import numpy as np
import tensorflow as tf
import joblib
from collections import deque
from .feature_extractor import extract_sequence

class DynamicGesturePredictor:
    def __init__(self, model_path, classes_path, window_size=30, threshold=0.7):
        self.model = tf.keras.models.load_model(model_path)
        with open(classes_path, 'rb') as f:
            self.classes = joblib.load(f)
        self.window_size = window_size
        self.buffer = deque(maxlen=window_size)
        self.threshold = threshold

    def add_frame(self, landmarks):
        if landmarks is None or np.isnan(landmarks).any():
            landmarks = np.zeros(99)
        self.buffer.append(landmarks)

    def predict(self):
        if len(self.buffer) < self.window_size:
            return None
        seq = extract_sequence(list(self.buffer))
        seq = np.expand_dims(seq, axis=0)  # (1, window, 99)
        probs = self.model.predict(seq, verbose=0)[0]
        idx = np.argmax(probs)
        if probs[idx] > self.threshold and self.classes[idx] != 'none':
            return self.classes[idx]
        return None

    def reset(self):
        self.buffer.clear()