import os
import gradio as gr
import requests
import json
import cv2
import numpy as np
import time
from PIL import Image
from fr.engine.header import *
from fl.engine.header import *
import fr.engine.header as fr_header
import fl.engine.header as fl_header

css = """
.example-image img{
    display: flex; /* Use flexbox to align items */
    justify-content: center; /* Center the image horizontally */
    align-items: center; /* Center the image vertically */
    height: 300px; /* Set the height of the container */
    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
}

.example-image{
    display: flex; /* Use flexbox to align items */
    justify-content: center; /* Center the image horizontally */
    align-items: center; /* Center the image vertically */
    height: 350px; /* Set the height of the container */
    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
}

.face-row {
    display: flex;
    justify-content: space-around; /* Distribute space evenly between elements */
    align-items: center; /* Align items vertically */
    width: 100%; /* Set the width of the row to 100% */
}

.face-image{
    justify-content: center; /* Center the image horizontally */
    align-items: center; /* Center the image vertically */
    height: 160px; /* Set the height of the container */
    width: 160px;
    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
}

.face-image img{
    justify-content: center; /* Center the image horizontally */
    align-items: center; /* Center the image vertically */
    height: 160px; /* Set the height of the container */
    object-fit: contain; /* Preserve aspect ratio while fitting the image within the container */
}

.markdown-success-container {
    background-color: #F6FFED;
    padding: 20px;
    margin: 20px;
    border-radius: 1px;
    border: 2px solid green;
    text-align: center;
}

.markdown-fail-container {
    background-color: #FFF1F0;
    padding: 20px;
    margin: 20px;
    border-radius: 1px;
    border: 2px solid red;
    text-align: center;
}

.markdown-attribute-container {
    display: flex;
    justify-content: space-around; /* Distribute space evenly between elements */
    align-items: center; /* Align items vertically */
    padding: 10px;
    margin: 10px;
}

.block-background {
    # background-color: #202020; /* Set your desired background color */
    border-radius: 5px;
}

"""

file_path = os.path.abspath(__file__)
root_path = os.path.dirname(file_path)

g_fr_activation_result = -1
g_fl_activation_result = -1
MATCH_THRESHOLD = 0.67
SPOOF_THRESHOLD = 0.5

def activate_fr_sdk():
    fr_key = os.environ.get("FR_LICENSE_KEY")
    fr_dict_path = os.path.join(root_path, "fr/engine/bin")

    ret = -1
    if fr_key is None:
        print_warning("Recognition online license key not found!")
    else:
        ret = fr_header.init_sdk(fr_dict_path.encode('utf-8'), fr_key.encode('utf-8'))

    if ret == 0:
        print_log("Successfully init FR SDK!")
    else:
        print_error(f"Falied to init FR SDK, Error code {ret}")

    return ret

def activate_fl_sdk():
    fl_key = os.environ.get("FL_LICENSE_KEY")
    fl_dict_path = os.path.join(root_path, "fl/engine/bin")

    ret = -1
    if fl_key is None:
        print_warning("Liveness Detection online license key not found!")
    else:
        ret = fl_header.init_sdk(fl_dict_path.encode('utf-8'), fl_key.encode('utf-8'))

    if ret == 0:
        print_log("Successfully init FL SDK!")
    else:
        print_error(f"Falied to init FL SDK, Error code {ret}")
        
    return ret
    
def convert_fun(input_str):
    # Remove line breaks and extra whitespaces
    return ' '.join(input_str.split())

# def get_attributes(frame):    
#     url = "https://recognito.p.rapidapi.com/api/analyze_face"
#     try:
#         files = {'image': open(frame, 'rb')}
#         headers = {"X-RapidAPI-Key": os.environ.get("API_KEY")}

#         r = requests.post(url=url, files=files, headers=headers)
#     except:
#         raise gr.Error("Please select images file!")

#     faces = None
#     face_crop, one_line_attribute = None, ""
#     try:
#         image = Image.open(frame)

#         face = Image.new('RGBA',(150, 150), (80,80,80,0))
        
#         res = r.json().get('image')
#         if res is not None and res:
#             face = res.get('detection')
#             x1 = face.get('x')
#             y1 = face.get('y')
#             x2 = x1 + face.get('w')
#             y2 = y1 + face.get('h')

#             if x1 < 0:
#                 x1 = 0
#             if y1 < 0:
#                 y1 = 0
#             if x2 >= image.width:
#                 x2 = image.width - 1
#             if y2 >= image.height:
#                 y2 = image.height - 1

#             face_crop = image.crop((x1, y1, x2, y2))
#             face_image_ratio = face_crop.width / float(face_crop.height)
#             resized_w = int(face_image_ratio * 150)
#             resized_h = 150

#             face_crop = face_crop.resize((int(resized_w), int(resized_h)))
            
#             attr = res.get('attribute')
            
#             age = attr.get('age')
#             gender = attr.get('gender')
#             emotion = attr.get('emotion')
#             ethnicity = attr.get('ethnicity')

#             mask = attr.get('face_mask')
#             glass = 'No Glasses'
#             if attr.get('glasses') == 'USUAL':
#                 glass = 'Glasses'
#             if attr.get('glasses') == 'DARK':
#                 glass = 'Sunglasses'
            
#             open_eye_thr = 0.3
#             left_eye = 'Close'
#             if attr.get('eye_left') >= open_eye_thr:
#                 left_eye = 'Open'

#             right_eye = 'Close'
#             if attr.get('eye_right') >= open_eye_thr:
#                 right_eye = 'Open'

#             facehair = attr.get('facial_hair')
#             haircolor = attr.get('hair_color')
#             hairtype = attr.get('hair_type')
#             headwear = attr.get('headwear')

#             pitch = attr.get('pitch')
#             roll = attr.get('roll')
#             yaw = attr.get('yaw')
#             quality = attr.get('quality')

#             attribute = f"""
#             <br/>
#             <div class="markdown-attribute-container">
#             <table>
#             <tr>
#                 <th style="text-align: center;">Attribute</th>
#                 <th style="text-align: center;">Result</th>
#                 <th style="text-align: center;">Score</th>
#                 <th style="text-align: center;">Threshold</th>
#             </tr>
#             <tr>
#                 <td>Gender</td>
#                 <td>{gender}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Age</td>
#                 <td>{int(age)}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Pitch</td>
#                 <td>{"{:.4f}".format(pitch)}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Yaw</td>
#                 <td>{"{:.4f}".format(yaw)}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Roll</td>
#                 <td>{"{:.4f}".format(roll)}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Emotion</td>
#                 <td>{emotion}</td>
#                 <td></td><td></td>
#             </tr>
#                 <tr>
#                 <td>Left Eye</td>
#                 <td>{left_eye}</td>
#                 <td>{"{:.4f}".format(attr.get('eye_left'))}</td>
#                 <td>{open_eye_thr}</td>
#             </tr>
#             <tr>
#                 <td>Right Eye</td>
#                 <td>{right_eye}</td>
#                 <td>{"{:.4f}".format(attr.get('eye_right'))}</td>
#                 <td>{open_eye_thr}</td>
#             </tr>
#             <tr>
#                 <td>Mask</td>
#                 <td>{mask}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Glass</td>
#                 <td>{glass}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>FaceHair</td>
#                 <td>{facehair}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>HairColor</td>
#                 <td>{haircolor}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>HairType</td>
#                 <td>{hairtype}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>HeadWear</td>
#                 <td>{headwear}</td>
#                 <td></td><td></td>
#             </tr>
#             <tr>
#                 <td>Image Quality</td>
#                 <td>{"{:.4f}".format(quality)}</td>
#                 <td></td><td></td>
#             </tr>
#             </table>
#             </div>
#             """
#             one_line_attribute = convert_fun(attribute)
#     except:
#         pass
    
#     return face_crop, one_line_attribute

def check_liveness(frame):
    # url = "https://recognito-faceliveness.p.rapidapi.com/api/check_liveness"
    # try:
    #     files = {'image': open(frame, 'rb')}
    #     headers = {"X-RapidAPI-Key": os.environ.get("API_KEY")}

    #     r = requests.post(url=url, files=files, headers=headers)
    # except:
    #     raise gr.Error("Please select images file!")

    # faces = None

    # face_crop, liveness_result, liveness_score = None, "", -200
    # try:
    #     image = Image.open(frame)

    #     face = Image.new('RGBA',(150, 150), (80,80,80,0))
    #     res = r.json().get('data')
    #     if res is not None and res:
    #         face = res.get('face_rect')
    #         x1 = face.get('x')
    #         y1 = face.get('y')
    #         x2 = x1 + face.get('w')
    #         y2 = y1 + face.get('h')

    #         if x1 < 0:
    #             x1 = 0
    #         if y1 < 0:
    #             y1 = 0
    #         if x2 >= image.width:
    #             x2 = image.width - 1
    #         if y2 >= image.height:
    #             y2 = image.height - 1

    #         face_crop = image.crop((x1, y1, x2, y2))
    #         face_image_ratio = face_crop.width / float(face_crop.height)
    #         resized_w = int(face_image_ratio * 150)
    #         resized_h = 150

    #         face_crop = face_crop.resize((int(resized_w), int(resized_h)))
    #         liveness_score = res.get('liveness_score')
    #         liveness = res.get('result')

    #         if liveness == 'REAL':
    #             liveness_result = f"""<br/><div class="markdown-success-container"><p style="text-align: center; font-size: 20px; color: green;">Liveness Check:  REAL<br/>Score: {liveness_score}</p></div>"""
    #         else:
    #             liveness_result = f"""<br/><div class="markdown-fail-container"><p style="text-align: center; font-size: 20px; color: red;">Liveness Check:  {liveness}<br/>Score: {liveness_score}</p></div>"""

    # except:
    #     pass
    
    # return face_crop, liveness_result, liveness_score
    
    global g_fl_activation_result
    if g_fl_activation_result != 0:
        gr.Warning("FL SDK Activation Failed!")
        return None, None, None

    try:
        image = open(frame, 'rb')
    except:
        raise gr.Error("Please select image file!")
    
    image_mat = cv2.imdecode(np.frombuffer(image.read(), np.uint8), cv2.IMREAD_COLOR)
    start_time = time.time()
    result, face_rect, score, angles = fl_header.check_liveness(image_mat, SPOOF_THRESHOLD)
    end_time = time.time()
    process_time = (end_time - start_time) * 1000

    face_crop, one_line_attribute = None, ""
    try:
        image = Image.open(frame)

        face = Image.new('RGBA',(150, 150), (80,80,80,0))
        
        if face_rect is not None:
            x1 = int(face_rect[0])
            y1 = int(face_rect[1])
            x2 = int(face_rect[2])
            y2 = int(face_rect[3])

            if x1 < 0:
                x1 = 0
            if y1 < 0:
                y1 = 0
            if x2 >= image.width:
                x2 = image.width - 1
            if y2 >= image.height:
                y2 = image.height - 1

            face_crop = image.crop((x1, y1, x2, y2))
            face_image_ratio = face_crop.width / float(face_crop.height)
            resized_w = int(face_image_ratio * 150)
            resized_h = 150

            face_crop = face_crop.resize((int(resized_w), int(resized_h)))

        if angles is not None:
            yaw = angles[0]
            roll = angles[1]
            pitch = angles[2]

        attribute = f"""
        <br/>
        <div class="markdown-attribute-container">
        <table>
        <tr>
            <th>Field</th>
            <th colspan="2">Value</th>
        </tr>
        <tr>
            <th rowspan="4">Face Rect</th>
            <td>x</td>
            <td>{x1}</td>
        </tr>
        <tr>
            <td>y</td>
            <td>{y1}</td>
        </tr>
        <tr>
            <td>width</td>
            <td>{x2 - x1 + 1}</td>
        </tr>
        <tr>
            <td>height</td>
            <td>{y2 - y1 + 1}</td>
        </tr>
        <tr>
            <th rowspan="3">Face Angle</th>
            <td>Pitch</td>
            <td>{"{:.4f}".format(pitch)}</td>
        </tr>
        <tr>
            <td>Yaw</td>
            <td>{"{:.4f}".format(yaw)}</td>
        </tr>
        <tr>
            <td>Roll</td>
            <td>{"{:.4f}".format(roll)}</td>
        </tr>
        </table>
        </div>
        """
        
        one_line_attribute = convert_fun(attribute)
    except:
        pass

    str_score = str("{:.4f}".format(score))
    if result == "REAL":
        liveness_result = f"""<br/><div class="markdown-success-container"><p style="text-align: center; font-size: 20px; color: green;">Liveness Check:  REAL<br/>Score: {str_score}</p></div>"""        
    else:
        liveness_result = f"""<br/><div class="markdown-fail-container"><p style="text-align: center; font-size: 20px; color: red;">Liveness Check:  {result}<br/>Score: {str_score}</p></div>"""  
        
    return face_crop, liveness_result, one_line_attribute

def analyze_face(frame):
    # face_crop_1, liveness_result, liveness_score = check_liveness(frame)
    # face_crop_2, attribute = get_attributes(frame)
    # face_crop = face_crop_1 if (face_crop_1 is not None) else face_crop_2
    face_crop, liveness_result, attribute = check_liveness(frame)
    return [face_crop, liveness_result, attribute]


def compare_face(frame1, frame2):
    """
    url = "https://recognito.p.rapidapi.com/api/compare_face"
    try:
        files = {'image1': open(frame1, 'rb'), 'image2': open(frame2, 'rb')}
        headers = {"X-RapidAPI-Key": os.environ.get("API_KEY")}

        r = requests.post(url=url, files=files, headers=headers)
    except:
        raise gr.Error("Please select images files!")

    faces = None

    try:
        image1 = Image.open(frame1)
        image2 = Image.open(frame2)

        face1 = Image.new('RGBA',(150, 150), (80,80,80,0))
        face2 = Image.new('RGBA',(150, 150), (80,80,80,0))

        res1 = r.json().get('image1')
        
        if res1 is not None and res1:
            face = res1.get('detection')
            x1 = face.get('x')
            y1 = face.get('y')
            x2 = x1 + face.get('w')
            y2 = y1 + face.get('h')
            if x1 < 0:
                x1 = 0
            if y1 < 0:
                y1 = 0
            if x2 >= image1.width:
                x2 = image1.width - 1
            if y2 >= image1.height:
                y2 = image1.height - 1

            face1 = image1.crop((x1, y1, x2, y2))
            face_image_ratio = face1.width / float(face1.height)
            resized_w = int(face_image_ratio * 150)
            resized_h = 150

            face1 = face1.resize((int(resized_w), int(resized_h)))

        res2 = r.json().get('image2')
        if res2 is not None and res2:
            face = res2.get('detection')
            x1 = face.get('x')
            y1 = face.get('y')
            x2 = x1 + face.get('w')
            y2 = y1 + face.get('h')

            if x1 < 0:
                x1 = 0
            if y1 < 0:
                y1 = 0
            if x2 >= image2.width:
                x2 = image2.width - 1
            if y2 >= image2.height:
                y2 = image2.height - 1

            face2 = image2.crop((x1, y1, x2, y2))
            face_image_ratio = face2.width / float(face2.height)
            resized_w = int(face_image_ratio * 150)
            resized_h = 150

            face2 = face2.resize((int(resized_w), int(resized_h)))
    except:
        pass
        
    matching_result = Image.open("icons/blank.png")
    similarity_score = ""
    if face1 is not None and face2 is not None:
        matching_score = r.json().get('matching_score')
        if matching_score is not None:
            str_score = str("{:.4f}".format(matching_score))
            if matching_score >= 0.7:
                matching_result = Image.open("icons/same.png")
                similarity_score = 
            else:
                matching_result = Image.open("icons/different.png")
                similarity_score = 
    
    return [face1, face2, matching_result, similarity_score]
    """
    global g_fr_activation_result
    if g_fr_activation_result != 0:
        gr.Warning("FR SDK Activation Failed!")
        return None, None, None, None
        
    try:
        image1 = open(frame1, 'rb')
        image2 = open(frame2, 'rb')
    except:
        raise gr.Error("Please select images files!")

    image_mat1 = cv2.imdecode(np.frombuffer(image1.read(), np.uint8), cv2.IMREAD_COLOR)
    image_mat2 = cv2.imdecode(np.frombuffer(image2.read(), np.uint8), cv2.IMREAD_COLOR)
    start_time = time.time()
    result, score, face_bboxes, face_features = fr_header.compare_face(image_mat1, image_mat2, MATCH_THRESHOLD)
    end_time = time.time()
    process_time = (end_time - start_time) * 1000

    try:
        image1 = Image.open(frame1)
        image2 = Image.open(frame2)
        images = [image1, image2]

        face1 = Image.new('RGBA',(150, 150), (80,80,80,0))
        face2 = Image.new('RGBA',(150, 150), (80,80,80,0))
        faces = [face1, face2]

        face_bboxes_result = []
        if face_bboxes is not None:
            for i, bbox in enumerate(face_bboxes):
                x1 = bbox[0]
                y1 = bbox[1]
                x2 = bbox[2]
                y2 = bbox[3]
                if x1 < 0:
                    x1 = 0
                if y1 < 0:
                    y1 = 0
                if x2 >= images[i].width:
                    x2 = images[i].width - 1
                if y2 >= images[i].height:
                    y2 = images[i].height - 1

                face_bbox_str = f"x1: {x1}, y1: {y1}, x2: {x2}, y2: {y2}"
                face_bboxes_result.append(face_bbox_str)

                faces[i] = images[i].crop((x1, y1, x2, y2))
                face_image_ratio = faces[i].width / float(faces[i].height)
                resized_w = int(face_image_ratio * 150)
                resized_h = 150

                faces[i] = faces[i].resize((int(resized_w), int(resized_h)))
    except:
        pass

    matching_result = Image.open("icons/blank.png")
    similarity_score = ""
    if faces[0] is not None and faces[1] is not None:
        if score is not None:
            str_score = str("{:.4f}".format(score))
            if result == "SAME PERSON":
                matching_result = Image.open("icons/same.png")
                similarity_score = f"""<br/><div class="markdown-success-container"><p style="text-align: center; font-size: 20px; color: green;">Similarity score: {str_score}</p></div>"""
            else:
                matching_result = Image.open("icons/different.png")
                similarity_score = f"""<br/><div class="markdown-fail-container"><p style="text-align: center; font-size: 20px; color: red;">Similarity score: {str_score}</p></div>"""
    
    return faces[0], faces[1], matching_result, similarity_score

        

def launch_demo(activate_fr_result, activate_fl_result):
    with gr.Blocks(css=css) as demo:
        gr.Markdown(
            """
            <a href="https://recognito.vision" style="display: flex; align-items: center;">
                <img src="https://recognito.vision/wp-content/uploads/2024/03/Recognito-modified.png" style="width: 8%; margin-right: 15px;"/>
                <div>
                    <p style="font-size: 32px; font-weight: bold; margin: 0;">Recognito</p>
                    <p style="font-size: 18px; margin: 0;">www.recognito.vision</p>
                </div>
            </a>
    
            <p style="font-size: 20px; font-weight: bold;">✨ NIST FRVT Top #1 Face Recognition Algorithm Developer</p>
            <div style="display: flex; align-items: center;">
                &emsp;&emsp;<a href="https://pages.nist.gov/frvt/html/frvt11.html"> <p style="font-size: 14px;">👉🏻 Latest NIST FRVT Report</p></a>
            </div>
            <p style="font-size: 20px; font-weight: bold;">📘 Product Documentation</p>
            <div style="display: flex; align-items: center;">          
                &emsp;&emsp;<a href="https://docs.recognito.vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/05/book.png" style="width: 48px; margin-right: 5px;"/></a>
            </div>
            <p style="font-size: 20px; font-weight: bold;">🏠 Visit Recognito</p>
            <div style="display: flex; align-items: center;">
                &emsp;&emsp;<a href="https://recognito.vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/recognito_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://www.linkedin.com/company/recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/linkedin_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://huggingface.co/recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/hf_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://github.com/recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/github_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://hub.docker.com/u/recognito" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/03/docker_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a href="https://www.youtube.com/@recognito-vision" style="display: flex; align-items: center;"><img src="https://recognito.vision/wp-content/uploads/2024/04/youtube_64_cl.png" style="width: 32px; margin-right: 5px;"/></a>
            </div>
            <p style="font-size: 20px; font-weight: bold;">🤝 Contact us for our on-premise Face Recognition, Liveness Detection SDKs deployment</p>
            <div style="display: flex; align-items: center;">
                &emsp;&emsp;<a target="_blank" href="mailto:hello@recognito.vision"><img src="https://img.shields.io/badge/email-hassan@recognito.vision-blue.svg?logo=gmail " alt="www.recognito.vision"></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://wa.me/+14158003112"><img src="https://img.shields.io/badge/whatsapp-+14158003112-blue.svg?logo=whatsapp " alt="www.recognito.vision"></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://t.me/recognito_vision"><img src="https://img.shields.io/badge/telegram-@recognito__vision-blue.svg?logo=telegram " alt="www.recognito.vision"></a>
                &nbsp;&nbsp;&nbsp;&nbsp;<a target="_blank" href="https://join.slack.com/t/recognito-workspace/shared_invite/zt-2d4kscqgn-"><img src="https://img.shields.io/badge/slack-recognito__workspace-blue.svg?logo=slack " alt="www.recognito.vision"></a>
            </div>
            <br/><br/><br/>
            """
        )
        
        with gr.Tabs():
            with gr.Tab("Face Recognition"):
                with gr.Row():
                    with gr.Column(scale=2):
                        with gr.Row():
                            with gr.Column(scale=1):
                                compare_face_input1 = gr.Image(label="Image1", type='filepath', elem_classes="example-image")
                                gr.Examples(['examples/1.jpg', 'examples/2.jpg', 'examples/3.jpg', 'examples/4.jpg'], 
                                            inputs=compare_face_input1)
                            with gr.Column(scale=1):
                                compare_face_input2 = gr.Image(label="Image2", type='filepath', elem_classes="example-image")
                                gr.Examples(['examples/5.jpg', 'examples/6.jpg', 'examples/7.jpg', 'examples/8.jpg'], 
                                            inputs=compare_face_input2)
                                
                    with gr.Blocks():
                        with gr.Column(scale=1, min_width=400, elem_classes="block-background"):     
                            compare_face_button = gr.Button("Compare Face", variant="primary", size="lg")
                            with gr.Row(elem_classes="face-row"):
                                face_output1 = gr.Image(value="icons/face.jpg", label="Face 1", scale=0, elem_classes="face-image", show_share_button=False, show_download_button=False, show_fullscreen_button=False)
                                compare_result = gr.Image(value="icons/blank.png", min_width=30, scale=0, show_download_button=False, show_label=False, show_share_button=False, show_fullscreen_button=False)
                                face_output2 = gr.Image(value="icons/face.jpg", label="Face 2", scale=0, elem_classes="face-image", show_share_button=False, show_download_button=False, show_fullscreen_button=False)
                            similarity_markdown = gr.Markdown("")
    
                            compare_face_button.click(compare_face, inputs=[compare_face_input1, compare_face_input2], outputs=[face_output1, face_output2, compare_result, similarity_markdown])
                    
            with gr.Tab("Face Liveness, Analysis"):
                with gr.Row():
                    with gr.Column(scale=1):
                        face_input = gr.Image(label="Image", type='filepath', elem_classes="example-image")
                        gr.Examples(['examples/att_1.jpg', 'examples/att_2.jpg', 'examples/att_3.jpg', 'examples/att_4.jpg', 'examples/att_5.jpg', 'examples/att_6.jpg', 'examples/att_7.jpg'], 
                                    inputs=face_input)
    
                    with gr.Blocks():
                        with gr.Column(scale=1, elem_classes="block-background"):     
                            analyze_face_button = gr.Button("Analyze Face", variant="primary", size="lg")
                            with gr.Row(elem_classes="face-row"):
                                face_output = gr.Image(value="icons/face.jpg", label="Face", scale=0, elem_classes="face-image", show_share_button=False, show_download_button=False, show_fullscreen_button=False)
                            
                            liveness_result = gr.Markdown("")
                            attribute_result = gr.Markdown("")
                        
                        analyze_face_button.click(analyze_face, inputs=face_input, outputs=[face_output, liveness_result, attribute_result])
    
        gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FRecognito%2FFaceRecognition-LivenessDetection-FaceAnalysis"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FRecognito%2FFaceRecognition-LivenessDetection-FaceAnalysis&countColor=%2337d67a&style=flat&labelStyle=upper" /></a>')
        
    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=False)

if __name__ == '__main__':
    g_fr_activation_result = activate_fr_sdk()
    g_fl_activation_result = activate_fl_sdk()
    launch_demo(g_fr_activation_result, g_fl_activation_result)