app.py

import os
import subprocess

REST_IP = os.environ['REST_IP']
SERVICE_PORT = int(os.environ['SERVICE_PORT'])
TRANSLATION_APIKEY_URL = os.environ['TRANSLATION_APIKEY_URL']
GOOGLE_APPLICATION_CREDENTIALS = os.environ['GOOGLE_APPLICATION_CREDENTIALS']
subprocess.call(f"wget --no-check-certificate -O {GOOGLE_APPLICATION_CREDENTIALS} {TRANSLATION_APIKEY_URL}", shell=True)

TOXICITY_THRESHOLD = float(os.getenv('TOXICITY_THRESHOLD', 0.7))

import gradio as gr
from toxicity_estimator import PerspectiveAPI
from translator import Translator
from client_rest import RestAPIApplication
from pathlib import Path
import argparse
import threading
import yaml

TITLE = Path("docs/title.txt").read_text()
DESCRIPTION = Path("docs/description.md").read_text()
    
    
class GradioApplication:
    def __init__(self, rest_ip, rest_port, max_seed):
        self.lang_list = {
            'ko': 'ko_KR',
            'en': 'en_US',
            'ja': 'ja_JP',
            'zh': 'zh_CN',
            'zh-CN': 'zh_CN'
        }
        self.background_list = [None,
                                "background_image/cvpr.png",
                                "background_image/black.png",
                                "background_image/river.mp4",
                                "background_image/sky.mp4"]
        
        self.perspective_api = PerspectiveAPI()
        self.translator = Translator()
        self.rest_application = RestAPIApplication(rest_ip, rest_port)
        self.output_dir = Path("output_file")

        inputs = prepare_input()
        outputs = prepare_output()

        self.iface = gr.Interface(fn=self.infer,
                                  title=TITLE,
                                  description=DESCRIPTION,
                                  inputs=inputs,
                                  outputs=outputs,
                                  allow_flagging='never',
                                  article=Path("docs/article.md").read_text())

        self.max_seed = max_seed
        self._file_seed = 0
        self.lock = threading.Lock()
        
    
    def _get_file_seed(self):
        return f"{self._file_seed % self.max_seed:02d}"

    def _reset_file_seed(self):
        self._file_seed = 0

    def _counter_file_seed(self):
        with self.lock:
            self._file_seed += 1

    def get_lang_code(self, lang):
        return self.lang_list[lang]

    def get_background_data(self, background_index):
        # get background filename and its extension
        data_path = self.background_list[background_index]

        if data_path is not None:
            with open(data_path, 'rb') as rf:
                background_data = rf.read()
            is_video_background = str(data_path).endswith(".mp4")
        else:
            background_data = None
            is_video_background = False

        return background_data, is_video_background
    
    @staticmethod
    def return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=""):
        return {'Toxicity': toxicity_prob}, f"Language: {lang_dest}\nText: {target_text}\n-\nDetails: {detail}", str(video_filename)   

    def infer(self, text, lang, duration_rate, action, background_index):
        self._counter_file_seed()
        print(f"File Seed: {self._file_seed}")
        toxicity_prob = 0.0
        target_text = ""
        lang_dest = ""
        video_filename = "vacant.mp4"
        
        # Toxicity estimation
        try:
            toxicity_prob = self.perspective_api.get_score(text)
        except Exception as e:  # when Perspective API doesn't work
            pass
        
        if toxicity_prob > TOXICITY_THRESHOLD:
            detail = "Sorry, it seems that the input text is too toxic."
            return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {detail}")
        
        # Google Translate API
        try:
            target_text, lang_dest = self.translator.get_translation(text, lang)
        except Exception as e:
            target_text = ""
            lang_dest = ""
            detail = f"Error from language translation: ({e})"
            return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {detail}")
        
        try:
            self.translator.length_check(lang_dest, target_text)  # assertion check
        except AssertionError as e:
            return self.return_format(toxicity_prob, target_text, lang_dest, video_filename, detail=f"Error: {str(e)}")
            
        lang_rpc_code = self.get_lang_code(lang_dest)

        # Video Inference
        background_data, is_video_background = self.get_background_data(background_index)
        
        video_data = self.rest_application.get_video(target_text, lang_rpc_code, duration_rate, action.lower(),
                                                     background_data, is_video_background)
        print(f"Video data size: {len(video_data)}")

        video_filename = self.output_dir / f"{self._file_seed:02d}.mkv"
        with open(video_filename, "wb") as video_file:
            video_file.write(video_data)
        
        return self.return_format(toxicity_prob, target_text, lang_dest, video_filename)     

    def run(self, server_port=7860, share=False):
        try:
            self.iface.launch(height=900,
                              share=share, server_port=server_port,
                              enable_queue=True)
        
        except KeyboardInterrupt:
            gr.close_all()


def prepare_input():
    text_input = gr.Textbox(lines=2,
                            placeholder="Type your text with English, Chinese, Korean, and Japanese.",
                            value="Hello, this is demonstration for talking face generation "
                            "with multilingual text-to-speech.",
                            label="Text")
    lang_input = gr.Radio(['Korean', 'English', 'Japanese', 'Chinese'],
                          type='value',
                          value=None,
                          label="Language")
    duration_rate_input = gr.Slider(minimum=0.8,
                                    maximum=1.2,
                                    step=0.01,
                                    value=1.0,
                                    label="Duration (The bigger the value, the slower the speech)")
    action_input = gr.Radio(['Default', 'Hand', 'BothHand', 'HandDown', 'Sorry'],
                            type='value',
                            value='Default',
                            label="Select an action ...")
    background_input = gr.Radio(['None', 'CVPR', 'Black', 'River', 'Sky'],
                                type='index',
                                value='None',
                                label="Select a background image/video ...")

    return [text_input, lang_input, duration_rate_input,
            action_input, background_input]


def prepare_output():
    toxicity_output = gr.Label(num_top_classes=1, label="Toxicity (from Perspective API)")
    translation_result_otuput = gr.Textbox(type="str", label="Translation Result")
    video_output = gr.Video(format='mp4')
    return [toxicity_output, translation_result_otuput, video_output]


def parse_args():
    parser = argparse.ArgumentParser(
        description='GRADIO DEMO for talking face generation submitted to CVPR2022')
    parser.add_argument('-p', '--port', dest='gradio_port', type=int, default=7860, help="Port for gradio")
    parser.add_argument('--rest_ip', type=str, default=REST_IP, help="IP for REST API")
    parser.add_argument('--rest_port', type=int, default=SERVICE_PORT, help="Port for REST API")
    parser.add_argument('--max_seed', type=int, default=20, help="Max seed for saving video")
    parser.add_argument('--share', action='store_true', help='get publicly sharable link')
    args = parser.parse_args()
    return args    


if __name__ == '__main__':
    args = parse_args()
    
    gradio_application = GradioApplication(args.rest_ip, args.rest_port, args.max_seed)
    gradio_application.run(server_port=args.gradio_port, share=args.share)