# import the rquired libraries.
import numpy as np
import cv2
from keras.models import load_model
import streamlit as st
from tensorflow import keras
from tensorflow.keras.preprocessing.image import img_to_array
from streamlit_webrtc import webrtc_streamer, VideoTransformerBase, RTCConfiguration, VideoProcessorBase, WebRtcMode

emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']

classifier =load_model('model_78.h5')

classifier.load_weights("model_weights_78.h5")

try:
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
except Exception:
    st.write("Error loading cascade classifiers")

class VideoTransformer(VideoTransformerBase):
    def transform(self, frame):
        img = frame.to_ndarray(format="bgr24")

        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(
            image=img_gray, scaleFactor=1.3, minNeighbors=5)
        for (x, y, w, h) in faces:
            cv2.rectangle(img=img, pt1=(x, y), pt2=(
                x + w, y + h), color=(0, 255, 255), thickness=2)
            roi_gray = img_gray[y:y + h, x:x + w]
            roi_gray = cv2.resize(roi_gray, (48, 48), interpolation=cv2.INTER_AREA)
            if np.sum([roi_gray]) != 0:
                roi = roi_gray.astype('float') / 255.0
                roi = img_to_array(roi)
                roi = np.expand_dims(roi, axis=0)
                prediction = classifier.predict(roi)[0]
                maxindex = int(np.argmax(prediction))
                finalout = emotion_labels[maxindex]
                output = str(finalout)
            label_position = (x, y-10)
            cv2.putText(img, output, label_position, cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

        return img

def main():
    # Face Analysis Application #
    st.title("Real Time Face Emotion Detection Application 😠🤮😨😀😐😔😮")
    activiteis = ["Home", "Live Face Emotion Detection"]
    choice = st.sidebar.selectbox("Select Activity", activiteis)

    # Homepage.
    if choice == "Home":
        html_temp_home1 = """<div style="background-color:#FC4C02;padding:0.5px">
                             <h4 style="color:white;text-align:center;">
                            Start Real Time Face Emotion Detection.
                             </h4>
                             </div>
                             </br>"""

        st.markdown(html_temp_home1, unsafe_allow_html=True)
        st.write("""
        How to use...
        1. Click the dropdown list in the top left corner and select Live Face Emotion Detection.
        2. This takes you to a page which will tell if it recognizes your emotions.
                 """)

    # Live Face Emotion Detection.
    elif choice == "Live Face Emotion Detection":
        st.header("Webcam Live Feed")
        st.subheader('''
        Welcome to the other side of the SCREEN!!!
        * Get ready with all the emotions you can express. 
        ''')
        st.write("1. Click Start to open your camera and give permission for prediction")
        st.write("2. This will predict your emotion.")
        st.write("3. When you done, click stop to end.")
        webrtc_streamer(key="example", video_processor_factory=VideoTransformer)

    else:
        pass


if __name__ == "__main__":
    main()