import pandas as pd
import joblib
import time
import re
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

ML_MODEL_PATH = "/home/office-7/support-intelligence-backend/core/services/models/spam_detection_model.pkl"
DATASET_PATH = "/home/office-7/Downloads/spam_dedection_dataset - synthetic_spam_50k.csv"

def preprocess_ml(text):
    text = str(text).lower()
    text = re.sub(r'[^\w\s]', '', text)
    text = re.sub(r'\d+', '', text)
    return text.strip()

def evaluate_large():
    logger.info("Loading ML model...")
    try:
        model = joblib.load(ML_MODEL_PATH)
    except Exception as e:
        logger.error(f"Failed to load model: {e}")
        return

    logger.info("Loading 50k dataset...")
    try:
        df = pd.read_csv(DATASET_PATH)
    except Exception as e:
        logger.error(f"Failed to load dataset: {e}")
        return

    results = []
    
    # Overall Evaluation
    logger.info("Evaluating Overall Performance...")
    start_time = time.time()
    clean_texts = [preprocess_ml(t) for t in df['text']]
    predictions = model.predict(clean_texts)
    end_time = time.time()
    
    overall_acc = accuracy_score(df['label'], predictions)
    overall_prec = precision_score(df['label'], predictions)
    overall_rec = recall_score(df['label'], predictions)
    overall_f1 = f1_score(df['label'], predictions)
    avg_speed = (end_time - start_time) / len(df)
    
    results.append({
        "Language": "OVERALL",
        "Samples": len(df),
        "Accuracy": overall_acc,
        "Precision": overall_prec,
        "Recall": overall_rec,
        "F1": overall_f1,
        "Avg Speed (s)": avg_speed
    })

    # Language-wise Evaluation
    for lang in df['language'].unique():
        logger.info(f"Evaluating Language: {lang}...")
        lang_df = df[df['language'] == lang]
        lang_texts = [preprocess_ml(t) for t in lang_df['text']]
        lang_preds = model.predict(lang_texts)
        
        acc = accuracy_score(lang_df['label'], lang_preds)
        prec = precision_score(lang_df['label'], lang_preds, zero_division=0)
        rec = recall_score(lang_df['label'], lang_preds, zero_division=0)
        f1 = f1_score(lang_df['label'], lang_preds, zero_division=0)
        
        results.append({
            "Language": lang,
            "Samples": len(lang_df),
            "Accuracy": acc,
            "Precision": prec,
            "Recall": rec,
            "F1": f1,
            "Avg Speed (s)": avg_speed # Speed is mostly uniform for ML model
        })

    results_df = pd.DataFrame(results)
    results_df.to_csv("ml_model_large_eval.csv", index=False)
    
    print("\n--- ML Model Performance Summary (50k Dataset) ---")
    print(results_df.to_string(index=False))
    print("\nResults saved to ml_model_large_eval.csv")

if __name__ == "__main__":
    evaluate_large()