import pandas as pd
import numpy as np
import re
import math
import joblib
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from thefuzz import fuzz
from collections import Counter
import logging
import sys

from google_sheet_handler import GoogleSheetHandler
from helpers import normalize_company_name

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[logging.StreamHandler(sys.stdout)])
log = logging.getLogger()

GOLD_STANDARD_FILE = 'erweitertes_matching.csv' 
CRM_SHEET_NAME = "CRM_Accounts"
MODEL_OUTPUT_FILE = 'xgb_model.json'
TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib'
CRM_PREDICTION_FILE = 'crm_for_prediction.pkl'
BEST_MATCH_COL = 'Best Match Option'
SUGGESTION_COLS = ['V2_Match_Suggestion', 'V3_Match_Suggestion', 'V4_Match_Suggestion']

STOP_TOKENS_BASE = {
    'gmbh','mbh','ag','kg','ug','ohg','se','co','kgaa','inc','llc','ltd','sarl', 'b.v', 'bv',
    'holding','gruppe','group','international','solutions','solution','service','services',
}
CITY_TOKENS = set()

def _tokenize(s: str):
    if not s: return []
    return re.split(r"[^a-z0-9äöüß]+", str(s).lower())

def clean_name_for_scoring(norm_name: str):
    if not norm_name: return "", set()
    tokens = [t for t in _tokenize(norm_name) if len(t) >= 3]
    stop_union = STOP_TOKENS_BASE | CITY_TOKENS
    final_tokens = [t for t in tokens if t not in stop_union]
    return " ".join(final_tokens), set(final_tokens)

def choose_rarest_token(norm_name: str, term_weights: dict):
    _, toks = clean_name_for_scoring(norm_name)
    if not toks: return None
    return max(toks, key=lambda t: term_weights.get(t, 0))

def create_features(mrec: dict, crec: dict, term_weights: dict):
    features = {}
    n1_raw = mrec.get('normalized_CRM Name', '')
    n2_raw = crec.get('normalized_name', '')
    clean1, toks1 = clean_name_for_scoring(n1_raw)
    clean2, toks2 = clean_name_for_scoring(n2_raw)

    features['fuzz_ratio'] = fuzz.ratio(n1_raw, n2_raw)
    features['fuzz_partial_ratio'] = fuzz.partial_ratio(n1_raw, n2_raw)
    features['fuzz_token_set_ratio'] = fuzz.token_set_ratio(clean1, clean2)
    features['fuzz_token_sort_ratio'] = fuzz.token_sort_ratio(clean1, clean2)
    
    domain1_raw = str(mrec.get('CRM Website', '')).lower()
    domain2_raw = str(crec.get('CRM Website', '')).lower()
    domain1 = domain1_raw.replace('www.', '').split('/')[0].strip()
    domain2 = domain2_raw.replace('www.', '').split('/')[0].strip()
    features['domain_match'] = 1 if domain1 and domain1 == domain2 else 0
    
    features['city_match'] = 1 if mrec.get('CRM Ort') and crec.get('CRM Ort') and mrec['CRM Ort'] == crec['CRM Ort'] else 0
    features['country_match'] = 1 if mrec.get('CRM Land') and crec.get('CRM Land') and mrec['CRM Land'] == crec['CRM Land'] else 0
    features['country_mismatch'] = 1 if (mrec.get('CRM Land') and crec.get('CRM Land') and mrec['CRM Land'] != crec['CRM Land']) else 0
    
    overlapping_tokens = toks1 & toks2
    rarest_token_mrec = choose_rarest_token(n1_raw, term_weights)
    
    features['rarest_token_overlap'] = 1 if rarest_token_mrec and rarest_token_mrec in toks2 else 0
    features['weighted_token_score'] = sum(term_weights.get(t, 0) for t in overlapping_tokens)
    features['jaccard_similarity'] = len(overlapping_tokens) / len(toks1 | toks2) if len(toks1 | toks2) > 0 else 0
    
    features['name_len_diff'] = abs(len(n1_raw) - len(n2_raw))
    features['candidate_is_shorter'] = 1 if len(n2_raw) < len(n1_raw) else 0

    return features

if __name__ == "__main__":
    log.info("Starte Trainingsprozess für Duplikats-Checker v5.0")
    try:
        gold_df = pd.read_csv(GOLD_STANDARD_FILE, sep=';', encoding='utf-8')
        sheet_handler = GoogleSheetHandler()
        crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
    except Exception as e:
        log.critical(f"Fehler beim Laden der Daten: {e}")
        sys.exit(1)

    crm_df.drop_duplicates(subset=['CRM Name'], keep='first', inplace=True)
    crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
    gold_df['normalized_CRM Name'] = gold_df['CRM Name'].astype(str).apply(normalize_company_name)
    
    term_weights = {token: math.log(len(crm_df) / (count + 1)) for token, count in Counter(t for n in crm_df['normalized_name'] for t in set(clean_name_for_scoring(n)[1])).items()}
    
    features_list, labels = [], []
    crm_lookup = crm_df.set_index('CRM Name').to_dict('index')
    
    suggestion_cols_found = [col for col in gold_df.columns if col in SUGGESTION_COLS]

    for _, row in gold_df.iterrows():
        mrec = row.to_dict()
        best_match_name = row.get(BEST_MATCH_COL)
        if pd.notna(best_match_name) and str(best_match_name).strip() != '' and best_match_name in crm_lookup:
            features_list.append(create_features(mrec, crm_lookup[best_match_name], term_weights))
            labels.append(1)
        for col_name in suggestion_cols_found:
            suggestion_name = row.get(col_name)
            if pd.notna(suggestion_name) and suggestion_name != best_match_name and suggestion_name in crm_lookup:
                features_list.append(create_features(mrec, crm_lookup[suggestion_name], term_weights))
                labels.append(0)

    X, y = pd.DataFrame(features_list), np.array(labels)
    log.info(f"Trainingsdatensatz erstellt mit {X.shape[0]} Beispielen. Klassenverteilung: {Counter(y)}")
    
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
    scale_pos_weight = sum(y_train == 0) / sum(y_train) if sum(y_train) > 0 else 1
    model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', scale_pos_weight=scale_pos_weight)
    model.fit(X_train, y_train)
    log.info("Modell erfolgreich trainiert.")
    
    y_pred = model.predict(X_test)
    log.info(f"\n--- Validierungsergebnis ---\nGenauigkeit: {accuracy_score(y_test, y_pred):.2%}\n" + classification_report(y_test, y_pred, zero_division=0))
    
    model.save_model(MODEL_OUTPUT_FILE)
    joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
    crm_df.to_pickle(CRM_PREDICTION_FILE)
    log.info("Alle Modelldateien erfolgreich erstellt.")