import cv2
import numpy as np
from tensorflow.keras.models import load_model
import os
from FeatureExtraction import FeatureExtractor

model = load_model('orignal_model_b32.h5')
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])


# Initialize the feature extractor
feature_extractor = FeatureExtractor(img_shape=(224, 224), channels=3)

def predict_fight(frames_buffer):
    # Extract feature
    features_sequence = feature_extractor.extract_feature(frames_buffer)

    # Transpose the feature sequence to match the shape
    features_sequence = np.transpose(features_sequence, (1, 0))  # From (2048, 40) to (40, 2048)
    features_sequence = np.expand_dims(features_sequence, axis=0)  # Add batch dimension (1, 40, 2048)

    # Predict
    prediction = model.predict(features_sequence)
    return prediction > 0.8  # Returning a boolean for fight detection