import os
import json
import numpy as np
import cv2
import tensorflow as tf
from video_compression_model import NUM_CHANNELS, VideoCompressionModel, PRESET_SPEED_CATEGORIES
from tensorflow.keras.callbacks import EarlyStopping

print(tf.config.list_physical_devices('GPU'))

# Constants
BATCH_SIZE = 8
EPOCHS = 50
TRAIN_SAMPLES = 5

def load_list(list_path):
    with open(list_path, "r") as json_file:
        video_details_list = json.load(json_file)
    return video_details_list

def load_frame_from_video(video_file):
    print("Extracting video frame...")
    cap = cv2.VideoCapture(video_file)
    ret, frame = cap.read()
    if not ret:
        return None
    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    cap.release()
    return frame

def preprocess(frame):
    return frame / 255.0

def save_model(model, file):
    os.makedirs("models", exist_ok=True)
    model.save(os.path.join("models/", file))
    print("Model saved successfully!")

def load_video_from_list(list_path):
    details_list = load_list(list_path)
    all_frames = []
    all_details = []
    for video_details in details_list:
        VIDEO_FILE = video_details["video_file"]
        CRF = video_details['crf'] / 63.0
        PRESET_SPEED = PRESET_SPEED_CATEGORIES.index(video_details['preset_speed'])
        video_details['preset_speed'] = PRESET_SPEED

        frame = load_frame_from_video(os.path.join("test_data/", VIDEO_FILE))
        
        if frame is not None:
            all_frames.append(preprocess(frame))
            all_details.append({
                "frame": frame,
                "crf": CRF,
                "preset_speed": PRESET_SPEED,
                "video_file": VIDEO_FILE
            })
    return all_details

def main():
    all_video_details_train = load_video_from_list("test_data/training.json")
    all_video_details_val = load_video_from_list("test_data/validation.json")

    model = VideoCompressionModel(NUM_CHANNELS)
    model.compile(loss='mean_squared_error', optimizer='adam')
    early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)

    # Prepare data
    all_train_frames = []
    all_val_frames = []
    all_crf_train = []
    all_crf_val = []
    all_preset_speed_train = []
    all_preset_speed_val = []
    
    for video_details_train, video_details_val in zip(all_video_details_train, all_video_details_val):
        all_train_frames.append(video_details_train["frame"])
        all_val_frames.append(video_details_val["frame"])
        all_crf_train.append(video_details_train['crf'])
        all_crf_val.append(video_details_val['crf'])
        all_preset_speed_train.append(video_details_train['preset_speed'])
        all_preset_speed_val.append(video_details_val['preset_speed'])

    # Convert lists to numpy arrays
    all_train_frames = np.array(all_train_frames)
    all_val_frames = np.array(all_val_frames)
    all_crf_train = np.array(all_crf_train)
    all_crf_val = np.array(all_crf_val)
    all_preset_speed_train = np.array(all_preset_speed_train)
    all_preset_speed_val = np.array(all_preset_speed_val)

    print("\nTraining the model on frame pairs...")
    model.fit(
        {"frame": all_train_frames, "crf": all_crf_train, "preset_speed": all_preset_speed_train},
        all_val_frames,  # Target is the compressed frame
        batch_size=BATCH_SIZE,
        epochs=EPOCHS,
        validation_data=({"frame": all_val_frames, "crf": all_crf_val, "preset_speed": all_preset_speed_val}, all_val_frames),
        callbacks=[early_stop]
    )
    print("\nTraining completed!")

    save_model(model, 'model.keras')

if __name__ == "__main__":
    main()