Flask API version for first-order-motion-model by [AliaksandrSiarohin](https://github.com/AliaksandrSiarohin/first-order-model)

app.py

import imageio
# imageio.plugins.ffmpeg.download()
import numpy as np  
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage.transform import resize
from IPython.display import HTML
import warnings
warnings.filterwarnings("ignore")

from demo import load_checkpoints, make_animation
from skimage import img_as_ubyte
from flask import Flask, request,jsonify,send_file,render_template
from flask_cors import CORS, cross_origin

from PIL import Image
import io
import requests
import urllib.request

app = Flask(__name__)
CORS(app, resources = {
        r"/*":{
            "origins": "*"
        }
    }, headers='Content-Type')  ##added for origin access
app.config['CORS_HEADERS'] = 'Content-Type'

@app.route("/")
def homepage():
    return render_template("index.html", title="JUST WORK")


@app.route('/post', methods=['GET', 'POST'])
@cross_origin(origin='*',headers=['Content-Type','Authorization'])
def post():

    if request.method == 'POST':
        
        image = request.files['image']
        print('image recieved')
        print(" ")

        print(type(image))
        print(" ")

        image = image.read()
        print('after image read')
        print(" ")

        print(type(image))
        print(" ")
        
        # image = Image.open(requests.get(image, stream=True).raw)
        image = Image.open(io.BytesIO(image))
        print('after image open')
        print(" ")

        print(type(image))
        print(" ")

        image =np.array(image)
        image = resize(image, (256, 256))[..., :3]

        print("image resized")
        print(" ")

        video = request.files['video']
        print('video recieved')
        print(" ")

        print(type(video))
        print(" ")

        video = video.read()
        print('video read')
        print(" ")

        print(type(video))
        print(" ")

        video = imageio.get_reader(video, 'mp4')
        # video = imageio.get_reader(requests.get(video, allow_redirects=True, stream=True).raw, 'mp4')
        print('video url open')
        print(" ")

        print(type(video))
        print(" ")

        fps = video.get_meta_data()['fps']
        print(fps)
        driving_video = []
        try:
            for im in video:
                driving_video.append(im)
        except RuntimeError:
            pass
        video.close()

        driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]

        print('video resized')

        # source_image, driving_video,fps = control(image, video)

        generator, kp_detector = load_checkpoints(config_path='config/vox-256.yaml',
                                                checkpoint_path='vox-cpk.pth.tar',
                                                cpu=True
                                                )
        print("generator done")

        predictions = make_animation(source_image=image,
                                    driving_video=driving_video,
                                    generator=generator,
                                    kp_detector=kp_detector,
                                    relative=True,
                                    cpu=True
                                    ) #cpu
        
        imageio.mimsave('generatedVideo.mp4',
                        [img_as_ubyte(frame) for frame in predictions],
                        fps=fps)    

    
        return send_file('generatedVideo.mp4',  
                        as_attachment=True)



if __name__ == '__main__':
        app.run(host='0.0.0.0', port=5000, debug=True)