#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
evaluate_dataset.py — Script d'évaluation du détecteur SOTA v2 sur le corpus public 'DiscoursPublics' de Hugging Face.

Ce script charge le dataset 'OpenLLM-France/Lucie-Training-Dataset' (config 'DiscoursPublics'),
extrait un échantillon représentatif, applique le modèle hybride v2,
puis calcule la proportion de discours flagués comme IA, agrégée par année.
"""

import os
import sys
import time
import re
import argparse
import yaml
import joblib
import numpy as np
import pandas as pd
from datasets import load_dataset
from joblib import Parallel, delayed
from tqdm import tqdm

sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from build_features_v2 import extract_sota_features, STYLOMETRIC_COLS_V2
from camembert_encoder import CamemBERTEncoder

def load_config(config_path="configs/config.yaml"):
    if os.path.exists(config_path):
        with open(config_path, "r", encoding="utf-8") as f:
            return yaml.safe_load(f)
    return {"paths": {"models_dir": "models", "output_dir": "output"}}

def parse_french_date_to_year(date_str):
    """Extrait l'année à 4 chiffres à partir d'une date textuelle en français (ex. '18 mai 1987')."""
    if not isinstance(date_str, str):
        return None
    date_str = date_str.lower().strip()
    match = re.search(r'\b(19\d{2}|20\d{2})\b', date_str)
    if match:
        return int(match.group(1))
    return None

def main():
    parser = argparse.ArgumentParser(description="Evaluate SOTA v2 model on Lucie DiscoursPublics dataset.")
    parser.add_argument("--sample_size", type=int, default=20000, help="Number of speeches to sample (0 for all)")
    parser.add_argument("--config", default="configs/config.yaml", help="Path to config file")
    args = parser.parse_args()

    config = load_config(args.config)
    models_dir = config.get("paths", {}).get("models_dir", "models")
    output_dir = config.get("paths", {}).get("output_dir", "output")
    os.makedirs(output_dir, exist_ok=True)
    
    # 1. Load SOTA v2 Model
    model_path = os.path.join(models_dir, "best_detector_v2.pkl")
    if not os.path.exists(model_path):
        print(f"Error: Model file {model_path} not found. Train the model first.")
        sys.exit(1)
        
    print("Loading SOTA v2 model package...")
    pkg = joblib.load(model_path)
    xgb_raw = pkg["xgb_raw"]
    scalers = pkg["scalers"]
    print(f"Loaded: {pkg['model_name']}")
    
    # 2. Load dataset
    print("\nLoading dataset 'OpenLLM-France/Lucie-Training-Dataset' (config: DiscoursPublics) from Hugging Face...")
    dataset = load_dataset("OpenLLM-France/Lucie-Training-Dataset", name="DiscoursPublics", split="train")
    n_total = len(dataset)
    print(f"Loaded {n_total} speeches.")
    
    # Create DataFrame to work with metadata
    df_raw = pd.DataFrame({
        "date": dataset["date"],
        "author": dataset["author"],
        "title": dataset["title"],
        "text": dataset["text"]
    })
    
    # Clean text inputs
    df_raw["text"] = df_raw["text"].fillna("").astype(str)
    # Parse years
    df_raw["year"] = df_raw["date"].apply(parse_french_date_to_year)
    
    # Filter out empty years or empty texts
    df_clean = df_raw[(df_raw["text"].str.strip().str.len() > 10) & (df_raw["year"].notna())].copy()
    df_clean["year"] = df_clean["year"].astype(int)
    
    print(f"Cleaned dataset has {len(df_clean)} speeches with valid years.")
    
    # Sample if requested
    if args.sample_size > 0 and args.sample_size < len(df_clean):
        print(f"Sampling {args.sample_size} speeches randomly (seed=42)...")
        df = df_clean.sample(n=args.sample_size, random_state=42).reset_index(drop=True)
    else:
        df = df_clean.reset_index(drop=True)
        print("Using the full cleaned dataset.")
        
    print("\nDistribution of sampled speeches by year:")
    year_counts = df["year"].value_counts().sort_index()
    for y, count in year_counts.items():
        print(f"  {y}: {count} speeches")
        
    # 3. Extract Stylometrics
    print("\n🔬 Step 1/3: Extracting 30 stylometric features (CPU Parallel)...")
    connecteurs = config.get("features", {}).get("connecteurs", [
        "en effet", "par conséquent", "en outre", "néanmoins", "toutefois", "cependant"
    ])
    
    start_time = time.time()
    sty_dicts = Parallel(n_jobs=-1)(
        delayed(extract_sota_features)(text, connecteurs) for text in tqdm(df["text"], desc="Stylometrics")
    )
    df_sty = pd.DataFrame(sty_dicts)
    sty_elapsed = time.time() - start_time
    print(f"Stylometrics extraction completed in {sty_elapsed:.1f}s ({len(df)/sty_elapsed:.2f} texts/s)")
    
    # 4. Extract CamemBERT Embeddings
    print("\n🧠 Step 2/3: Extracting CamemBERT embeddings (GPU)...")
    encoder = CamemBERTEncoder(device="cuda")
    
    start_time = time.time()
    embeddings = encoder.encode_batch(df["text"].tolist(), batch_size=128)
    emb_elapsed = time.time() - start_time
    print(f"CamemBERT encoding completed in {emb_elapsed:.1f}s ({len(df)/emb_elapsed:.2f} texts/s)")
    
    # 5. Scale and Concat
    print("\n⚙️ Step 3/3: Scaling and running XGBoost classification...")
    X_sty = df_sty[STYLOMETRIC_COLS_V2].values
    X_sty_scaled = scalers["sty"].transform(X_sty)
    X_emb_scaled = scalers["emb"].transform(embeddings)
    X_combined = np.hstack([X_sty_scaled, X_emb_scaled])
    
    # Run prediction
    prob_ai = xgb_raw.predict_proba(X_combined)[:, 1]
    predictions = (prob_ai >= 0.5).astype(int)
    
    df["prob_ai"] = prob_ai
    df["prediction_ai"] = predictions
    
    # Save detailed predictions
    preds_out_path = os.path.join(output_dir, "lucie_speeches_predictions.csv")
    df[["date", "author", "title", "year", "prob_ai", "prediction_ai"]].to_csv(preds_out_path, index=False)
    print(f"Detailed predictions saved to {preds_out_path}")
    
    # 6. Aggregate results by year
    print("\n" + "=" * 70)
    print("ANALYSIS RESULTS BY YEAR (LUCIE DISCOURSPUBLICS)")
    print("=" * 70)
    
    summary = df.groupby("year").agg(
        total_speeches=("prediction_ai", "count"),
        flagged_ai=("prediction_ai", "sum"),
        mean_prob_ai=("prob_ai", "mean")
    ).reset_index()
    
    summary["pct_flagged_ai"] = (summary["flagged_ai"] / summary["total_speeches"]) * 100
    
    # Sort and save summary
    summary = summary.sort_values(by="year")
    summary_path = os.path.join(output_dir, "lucie_speeches_summary_by_year.csv")
    summary.to_csv(summary_path, index=False)
    print(f"Yearly summary saved to {summary_path}\n")
    
    # Display table in output
    print(f"{'Year':<6} | {'Total Speeches':<15} | {'Flagged AI':<12} | {'% Flagged':<10} | {'Mean Prob AI':<12}")
    print("-" * 65)
    for _, row in summary.iterrows():
        print(f"{int(row['year']):<6} | {int(row['total_speeches']):<15} | {int(row['flagged_ai']):<12} | {row['pct_flagged_ai']:<9.2f}% | {row['mean_prob_ai']:<12.4f}")
        
    # Overall summary metrics
    pre_2022 = df[df["year"] < 2022]
    post_2022 = df[df["year"] >= 2022]
    
    print("\n" + "=" * 70)
    print("SUMMARY METRICS")
    print("=" * 70)
    if len(pre_2022) > 0:
        pre_flagged = pre_2022["prediction_ai"].sum()
        pre_pct = (pre_flagged / len(pre_2022)) * 100
        print(f"Pre-2022 False Positive Rate (All human speeches): {pre_pct:.2f}% ({pre_flagged}/{len(pre_2022)})")
    if len(post_2022) > 0:
        post_flagged = post_2022["prediction_ai"].sum()
        post_pct = (post_flagged / len(post_2022)) * 100
        print(f"Post-2022 Flagged AI Rate (Speeches with potential AI support): {post_pct:.2f}% ({post_flagged}/{len(post_2022)})")
        
    print("\n✅ Evaluation complete!")

if __name__ == "__main__":
    main()