# evaluate.py

import pandas as pd
from sklearn.metrics import (
    accuracy_score, precision_score,
    recall_score, f1_score, classification_report
)
from ml_utils import ScamDetectionService

def run_evaluation(csv_path: str = "spam.csv", sample_size: int = 200):
    """
    Evaluates the scam detection model on the SMS Spam Collection dataset.
    Uses a sample to keep runtime reasonable (zero-shot is slow on CPU).

    Args:
        csv_path: path to the downloaded spam.csv
        sample_size: how many rows to evaluate (200 is enough for metrics)
    """

    print("Loading dataset...")
    df = pd.read_csv(csv_path, encoding='latin-1')[['v1', 'v2']]
    df.columns = ['label', 'text']

    # Map ham/spam -> Safe/Scam
    df['true_label'] = df['label'].map({'ham': 'Safe', 'spam': 'Scam'})

    # Sample evenly across both classes
    sample_df = df.groupby('true_label', group_keys=False).apply(
        lambda x: x.sample(min(len(x), sample_size // 2), random_state=42)
    ).reset_index(drop=True)

    print(f"Evaluating on {len(sample_df)} samples...")

    service = ScamDetectionService()

    predictions = []
    for i, row in sample_df.iterrows():
        result = service.analyze_text_scam(row['text'])
        # Collapse Suspicious -> Scam for binary eval
        pred = "Scam" if result["risk_level"] in ["Scam", "Suspicious"] else "Safe"
        predictions.append(pred)

        if (i + 1) % 20 == 0:
            print(f"  Progress: {i + 1}/{len(sample_df)}")

    true_labels = sample_df['true_label'].tolist()

    print("\n========== EVALUATION RESULTS ==========")
    print(f"Accuracy  : {accuracy_score(true_labels, predictions):.4f}")
    print(f"Precision : {precision_score(true_labels, predictions, pos_label='Scam'):.4f}")
    print(f"Recall    : {recall_score(true_labels, predictions, pos_label='Scam'):.4f}")
    print(f"F1 Score  : {f1_score(true_labels, predictions, pos_label='Scam'):.4f}")
    print("\nFull Report:")
    print(classification_report(true_labels, predictions))
    print("=========================================")
    print("\nCopy these numbers into your README and resume.")


if __name__ == "__main__":
    run_evaluation()