tab():
    """Creates the UI for the OdeNet tab."""
    gr.Markdown("# 🇩🇪 German Thesaurus (WordNet) Service")
    gr.Markdown("Powered by `wn` and `OdeNet (odenet:1.4)`. Finds synonyms, antonyms, and other semantic relations for German words.")
    with gr.Column():
        word_input = gr.Textbox(
            label="German Word",
            placeholder="e.g., Haus, schnell, gut, Katze"
        )
        check_button = gr.Button("Find Relations", variant="primary")
    output = gr.JSON(label="Thesaurus Information (JSON)")
    check_button.click(
        fn=odenet_get_thesaurus_info,
        inputs=[word_input],
        outputs=[output],
        api_name="get_thesaurus"
    )
    gr.Examples(
        [["Hund"], ["gut"], ["laufen"], ["Haus"], ["schnell"]],
        inputs=[word_input], outputs=[output], fn=odenet_get_thesaurus_info
    )

def create_pattern_tab():
    """Creates the UI for the Pattern.de tab."""
    gr.Markdown("# 🇩🇪 Complete German Word Inflection System")
    gr.Markdown("Powered by `PatternLite`. Generates complete inflection tables (declension, conjugation) for German words. Robustly handles ambiguity (e.g., 'Lauf' vs 'lauf').")
    with gr.Column():
        word_input = gr.Textbox(
            label="German Word",
            placeholder="z.B. Haus, gehen, schön, besser, lief, Lauf, See"
        )
        generate_button = gr.Button("Generate All Forms", variant="primary")
    output = gr.JSON(label="Complete Inflection Analysis")
    generate_button.click(
        fn=pattern_get_all_inflections,
        inputs=[word_input],
        outputs=[output],
        api_name="get_all_inflections"
    )
    gr.Examples(
        [["Haus"], ["gehen"], ["schön"], ["besser"], ["ging"], ["schnellem"], ["Katze"], ["Lauf"], ["See"]],
        inputs=[word_input], outputs=[output], fn=pattern_get_all_inflections
    )

def create_conceptnet_tab():
    """--- NEW: Creates the UI for the ConceptNet tab ---"""
    gr.Markdown("# 🌍 ConceptNet Knowledge Graph (Direct API)")
    gr.Markdown("Powered by `api.conceptnet.io`. Fetches semantic relations for a word in any language.")
    with gr.Row():
        word_input = gr.Textbox(
            label="Word or Phrase",
            placeholder="e.g., Baum, tree, Katze"
        )
        lang_input = gr.Textbox(
            label="Language Code",
            placeholder="de",
            value="de"
        )
    check_button = gr.Button("Find Relations", variant="primary")
    output = gr.JSON(label="ConceptNet Relations (JSON)")

    check_button.click(
        fn=conceptnet_get_relations,
        inputs=[word_input, lang_input],
        outputs=[output],
        api_name="get_conceptnet"
    )
    gr.Examples(
        [["Baum", "de"], ["tree", "en"], ["Katze", "de"], ["gato", "es"]],
        inputs=[word_input, lang_input], outputs=[output], fn=conceptnet_get_relations
    )

def create_combined_tab():
    """Creates the UI for the CONTEXTUAL Comprehensive Analyzer tab."""
    gr.Markdown("# 🚀 Comprehensive Analyzer (Contextual)")
    gr.Markdown("This tool provides a deep, **lemma-based** analysis *in context*. It integrates all tools and uses the **full sentence** to rank semantic senses by relevance.")
    with gr.Column():
        text_input = gr.Textbox(
            label="German Text",
            placeholder="e.g., Die schnelle Katze springt über den faulen Hund.",
            lines=5
        )
        top_n_number = gr.Number(
            label="Limit Semantic Senses per POS (0 for all)",
            value=0,
            step=1,
            minimum=0,
            interactive=True
        )
        analyze_button = gr.Button("Run Comprehensive Analysis", variant="primary")
    
    # *** ADD STATUS OUTPUT ***
    status_output = gr.Markdown(value="", visible=True)
    output = gr.JSON(label="Comprehensive Analysis (JSON)")
    
    # *** WRAPPER FUNCTION TO FORCE REFRESH ***
    def run_analysis_with_status(text, top_n):
        try:
            status = "🔄 Analyzing..."
            yield status, {}
            
            result = comprehensive_german_analysis(text, top_n)
            
            status = f"✅ Analysis complete! Found {len(result.get('lemma_deep_dive', {}))} lemmas."
            yield status, result
            
        except Exception as e:
            error_status = f"❌ Error: {str(e)}"
            error_result = {"error": str(e), "traceback": traceback.format_exc()}
            yield error_status, error_result
    
    analyze_button.click(
        fn=run_analysis_with_status,
        inputs=[text_input, top_n_number],
        outputs=[status_output, output],
        api_name="comprehensive_analysis"
    )
    
    gr.Examples(
        [["Die Katze schlafen auf dem Tisch.", 3], 
         ["Das ist ein Huas.", 0], 
         ["Ich laufe schnell.", 3],
         ["Der Gärtner pflanzt einen Baum.", 5],
         ["Ich fahre an den See.", 3]],
        inputs=[text_input, top_n_number],
        outputs=[status_output, output], 
        fn=run_analysis_with_status
    )

def create_word_encyclopedia_tab():
    """--- NEW: Creates the UI for the NON-CONTEXTUAL Word Analyzer tab ---"""
    gr.Markdown("# 📖 Word Encyclopedia (Non-Contextual)")
    gr.Markdown("This tool analyzes a **single word** for *all possible* grammatical and semantic forms. It's ideal for enriching word lists. It finds ambiguities (e.g., 'Lauf' as noun and verb) and groups all data by Part-of-Speech.")
    with gr.Column():
        word_input = gr.Textbox(
            label="Single German Word",
            placeholder="e.g., Lauf, See, schnell"
        )
        top_n_number = gr.Number(
            label="Limit Semantic Senses per POS (0 for all)",
            value=0,
            step=1,
            minimum=0,
            interactive=True
        )
        analyze_button = gr.Button("Analyze Word", variant="primary")
    
    output = gr.JSON(label="Word Encyclopedia Analysis (JSON)")
    
    analyze_button.click(
        fn=analyze_word_encyclopedia,
        inputs=[word_input, top_n_number],
        outputs=[output],
        api_name="analyze_word"
    )
    
    gr.Examples(
        [["Lauf", 3], 
         ["See", 0], 
         ["schnell", 3],
         ["Hund", 5]],
        inputs=[word_input, top_n_number],
        outputs=[output], 
        fn=analyze_word_encyclopedia
    )

# --- Main UI Builder ---
def create_consolidated_interface():
    """Builds the final Gradio app with all tabs."""
    with gr.Blocks(title="Consolidated Linguistics Hub", theme=gr.themes.Soft()) as demo:
        gr.Markdown("# 🏛️ Consolidated Linguistics Hub")
        gr.Markdown("A suite of advanced tools for German linguistics, providing both contextual and non-contextual analysis.")
        
        with gr.Tabs():
            # --- NEW "Word Encyclopedia" TAB ---
            with gr.Tab("📖 Word Encyclopedia (DE)"):
                create_word_encyclopedia_tab()
                
            with gr.Tab("🚀 Comprehensive Analyzer (DE)"):
                create_combined_tab()

            with gr.Tab("🔬 spaCy Analyzer (Multi-lingual)"):
                create_spacy_tab()
            
            with gr.Tab("✅ Grammar Check (DE)"):
                create_languagetool_tab()

            with gr.Tab("📚 Inflections (DE)"):
                create_pattern_tab()

            with gr.Tab("📖 Thesaurus (DE)"):
                create_odenet_tab()
            
            with gr.Tab("🌐 ConceptNet (Direct)"):
                create_conceptnet_tab()
    
    return demo

# ============================================================================
# 9. MAIN EXECUTION BLOCK
# ============================================================================

if __name__ == "__main__":
    print("\n" + "="*70)
    print("CONSOLIDATED LINGUISTICS HUB (STARTING)")
    print("="*70 + "\n")

    # --- 1. Initialize spaCy Models ---
    print("--- Initializing spaCy Models ---")
    spacy_initialize_models()
    print("--- spaCy Done ---\n")
    
    # --- 2. Initialize OdeNet Worker ---
    print("--- Initializing OdeNet Worker ---")
    if WN_AVAILABLE:
        try:
            odenet_start_worker()
            print("✓ OdeNet worker is starting/ready.")
        except Exception as e:
            print(f"✗ FAILED to start OdeNet worker: {e}")
            print("  'Thesaurus' and 'Comprehensive' tabs may fail.")
    else:
        print("INFO: OdeNet ('wn') library not available, skipping worker.")
    print("--- OdeNet Done ---\n")

    # --- 3. NEW: Initialize HanTa Tagger ---
    print("--- Initializing HanTa Tagger ---")
    if HANTA_AVAILABLE:
        try:
            hanta_get_tagger() # Call the function to load the model
        except Exception as e:
            print(f"✗ FAILED to start HanTa tagger: {e}")
            print("  'Word Encyclopedia' tab will fail.")
    else:
        print("INFO: HanTa library not available, skipping tagger.")
    print("--- HanTa Done ---\n")

    # --- 4. Check LanguageTool ---
    print("--- Checking LanguageTool ---")
    if not LT_AVAILABLE:
        print("WARNING: language-tool-python not available. 'Grammar' tab will fail.")
    else:
        print("✓ LanguageTool library is available (will lazy-load on first use).")
    print("--- LanguageTool Done ---\n")

    # --- 5. Check Pattern.de ---
    print("--- Checking Pattern.de ---")
    if not PATTERN_DE_AVAILABLE:
        print("WARNING: pattern.de library not available. 'Inflections' tab will fail.")
    else:
        print("✓ Pattern.de library is available.")
    print("--- Pattern.de Done ---\n")
    
    # --- 6. Check Requests (for ConceptNet) ---
    print("--- Checking Requests (for ConceptNet) ---")
    if not REQUESTS_AVAILABLE:
        print("WARNING: requests library not available. 'ConceptNet' features will fail.")
    else:
        print("✓ Requests library is available.")
    print("--- Requests Done ---\n")

    print("="*70)
    print("All services initialized. Launching Gradio Hub...")
    print("="*70 + "\n")
    
    # --- 6. Launch Gradio ---
    demo = create_consolidated_interface()
    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)