import gradio as gr
import os
import requests

from PIL import Image

def face_compare(frame1, frame2):
    url = "https://faceapi.miniai.live/face_compare"
    files = {'file1': open(frame1, 'rb'), 'file2': open(frame2, 'rb')}

    r = requests.post(url=url, files=files)

    html = None
    faces = None

    compare_result = r.json().get('compare_result')
    compare_similarity = r.json().get('compare_similarity')

    html = ("<table>"
                "<tr>"
                    "<th>State</th>"
                    "<th>Value</th>"
                "</tr>"
                "<tr>"
                    "<td>Is same person? </td>"
                    "<td>{compare_result}</td>"
                "</tr>"
                "<tr>"
                    "<td>Similarity</td>"
                    "<td>{compare_similarity}</td>"
                "</tr>"
                "</table>".format(compare_result=compare_result, compare_similarity=compare_similarity))

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

        face1 = None
        face2 = None

        if r.json().get('face1') is not None:
            face = r.json().get('face1')
            x1 = face.get('x1')
            y1 = face.get('y1')
            x2 = face.get('x2')
            y2 = face.get('y2')

            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)))

        if r.json().get('face2') is not None:
            face = r.json().get('face2')
            x1 = face.get('x1')
            y1 = face.get('y1')
            x2 = face.get('x2')
            y2 = face.get('y2')

            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)))

        if face1 is not None and face2 is not None:
            new_image = Image.new('RGB',(face1.width + face2.width + 10, 150), (80,80,80))

            new_image.paste(face1,(0,0))
            new_image.paste(face2,(face1.width + 10, 0))
            faces = new_image.copy()
        elif face1 is not None and face2 is None:
            new_image = Image.new('RGB',(face1.width + face1.width + 10, 150), (80,80,80))

            new_image.paste(face1,(0,0))
            faces = new_image.copy()
        elif face1 is None and face2 is not None:
            new_image = Image.new('RGB',(face2.width + face2.width + 10, 150), (80,80,80))

            new_image.paste(face2,(face2.width + 10, 0))
            faces = new_image.copy()

    except:
        pass

    return [faces, html]

def check_liveness(frame):
    url = "https://faceapi.miniai.live/face_liveness_check"
    file = {'file': open(frame, 'rb')}

    r = requests.post(url=url, files=file)

    faceCount = None

    response_data = r.json()

    for item in response_data.get('face_state', []):
        if 'faceCount' in item:
            faceCount = item['faceCount']
            break

    faces = None
    live_result = []
    live_result.append(f"<table><tr><th>FaceID</th><th>Age</th><th>Gender</th><th>Liveness</th></tr>")

    for item in response_data.get('face_state', []):
        if item.get('FaceID'):
            faceID = item.get('FaceID')
            result = item.get('LivenessCheck')
            age = item.get('Age')
            gender = item.get('Gender')
            live_result.append(f"<tr><td>{faceID}</td><td>{age}</td><td>{gender}</td><td>{result}</td></tr>")
    live_result.append(f"</table>")
    live_result = ''.join(live_result)

    try:
        image = Image.open(frame)        

        for face in r.json().get('faces'):
            x1 = face.get('x1')
            y1 = face.get('y1')
            x2 = face.get('x2')
            y2 = face.get('y2')

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

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

            if faces is None:
                faces = face_image
            else:
                new_image = Image.new('RGB',(faces.width + face_image.width + 10, 150), (80,80,80))

                new_image.paste(faces,(0,0))
                new_image.paste(face_image,(faces.width + 10, 0))
                faces = new_image.copy()
    except:
        pass

    return [faces, live_result]

def face_emotion(frame):
    url = "https://faceapi.miniai.live/face_emotion"
    file = {'file': open(frame, 'rb')}

    r = requests.post(url=url, files=file)

    emotion_result = []
    emotion_result.append(f"<table><tr><td>Emotional Result : </td><td>{r.json().get('emotion_result')}</td></tr>")
    emotion_result.append(f"</table>")
    emotion_result = ''.join(emotion_result)

    faces = None

    try:
        image = Image.open(frame)        

        for face in r.json().get('faces'):
            x1 = face.get('x1')
            y1 = face.get('y1')
            x2 = face.get('x2')
            y2 = face.get('y2')

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

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

            if faces is None:
                faces = face_image
            else:
                new_image = Image.new('RGB',(faces.width + face_image.width + 10, 150), (80,80,80))

                new_image.paste(faces,(0,0))
                new_image.paste(face_image,(faces.width + 10, 0))
                faces = new_image.copy()
    except:
        pass

    return [faces, emotion_result]

# APP Interface
with gr.Blocks() as MiniAIdemo:
    gr.Markdown(
        """
        <a href="https://miniai.live" style="display: flex; align-items: center;">
            <img src="https://miniai.live/wp-content/uploads/2024/02/logo_name-1-768x426-1.png" style="width: 18%; margin-right: 15px;"/>
            <div>
                <p style="font-size: 40px; font-weight: bold; margin-right: 20px;">FaceRecognition-LivenessDetection SDK Demo</p>
                <p style="font-size: 20px; margin-right: 0;">Experience our NIST FRVT Top Ranked FaceRecognition, iBeta 2 Certified Face Liveness Detection Engine</p>
            </div>
        </a>

        <br/>
        <ul>
            <li style="font-size: 18px;">Visit and learn more about our Service : <a href="https://miniai.live" target="_blank" style="font-size: 18px;">https://www.miniai.live</a></li>
            <li style="font-size: 18px;">Check our SDK for cross-platform from Github : <a href="https://github.com/MiniAiLive" target="_blank" style="font-size: 18px;">https://github.com/MiniAiLive</a></li>
            <li style="font-size: 18px;">Quick view our Youtube Demo Video : <a href="https://www.youtube.com/@miniailive" target="_blank" style="font-size: 18px;">MiniAiLive Youtube Channel</a></li>
            <li style="font-size: 18px;">Demo with Android device from Google Play : <a href="https://play.google.com/store/apps/dev?id=5831076207730531667" target="_blank" style="font-size: 18px;">MiniAiLive Google Play</a></li>
        </ul>
        <br/>
        """
    )
    with gr.Tabs():
        with gr.Tab("Face Recognition"):
            with gr.Row():
                with gr.Column():
                    im_match_in1 = gr.Image(type='filepath', height=300)
                    gr.Examples(
                        [
                            "images/compare/demo-pic22.jpg",
                            "images/compare/demo-pic60.jpg",
                            "images/compare/demo-pic35.jpg",
                            "images/compare/demo-pic33.jpg",
                            "images/compare/demo-pic34.jpg",
                        ],
                        inputs=im_match_in1
                    )
                with gr.Column():
                    im_match_in2 = gr.Image(type='filepath', height=300)
                    gr.Examples(
                        [
                            "images/compare/demo-pic41.jpg",
                            "images/compare/demo-pic32.jpg",
                            "images/compare/demo-pic39.jpg",
                            "images/compare/demo-pic61.jpg",
                            "images/compare/demo-pic40.jpg",
                        ],
                        inputs=im_match_in2
                    )
                with gr.Column():
                    im_match_crop = gr.Image(type="pil", height=256)
                    txt_compare_out = gr.HTML()
            btn_f_match = gr.Button("Check Comparing!", variant='primary')
            btn_f_match.click(face_compare, inputs=[im_match_in1, im_match_in2], outputs=[im_match_crop, txt_compare_out])
        with gr.Tab("Face Liveness Detection"):
            with gr.Row():
                with gr.Column(scale=1):
                    im_liveness_in = gr.Image(type='filepath', height=300)
                    gr.Examples(
                        [
                            "images/liveness/f_real_andr.jpg",
                            "images/liveness/f_fake_andr_mask3d.jpg",
                            "images/liveness/f_fake_andr_monitor.jpg",
                            "images/liveness/f_fake_andr_outline.jpg",
                            "images/liveness/f_fake_andr_outline3d.jpg",
                            "images/liveness/1.jpg",
                            "images/liveness/3.png",
                            "images/liveness/4.jpg",
                        ],
                        inputs=im_liveness_in
                    )
                    btn_f_liveness = gr.Button("Check Liveness!", variant='primary')
                with gr.Blocks():
                    with gr.Row():
                        with gr.Column():
                            im_liveness_out = gr.Image(label="Croped Face", type="pil", scale=1)
                        with gr.Column():
                            livness_result_output = gr.HTML()
            btn_f_liveness.click(check_liveness, inputs=im_liveness_in, outputs=[im_liveness_out, livness_result_output])
        with gr.Tab("Face Emotional Recognition"):
            with gr.Row():
                with gr.Column():
                    im_emotion_in = gr.Image(type='filepath', height=300)
                    gr.Examples(
                        [
                            "images/emotion/1.jpg",
                            "images/emotion/2.jpg",
                            "images/emotion/3.jpg",
                            "images/emotion/4.jpg",
                            "images/emotion/5.jpg",
                            "images/emotion/6.jpg",
                        ],
                        inputs=im_emotion_in
                    )
                    btn_f_emotion = gr.Button("Check Emotion!", variant='primary')
                with gr.Blocks():
                    with gr.Row():
                        with gr.Column():
                            im_emotion_out = gr.Image(label="Result Image", type="pil", scale=1)
                        with gr.Column():
                            txt_emotion_out = gr.HTML()
            btn_f_emotion.click(face_emotion, inputs=im_emotion_in, outputs=[im_emotion_out, txt_emotion_out])
    gr.HTML('<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FMiniAiLive%2FFaceRecognition-LivenessDetection-Demo"><img src="https://api.visitorbadge.io/api/combined?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FMiniAiLive%2FFaceRecognition-LivenessDetection-Demo&label=VISITORS&labelColor=%2337d67a&countColor=%23ff8a65&style=plastic&labelStyle=none" /></a>')
if __name__ == "__main__":
    MiniAIdemo.launch()