scoring/viral_hooks.py

"""
ShortSmith v2 - Viral Hooks Module

Optimizes clip start points for maximum viral potential.
The first 1-3 seconds determine if viewers keep watching.

Research-backed viral triggers by content type:
- Sports: Peak action moments, crowd eruptions, commentator hype
- Music: Beat drops, chorus hits, dance peaks
- Gaming: Clutch plays, reactions, unexpected moments
- Vlogs: Emotional peaks, reveals, punch lines
- Podcasts: Hot takes, laughs, controversial statements

Each domain has specific "hook triggers" that maximize retention.
"""

from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from enum import Enum
import numpy as np

from utils.logger import get_logger

logger = get_logger("scoring.viral_hooks")


class HookType(Enum):
    """Types of viral hook moments."""
    # Universal hooks
    PEAK_ENERGY = "peak_energy"           # Maximum audio/visual energy
    SUDDEN_CHANGE = "sudden_change"       # Dramatic shift in content
    EMOTIONAL_PEAK = "emotional_peak"     # High emotion moment

    # Sports-specific
    GOAL_MOMENT = "goal_moment"           # Scoring play
    CROWD_ERUPTION = "crowd_eruption"     # Crowd going wild
    COMMENTATOR_HYPE = "commentator_hype" # Excited commentary
    REPLAY_WORTHY = "replay_worthy"       # Highlight reel moment

    # Music-specific
    BEAT_DROP = "beat_drop"               # Bass drop / beat switch
    CHORUS_HIT = "chorus_hit"             # Chorus start
    DANCE_PEAK = "dance_peak"             # Peak choreography
    VISUAL_CLIMAX = "visual_climax"       # Visual spectacle

    # Gaming-specific
    CLUTCH_PLAY = "clutch_play"           # Skill moment
    ELIMINATION = "elimination"           # Kill/win moment
    RAGE_REACTION = "rage_reaction"       # Streamer reaction
    UNEXPECTED = "unexpected"             # Plot twist / surprise

    # Vlog-specific
    REVEAL = "reveal"                     # Surprise reveal
    PUNCHLINE = "punchline"               # Joke landing
    EMOTIONAL_MOMENT = "emotional_moment" # Tears/joy/shock
    CONFRONTATION = "confrontation"       # Drama/tension

    # Podcast-specific
    HOT_TAKE = "hot_take"                 # Controversial opinion
    BIG_LAUGH = "big_laugh"               # Group laughter
    REVELATION = "revelation"             # Surprising info
    HEATED_DEBATE = "heated_debate"       # Argument/passion


@dataclass
class HookSignal:
    """A detected hook signal at a specific timestamp."""
    timestamp: float
    hook_type: HookType
    confidence: float          # 0-1, how confident we are this is a hook
    intensity: float           # 0-1, how strong the hook is
    description: str           # Human readable description

    @property
    def score(self) -> float:
        """Combined hook score."""
        return self.confidence * self.intensity


@dataclass
class ViralHookConfig:
    """Configuration for viral hook detection per domain."""
    domain: str

    # Which hook types to look for (in priority order)
    priority_hooks: List[HookType] = field(default_factory=list)

    # Audio thresholds
    audio_spike_threshold: float = 0.7      # RMS energy spike to detect
    audio_spike_window: float = 0.5         # Seconds to detect spike
    crowd_noise_threshold: float = 0.6      # Spectral centroid for crowd
    speech_energy_threshold: float = 0.8    # For commentator/speaker hype

    # Visual thresholds
    motion_spike_threshold: float = 0.7     # Sudden motion increase
    scene_change_weight: float = 0.3        # Weight for scene transitions
    emotion_threshold: float = 0.7          # For detected emotions

    # Timing preferences
    ideal_hook_window: Tuple[float, float] = (0.0, 2.0)  # Seconds from clip start
    max_hook_search_window: float = 5.0     # How far to search for hook

    # Hook scoring weights
    hook_type_weights: Dict[HookType, float] = field(default_factory=dict)

    # Minimum score to consider a valid hook
    min_hook_score: float = 0.5


# Domain-specific viral hook configurations
VIRAL_HOOK_CONFIGS: Dict[str, ViralHookConfig] = {

    "sports": ViralHookConfig(
        domain="sports",
        priority_hooks=[
            HookType.GOAL_MOMENT,
            HookType.CROWD_ERUPTION,
            HookType.COMMENTATOR_HYPE,
            HookType.REPLAY_WORTHY,
            HookType.PEAK_ENERGY,
        ],
        audio_spike_threshold=0.75,        # Sports has loud moments
        crowd_noise_threshold=0.65,        # Crowd detection
        speech_energy_threshold=0.85,      # Commentator excitement
        motion_spike_threshold=0.7,        # Action detection
        ideal_hook_window=(0.0, 1.5),      # Sports hooks need to be immediate
        hook_type_weights={
            HookType.GOAL_MOMENT: 1.0,
            HookType.CROWD_ERUPTION: 0.95,
            HookType.COMMENTATOR_HYPE: 0.9,
            HookType.REPLAY_WORTHY: 0.85,
            HookType.PEAK_ENERGY: 0.8,
            HookType.SUDDEN_CHANGE: 0.6,
        },
        min_hook_score=0.6,
    ),

    "music": ViralHookConfig(
        domain="music",
        priority_hooks=[
            HookType.BEAT_DROP,
            HookType.CHORUS_HIT,
            HookType.DANCE_PEAK,
            HookType.VISUAL_CLIMAX,
            HookType.PEAK_ENERGY,
        ],
        audio_spike_threshold=0.8,         # Beat drops are loud
        audio_spike_window=0.3,            # Quick detection for beats
        motion_spike_threshold=0.65,       # Dance moves
        ideal_hook_window=(0.0, 2.0),      # Can build slightly
        hook_type_weights={
            HookType.BEAT_DROP: 1.0,
            HookType.CHORUS_HIT: 0.95,
            HookType.DANCE_PEAK: 0.85,
            HookType.VISUAL_CLIMAX: 0.8,
            HookType.PEAK_ENERGY: 0.75,
            HookType.SUDDEN_CHANGE: 0.7,
        },
        min_hook_score=0.55,
    ),

    "gaming": ViralHookConfig(
        domain="gaming",
        priority_hooks=[
            HookType.CLUTCH_PLAY,
            HookType.ELIMINATION,
            HookType.RAGE_REACTION,
            HookType.UNEXPECTED,
            HookType.PEAK_ENERGY,
        ],
        audio_spike_threshold=0.7,         # Streamer reactions
        speech_energy_threshold=0.75,      # Voice reactions
        motion_spike_threshold=0.6,        # Gameplay action
        ideal_hook_window=(0.0, 2.5),      # Gaming can have slight buildup
        hook_type_weights={
            HookType.CLUTCH_PLAY: 1.0,
            HookType.ELIMINATION: 0.95,
            HookType.RAGE_REACTION: 0.9,
            HookType.UNEXPECTED: 0.85,
            HookType.PEAK_ENERGY: 0.75,
            HookType.EMOTIONAL_PEAK: 0.7,
        },
        min_hook_score=0.5,
    ),

    "vlogs": ViralHookConfig(
        domain="vlogs",
        priority_hooks=[
            HookType.REVEAL,
            HookType.PUNCHLINE,
            HookType.EMOTIONAL_MOMENT,
            HookType.CONFRONTATION,
            HookType.EMOTIONAL_PEAK,
        ],
        audio_spike_threshold=0.65,        # Reactions less loud
        speech_energy_threshold=0.7,       # Speaking emphasis
        emotion_threshold=0.65,            # Facial emotions
        ideal_hook_window=(0.0, 3.0),      # Vlogs can have more buildup
        hook_type_weights={
            HookType.REVEAL: 1.0,
            HookType.PUNCHLINE: 0.95,
            HookType.EMOTIONAL_MOMENT: 0.9,
            HookType.CONFRONTATION: 0.85,
            HookType.EMOTIONAL_PEAK: 0.8,
            HookType.SUDDEN_CHANGE: 0.7,
        },
        min_hook_score=0.45,
    ),

    "podcasts": ViralHookConfig(
        domain="podcasts",
        priority_hooks=[
            HookType.HOT_TAKE,
            HookType.BIG_LAUGH,
            HookType.REVELATION,
            HookType.HEATED_DEBATE,
            HookType.EMOTIONAL_PEAK,
        ],
        audio_spike_threshold=0.6,         # Speech-based
        speech_energy_threshold=0.8,       # Emphasis detection
        crowd_noise_threshold=0.7,         # Group laughter
        ideal_hook_window=(0.0, 2.0),      # Podcasts need quick hooks
        hook_type_weights={
            HookType.HOT_TAKE: 1.0,
            HookType.BIG_LAUGH: 0.95,
            HookType.REVELATION: 0.9,
            HookType.HEATED_DEBATE: 0.85,
            HookType.EMOTIONAL_PEAK: 0.75,
            HookType.SUDDEN_CHANGE: 0.6,
        },
        min_hook_score=0.5,
    ),

    "comedy": ViralHookConfig(
        domain="comedy",
        priority_hooks=[
            HookType.BIG_LAUGH,
            HookType.PUNCHLINE,
            HookType.EMOTIONAL_PEAK,
            HookType.SUDDEN_CHANGE,
            HookType.PEAK_ENERGY,
        ],
        audio_spike_threshold=0.55,
        audio_spike_window=0.8,
        crowd_noise_threshold=0.50,
        speech_energy_threshold=0.70,
        motion_spike_threshold=0.55,
        scene_change_weight=0.2,
        emotion_threshold=0.60,
        ideal_hook_window=(0.0, 2.5),
        max_hook_search_window=6.0,
        hook_type_weights={
            HookType.BIG_LAUGH: 1.0,
            HookType.PUNCHLINE: 0.90,
            HookType.EMOTIONAL_PEAK: 0.80,
            HookType.SUDDEN_CHANGE: 0.75,
            HookType.PEAK_ENERGY: 0.70,
            HookType.REVEAL: 0.65,
        },
        min_hook_score=0.40,
    ),

    "general": ViralHookConfig(
        domain="general",
        priority_hooks=[
            HookType.PEAK_ENERGY,
            HookType.SUDDEN_CHANGE,
            HookType.EMOTIONAL_PEAK,
        ],
        audio_spike_threshold=0.7,
        motion_spike_threshold=0.65,
        ideal_hook_window=(0.0, 2.5),
        hook_type_weights={
            HookType.PEAK_ENERGY: 1.0,
            HookType.SUDDEN_CHANGE: 0.9,
            HookType.EMOTIONAL_PEAK: 0.85,
        },
        min_hook_score=0.5,
    ),
}


class ViralHookDetector:
    """
    Detects viral hook moments in video segments.

    Analyzes audio, visual, and motion signals to find the best
    starting point for maximum viewer retention.
    """

    def __init__(self, domain: str = "general"):
        """
        Initialize hook detector.

        Args:
            domain: Content domain for hook detection
        """
        self.domain = domain
        self.config = VIRAL_HOOK_CONFIGS.get(domain, VIRAL_HOOK_CONFIGS["general"])
        logger.info(f"ViralHookDetector initialized for domain: {domain}")

    def detect_hooks(
        self,
        timestamps: List[float],
        audio_energy: Optional[List[float]] = None,
        audio_flux: Optional[List[float]] = None,
        audio_centroid: Optional[List[float]] = None,
        visual_scores: Optional[List[float]] = None,
        motion_scores: Optional[List[float]] = None,
        emotions: Optional[List[str]] = None,
        actions: Optional[List[str]] = None,
    ) -> List[HookSignal]:
        """
        Detect hook moments from multi-modal signals.

        Args:
            timestamps: Time points for each data sample
            audio_energy: RMS energy values (0-1)
            audio_flux: Spectral flux values (0-1)
            audio_centroid: Spectral centroid values (0-1)
            visual_scores: Visual hype scores (0-1)
            motion_scores: Motion intensity scores (0-1)
            emotions: Detected emotions per timestamp
            actions: Detected actions per timestamp

        Returns:
            List of detected HookSignals sorted by score
        """
        hooks = []

        # Detect audio-based hooks
        if audio_energy is not None:
            hooks.extend(self._detect_audio_spikes(timestamps, audio_energy, audio_flux))

        # Detect crowd/laughter from spectral centroid
        if audio_centroid is not None:
            hooks.extend(self._detect_crowd_moments(timestamps, audio_centroid, audio_energy))

        # Detect motion-based hooks
        if motion_scores is not None:
            hooks.extend(self._detect_motion_peaks(timestamps, motion_scores))

        # Detect visual peaks
        if visual_scores is not None:
            hooks.extend(self._detect_visual_peaks(timestamps, visual_scores))

        # Detect emotion-based hooks
        if emotions is not None:
            hooks.extend(self._detect_emotion_hooks(timestamps, emotions))

        # Detect action-based hooks
        if actions is not None:
            hooks.extend(self._detect_action_hooks(timestamps, actions))

        # Sort by score descending
        hooks.sort(key=lambda h: h.score, reverse=True)

        # Filter by minimum score
        hooks = [h for h in hooks if h.score >= self.config.min_hook_score]

        logger.info(f"Detected {len(hooks)} potential hook moments")
        return hooks

    def _detect_audio_spikes(
        self,
        timestamps: List[float],
        energy: List[float],
        flux: Optional[List[float]] = None,
    ) -> List[HookSignal]:
        """Detect sudden audio energy spikes (beat drops, reactions, etc.)"""
        hooks = []

        if len(energy) < 3:
            return hooks

        energy_arr = np.array(energy)
        threshold = self.config.audio_spike_threshold

        # Calculate rolling mean and detect spikes
        window = max(3, int(len(energy) * 0.1))
        rolling_mean = np.convolve(energy_arr, np.ones(window)/window, mode='same')

        for i in range(1, len(energy) - 1):
            # Spike detection: current value significantly above local average
            if energy[i] > threshold and energy[i] > rolling_mean[i] * 1.3:
                # Check if it's a peak (higher than neighbors)
                if energy[i] >= energy[i-1] and energy[i] >= energy[i+1]:
                    # Determine hook type based on domain
                    if self.domain == "music":
                        hook_type = HookType.BEAT_DROP
                    elif self.domain == "sports":
                        hook_type = HookType.COMMENTATOR_HYPE
                    elif self.domain == "gaming":
                        hook_type = HookType.RAGE_REACTION
                    else:
                        hook_type = HookType.PEAK_ENERGY

                    intensity = min(1.0, energy[i])
                    confidence = min(1.0, (energy[i] - rolling_mean[i]) / 0.3)

                    hooks.append(HookSignal(
                        timestamp=timestamps[i],
                        hook_type=hook_type,
                        confidence=confidence,
                        intensity=intensity,
                        description=f"Audio spike at {timestamps[i]:.1f}s (energy: {energy[i]:.2f})"
                    ))

        return hooks

    def _detect_crowd_moments(
        self,
        timestamps: List[float],
        centroid: List[float],
        energy: Optional[List[float]] = None,
    ) -> List[HookSignal]:
        """Detect crowd noise / group reactions from spectral characteristics."""
        hooks = []

        threshold = self.config.crowd_noise_threshold

        for i, (ts, cent) in enumerate(zip(timestamps, centroid)):
            # High centroid + high energy = crowd/cheering
            energy_val = energy[i] if energy else 0.5

            if cent > threshold and energy_val > 0.5:
                if self.domain == "sports":
                    hook_type = HookType.CROWD_ERUPTION
                elif self.domain in ("podcasts", "comedy"):
                    hook_type = HookType.BIG_LAUGH
                else:
                    hook_type = HookType.PEAK_ENERGY

                intensity = min(1.0, cent * energy_val * 1.5)
                confidence = min(1.0, cent)

                hooks.append(HookSignal(
                    timestamp=ts,
                    hook_type=hook_type,
                    confidence=confidence,
                    intensity=intensity,
                    description=f"Crowd/group moment at {ts:.1f}s"
                ))

        return hooks

    def _detect_motion_peaks(
        self,
        timestamps: List[float],
        motion: List[float],
    ) -> List[HookSignal]:
        """Detect peak motion moments (action, dance, etc.)"""
        hooks = []

        threshold = self.config.motion_spike_threshold

        # Find local maxima above threshold
        for i in range(1, len(motion) - 1):
            if motion[i] > threshold:
                if motion[i] >= motion[i-1] and motion[i] >= motion[i+1]:
                    if self.domain == "music":
                        hook_type = HookType.DANCE_PEAK
                    elif self.domain == "sports":
                        hook_type = HookType.REPLAY_WORTHY
                    elif self.domain == "gaming":
                        hook_type = HookType.CLUTCH_PLAY
                    else:
                        hook_type = HookType.PEAK_ENERGY

                    hooks.append(HookSignal(
                        timestamp=timestamps[i],
                        hook_type=hook_type,
                        confidence=min(1.0, motion[i]),
                        intensity=motion[i],
                        description=f"High motion at {timestamps[i]:.1f}s"
                    ))

        return hooks

    def _detect_visual_peaks(
        self,
        timestamps: List[float],
        visual: List[float],
    ) -> List[HookSignal]:
        """Detect visual hype peaks."""
        hooks = []

        # Find timestamps with high visual scores
        threshold = 0.7

        for i, (ts, score) in enumerate(zip(timestamps, visual)):
            if score > threshold:
                hooks.append(HookSignal(
                    timestamp=ts,
                    hook_type=HookType.VISUAL_CLIMAX if self.domain == "music" else HookType.PEAK_ENERGY,
                    confidence=score,
                    intensity=score,
                    description=f"Visual peak at {ts:.1f}s (score: {score:.2f})"
                ))

        return hooks

    def _detect_emotion_hooks(
        self,
        timestamps: List[float],
        emotions: List[str],
    ) -> List[HookSignal]:
        """Detect emotion-based hook moments."""
        hooks = []

        # High-engagement emotions
        hook_emotions = {
            "excitement": (HookType.EMOTIONAL_PEAK, 0.9),
            "joy": (HookType.EMOTIONAL_MOMENT, 0.85),
            "surprise": (HookType.REVEAL if self.domain == "vlogs" else HookType.UNEXPECTED, 0.9),
            "tension": (HookType.CONFRONTATION if self.domain == "vlogs" else HookType.EMOTIONAL_PEAK, 0.8),
            "anger": (HookType.HEATED_DEBATE if self.domain == "podcasts" else HookType.RAGE_REACTION, 0.85),
        }

        for ts, emotion in zip(timestamps, emotions):
            emotion_lower = emotion.lower()
            if emotion_lower in hook_emotions:
                hook_type, intensity = hook_emotions[emotion_lower]
                hooks.append(HookSignal(
                    timestamp=ts,
                    hook_type=hook_type,
                    confidence=0.8,
                    intensity=intensity,
                    description=f"Emotion '{emotion}' at {ts:.1f}s"
                ))

        return hooks

    def _detect_action_hooks(
        self,
        timestamps: List[float],
        actions: List[str],
    ) -> List[HookSignal]:
        """Detect action-based hook moments."""
        hooks = []

        # High-engagement actions by domain
        hook_actions = {
            "sports": {
                "celebration": (HookType.GOAL_MOMENT, 1.0),
                "action": (HookType.REPLAY_WORTHY, 0.85),
                "reaction": (HookType.CROWD_ERUPTION, 0.8),
            },
            "music": {
                "performance": (HookType.VISUAL_CLIMAX, 0.9),
                "action": (HookType.DANCE_PEAK, 0.85),
            },
            "gaming": {
                "action": (HookType.CLUTCH_PLAY, 0.9),
                "reaction": (HookType.RAGE_REACTION, 0.85),
                "celebration": (HookType.ELIMINATION, 0.9),
            },
            "vlogs": {
                "reaction": (HookType.REVEAL, 0.9),
                "celebration": (HookType.EMOTIONAL_MOMENT, 0.85),
            },
            "podcasts": {
                "reaction": (HookType.BIG_LAUGH, 0.85),
                "speech": (HookType.HOT_TAKE, 0.8),
            },
            "comedy": {
                "reaction": (HookType.BIG_LAUGH, 0.95),
                "celebration": (HookType.EMOTIONAL_MOMENT, 0.85),
                "action": (HookType.PUNCHLINE, 0.80),
            },
        }

        domain_actions = hook_actions.get(self.domain, {})

        for ts, action in zip(timestamps, actions):
            action_lower = action.lower()
            if action_lower in domain_actions:
                hook_type, intensity = domain_actions[action_lower]
                hooks.append(HookSignal(
                    timestamp=ts,
                    hook_type=hook_type,
                    confidence=0.85,
                    intensity=intensity,
                    description=f"Action '{action}' at {ts:.1f}s"
                ))

        return hooks

    def find_best_clip_start(
        self,
        clip_start: float,
        clip_end: float,
        hooks: List[HookSignal],
        allow_adjustment: float = 3.0,
    ) -> Tuple[float, Optional[HookSignal]]:
        """
        Find the best starting point for a clip based on detected hooks.

        Args:
            clip_start: Original clip start time
            clip_end: Original clip end time
            hooks: Detected hook signals
            allow_adjustment: Max seconds to adjust start backwards

        Returns:
            Tuple of (adjusted_start_time, best_hook_signal)
        """
        # Find hooks within the ideal window from clip start
        search_start = max(0, clip_start - allow_adjustment)
        search_end = clip_start + self.config.max_hook_search_window

        # Filter hooks in search range
        candidate_hooks = [
            h for h in hooks
            if search_start <= h.timestamp <= search_end
        ]

        if not candidate_hooks:
            logger.debug(f"No hooks found for clip at {clip_start:.1f}s")
            return clip_start, None

        # Score each hook based on:
        # 1. Hook quality (score)
        # 2. Position preference (earlier in ideal window = better)
        # 3. Hook type priority for domain

        best_hook = None
        best_score = 0

        for hook in candidate_hooks:
            # Base score from hook quality
            score = hook.score

            # Apply hook type weight
            type_weight = self.config.hook_type_weights.get(hook.hook_type, 0.5)
            score *= type_weight

            # Prefer hooks that land in ideal window
            ideal_start, ideal_end = self.config.ideal_hook_window
            time_from_original = hook.timestamp - clip_start

            if ideal_start <= time_from_original <= ideal_end:
                # Perfect position
                score *= 1.2
            elif time_from_original < ideal_start:
                # Hook is before clip start - we'd need to adjust
                adjustment_needed = clip_start - hook.timestamp
                if adjustment_needed <= allow_adjustment:
                    # Penalize based on adjustment needed
                    score *= (1.0 - adjustment_needed / allow_adjustment * 0.3)
                else:
                    score *= 0.3  # Heavy penalty
            else:
                # Hook is after ideal window
                score *= 0.8

            if score > best_score:
                best_score = score
                best_hook = hook

        if best_hook:
            # Adjust start to put hook in ideal position
            ideal_position = self.config.ideal_hook_window[0] + 0.5  # Aim for middle of ideal window
            adjusted_start = best_hook.timestamp - ideal_position

            # Don't go before search_start or make clip too short
            adjusted_start = max(search_start, adjusted_start)
            adjusted_start = min(adjusted_start, clip_end - 5.0)  # Keep at least 5s

            logger.info(
                f"Adjusted clip start: {clip_start:.1f}s -> {adjusted_start:.1f}s "
                f"(hook: {best_hook.hook_type.value} at {best_hook.timestamp:.1f}s)"
            )

            return adjusted_start, best_hook

        return clip_start, None

    def score_clip_hook_potential(
        self,
        clip_start: float,
        clip_duration: float,
        hooks: List[HookSignal],
    ) -> float:
        """
        Score a clip's viral potential based on hook placement.

        Args:
            clip_start: Clip start time
            clip_duration: Clip duration
            hooks: All detected hooks

        Returns:
            Hook potential score (0-1)
        """
        clip_end = clip_start + clip_duration

        # Find hooks in the first few seconds of clip
        hook_window = self.config.ideal_hook_window[1]
        early_hooks = [
            h for h in hooks
            if clip_start <= h.timestamp <= clip_start + hook_window
        ]

        if not early_hooks:
            return 0.3  # Base score for clips without clear hooks

        # Score based on best hook in opening
        best_hook = max(early_hooks, key=lambda h: h.score)

        # Apply type weight
        type_weight = self.config.hook_type_weights.get(best_hook.hook_type, 0.5)

        return min(1.0, best_hook.score * type_weight * 1.2)


def get_viral_hook_config(domain: str) -> ViralHookConfig:
    """Get viral hook configuration for a domain."""
    return VIRAL_HOOK_CONFIGS.get(domain, VIRAL_HOOK_CONFIGS["general"])


def get_viral_hook_detector(domain: str) -> ViralHookDetector:
    """Get a viral hook detector for a domain."""
    return ViralHookDetector(domain)


# Export public interface
__all__ = [
    "HookType",
    "HookSignal",
    "ViralHookConfig",
    "ViralHookDetector",
    "VIRAL_HOOK_CONFIGS",
    "get_viral_hook_config",
    "get_viral_hook_detector",
]