# import basic libraries
import numpy as np
import streamlit as st
import cv2
from deepface import DeepFace as dfc
from PIL import Image
import os

st.set_page_config(page_title='Face-detection-analysis', page_icon=None, layout='centered', initial_sidebar_state='auto')

# function to load image
try:
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
except Exception:
    st.write("Error loading cascade classifiers")

@st.cache
def face_detect(img):
    img = np.array(img.convert("RGB"))
    face = face_cascade.detectMultiScale(image=img)

    # draw rectangle around face
    for (x, y, w, h) in face:
        cv2.rectangle(img=img, pt1=(x, y), pt2=(x + w, y + h), color=(255, 0, 0), thickness=2)
        roi = img[y:y + h, x:x + w]
    return img, face

# analyze image
def analyze_image(img):
    prediction = dfc.analyze(img_path=img)
    return prediction

#function for webcam
def detect_web(image):

    faces = face_cascade.detectMultiScale(
        image=image, scaleFactor=1.3, minNeighbors=5)

    for (x, y, w, h) in faces:
        cv2.rectangle(img=image, pt1=(x, y), pt2=(
            x + w, y + h), color=(255, 0, 0), thickness=2)
    return image, faces

def main():
    # Face Analysis Application #
    st.markdown("<h1 style='text-align: center;'>Face Detection and Analysis </h1>", unsafe_allow_html=True)
    activiteis = ["Home", "Analyze Face", "About"]
    choice = st.sidebar.selectbox("Select Activity", activiteis)
    st.sidebar.markdown(
        """ Developed by [Vivek] (https://github.com/7Vivek)""")
    st.sidebar.markdown(
        """ Checkout complete project [here] (https://github.com/7Vivek/Face-detection-analysis)""")
    # C0C0C0
    if choice == "Home":
        html_temp_home1 = """<div style="background-color:#1E2839;padding:10px">
                                            <h4 style="color:white;text-align:center;">
                                            Face detection and Face feature analysis application using OpenCV, DeepFace and Streamlit.</h4>
                                            </div>
                                            </br>"""
        st.image('https://cdn.dribbble.com/users/1373613/screenshots/5510801/media/b82469d51c432c2ff65c0158334cfabf.gif',use_column_width=True)
        st.markdown(html_temp_home1, unsafe_allow_html=True)
        st.write("""
                 Application Functionalities.
                 
                 1. Face feature analysis such as emotion, gender and age.""")
    elif choice == "Analyze Face":
        st.subheader("Analyze facial features such as emotion, age and gender.")
        image_file = st.file_uploader("Upload image you want to analyze", type=['jpg', 'png', 'jpeg'])

        if image_file is not None:
            #read image using PIL
            image_loaded = Image.open(image_file)
            #detect faces in image
            result_img, result_face = face_detect(image_loaded)
            st.image(result_img, use_column_width=True)
            st.success("found {} face\n".format(len(result_face)))

            if st.button("Analyze image"):
                # convert image to array
                new_image = np.array(image_loaded.convert('RGB'))
                img = cv2.cvtColor(new_image, 1)
                gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
                #analyze features of face
                result = analyze_image(img)
                # st.write(result)
                st.write("Analysis summary")
                st.write("Face emotion is ", result["dominant_emotion"], "in image.")
                st.write("Gender recognized as", result["gender"], "in image.")
                st.write("Age is", result["age"], "years.")
            else:
                pass
                #st.write("Click on Analyze image ")

    elif choice == "About":
        st.subheader("About this app")
        html_temp_about1= """<div style="background-color:#283347;padding:10px">
                                    <h4 style="color:white;text-align:center;">
                                    Face detection and Face feature analysis application using OpenCV, DeepFace and Streamlit.</h4>
                                    </div>
                                    </br>"""
        st.markdown(html_temp_about1, unsafe_allow_html=True)

        html_temp4 = """
                             		<div style="background-color:#434E61;padding:10px">
                             		<h4 style="color:white;text-align:center;">This Application is developed by Vivek Limbad using Streamlit Framework, Opencv and DeepFace library for demonstration purpose. If you have any suggestion or want to comment just write a mail at viveklimbad1010@gmail.com. </h4>
                             		<h4 style="color:white;text-align:center;">Thanks for Visiting </h4>
                             		</div>
                             		<br></br>
                             		<br></br>"""

        st.markdown(html_temp4, unsafe_allow_html=True)

    else:
        pass

if __name__ == '__main__':
    main()