import cv2
import numpy as np
from keras.models import model_from_json
from collections import Counter
import time

emotion_dict = {0: "Happy", 1: "Neutral/Sad", 2: "Sad"}
detected_emotions = []  # List to store detected emotions

# Function to reset the list of detected emotions
def reset_detected_emotions():
    global detected_emotions
    detected_emotions = []

# Function to process the frame and update the detected emotions
def process_frame(cap2, emotion_model):
    global detected_emotions
    ret, frame = cap2.read()
    frame = cv2.resize(frame, (1280, 720))

    face_detector = cv2.CascadeClassifier('emotion/haarcascades/haarcascade_frontalface_default.xml')
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)

    for (x, y, w, h) in num_faces:
        roi_gray_frame = gray_frame[y:y + h, x:x + w]
        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)

        emotion_prediction = emotion_model.predict(cropped_img)
        maxindex = int(np.argmax(emotion_prediction))
        detected_emotions.append(emotion_dict[maxindex])

# Function to get the most common emotion from the list
def get_most_common_emotion():
    global detected_emotions
    if detected_emotions:
        counter = Counter(detected_emotions)
        most_common_emotion = counter.most_common(1)[0][0]
        return most_common_emotion
    else:
        return None

def call_me():
    # Load the emotion model
    json_file = open('emotion/model/emotion_model.json', 'r')
    loaded_model_json = json_file.read()
    json_file.close()
    emotion_model = model_from_json(loaded_model_json)
    emotion_model.load_weights("emotion/model/emotion_model.h5")
    print("Loaded model from disk")
        
    # Start the webcam feed
    cap2 = cv2.VideoCapture(0)
    
    duration = 5  # seconds
    end_time = time.time() + duration

    # Example usage of the functions
    while time.time() < end_time:
        process_frame(cap2, emotion_model)

    cap2.release()
    # print(cap)
    cv2.destroyAllWindows()

    # Get the most common emotion detected during the session
    most_common_emotion = get_most_common_emotion()
    return most_common_emotion
    # print("Most Common Emotion:", most_common_emotion)
    # print("User's current Emotion:", most_common_emotion)