import os
import sys
import yaml
import argparse
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix
import xgboost as xgb
import joblib

STYLOMETRIC_COLS = [
    "num_chars", "num_words", "num_sentences", "avg_sentence_len", "std_sentence_len",
    "avg_word_len", "ratio_long_words", "ratio_punctuation", "freq_uppercase",
    "freq_digits", "freq_symbols", "vocabulary_diversity", "hapax_ratio",
    "stopword_ratio", "connector_ratio", "repetition_ratio", "syntactic_complexity_score",
    "ratio_interrogative", "ratio_exclamative", "ratio_declarative", "imparfait_ratio",
    "futur_ratio", "conditional_ratio"
]

def load_config(config_path):
    with open(config_path, "r", encoding="utf-8") as f:
        return yaml.safe_load(f)

def evaluate_model(model_name, y_true, y_pred, y_prob):
    """Calculates all metrics for model evaluation."""
    acc = accuracy_score(y_true, y_pred)
    prec = precision_score(y_true, y_pred, zero_division=0)
    rec = recall_score(y_true, y_pred, zero_division=0)
    f1 = f1_score(y_true, y_pred, zero_division=0)
    auc = roc_auc_score(y_true, y_prob)
    cm = confusion_matrix(y_true, y_pred)
    
    return {
        "model": model_name,
        "accuracy": acc,
        "precision": prec,
        "recall": rec,
        "f1": f1,
        "auc": auc,
        "cm": cm
    }

def main():
    parser = argparse.ArgumentParser(description="Train and evaluate French political AI text detectors.")
    parser.add_argument("--config", default="configs/config.yaml", help="Path to config file")
    args = parser.parse_args()
    
    config = load_config(args.config)
    processed_dir = config["paths"]["processed_dir"]
    models_dir = config["paths"]["models_dir"]
    os.makedirs(models_dir, exist_ok=True)
    
    # Load training features
    train_data_path = os.path.join(processed_dir, "train_features.csv")
    if not os.path.exists(train_data_path):
        print(f"Error: Processed training features not found at {train_data_path}. Please run build_features.py first.")
        sys.exit(1)
        
    df = pd.read_csv(train_data_path)
    
    # Separate stylometric and n-gram features
    ngram_cols = [c for c in df.columns if c.startswith("ngram_word_") or c.startswith("ngram_char_")]
    hybrid_cols = STYLOMETRIC_COLS + ngram_cols
    
    y = df["label_human_ai"].values
    
    # Train-test split
    test_size = config["training"]["test_size"]
    random_state = config["training"]["random_state"]
    
    df_train, df_test, y_train, y_test = train_test_split(df, y, test_size=test_size, random_state=random_state, stratify=y)
    
    print(f"Train size: {len(df_train)}, Test size: {len(df_test)}")
    
    results = []
    trained_models = {}
    scalers = {}
    
    # Set up models
    # 1. Baseline 1: Logistic Regression on Stylometrics
    print("\nTraining Model 1: Logistic Regression on Stylometrics...")
    scaler_sty = StandardScaler()
    X_train_sty = scaler_sty.fit_transform(df_train[STYLOMETRIC_COLS])
    X_test_sty = scaler_sty.transform(df_test[STYLOMETRIC_COLS])
    
    lr_sty = LogisticRegression(C=config["training"]["logistic_regression"]["C"], 
                                max_iter=config["training"]["logistic_regression"]["max_iter"],
                                random_state=random_state)
    lr_sty.fit(X_train_sty, y_train)
    y_pred = lr_sty.predict(X_test_sty)
    y_prob = lr_sty.predict_proba(X_test_sty)[:, 1]
    
    results.append(evaluate_model("Logistic Regression (Stylometrics)", y_test, y_pred, y_prob))
    trained_models["logistic_regression_sty"] = lr_sty
    scalers["sty"] = scaler_sty
    
    # 2. Baseline 2: XGBoost on Stylometrics
    print("Training Model 2: XGBoost on Stylometrics...")
    xgb_sty = xgb.XGBClassifier(n_estimators=config["training"]["xgboost"]["n_estimators"],
                                 learning_rate=config["training"]["xgboost"]["learning_rate"],
                                 max_depth=config["training"]["xgboost"]["max_depth"],
                                 random_state=random_state,
                                 eval_metric="logloss")
    xgb_sty.fit(df_train[STYLOMETRIC_COLS], y_train)
    y_pred = xgb_sty.predict(df_test[STYLOMETRIC_COLS])
    y_prob = xgb_sty.predict_proba(df_test[STYLOMETRIC_COLS])[:, 1]
    
    results.append(evaluate_model("XGBoost (Stylometrics)", y_test, y_pred, y_prob))
    trained_models["xgb_sty"] = xgb_sty
    
    # 3. Model 3: Logistic Regression on N-grams
    print("Training Model 3: Logistic Regression on N-grams...")
    X_train_ng = df_train[ngram_cols].values
    X_test_ng = df_test[ngram_cols].values
    
    lr_ng = LogisticRegression(C=config["training"]["logistic_regression"]["C"],
                               max_iter=config["training"]["logistic_regression"]["max_iter"],
                               random_state=random_state)
    lr_ng.fit(X_train_ng, y_train)
    y_pred = lr_ng.predict(X_test_ng)
    y_prob = lr_ng.predict_proba(X_test_ng)[:, 1]
    
    results.append(evaluate_model("Logistic Regression (N-grams)", y_test, y_pred, y_prob))
    trained_models["logistic_regression_ng"] = lr_ng
    
    # 4. Model 4: Hybrid Model (Logistic Regression on Stylometrics + N-grams)
    print("Training Model 4: Hybrid Model (Logistic Regression on All Features)...")
    scaler_hybrid = StandardScaler()
    # Let's scale everything together
    X_train_hyb = scaler_hybrid.fit_transform(df_train[hybrid_cols])
    X_test_hyb = scaler_hybrid.transform(df_test[hybrid_cols])
    
    lr_hybrid = LogisticRegression(C=config["training"]["logistic_regression"]["C"],
                                   max_iter=config["training"]["logistic_regression"]["max_iter"],
                                   random_state=random_state)
    lr_hybrid.fit(X_train_hyb, y_train)
    y_pred = lr_hybrid.predict(X_test_hyb)
    y_prob = lr_hybrid.predict_proba(X_test_hyb)[:, 1]
    
    results.append(evaluate_model("Hybrid Model (Logistic Regression)", y_test, y_pred, y_prob))
    trained_models["hybrid"] = lr_hybrid
    scalers["hybrid"] = scaler_hybrid
    
    # Compare models
    df_results = pd.DataFrame(results)
    print("\nEvaluation Results:")
    print(df_results[["model", "accuracy", "precision", "recall", "f1", "auc"]].to_string(index=False))
    
    # Pick the best model based on F1-Score
    best_idx = df_results["f1"].idxmax()
    best_row = df_results.iloc[best_idx]
    best_model_name = best_row["model"]
    print(f"\nBest model: {best_model_name} with F1-score: {best_row['f1']:.4f}")
    
    # Map friendly name to key
    model_mapping = {
        "Logistic Regression (Stylometrics)": "logistic_regression_sty",
        "XGBoost (Stylometrics)": "xgb_sty",
        "Logistic Regression (N-grams)": "logistic_regression_ng",
        "Hybrid Model (Logistic Regression)": "hybrid"
    }
    best_key = model_mapping[best_model_name]
    best_model = trained_models[best_key]
    
    # Save the best model details
    package = {
        "model_name": best_model_name,
        "model_key": best_key,
        "model": best_model,
        "stylometric_cols": STYLOMETRIC_COLS,
        "ngram_cols": ngram_cols,
        "hybrid_cols": hybrid_cols,
        "scalers": scalers,
        "vectorizer_words_path": os.path.join(models_dir, "word_vectorizer.pkl"),
        "vectorizer_chars_path": os.path.join(models_dir, "char_vectorizer.pkl")
    }
    
    best_model_path = os.path.join(models_dir, "best_detector.pkl")
    joblib.dump(package, best_model_path)
    print(f"Saved best model package to {best_model_path}")
    
    # Retrieve feature importance / coefficients for explanation
    # Only applicable if best model is linear (Logistic Regression) or tree-based (XGBoost)
    importance_report = ""
    if "Logistic Regression" in best_model_name:
        coefs = best_model.coef_[0]
        feats = STYLOMETRIC_COLS if best_key == "logistic_regression_sty" else (ngram_cols if best_key == "logistic_regression_ng" else hybrid_cols)
        
        # Load vectorizers to map n-gram index back to text if hybrid or n-gram
        word_vectorizer = joblib.load(os.path.join(models_dir, "word_vectorizer.pkl"))
        char_vectorizer = joblib.load(os.path.join(models_dir, "char_vectorizer.pkl"))
        
        feature_names = []
        for f in feats:
            if f.startswith("ngram_word_"):
                idx = int(f.split("_")[-1])
                feature_names.append(f"Word n-gram: '{word_vectorizer.get_feature_names_out()[idx]}'")
            elif f.startswith("ngram_char_"):
                idx = int(f.split("_")[-1])
                feature_names.append(f"Char n-gram: '{char_vectorizer.get_feature_names_out()[idx]}'")
            else:
                feature_names.append(f)
                
        coef_df = pd.DataFrame({"feature": feature_names, "coefficient": coefs})
        coef_df["abs_coef"] = coef_df["coefficient"].abs()
        coef_df = coef_df.sort_values(by="coefficient", ascending=False)
        
        print("\nTop 10 features indicating AI:")
        print(coef_df.head(10)[["feature", "coefficient"]].to_string(index=False))
        print("\nTop 10 features indicating Human:")
        print(coef_df.tail(10)[["feature", "coefficient"]].to_string(index=False))
        
        # Create report string
        importance_report = "### Feature Explanations (Logistic Regression Coefficients)\n\n"
        importance_report += "#### Features predicting AI (positive coefficients):\n"
        for _, row in coef_df.head(10).iterrows():
            importance_report += f"- **{row['feature']}**: {row['coefficient']:.4f}\n"
        importance_report += "\n#### Features predicting Human (negative coefficients):\n"
        for _, row in coef_df.tail(10).iterrows():
            importance_report += f"- **{row['feature']}**: {row['coefficient']:.4f}\n"
            
    elif "XGBoost" in best_model_name:
        importances = best_model.feature_importances_
        imp_df = pd.DataFrame({"feature": STYLOMETRIC_COLS, "importance": importances})
        imp_df = imp_df.sort_values(by="importance", ascending=False)
        
        print("\nTop 10 feature importances (XGBoost):")
        print(imp_df.head(10).to_string(index=False))
        
        importance_report = "### Feature Importances (XGBoost)\n\n"
        for _, row in imp_df.head(10).iterrows():
            importance_report += f"- **{row['feature']}**: {row['importance']:.4f}\n"
            
    # Generate reports/evaluation_report.md
    report_path = os.path.join(config["paths"]["reports_dir"], "evaluation_report.md")
    
    report_content = f"""# Rapport d'Évaluation des Modèles de Détection d'IA

Ce rapport présente les performances des différents modèles entraînés pour distinguer les discours politiques rédigés par des humains de ceux générés par intelligence artificielle.

## Performances des Modèles

| Modèle | Accuracy | Précision | Rappel | F1-Score | ROC-AUC |
| :--- | :---: | :---: | :---: | :---: | :---: |
"""
    for r in results:
        report_content += f"| {r['model']} | {r['accuracy']:.4f} | {r['precision']:.4f} | {r['recall']:.4f} | {r['f1']:.4f} | {r['auc']:.4f} |\n"
        
    report_content += f"""
### Meilleur Modèle Sélectionné
Le modèle **{best_model_name}** a été sélectionné comme le meilleur détecteur. Il offre un F1-score de **{best_row['f1']:.4f}** et une aire sous la courbe ROC de **{best_row['auc']:.4f}**.

{importance_report}

## Matrices de Confusion

"""
    for r in results:
        cm = r["cm"]
        report_content += f"""#### {r['model']}
- Vrais Humains (Corrects): {cm[0, 0]} | Faux IA (Faux Positifs): {cm[0, 1]}
- Faux Humains (Faux Négatifs): {cm[1, 0]} | Vrais IA (Corrects): {cm[1, 1]}

"""
    with open(report_path, "w", encoding="utf-8") as f:
        f.write(report_content)
        
    print(f"\nWritten model evaluation report to {report_path}")

if __name__ == "__main__":
    main()