"""
core/health_detector.py
Identifies inactive, legacy, zombie, and hyper-viral channels
based on engagement-to-audience ratios and upload cadence.
"""

from typing import Dict, Any
import numpy as np
import pandas as pd


class ChannelHealthDetector:
    """
    Evaluates channel vitality by examining daily velocity against subscriber scale.
    """

    def __init__(self, zombie_sub_threshold: int = 25000, zombie_velocity_cutoff: float = 100.0):
        self.zombie_sub_threshold = zombie_sub_threshold
        self.zombie_velocity_cutoff = zombie_velocity_cutoff

    def classify_channel(self, row: pd.Series) -> Dict[str, Any]:
        subs = row.get("subscriber_count")
        daily_vel = row.get("daily_view_velocity")
        status_flag = row.get("status_flag", "")
        
        # Handle unresolvable or missing data
        if pd.isna(subs) or status_flag == "FAILED_LOOKUP":
            return {
                "health_status": "UNRESOLVED",
                "is_zombie": False,
                "health_notes": "Channel could not be resolved via YouTube API."
            }

        subs = float(subs)
        daily_vel = float(daily_vel) if pd.notna(daily_vel) else 0.0

        engagement_ratio = daily_vel / max(subs, 1000.0)

        # 1. Zombie Channel: Large subscriber base with dead viewership
        if subs >= self.zombie_sub_threshold and daily_vel < self.zombie_velocity_cutoff:
            return {
                "health_status": "ZOMBIE_CHANNEL",
                "is_zombie": True,
                "health_notes": f"High subscriber base ({int(subs):,}) with negligible viewership ({daily_vel:.1f} views/day)."
            }

        # 2. Dormant Catalog: No recent uploads and negligible velocity
        if status_flag == "INACTIVE_OR_DORMANT" or daily_vel == 0.0:
            return {
                "health_status": "DORMANT_CATALOG",
                "is_zombie": subs >= 10000,
                "health_notes": "Zero recent uploads and stalled catalog velocity."
            }

        # 3. Viral Surge: Recent views exceed subscriber base
        if engagement_ratio >= 1.0:
            return {
                "health_status": "VIRAL_SURGE",
                "is_zombie": False,
                "health_notes": "Audience reach is exploding beyond existing subscriber base."
            }

        # 4. Catalog Evergreen: Big catalog keeping views high despite no new release
        if status_flag == "CATALOG_ACTIVE_NO_RECENT_UPLOAD":
            return {
                "health_status": "CATALOG_EVERGREEN",
                "is_zombie": False,
                "health_notes": "Strong passive catalog views despite release pause."
            }

        # 5. Healthy Active Channel
        return {
            "health_status": "ACTIVE_HEALTHY",
            "is_zombie": False,
            "health_notes": "Consistent release schedule and steady audience engagement."
        }

    def evaluate_dataframe(self, df: pd.DataFrame) -> pd.DataFrame:
        """Applies health classification across all artist records."""
        out = df.copy()
        health_results = out.apply(self.classify_channel, axis=1)
        
        out["health_status"] = [r["health_status"] for r in health_results]
        out["is_zombie"] = [r["is_zombie"] for r in health_results]
        out["health_notes"] = [r["health_notes"] for r in health_results]
        
        return out