# Fuses calibrated gaze position with eye openness (EAR) for focus detection.
# Takes L2CS gaze angles + MediaPipe landmarks, outputs screen coords + focus decision.

import math
import numpy as np

from .gaze_calibration import GazeCalibration
from .eye_scorer import compute_avg_ear

_EAR_BLINK = 0.18
_ON_SCREEN_MARGIN = 0.08


class GazeEyeFusion:

    def __init__(self, calibration, ear_weight=0.3, gaze_weight=0.7, focus_threshold=0.52):
        if not calibration.is_fitted:
            raise ValueError("Calibration must be fitted first")
        self._cal = calibration
        self._ear_w = ear_weight
        self._gaze_w = gaze_weight
        self._threshold = focus_threshold
        self._smooth_x = 0.5
        self._smooth_y = 0.5
        self._alpha = 0.5

    def update(self, yaw_rad, pitch_rad, landmarks):
        gx, gy = self._cal.predict(yaw_rad, pitch_rad)

        # EMA smooth the gaze position
        self._smooth_x += self._alpha * (gx - self._smooth_x)
        self._smooth_y += self._alpha * (gy - self._smooth_y)
        gx, gy = self._smooth_x, self._smooth_y

        on_screen = (
            -_ON_SCREEN_MARGIN <= gx <= 1.0 + _ON_SCREEN_MARGIN and
            -_ON_SCREEN_MARGIN <= gy <= 1.0 + _ON_SCREEN_MARGIN
        )

        ear = None
        ear_score = 1.0
        if landmarks is not None:
            ear = compute_avg_ear(landmarks)
            ear_score = 0.0 if ear < _EAR_BLINK else min(ear / 0.30, 1.0)

        # penalise gaze near screen edges
        gaze_score = 1.0 if on_screen else 0.0
        if on_screen:
            dx = max(0.0, abs(gx - 0.5) - 0.3)
            dy = max(0.0, abs(gy - 0.5) - 0.3)
            gaze_score = max(0.0, 1.0 - math.sqrt(dx**2 + dy**2) * 5.0)

        score = float(np.clip(self._gaze_w * gaze_score + self._ear_w * ear_score, 0, 1))

        return {
            "gaze_x": round(float(gx), 4),
            "gaze_y": round(float(gy), 4),
            "on_screen": on_screen,
            "ear": round(ear, 4) if ear is not None else None,
            "focus_score": round(score, 4),
            "focused": score >= self._threshold,
        }

    def reset(self):
        self._smooth_x = 0.5
        self._smooth_y = 0.5