import os
from datetime import datetime, timedelta

import mlflow
import pandas as pd
from evidently.metric_preset import DataDriftPreset, TextOverviewPreset
from evidently.report import Report
from sqlalchemy import create_engine


def main():
    # Attempt to fetch database URL, fallback to sqlite for local tests
    db_url = os.getenv("DATABASE_URL", "sqlite:///./test.db")

    # We would normally load the reference data (e.g. from training data CSV)
    # For this script, we'll assume a local path or create a dummy reference if missing
    ref_path = "data/reference.csv"
    if os.path.exists(ref_path):
        ref_df = pd.read_csv(ref_path)
    else:
        print(f"Reference data not found at {ref_path}. Creating dummy reference data for testing.")
        ref_df = pd.DataFrame(
            {"text": ["This is great", "I hate this", "Neutral statement"], "language": ["en", "en", "en"]}
        )

    try:
        # Load production data from the last 7 days
        engine = create_engine(db_url)
        seven_days_ago = datetime.now() - timedelta(days=7)

        # Load directly from SQLAlchemy using pandas with parameterized query
        query = "SELECT text, language FROM reviews WHERE created_at >= %(cutoff)s"
        curr_df = pd.read_sql(query, engine, params={"cutoff": seven_days_ago})
    except Exception as e:
        print(f"Failed to fetch production data: {e}")
        curr_df = pd.DataFrame(columns=["text", "language"])

    if len(curr_df) < 50:
        print(
            f"Not enough data to run drift monitor "
            f"(found {len(curr_df)} records, need at least 50). Exiting gracefully."
        )
        return

    # Run Evidently report
    print("Running Evidently drift report...")
    report = Report(metrics=[DataDriftPreset(), TextOverviewPreset(column_name="text")])

    report.run(reference_data=ref_df, current_data=curr_df)

    # Create monitoring/reports dir if missing
    os.makedirs("monitoring/reports", exist_ok=True)

    report_path = f"monitoring/reports/drift_{datetime.now().strftime('%Y%m%d')}.html"
    report.save_html(report_path)
    print(f"Report saved to {report_path}")

    # Extract drift metrics as a dict
    report_dict = report.as_dict()

    # Simplified check for drift (using Dataset Drift metric from DataDriftPreset)
    dataset_drift = report_dict["metrics"][0]["result"]["dataset_drift"]
    drift_share = report_dict["metrics"][0]["result"]["drift_share"]

    if dataset_drift and drift_share > 0.3:
        print(f"⚠️ Drift detected — consider retraining. Drift share: {drift_share:.2f}")
        try:
            mlflow.set_tracking_uri(os.getenv("MLFLOW_TRACKING_URI", "file:./mlruns"))
            with mlflow.start_run(run_name="drift_monitoring"):
                mlflow.log_metric("drift_share", drift_share)
                mlflow.log_artifact(report_path)
        except Exception as e:
            print(f"Failed to log warning to MLflow: {e}")


if __name__ == "__main__":
    main()