copied original project

+# Created by https://www.gitignore.io/api/django
+# Edit at https://www.gitignore.io/?templates=django
+
+### Django ###
+*.log
+*.pot
+*.pyc
+__pycache__/
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+media
+static/
+/static
+/backend/env
+env/
+/env
+
+# If your build process includes running collectstatic, then you probably don't need or want to include staticfiles/
+# in your Git repository. Update and uncomment the following line accordingly.
+# <django-project-name>/staticfiles/
+
+### Django.Python Stack ###
+# Byte-compiled / optimized / DLL files
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# Mr Developer
+.mr.developer.cfg
+.project
+.pydevproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# End of https://www.gitignore.io/api/django
+
+.DS_Store
+node_modules
+/dist
+
+# local env files
+.env.local
+.env.*.local
+
+# Log files
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Editor directories and files
+.idea
+.vscode
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?
+

+import os
+from django.conf.global_settings import FILE_UPLOAD_MAX_MEMORY_SIZE
+import datetime
+import uuid
+
+# 각 media 파일에 대한 URL Prefix
+MEDIA_URL = '/media/' # 항상 / 로 끝나도록 설정
+# MEDIA_URL = 'http://static.myservice.com/media/' 다른 서버로 media 파일 복사시
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+MEDIA_ROOT = os.path.join(BASE_DIR, 'media')
+
+# 파일 업로드 사이즈 100M ( 100 * 1024 * 1024 )
+#FILE_UPLOAD_MAX_MEMORY_SIZE = 104857600 
+
+# 실제 파일을 저장할 경로 및 파일 명 생성
+# 폴더는 일별로 생성됨
+def file_upload_path( filename):
+    ext = filename.split('.')[-1]
+    d = datetime.datetime.now()
+    filepath = d.strftime('%Y-%m-%d')
+    suffix = d.strftime("%Y%m%d%H%M%S")
+    filename = "%s.%s"%(suffix, ext)
+    return os.path.join( MEDIA_ROOT , filepath, filename)
+
+# DB 필드에서 호출
+def file_upload_path_for_db( intance, filename):
+    return file_upload_path(filename)
\ No newline at end of file

+from django.contrib import admin
+
+# Register your models here.

+from django.apps import AppConfig
+
+
+class ApiConfig(AppConfig):
+    name = 'api'

+# Generated by Django 3.0.5 on 2020-05-02 12:19
+
+import api
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Video',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('videourl', models.CharField(blank=True, max_length=1000)),
+                ('title', models.CharField(max_length=200)),
+                ('tags', models.CharField(max_length=500)),
+            ],
+        ),
+        migrations.CreateModel(
+            name='VideoFile',
+            fields=[
+                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('file_save_name', models.FileField(upload_to=api.file_upload_path_for_db)),
+                ('file_origin_name', models.CharField(max_length=100)),
+                ('file_path', models.CharField(max_length=100)),
+            ],
+        ),
+    ]

+# Generated by Django 3.0.5 on 2020-05-31 05:39
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('api', '0001_initial'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='video',
+            name='threshold',
+            field=models.CharField(default=20, max_length=20),
+            preserve_default=False,
+        ),
+    ]

+from django.db import models
+from api import file_upload_path_for_db
+# Create your models here.
+
+
+class Video(models.Model):
+    videourl = models.CharField(max_length=1000, blank=True)
+    title = models.CharField(max_length=200)
+    threshold = models.CharField(max_length=20)
+    tags = models.CharField(max_length=500)
+
+
+class VideoFile(models.Model):
+    file_save_name = models.FileField(upload_to=file_upload_path_for_db, blank=False, null=False)
+    # 파일의 원래 이름
+    file_origin_name = models.CharField(max_length=100)    
+    # 파일 저장 경로
+    file_path = models.CharField(max_length=100)
+    
+    def __str__(self):
+        return self.file.name

+from rest_framework import serializers
+from api.models import Video, VideoFile
+
+
+class VideoSerializer(serializers.ModelSerializer):
+
+    class Meta:
+        model = Video
+        fields = '__all__'
+
+
+class VideoFileSerializer(serializers.ModelSerializer):
+
+    class Meta:
+        model = VideoFile
+        fields = '__all__'

+from django.test import TestCase
+
+# Create your tests here.

+from django.urls import path, include
+from django.conf.urls import url
+from api.views import VideoFileUploadView, VideoFileList, FileListView
+from . import views
+from rest_framework.routers import DefaultRouter
+from django.views.generic import TemplateView
+
+router = DefaultRouter()
+router.register('db/videofile', views.VideoFileViewSet)
+router.register('db/video', views.VideoViewSet)
+
+urlpatterns = [
+    # FBV
+    path('api/upload', VideoFileUploadView.as_view(), name="file-upload"),
+    path('api/upload/<int:pk>/', VideoFileList.as_view(), name="file-list"),
+    path('api/file', FileListView.as_view(), name="file"),
+    url(r'^(?P<path>.*)$', TemplateView.as_view(template_name='index.html')),
+    # path('api/upload', views.VideoFile_Upload),
+    path('', include(router.urls)),
+]

+from rest_framework import status
+from rest_framework.views import APIView
+from rest_framework.response import Response
+from rest_framework.parsers import MultiPartParser, FormParser
+from rest_framework.response import Response
+from rest_framework import viewsets
+import os
+from django.http.request import QueryDict
+from django.http import Http404
+from django.http import JsonResponse
+from django.shortcuts import get_object_or_404, render
+from api.models import Video, VideoFile
+from api.serializers import VideoSerializer, VideoFileSerializer
+from api import file_upload_path 
+
+import subprocess
+import shlex
+import json
+# Create your views here.
+import sys
+sys.path.insert(0, "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria")
+import inference_pb
+
+def with_ffprobe(filename):
+
+    result = subprocess.check_output(
+            f'ffprobe -v quiet -show_streams -select_streams v:0 -of json "{filename}"',
+            shell=True).decode()
+    fields = json.loads(result)['streams'][0]
+    duration = int(float(fields['duration']))
+    return duration
+
+def index(request):
+    return render(request, template_name='index.html')
+
+class VideoViewSet(viewsets.ModelViewSet):
+    queryset = Video.objects.all()
+    serializer_class = VideoSerializer
+
+class VideoFileViewSet(viewsets.ModelViewSet):
+    queryset = VideoFile.objects.all()
+    serializer_class = VideoFileSerializer
+
+
+class VideoFileUploadView(APIView):
+    parser_classes = (MultiPartParser, FormParser)
+
+    def get(self, request, format=None):
+        videoFiles = VideoFile.objects.all()
+        serializer = VideoFileSerializer(videoFiles, many=True)
+        return Response(serializer.data)
+
+    def post(self, req, *args, **kwargs):
+        # 동영상 길이
+        runTime = 0
+       # 요청된 데이터를 꺼냄( QueryDict)         
+        new_data = req.data.dict()
+        
+        # 요청된 파일 객체
+        file_name = req.data['file']
+        threshold = req.data['threshold']
+        
+        # 저장될 파일의 풀path를 생성        
+        new_file_full_name = file_upload_path(file_name.name)
+        # 새롭게 생성된 파일의 경로
+        file_path = '-'.join(new_file_full_name.split('-')[0:-1])        
+
+        new_data['file_path'] = file_path
+        new_data['file_origin_name'] = req.data['file'].name
+        new_data['file_save_name'] = req.data['file']
+
+        new_query_dict = QueryDict('', mutable=True)
+        new_query_dict.update(new_data)
+        file_serializer = VideoFileSerializer(data = new_query_dict)
+        
+        if file_serializer.is_valid():
+            file_serializer.save()     
+            # 동영상 길이 출력  
+            runTime = with_ffprobe('/'+file_serializer.data['file_save_name'])
+            print(runTime)
+            print(threshold)
+            process = subprocess.Popen(['./runMediaPipe.sh %s %s' %(file_serializer.data['file_save_name'],runTime,)], shell = True)
+            process.wait()
+
+
+            result =  inference_pb.inference_pb('/tmp/mediapipe/features.pb', threshold)
+
+            return Response(result, status=status.HTTP_201_CREATED)
+        else:
+            return Response(file_serializer.errors, status=status.HTTP_400_BAD_REQUEST)
+
+    
+class VideoFileList(APIView):
+
+    def get_object(self, pk):
+        try:
+            return VideoFile.objects.get(pk=pk)
+        except VideoFile.DoesNotExist:
+            raise Http404
+
+    def get(self, request, pk, format=None):
+        video = self.get_object(pk)
+        serializer = VideoFileSerializer(video)
+        return Response(serializer.data)
+
+    def put(self, request, pk, format=None):
+        video = self.get_object(pk)
+        serializer = VideoFileSerializer(video, data=request.data)
+        if serializer.is_valid():
+            serializer.save()
+            return Response(serializer.data)
+        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)
+
+    def delete(self, request, pk, format=None):
+        video = self.get_object(pk)
+        video.delete()
+        return Response(status=status.HTTP_204_NO_CONTENT)
+
+
+class FileListView(APIView):
+    def get(self, request):
+        data = {
+            "search": '',
+            "limit": 10,
+            "skip": 0,
+            "order": "time",
+            "fileList": [
+                {
+                    "name": "1.png",
+                    "created": "2020-04-30",
+                    "size": 10234,
+                    "isFolder": False,
+                    "deletedDate": "",
+                },
+                {
+                    "name": "2.png",
+                    "created": "2020-04-30",
+                    "size": 3145,
+                    "isFolder": False,
+                    "deletedDate": "",
+                },
+                {
+                    "name": "3.png",
+                    "created": "2020-05-01",
+                    "size": 5653,
+                    "isFolder": False,
+                    "deletedDate": "",
+                },
+            ]
+        }
+        return Response(data)
+    def post(self, request, format=None):
+        data = {
+            "isSuccess": True,
+            "File": {
+                "name": "test.jpg",
+                "created": "2020-05-02",
+                "deletedDate": "",
+                "size": 2312,
+                "isFolder": False
+            }
+        }
+        return Response(data)
\ No newline at end of file

+"""
+ASGI config for backend project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
+
+application = get_asgi_application()

+"""
+Django settings for backend project.
+
+Generated by 'django-admin startproject' using Django 3.0.5.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.0/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/3.0/ref/settings/
+"""
+
+import os
+
+# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = '7e^4!u6019jww&=-!mu%r$hz6jy#=i+i9@9m_44+ga^#%7#e0l'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = ['*']
+
+
+# Static File DIR Settings
+STATIC_URL = '/static/'
+STATIC_ROOT = os.path.join(BASE_DIR, 'static')
+
+
+FRONTEND_DIR = os.path.join(os.path.abspath('../'), 'frontend')
+STATICFILES_DIRS = [
+    os.path.join(FRONTEND_DIR, 'dist/'),
+]
+# Application definition
+
+REST_FRAMEWORK = {
+    'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',
+    'PAGE_SIZE': 10
+}
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'rest_framework',
+    'corsheaders',
+    'api'
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    # 'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+    'corsheaders.middleware.CorsMiddleware',
+]
+
+ROOT_URLCONF = 'backend.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [
+            os.path.join(BASE_DIR, 'templates'),
+        ],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'backend.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': os.path.join(BASE_DIR, 'db.sqlite3'),
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/3.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_L10N = True
+
+USE_TZ = True
+
+
+# CORS settings
+
+CORS_ORIGIN_ALLOW_ALL = True
+CORS_ALLOW_CREDENTIALS = True
+CORS_ORIGIN_WHITELIST = [
+    "http://127.0.0.1:12233",
+    "http://localhost:8080",
+    "http://127.0.0.1:9000"
+]
+CORS_URLS_REGEX = r'^/api/.*$'
+CORS_ALLOW_METHODS = (
+    'DELETE',
+    'GET',
+    'OPTIONS',
+    'PATCH',
+    'POST',
+    'PUT',
+)
+
+CORS_ALLOW_HEADERS = (
+    'accept',
+    'accept-encoding',
+    'authorization',
+    'content-type',
+    'dnt',
+    'origin',
+    'user-agent',
+    'x-csrftoken',
+    'x-requested-with',
+)

+"""backend URL Configuration
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/3.0/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.urls import path
+from django.conf.urls import url, include
+from django.conf import settings
+from django.conf.urls.static import static
+
+urlpatterns = [
+    path('', include('api.urls')),
+    path('admin/', admin.site.urls),
+]
\ No newline at end of file

+"""
+WSGI config for backend project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.0/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
+
+application = get_wsgi_application()

+import tensorflow as tf
+import glob, os
+import numpy
+
+def _make_bytes(int_array):
+    if bytes == str:  # Python2
+        return ''.join(map(chr, int_array))
+    else:
+        return bytes(int_array)
+
+
+def quantize(features, min_quantized_value=-2.0, max_quantized_value=2.0):
+    """Quantizes float32 `features` into string."""
+    assert features.dtype == 'float32'
+    assert len(features.shape) == 1  # 1-D array
+    features = numpy.clip(features, min_quantized_value, max_quantized_value)
+    quantize_range = max_quantized_value - min_quantized_value
+    features = (features - min_quantized_value) * (255.0 / quantize_range)
+    features = [int(round(f)) for f in features]
+
+    return _make_bytes(features)
+
+
+# for parse feature.pb
+
+contexts = {
+    'AUDIO/feature/dimensions': tf.io.FixedLenFeature([], tf.int64),
+    'AUDIO/feature/rate': tf.io.FixedLenFeature([], tf.float32),
+    'RGB/feature/dimensions': tf.io.FixedLenFeature([], tf.int64),
+    'RGB/feature/rate': tf.io.FixedLenFeature([], tf.float32),
+    'clip/data_path': tf.io.FixedLenFeature([], tf.string),
+    'clip/end/timestamp': tf.io.FixedLenFeature([], tf.int64),
+    'clip/start/timestamp': tf.io.FixedLenFeature([], tf.int64)
+}
+
+features = {
+    'AUDIO/feature/floats': tf.io.VarLenFeature(dtype=tf.float32),
+    'AUDIO/feature/timestamp': tf.io.VarLenFeature(tf.int64),
+    'RGB/feature/floats': tf.io.VarLenFeature(dtype=tf.float32),
+    'RGB/feature/timestamp': tf.io.VarLenFeature(tf.int64)
+
+}
+
+
+def parse_exmp(serial_exmp):
+    _, sequence_parsed = tf.io.parse_single_sequence_example(
+        serialized=serial_exmp,
+        context_features=contexts,
+        sequence_features=features)
+
+    sequence_parsed = tf.contrib.learn.run_n(sequence_parsed)[0]
+
+    audio = sequence_parsed['AUDIO/feature/floats'].values
+    rgb = sequence_parsed['RGB/feature/floats'].values
+
+    # print(audio.values)
+    # print(type(audio.values))
+
+    # audio is 128 8bit, rgb is 1024 8bit for every second
+    audio_slices = [audio[128 * i: 128 * (i + 1)] for i in range(len(audio) // 128)]
+    rgb_slices = [rgb[1024 * i: 1024 * (i + 1)] for i in range(len(rgb) // 1024)]
+
+    byte_audio = []
+    byte_rgb = []
+
+    for seg in audio_slices:
+        audio_seg = quantize(seg)
+        byte_audio.append(audio_seg)
+
+    for seg in rgb_slices:
+        rgb_seg = quantize(seg)
+        byte_rgb.append(rgb_seg)
+
+    return byte_audio, byte_rgb
+
+
+def make_exmp(id, labels, audio, rgb):
+    audio_features = []
+    rgb_features = []
+
+    for embedding in audio:
+        embedding_feature = tf.train.Feature(
+            bytes_list=tf.train.BytesList(value=[embedding]))
+        audio_features.append(embedding_feature)
+
+    for embedding in rgb:
+        embedding_feature = tf.train.Feature(
+            bytes_list=tf.train.BytesList(value=[embedding]))
+        rgb_features.append(embedding_feature)
+
+    # for construct yt8m data
+    seq_exmp = tf.train.SequenceExample(
+        context=tf.train.Features(
+            feature={
+                'id': tf.train.Feature(bytes_list=tf.train.BytesList(
+                    value=[id.encode('utf-8')])),
+                'labels': tf.train.Feature(int64_list=tf.train.Int64List(
+                    value=[labels]))
+            }),
+        feature_lists=tf.train.FeatureLists(
+            feature_list={
+                'audio': tf.train.FeatureList(
+                    feature=audio_features
+                ),
+                'rgb': tf.train.FeatureList(
+                    feature=rgb_features
+                )
+            })
+    )
+    serialized = seq_exmp.SerializeToString()
+    return serialized
+
+
+if __name__ == '__main__':
+    filename = '/tmp/mediapipe/features.pb'
+
+    sequence_example = open(filename, 'rb').read()
+
+    audio, rgb = parse_exmp(sequence_example)
+
+    id = 'test_001'
+
+    labels = 1
+
+    tmp_example = make_exmp(id, labels, audio, rgb)
+
+    decoded = tf.train.SequenceExample.FromString(tmp_example)
+    print(decoded)
+
+    # then you can write tmp_example to tfrecord files
\ No newline at end of file

+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == '__main__':
+    main()

+Django==3.0.5
+django-cors-headers==3.2.1
+djangorestframework==3.11.0
+tensorflow==1.15
+pandas==1.0.4
+gensim==3.8.3
\ No newline at end of file

+#!/bin/bash
+. env/bin/activate
+python manage.py migrate
+python manage.py runserver 0.0.0.0:8000
\ No newline at end of file

+#!/bin/bash
+cd ../../../mediapipe
+. venv/bin/activate
+
+/usr/local/bazel/2.0.0/lib/bazel/bin/bazel version && \
+alias bazel='/usr/local/bazel/2.0.0/lib/bazel/bin/bazel'
+
+python -m mediapipe.examples.desktop.youtube8m.generate_input_sequence_example \
+  --path_to_input_video=/$1 \
+  --clip_end_time_sec=$2
+
+GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features \
+  --calculator_graph_config_file=mediapipe/graphs/youtube8m/feature_extraction.pbtxt \
+  --input_side_packets=input_sequence_example=/tmp/mediapipe/metadata.pb  \
+  --output_side_packets=output_sequence_example=/tmp/mediapipe/features.pb
\ No newline at end of file

+{% load static %}
+<!doctype html>
+<html lang="en">
+
+<head>
+  <meta charset=utf-8>
+  <meta http-equiv=X-UA-Compatible content="IE=edge">
+  <meta name=viewport content="width=device-width,initial-scale=1">
+  <link rel=icon href="{% static 'favicon.ico' %}">
+  <title>ThrowBox</title>
+  <link href="{% static '/css/app.1dc1d4aa.css' %}" rel=preload as=style>
+  <link href="{% static '/css/chunk-vendors.e4bdc0d1.css' %}" rel=preload as=style>
+  <link href="{% static '/js/app.1e0612ff.js' %}" rel=preload as=script>
+  <link href="{% static '/js/chunk-vendors.951af5fd.js' %}" rel=preload as=script>
+  <link href="{% static '/css/chunk-vendors.e4bdc0d1.css' %}" rel=stylesheet>
+  <link href="{% static '/css/app.1dc1d4aa.css' %}" rel=stylesheet>
+</head>
+
+<body>
+  <noscript><strong>We're sorry but ThrowBox doesn't work properly without JavaScript enabled. Please enable it to
+      continue.</strong></noscript>
+  <div id=app>
+  </div>
+  <script src="{% static '/js/chunk-vendors.951af5fd.js' %}"></script>
+  <script src="{% static '/js/app.1e0612ff.js' %}"></script>
+</body>
+
+</html>
\ No newline at end of file

+import os
+read = open("static/index.html", 'r')
+write = open("templates/index.html", 'w')
+

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Calculate or keep track of the interpolated average precision.
+
+It provides an interface for calculating interpolated average precision for an
+entire list or the top-n ranked items. For the definition of the
+(non-)interpolated average precision:
+http://trec.nist.gov/pubs/trec15/appendices/CE.MEASURES06.pdf
+
+Example usages:
+1) Use it as a static function call to directly calculate average precision for
+a short ranked list in the memory.
+
+```
+import random
+
+p = np.array([random.random() for _ in xrange(10)])
+a = np.array([random.choice([0, 1]) for _ in xrange(10)])
+
+ap = average_precision_calculator.AveragePrecisionCalculator.ap(p, a)
+```
+
+2) Use it as an object for long ranked list that cannot be stored in memory or
+the case where partial predictions can be observed at a time (Tensorflow
+predictions). In this case, we first call the function accumulate many times
+to process parts of the ranked list. After processing all the parts, we call
+peek_interpolated_ap_at_n.
+```
+p1 = np.array([random.random() for _ in xrange(5)])
+a1 = np.array([random.choice([0, 1]) for _ in xrange(5)])
+p2 = np.array([random.random() for _ in xrange(5)])
+a2 = np.array([random.choice([0, 1]) for _ in xrange(5)])
+
+# interpolated average precision at 10 using 1000 break points
+calculator = average_precision_calculator.AveragePrecisionCalculator(10)
+calculator.accumulate(p1, a1)
+calculator.accumulate(p2, a2)
+ap3 = calculator.peek_ap_at_n()
+```
+"""
+
+import heapq
+import random
+import numbers
+
+import numpy
+
+
+class AveragePrecisionCalculator(object):
+  """Calculate the average precision and average precision at n."""
+
+  def __init__(self, top_n=None):
+    """Construct an AveragePrecisionCalculator to calculate average precision.
+
+    This class is used to calculate the average precision for a single label.
+
+    Args:
+      top_n: A positive Integer specifying the average precision at n, or None
+        to use all provided data points.
+
+    Raises:
+      ValueError: An error occurred when the top_n is not a positive integer.
+    """
+    if not ((isinstance(top_n, int) and top_n >= 0) or top_n is None):
+      raise ValueError("top_n must be a positive integer or None.")
+
+    self._top_n = top_n  # average precision at n
+    self._total_positives = 0  # total number of positives have seen
+    self._heap = []  # max heap of (prediction, actual)
+
+  @property
+  def heap_size(self):
+    """Gets the heap size maintained in the class."""
+    return len(self._heap)
+
+  @property
+  def num_accumulated_positives(self):
+    """Gets the number of positive samples that have been accumulated."""
+    return self._total_positives
+
+  def accumulate(self, predictions, actuals, num_positives=None):
+    """Accumulate the predictions and their ground truth labels.
+
+    After the function call, we may call peek_ap_at_n to actually calculate
+    the average precision.
+    Note predictions and actuals must have the same shape.
+
+    Args:
+      predictions: a list storing the prediction scores.
+      actuals: a list storing the ground truth labels. Any value larger than 0
+        will be treated as positives, otherwise as negatives. num_positives = If
+        the 'predictions' and 'actuals' inputs aren't complete, then it's
+        possible some true positives were missed in them. In that case, you can
+        provide 'num_positives' in order to accurately track recall.
+
+    Raises:
+      ValueError: An error occurred when the format of the input is not the
+      numpy 1-D array or the shape of predictions and actuals does not match.
+    """
+    if len(predictions) != len(actuals):
+      raise ValueError("the shape of predictions and actuals does not match.")
+
+    if num_positives is not None:
+      if not isinstance(num_positives, numbers.Number) or num_positives < 0:
+        raise ValueError(
+            "'num_positives' was provided but it was a negative number.")
+
+    if num_positives is not None:
+      self._total_positives += num_positives
+    else:
+      self._total_positives += numpy.size(
+          numpy.where(numpy.array(actuals) > 1e-5))
+    topk = self._top_n
+    heap = self._heap
+
+    for i in range(numpy.size(predictions)):
+      if topk is None or len(heap) < topk:
+        heapq.heappush(heap, (predictions[i], actuals[i]))
+      else:
+        if predictions[i] > heap[0][0]:  # heap[0] is the smallest
+          heapq.heappop(heap)
+          heapq.heappush(heap, (predictions[i], actuals[i]))
+
+  def clear(self):
+    """Clear the accumulated predictions."""
+    self._heap = []
+    self._total_positives = 0
+
+  def peek_ap_at_n(self):
+    """Peek the non-interpolated average precision at n.
+
+    Returns:
+      The non-interpolated average precision at n (default 0).
+      If n is larger than the length of the ranked list,
+      the average precision will be returned.
+    """
+    if self.heap_size <= 0:
+      return 0
+    predlists = numpy.array(list(zip(*self._heap)))
+
+    ap = self.ap_at_n(predlists[0],
+                      predlists[1],
+                      n=self._top_n,
+                      total_num_positives=self._total_positives)
+    return ap
+
+  @staticmethod
+  def ap(predictions, actuals):
+    """Calculate the non-interpolated average precision.
+
+    Args:
+      predictions: a numpy 1-D array storing the sparse prediction scores.
+      actuals: a numpy 1-D array storing the ground truth labels. Any value
+        larger than 0 will be treated as positives, otherwise as negatives.
+
+    Returns:
+      The non-interpolated average precision at n.
+      If n is larger than the length of the ranked list,
+      the average precision will be returned.
+
+    Raises:
+      ValueError: An error occurred when the format of the input is not the
+      numpy 1-D array or the shape of predictions and actuals does not match.
+    """
+    return AveragePrecisionCalculator.ap_at_n(predictions, actuals, n=None)
+
+  @staticmethod
+  def ap_at_n(predictions, actuals, n=20, total_num_positives=None):
+    """Calculate the non-interpolated average precision.
+
+    Args:
+      predictions: a numpy 1-D array storing the sparse prediction scores.
+      actuals: a numpy 1-D array storing the ground truth labels. Any value
+        larger than 0 will be treated as positives, otherwise as negatives.
+      n: the top n items to be considered in ap@n.
+      total_num_positives : (optionally) you can specify the number of total
+        positive in the list. If specified, it will be used in calculation.
+
+    Returns:
+      The non-interpolated average precision at n.
+      If n is larger than the length of the ranked list,
+      the average precision will be returned.
+
+    Raises:
+      ValueError: An error occurred when
+      1) the format of the input is not the numpy 1-D array;
+      2) the shape of predictions and actuals does not match;
+      3) the input n is not a positive integer.
+    """
+    if len(predictions) != len(actuals):
+      raise ValueError("the shape of predictions and actuals does not match.")
+
+    if n is not None:
+      if not isinstance(n, int) or n <= 0:
+        raise ValueError("n must be 'None' or a positive integer."
+                         " It was '%s'." % n)
+
+    ap = 0.0
+
+    predictions = numpy.array(predictions)
+    actuals = numpy.array(actuals)
+
+    # add a shuffler to avoid overestimating the ap
+    predictions, actuals = AveragePrecisionCalculator._shuffle(
+        predictions, actuals)
+    sortidx = sorted(range(len(predictions)),
+                     key=lambda k: predictions[k],
+                     reverse=True)
+
+    if total_num_positives is None:
+      numpos = numpy.size(numpy.where(actuals > 0))
+    else:
+      numpos = total_num_positives
+
+    if numpos == 0:
+      return 0
+
+    if n is not None:
+      numpos = min(numpos, n)
+    delta_recall = 1.0 / numpos
+    poscount = 0.0
+
+    # calculate the ap
+    r = len(sortidx)
+    if n is not None:
+      r = min(r, n)
+    for i in range(r):
+      if actuals[sortidx[i]] > 0:
+        poscount += 1
+        ap += poscount / (i + 1) * delta_recall
+    return ap
+
+  @staticmethod
+  def _shuffle(predictions, actuals):
+    random.seed(0)
+    suffidx = random.sample(range(len(predictions)), len(predictions))
+    predictions = predictions[suffidx]
+    actuals = actuals[suffidx]
+    return predictions, actuals
+
+  @staticmethod
+  def _zero_one_normalize(predictions, epsilon=1e-7):
+    """Normalize the predictions to the range between 0.0 and 1.0.
+
+    For some predictions like SVM predictions, we need to normalize them before
+    calculate the interpolated average precision. The normalization will not
+    change the rank in the original list and thus won't change the average
+    precision.
+
+    Args:
+      predictions: a numpy 1-D array storing the sparse prediction scores.
+      epsilon: a small constant to avoid denominator being zero.
+
+    Returns:
+      The normalized prediction.
+    """
+    denominator = numpy.max(predictions) - numpy.min(predictions)
+    ret = (predictions - numpy.min(predictions)) / numpy.max(
+        denominator, epsilon)
+    return ret

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utility to convert the output of batch prediction into a CSV submission.
+
+It converts the JSON files created by the command
+'gcloud beta ml jobs submit prediction' into a CSV file ready for submission.
+"""
+
+import json
+import tensorflow as tf
+
+from builtins import range
+from tensorflow import app
+from tensorflow import flags
+from tensorflow import gfile
+from tensorflow import logging
+
+FLAGS = flags.FLAGS
+
+if __name__ == "__main__":
+
+  flags.DEFINE_string(
+      "json_prediction_files_pattern", None,
+      "Pattern specifying the list of JSON files that the command "
+      "'gcloud beta ml jobs submit prediction' outputs. These files are "
+      "located in the output path of the prediction command and are prefixed "
+      "with 'prediction.results'.")
+  flags.DEFINE_string(
+      "csv_output_file", None,
+      "The file to save the predictions converted to the CSV format.")
+
+
+def get_csv_header():
+  return "VideoId,LabelConfidencePairs\n"
+
+
+def to_csv_row(json_data):
+
+  video_id = json_data["video_id"]
+
+  class_indexes = json_data["class_indexes"]
+  predictions = json_data["predictions"]
+
+  if isinstance(video_id, list):
+    video_id = video_id[0]
+    class_indexes = class_indexes[0]
+    predictions = predictions[0]
+
+  if len(class_indexes) != len(predictions):
+    raise ValueError(
+        "The number of indexes (%s) and predictions (%s) must be equal." %
+        (len(class_indexes), len(predictions)))
+
+  return (video_id.decode("utf-8") + "," +
+          " ".join("%i %f" % (class_indexes[i], predictions[i])
+                   for i in range(len(class_indexes))) + "\n")
+
+
+def main(unused_argv):
+  logging.set_verbosity(tf.logging.INFO)
+
+  if not FLAGS.json_prediction_files_pattern:
+    raise ValueError(
+        "The flag --json_prediction_files_pattern must be specified.")
+
+  if not FLAGS.csv_output_file:
+    raise ValueError("The flag --csv_output_file must be specified.")
+
+  logging.info("Looking for prediction files with pattern: %s",
+               FLAGS.json_prediction_files_pattern)
+
+  file_paths = gfile.Glob(FLAGS.json_prediction_files_pattern)
+  logging.info("Found files: %s", file_paths)
+
+  logging.info("Writing submission file to: %s", FLAGS.csv_output_file)
+  with gfile.Open(FLAGS.csv_output_file, "w+") as output_file:
+    output_file.write(get_csv_header())
+
+    for file_path in file_paths:
+      logging.info("processing file: %s", file_path)
+
+      with gfile.Open(file_path) as input_file:
+
+        for line in input_file:
+          json_data = json.loads(line)
+          output_file.write(to_csv_row(json_data))
+
+    output_file.flush()
+  logging.info("done")
+
+
+if __name__ == "__main__":
+  app.run()

+import numpy as np
+import tensorflow as tf
+from tensorflow import logging
+from tensorflow import gfile
+import operator
+import pb_util as pbutil
+import video_recommender as recommender
+import video_util as videoutil
+
+# Define file paths.
+MODEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/model/inference_model/segment_inference_model"
+VOCAB_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/vocabulary.csv"
+VIDEO_TAGS_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/kaggle_solution_40k.csv"
+TAG_VECTOR_MODEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/tag_vectors.model"
+VIDEO_VECTOR_MODEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/esot3ria/video_vectors.model"
+SEGMENT_LABEL_PATH = "/home/jun/documents/univ/PKH_Project1/web/backend/yt8m/segment_label_ids.csv"
+
+# Define parameters.
+TAG_TOP_K = 5
+VIDEO_TOP_K = 10
+
+
+def get_segments(batch_video_mtx, batch_num_frames, segment_size):
+    """Get segment-level inputs from frame-level features."""
+    video_batch_size = batch_video_mtx.shape[0]
+    max_frame = batch_video_mtx.shape[1]
+    feature_dim = batch_video_mtx.shape[-1]
+    padded_segment_sizes = (batch_num_frames + segment_size - 1) // segment_size
+    padded_segment_sizes *= segment_size
+    segment_mask = (
+            0 < (padded_segment_sizes[:, np.newaxis] - np.arange(0, max_frame)))
+
+    # Segment bags.
+    frame_bags = batch_video_mtx.reshape((-1, feature_dim))
+    segment_frames = frame_bags[segment_mask.reshape(-1)].reshape(
+        (-1, segment_size, feature_dim))
+
+    # Segment num frames.
+    segment_start_times = np.arange(0, max_frame, segment_size)
+    num_segments = batch_num_frames[:, np.newaxis] - segment_start_times
+    num_segment_bags = num_segments.reshape((-1))
+    valid_segment_mask = num_segment_bags > 0
+    segment_num_frames = num_segment_bags[valid_segment_mask]
+    segment_num_frames[segment_num_frames > segment_size] = segment_size
+
+    max_segment_num = (max_frame + segment_size - 1) // segment_size
+    video_idxs = np.tile(
+        np.arange(0, video_batch_size)[:, np.newaxis], [1, max_segment_num])
+    segment_idxs = np.tile(segment_start_times, [video_batch_size, 1])
+    idx_bags = np.stack([video_idxs, segment_idxs], axis=-1).reshape((-1, 2))
+    video_segment_ids = idx_bags[valid_segment_mask]
+
+    return {
+        "video_batch": segment_frames,
+        "num_frames_batch": segment_num_frames,
+        "video_segment_ids": video_segment_ids
+    }
+
+
+def format_predictions(video_ids, predictions, top_k, whitelisted_cls_mask=None):
+    batch_size = len(video_ids)
+    for video_index in range(batch_size):
+        video_prediction = predictions[video_index]
+        if whitelisted_cls_mask is not None:
+            # Whitelist classes.
+            video_prediction *= whitelisted_cls_mask
+        top_indices = np.argpartition(video_prediction, -top_k)[-top_k:]
+        line = [(class_index, predictions[video_index][class_index])
+                for class_index in top_indices]
+        line = sorted(line, key=lambda p: -p[1])
+        yield (video_ids[video_index] + "," +
+               " ".join("%i %g" % (label, score) for (label, score) in line) +
+               "\n").encode("utf8")
+
+
+def normalize_tag(tag):
+    if isinstance(tag, str):
+        new_tag = tag.lower().replace('[^a-zA-Z]', ' ')
+        if new_tag.find(" (") != -1:
+            new_tag = new_tag[:new_tag.find(" (")]
+        new_tag = new_tag.replace(" ", "-")
+        return new_tag
+    else:
+        return tag
+
+
+def inference_pb(file_path, threshold):
+    VIDEO_TOP_K = int(threshold)
+    inference_result = {}
+    with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
+
+        # 0. Import SequenceExample type target from pb.
+        target_video = pbutil.convert_pb(file_path)
+
+        # 1. Load video features from pb.
+        video_id_batch_val = np.array([b'video'])
+        n_frames = len(target_video.feature_lists.feature_list['rgb'].feature)
+        # Restrict frame size to 300
+        if n_frames > 300:
+            n_frames = 300
+        video_batch_val = np.zeros((300, 1152))
+        for i in range(n_frames):
+            video_batch_rgb_raw = target_video.feature_lists.feature_list['rgb'].feature[i].bytes_list.value[0]
+            video_batch_rgb = np.array(tf.cast(tf.decode_raw(video_batch_rgb_raw, tf.float32), tf.float32).eval())
+            video_batch_audio_raw = target_video.feature_lists.feature_list['audio'].feature[i].bytes_list.value[0]
+            video_batch_audio = np.array(tf.cast(tf.decode_raw(video_batch_audio_raw, tf.float32), tf.float32).eval())
+            video_batch_val[i] = np.concatenate([video_batch_rgb, video_batch_audio], axis=0)
+        video_batch_val = np.array([video_batch_val])
+        num_frames_batch_val = np.array([n_frames])
+
+        # Restore checkpoint and meta-graph file.
+        if not gfile.Exists(MODEL_PATH + ".meta"):
+            raise IOError("Cannot find %s. Did you run eval.py?" % MODEL_PATH)
+        meta_graph_location = MODEL_PATH + ".meta"
+        logging.info("loading meta-graph: " + meta_graph_location)
+
+        with tf.device("/cpu:0"):
+            saver = tf.train.import_meta_graph(meta_graph_location, clear_devices=True)
+        logging.info("restoring variables from " + MODEL_PATH)
+        saver.restore(sess, MODEL_PATH)
+        input_tensor = tf.get_collection("input_batch_raw")[0]
+        num_frames_tensor = tf.get_collection("num_frames")[0]
+        predictions_tensor = tf.get_collection("predictions")[0]
+
+        # Workaround for num_epochs issue.
+        def set_up_init_ops(variables):
+            init_op_list = []
+            for variable in list(variables):
+                if "train_input" in variable.name:
+                    init_op_list.append(tf.assign(variable, 1))
+                    variables.remove(variable)
+            init_op_list.append(tf.variables_initializer(variables))
+            return init_op_list
+
+        sess.run(
+            set_up_init_ops(tf.get_collection_ref(tf.GraphKeys.LOCAL_VARIABLES)))
+
+        whitelisted_cls_mask = np.zeros((predictions_tensor.get_shape()[-1],),
+                                        dtype=np.float32)
+        with tf.io.gfile.GFile(SEGMENT_LABEL_PATH) as fobj:
+            for line in fobj:
+                try:
+                    cls_id = int(line)
+                    whitelisted_cls_mask[cls_id] = 1.
+                except ValueError:
+                    # Simply skip the non-integer line.
+                    continue
+
+        # 2. Make segment features.
+        results = get_segments(video_batch_val, num_frames_batch_val, 5)
+        video_segment_ids = results["video_segment_ids"]
+        video_id_batch_val = video_id_batch_val[video_segment_ids[:, 0]]
+        video_id_batch_val = np.array([
+            "%s:%d" % (x.decode("utf8"), y)
+            for x, y in zip(video_id_batch_val, video_segment_ids[:, 1])
+        ])
+        video_batch_val = results["video_batch"]
+        num_frames_batch_val = results["num_frames_batch"]
+        if input_tensor.get_shape()[1] != video_batch_val.shape[1]:
+            raise ValueError("max_frames mismatch. Please re-run the eval.py "
+                             "with correct segment_labels settings.")
+
+        predictions_val, = sess.run([predictions_tensor],
+                                    feed_dict={
+                                        input_tensor: video_batch_val,
+                                        num_frames_tensor: num_frames_batch_val
+                                    })
+
+        # 3. Make vocabularies.
+        voca_dict = {}
+        vocabs = open(VOCAB_PATH, 'r')
+        while True:
+            line = vocabs.readline()
+            if not line: break
+            vocab_dict_item = line.split(",")
+            if vocab_dict_item[0] != "Index":
+                voca_dict[vocab_dict_item[0]] = vocab_dict_item[3]
+        vocabs.close()
+
+        # 4. Make combined scores.
+        combined_scores = {}
+        for line in format_predictions(video_id_batch_val, predictions_val, TAG_TOP_K, whitelisted_cls_mask):
+            segment_id, preds = line.decode("utf8").split(",")
+            preds = preds.split(" ")
+            pred_cls_ids = [int(preds[idx]) for idx in range(0, len(preds), 2)]
+            pred_cls_scores = [float(preds[idx]) for idx in range(1, len(preds), 2)]
+            for i in range(len(pred_cls_ids)):
+                if pred_cls_ids[i] in combined_scores:
+                    combined_scores[pred_cls_ids[i]] += pred_cls_scores[i]
+                else:
+                    combined_scores[pred_cls_ids[i]] = pred_cls_scores[i]
+
+        combined_scores = sorted(combined_scores.items(), key=operator.itemgetter(1), reverse=True)
+        demoninator = float(combined_scores[0][1] + combined_scores[1][1]
+                            + combined_scores[2][1] + combined_scores[3][1] + combined_scores[4][1])
+
+        tag_result = []
+        for itemIndex in range(TAG_TOP_K):
+            segment_tag = str(voca_dict[str(combined_scores[itemIndex][0])])
+            normalized_tag = normalize_tag(segment_tag)
+            tag_percentage = format(combined_scores[itemIndex][1] / demoninator, ".3f")
+            tag_result.append((normalized_tag, tag_percentage))
+
+        # 5. Create recommend videos info, Combine results.
+        recommend_video_ids = recommender.recommend_videos(tag_result, TAG_VECTOR_MODEL_PATH,
+                                                           VIDEO_VECTOR_MODEL_PATH, VIDEO_TOP_K)
+        video_result = [videoutil.getVideoInfo(ids, VIDEO_TAGS_PATH, TAG_TOP_K) for ids in recommend_video_ids]
+
+        inference_result = {
+            "tag_result": tag_result,
+            "video_result": video_result
+        }
+
+        # 6. Dispose instances.
+        sess.close()
+
+    return inference_result
+
+
+if __name__ == '__main__':
+    filepath = "/tmp/mediapipe/features.pb"
+    result = inference_pb(filepath)
+    print(result)

+model_checkpoint_path: "/root/volume/youtube-8m/saved_model/inference_model/segment_inference_model"
+all_model_checkpoint_paths: "/root/volume/youtube-8m/saved_model/inference_model/segment_inference_model"

+{"model": "FrameLevelLogisticModel", "feature_sizes": "1024,128", "feature_names": "rgb,audio", "frame_features": true, "label_loss": "CrossEntropyLoss"}
\ No newline at end of file

+import tensorflow as tf
+import numpy
+
+
+def _make_bytes(int_array):
+    if bytes == str:  # Python2
+        return ''.join(map(chr, int_array))
+    else:
+        return bytes(int_array)
+
+
+def quantize(features, min_quantized_value=-2.0, max_quantized_value=2.0):
+    """Quantizes float32 `features` into string."""
+    assert features.dtype == 'float32'
+    assert len(features.shape) == 1  # 1-D array
+    features = numpy.clip(features, min_quantized_value, max_quantized_value)
+    quantize_range = max_quantized_value - min_quantized_value
+    features = (features - min_quantized_value) * (255.0 / quantize_range)
+    features = [int(round(f)) for f in features]
+
+    return _make_bytes(features)
+
+
+# for parse feature.pb
+
+contexts = {
+    'AUDIO/feature/dimensions': tf.io.FixedLenFeature([], tf.int64),
+    'AUDIO/feature/rate': tf.io.FixedLenFeature([], tf.float32),
+    'RGB/feature/dimensions': tf.io.FixedLenFeature([], tf.int64),
+    'RGB/feature/rate': tf.io.FixedLenFeature([], tf.float32),
+    'clip/data_path': tf.io.FixedLenFeature([], tf.string),
+    'clip/end/timestamp': tf.io.FixedLenFeature([], tf.int64),
+    'clip/start/timestamp': tf.io.FixedLenFeature([], tf.int64)
+}
+
+features = {
+    'AUDIO/feature/floats': tf.io.VarLenFeature(dtype=tf.float32),
+    'AUDIO/feature/timestamp': tf.io.VarLenFeature(tf.int64),
+    'RGB/feature/floats': tf.io.VarLenFeature(dtype=tf.float32),
+    'RGB/feature/timestamp': tf.io.VarLenFeature(tf.int64)
+
+}
+
+
+def parse_exmp(serial_exmp):
+    _, sequence_parsed = tf.io.parse_single_sequence_example(
+        serialized=serial_exmp,
+        context_features=contexts,
+        sequence_features=features)
+
+    sequence_parsed = tf.contrib.learn.run_n(sequence_parsed)[0]
+
+    audio = sequence_parsed['AUDIO/feature/floats'].values
+    rgb = sequence_parsed['RGB/feature/floats'].values
+
+    # print(audio.values)
+    # print(type(audio.values))
+
+    # audio is 128 8bit, rgb is 1024 8bit for every second
+    audio_slices = [audio[128 * i: 128 * (i + 1)] for i in range(len(audio) // 128)]
+    rgb_slices = [rgb[1024 * i: 1024 * (i + 1)] for i in range(len(rgb) // 1024)]
+
+    byte_audio = []
+    byte_rgb = []
+
+    for seg in audio_slices:
+        # audio_seg = quantize(seg)
+        audio_seg = _make_bytes(seg)
+        byte_audio.append(audio_seg)
+
+    for seg in rgb_slices:
+        # rgb_seg = quantize(seg)
+        rgb_seg = _make_bytes(seg)
+        byte_rgb.append(rgb_seg)
+
+    return byte_audio, byte_rgb
+
+
+def make_exmp(id, audio, rgb):
+    audio_features = []
+    rgb_features = []
+
+    for embedding in audio:
+        embedding_feature = tf.train.Feature(
+            bytes_list=tf.train.BytesList(value=[embedding]))
+        audio_features.append(embedding_feature)
+
+    for embedding in rgb:
+        embedding_feature = tf.train.Feature(
+            bytes_list=tf.train.BytesList(value=[embedding]))
+        rgb_features.append(embedding_feature)
+
+    # for construct yt8m data
+    seq_exmp = tf.train.SequenceExample(
+        context=tf.train.Features(
+            feature={
+                'id': tf.train.Feature(bytes_list=tf.train.BytesList(
+                    value=[id.encode('utf-8')]))
+            }),
+        feature_lists=tf.train.FeatureLists(
+            feature_list={
+                'audio': tf.train.FeatureList(
+                    feature=audio_features
+                ),
+                'rgb': tf.train.FeatureList(
+                    feature=rgb_features
+                )
+            })
+    )
+    serialized = seq_exmp.SerializeToString()
+    return serialized
+
+
+def convert_pb(filename):
+    sequence_example = open(filename, 'rb').read()
+
+    audio, rgb = parse_exmp(sequence_example)
+    tmp_example = make_exmp('video', audio, rgb)
+
+    decoded = tf.train.SequenceExample.FromString(tmp_example)
+    return decoded

+import tensorflow as tf
+import numpy as np
+
+frame_lvl_record = "test0000.tfrecord"
+
+feat_rgb = []
+feat_audio = []
+
+for example in tf.python_io.tf_record_iterator(frame_lvl_record):
+    tf_seq_example = tf.train.SequenceExample.FromString(example)
+    test = tf_seq_example.SerializeToString()
+    n_frames = len(tf_seq_example.feature_lists.feature_list['audio'].feature)
+    sess = tf.InteractiveSession()
+    rgb_frame = []
+    audio_frame = []
+    # iterate through frames
+    for i in range(n_frames):
+        rgb_frame.append(tf.cast(tf.decode_raw(
+            tf_seq_example.feature_lists.feature_list['rgb']
+                .feature[i].bytes_list.value[0], tf.uint8)
+            , tf.float32).eval())
+        audio_frame.append(tf.cast(tf.decode_raw(
+            tf_seq_example.feature_lists.feature_list['audio']
+                .feature[i].bytes_list.value[0], tf.uint8)
+            , tf.float32).eval())
+
+    sess.close()
+
+    feat_audio.append(audio_frame)
+    feat_rgb.append(rgb_frame)
+    break
+
+print('The first video has %d frames' %len(feat_rgb[0]))
\ No newline at end of file

+import nltk
+import gensim
+import pandas as pd
+
+# Load files.
+nltk.download('stopwords')
+vocab = pd.read_csv('../vocabulary.csv')
+
+# Lower corpus and Remove () from name.
+vocab['WikiDescription'] = vocab['WikiDescription'].str.lower().str.replace('[^a-zA-Z0-9]', ' ')
+for i in range(vocab['Name'].__len__()):
+    name = vocab['Name'][i]
+    if isinstance(name, str) and name.find(" (") != -1:
+        vocab['Name'][i] = name[:name.find(" (")]
+vocab['Name'] = vocab['Name'].str.lower()
+
+# Combine separated names.(mobile phone -> mobile-phone)
+for name in vocab['Name']:
+    if isinstance(name, str) and name.find(" ") != -1:
+        combined_name = name.replace(" ", "-")
+        for i in range(vocab['WikiDescription'].__len__()):
+            if isinstance(vocab['WikiDescription'][i], str):
+                vocab['WikiDescription'][i] = vocab['WikiDescription'][i].replace(name, combined_name)
+
+
+# Remove stopwords from corpus.
+stop_re = '\\b'+'\\b|\\b'.join(nltk.corpus.stopwords.words('english'))+'\\b'
+vocab['WikiDescription'] = vocab['WikiDescription'].str.replace(stop_re, '')
+vocab['WikiDescription'] = vocab['WikiDescription'].str.split()
+
+# Tokenize corpus.
+tokenlist = [x for x in vocab['WikiDescription'] if str(x) != 'nan']
+phrases = gensim.models.phrases.Phrases(tokenlist)
+phraser = gensim.models.phrases.Phraser(phrases)
+vocab_phrased = phraser[tokenlist]
+
+# Vectorize tags.
+w2v = gensim.models.word2vec.Word2Vec(sentences=tokenlist, min_count=1)
+w2v.save('tag_vectors.model')
+
+# word_vectors = w2v.wv
+# vocabs = word_vectors.vocab.keys()
+# word_vectors_list = [word_vectors[v] for v in vocabs]

+from gensim.models import Word2Vec
+import numpy as np
+
+def recommend_videos(tags, tag_model_path, video_model_path, top_k):
+    tag_vectors = Word2Vec.load(tag_model_path).wv
+    video_vectors = Word2Vec().wv.load(video_model_path)
+    error_tags = []
+
+    video_vector = np.zeros(100)
+    for (tag, weight) in tags:
+        if tag in tag_vectors.vocab:
+            video_vector = video_vector + (tag_vectors[tag] * float(weight))
+        else:
+            # Pass if tag is unknown
+            if tag not in error_tags:
+                error_tags.append(tag)
+
+    similar_ids = [x[0] for x in video_vectors.similar_by_vector(video_vector, top_k)]
+    return similar_ids

+import requests
+import pandas as pd
+
+base_URL = 'https://data.yt8m.org/2/j/i/'
+youtube_url = 'https://www.youtube.com/watch?v='
+
+
+def getURL(vid_id):
+    URL = base_URL + vid_id[:-2] + '/' + vid_id + '.js'
+    response = requests.get(URL, verify = False)
+    if response.status_code == 200:
+        return youtube_url + response.text[10:-3]
+
+
+def getVideoInfo(vid_id, video_tags_path, top_k):
+    video_url = getURL(vid_id)
+
+    entire_video_tags = pd.read_csv(video_tags_path)
+    video_tags_info = entire_video_tags.loc[entire_video_tags["vid_id"] == vid_id]
+    video_tags = []
+    for i in range(1, top_k + 1):
+        video_tag_tuple = video_tags_info["segment" + str(i)].values[0]     # ex: "mobile-phone:0.361"
+        video_tags.append(video_tag_tuple.split(":")[0])
+
+    return {
+        "video_url": video_url,
+        "video_tags": video_tags
+    }

+import pandas as pd
+import numpy as np
+from gensim.models import Word2Vec
+
+BATCH_SIZE = 1000
+
+
+def vectorization_video():
+    print('[0.1 0.2]')
+
+
+if __name__ == '__main__':
+    tag_vectors = Word2Vec.load("tag_vectors.model").wv
+    video_vectors = Word2Vec().wv   # Empty model
+
+    # Load video recommendation tags.
+    video_tags = pd.read_csv('kaggle_solution_40k.csv')
+
+    # Define batch variables.
+    batch_video_ids = []
+    batch_video_vectors = []
+    error_tags = []
+
+    for i, row in video_tags.iterrows():
+        video_id = row[0]
+        video_vector = np.zeros(100)
+        for segment_index in range(1, 6):
+            tag, weight = row[segment_index].split(":")
+            if tag in tag_vectors.vocab:
+                video_vector = video_vector + (tag_vectors[tag] * float(weight))
+            else:
+                # Pass if tag is unknown
+                if tag not in error_tags:
+                    error_tags.append(tag)
+
+        batch_video_ids.append(video_id)
+        batch_video_vectors.append(video_vector)
+        # Add video vectors.
+        if (i+1) % BATCH_SIZE == 0:
+            video_vectors.add(batch_video_ids, batch_video_vectors)
+            batch_video_ids = []
+            batch_video_vectors = []
+            print("Video vectors created: ", i+1)
+
+    # Add rest of video vectors.
+    video_vectors.add(batch_video_ids, batch_video_vectors)
+    print("error tags: ")
+    print(error_tags)
+
+    video_vectors.save("video_vectors.model")
+
+    # Usage
+    # video_vectors = Word2Vec().wv.load("video_vectors.model")
+    # video_vectors.most_similar("XwFj", topn=5)

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Provides functions to help with evaluating models."""
+import average_precision_calculator as ap_calculator
+import mean_average_precision_calculator as map_calculator
+import numpy
+from tensorflow.python.platform import gfile
+
+
+def flatten(l):
+  """Merges a list of lists into a single list. """
+  return [item for sublist in l for item in sublist]
+
+
+def calculate_hit_at_one(predictions, actuals):
+  """Performs a local (numpy) calculation of the hit at one.
+
+  Args:
+    predictions: Matrix containing the outputs of the model. Dimensions are
+      'batch' x 'num_classes'.
+    actuals: Matrix containing the ground truth labels. Dimensions are 'batch' x
+      'num_classes'.
+
+  Returns:
+    float: The average hit at one across the entire batch.
+  """
+  top_prediction = numpy.argmax(predictions, 1)
+  hits = actuals[numpy.arange(actuals.shape[0]), top_prediction]
+  return numpy.average(hits)
+
+
+def calculate_precision_at_equal_recall_rate(predictions, actuals):
+  """Performs a local (numpy) calculation of the PERR.
+
+  Args:
+    predictions: Matrix containing the outputs of the model. Dimensions are
+      'batch' x 'num_classes'.
+    actuals: Matrix containing the ground truth labels. Dimensions are 'batch' x
+      'num_classes'.
+
+  Returns:
+    float: The average precision at equal recall rate across the entire batch.
+  """
+  aggregated_precision = 0.0
+  num_videos = actuals.shape[0]
+  for row in numpy.arange(num_videos):
+    num_labels = int(numpy.sum(actuals[row]))
+    top_indices = numpy.argpartition(predictions[row],
+                                     -num_labels)[-num_labels:]
+    item_precision = 0.0
+    for label_index in top_indices:
+      if predictions[row][label_index] > 0:
+        item_precision += actuals[row][label_index]
+    item_precision /= top_indices.size
+    aggregated_precision += item_precision
+  aggregated_precision /= num_videos
+  return aggregated_precision
+
+
+def calculate_gap(predictions, actuals, top_k=20):
+  """Performs a local (numpy) calculation of the global average precision.
+
+  Only the top_k predictions are taken for each of the videos.
+
+  Args:
+    predictions: Matrix containing the outputs of the model. Dimensions are
+      'batch' x 'num_classes'.
+    actuals: Matrix containing the ground truth labels. Dimensions are 'batch' x
+      'num_classes'.
+    top_k: How many predictions to use per video.
+
+  Returns:
+    float: The global average precision.
+  """
+  gap_calculator = ap_calculator.AveragePrecisionCalculator()
+  sparse_predictions, sparse_labels, num_positives = top_k_by_class(
+      predictions, actuals, top_k)
+  gap_calculator.accumulate(flatten(sparse_predictions), flatten(sparse_labels),
+                            sum(num_positives))
+  return gap_calculator.peek_ap_at_n()
+
+
+def top_k_by_class(predictions, labels, k=20):
+  """Extracts the top k predictions for each video, sorted by class.
+
+  Args:
+    predictions: A numpy matrix containing the outputs of the model. Dimensions
+      are 'batch' x 'num_classes'.
+    k: the top k non-zero entries to preserve in each prediction.
+
+  Returns:
+    A tuple (predictions,labels, true_positives). 'predictions' and 'labels'
+    are lists of lists of floats. 'true_positives' is a list of scalars. The
+    length of the lists are equal to the number of classes. The entries in the
+    predictions variable are probability predictions, and
+    the corresponding entries in the labels variable are the ground truth for
+    those predictions. The entries in 'true_positives' are the number of true
+    positives for each class in the ground truth.
+
+  Raises:
+    ValueError: An error occurred when the k is not a positive integer.
+  """
+  if k <= 0:
+    raise ValueError("k must be a positive integer.")
+  k = min(k, predictions.shape[1])
+  num_classes = predictions.shape[1]
+  prediction_triplets = []
+  for video_index in range(predictions.shape[0]):
+    prediction_triplets.extend(
+        top_k_triplets(predictions[video_index], labels[video_index], k))
+  out_predictions = [[] for _ in range(num_classes)]
+  out_labels = [[] for _ in range(num_classes)]
+  for triplet in prediction_triplets:
+    out_predictions[triplet[0]].append(triplet[1])
+    out_labels[triplet[0]].append(triplet[2])
+  out_true_positives = [numpy.sum(labels[:, i]) for i in range(num_classes)]
+
+  return out_predictions, out_labels, out_true_positives
+
+
+def top_k_triplets(predictions, labels, k=20):
+  """Get the top_k for a 1-d numpy array.
+
+  Returns a sparse list of tuples in
+  (prediction, class) format
+  """
+  m = len(predictions)
+  k = min(k, m)
+  indices = numpy.argpartition(predictions, -k)[-k:]
+  return [(index, predictions[index], labels[index]) for index in indices]
+
+
+class EvaluationMetrics(object):
+  """A class to store the evaluation metrics."""
+
+  def __init__(self, num_class, top_k, top_n):
+    """Construct an EvaluationMetrics object to store the evaluation metrics.
+
+    Args:
+      num_class: A positive integer specifying the number of classes.
+      top_k: A positive integer specifying how many predictions are considered
+        per video.
+      top_n: A positive Integer specifying the average precision at n, or None
+        to use all provided data points.
+
+    Raises:
+      ValueError: An error occurred when MeanAveragePrecisionCalculator cannot
+        not be constructed.
+    """
+    self.sum_hit_at_one = 0.0
+    self.sum_perr = 0.0
+    self.sum_loss = 0.0
+    self.map_calculator = map_calculator.MeanAveragePrecisionCalculator(
+        num_class, top_n=top_n)
+    self.global_ap_calculator = ap_calculator.AveragePrecisionCalculator()
+    self.top_k = top_k
+    self.num_examples = 0
+
+  def accumulate(self, predictions, labels, loss):
+    """Accumulate the metrics calculated locally for this mini-batch.
+
+    Args:
+      predictions: A numpy matrix containing the outputs of the model.
+        Dimensions are 'batch' x 'num_classes'.
+      labels: A numpy matrix containing the ground truth labels. Dimensions are
+        'batch' x 'num_classes'.
+      loss: A numpy array containing the loss for each sample.
+
+    Returns:
+      dictionary: A dictionary storing the metrics for the mini-batch.
+
+    Raises:
+      ValueError: An error occurred when the shape of predictions and actuals
+        does not match.
+    """
+    batch_size = labels.shape[0]
+    mean_hit_at_one = calculate_hit_at_one(predictions, labels)
+    mean_perr = calculate_precision_at_equal_recall_rate(predictions, labels)
+    mean_loss = numpy.mean(loss)
+
+    # Take the top 20 predictions.
+    sparse_predictions, sparse_labels, num_positives = top_k_by_class(
+        predictions, labels, self.top_k)
+    self.map_calculator.accumulate(sparse_predictions, sparse_labels,
+                                   num_positives)
+    self.global_ap_calculator.accumulate(flatten(sparse_predictions),
+                                         flatten(sparse_labels),
+                                         sum(num_positives))
+
+    self.num_examples += batch_size
+    self.sum_hit_at_one += mean_hit_at_one * batch_size
+    self.sum_perr += mean_perr * batch_size
+    self.sum_loss += mean_loss * batch_size
+
+    return {"hit_at_one": mean_hit_at_one, "perr": mean_perr, "loss": mean_loss}
+
+  def get(self):
+    """Calculate the evaluation metrics for the whole epoch.
+
+    Raises:
+      ValueError: If no examples were accumulated.
+
+    Returns:
+      dictionary: a dictionary storing the evaluation metrics for the epoch. The
+        dictionary has the fields: avg_hit_at_one, avg_perr, avg_loss, and
+        aps (default nan).
+    """
+    if self.num_examples <= 0:
+      raise ValueError("total_sample must be positive.")
+    avg_hit_at_one = self.sum_hit_at_one / self.num_examples
+    avg_perr = self.sum_perr / self.num_examples
+    avg_loss = self.sum_loss / self.num_examples
+
+    aps = self.map_calculator.peek_map_at_n()
+    gap = self.global_ap_calculator.peek_ap_at_n()
+
+    epoch_info_dict = {
+        "avg_hit_at_one": avg_hit_at_one,
+        "avg_perr": avg_perr,
+        "avg_loss": avg_loss,
+        "aps": aps,
+        "gap": gap
+    }
+    return epoch_info_dict
+
+  def clear(self):
+    """Clear the evaluation metrics and reset the EvaluationMetrics object."""
+    self.sum_hit_at_one = 0.0
+    self.sum_perr = 0.0
+    self.sum_loss = 0.0
+    self.map_calculator.clear()
+    self.global_ap_calculator.clear()
+    self.num_examples = 0

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities to export a model for batch prediction."""
+
+import tensorflow as tf
+import tensorflow.contrib.slim as slim
+
+from tensorflow.python.saved_model import builder as saved_model_builder
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import signature_def_utils
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.saved_model import utils as saved_model_utils
+
+_TOP_PREDICTIONS_IN_OUTPUT = 20
+
+
+class ModelExporter(object):
+
+  def __init__(self, frame_features, model, reader):
+    self.frame_features = frame_features
+    self.model = model
+    self.reader = reader
+
+    with tf.Graph().as_default() as graph:
+      self.inputs, self.outputs = self.build_inputs_and_outputs()
+      self.graph = graph
+      self.saver = tf.train.Saver(tf.trainable_variables(), sharded=True)
+
+  def export_model(self, model_dir, global_step_val, last_checkpoint):
+    """Exports the model so that it can used for batch predictions."""
+
+    with self.graph.as_default():
+      with tf.Session() as session:
+        session.run(tf.global_variables_initializer())
+        self.saver.restore(session, last_checkpoint)
+
+        signature = signature_def_utils.build_signature_def(
+            inputs=self.inputs,
+            outputs=self.outputs,
+            method_name=signature_constants.PREDICT_METHOD_NAME)
+
+        signature_map = {
+            signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature
+        }
+
+        model_builder = saved_model_builder.SavedModelBuilder(model_dir)
+        model_builder.add_meta_graph_and_variables(
+            session,
+            tags=[tag_constants.SERVING],
+            signature_def_map=signature_map,
+            clear_devices=True)
+        model_builder.save()
+
+  def build_inputs_and_outputs(self):
+    if self.frame_features:
+      serialized_examples = tf.placeholder(tf.string, shape=(None,))
+
+      fn = lambda x: self.build_prediction_graph(x)
+      video_id_output, top_indices_output, top_predictions_output = (tf.map_fn(
+          fn, serialized_examples, dtype=(tf.string, tf.int32, tf.float32)))
+
+    else:
+      serialized_examples = tf.placeholder(tf.string, shape=(None,))
+
+      video_id_output, top_indices_output, top_predictions_output = (
+          self.build_prediction_graph(serialized_examples))
+
+    inputs = {
+        "example_bytes":
+            saved_model_utils.build_tensor_info(serialized_examples)
+    }
+
+    outputs = {
+        "video_id":
+            saved_model_utils.build_tensor_info(video_id_output),
+        "class_indexes":
+            saved_model_utils.build_tensor_info(top_indices_output),
+        "predictions":
+            saved_model_utils.build_tensor_info(top_predictions_output)
+    }
+
+    return inputs, outputs
+
+  def build_prediction_graph(self, serialized_examples):
+    input_data_dict = (
+        self.reader.prepare_serialized_examples(serialized_examples))
+    video_id = input_data_dict["video_ids"]
+    model_input_raw = input_data_dict["video_matrix"]
+    labels_batch = input_data_dict["labels"]
+    num_frames = input_data_dict["num_frames"]
+
+    feature_dim = len(model_input_raw.get_shape()) - 1
+    model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
+
+    with tf.variable_scope("tower"):
+      result = self.model.create_model(model_input,
+                                       num_frames=num_frames,
+                                       vocab_size=self.reader.num_classes,
+                                       labels=labels_batch,
+                                       is_training=False)
+
+      for variable in slim.get_model_variables():
+        tf.summary.histogram(variable.op.name, variable)
+
+      predictions = result["predictions"]
+
+      top_predictions, top_indices = tf.nn.top_k(predictions,
+                                                 _TOP_PREDICTIONS_IN_OUTPUT)
+    return video_id, top_indices, top_predictions

+# Lint as: python3
+import numpy as np
+import tensorflow as tf
+from tensorflow import app
+from tensorflow import flags
+
+FLAGS = flags.FLAGS
+
+
+def main(unused_argv):
+  # Get the input tensor names to be replaced.
+  tf.reset_default_graph()
+  meta_graph_location = FLAGS.checkpoint_file + ".meta"
+  tf.train.import_meta_graph(meta_graph_location, clear_devices=True)
+
+  input_tensor_name = tf.get_collection("input_batch_raw")[0].name
+  num_frames_tensor_name = tf.get_collection("num_frames")[0].name
+
+  # Create output graph.
+  saver = tf.train.Saver()
+  tf.reset_default_graph()
+
+  input_feature_placeholder = tf.placeholder(
+        tf.float32, shape=(None, None, 1152))
+  num_frames_placeholder = tf.placeholder(tf.int32, shape=(None, 1))
+
+  saver = tf.train.import_meta_graph(
+      meta_graph_location,
+      input_map={
+          input_tensor_name: input_feature_placeholder,
+          num_frames_tensor_name: tf.squeeze(num_frames_placeholder, axis=1)
+      },
+      clear_devices=True)
+  predictions_tensor = tf.get_collection("predictions")[0]
+
+  with tf.Session() as sess:
+    print("restoring variables from " + FLAGS.checkpoint_file)
+    saver.restore(sess, FLAGS.checkpoint_file)
+    tf.saved_model.simple_save(
+        sess,
+        FLAGS.output_dir,
+        inputs={'rgb_and_audio': input_feature_placeholder,
+                'num_frames': num_frames_placeholder},
+        outputs={'predictions': predictions_tensor})
+
+    # Try running inference.
+    predictions = sess.run(
+       [predictions_tensor],
+       feed_dict={
+          input_feature_placeholder: np.zeros((3, 7, 1152), dtype=np.float32),
+          num_frames_placeholder: np.array([[7]], dtype=np.int32)})
+    print('Test inference:', predictions)
+
+    print('Model saved to ', FLAGS.output_dir)
+
+
+if __name__ == '__main__':
+  flags.DEFINE_string('checkpoint_file', None, 'Path to the checkpoint file.')
+  flags.DEFINE_string('output_dir', None, 'SavedModel output directory.')
+  app.run(main)

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Provides definitions for non-regularized training or test losses."""
+
+import tensorflow as tf
+
+
+class BaseLoss(object):
+  """Inherit from this class when implementing new losses."""
+
+  def calculate_loss(self, unused_predictions, unused_labels, **unused_params):
+    """Calculates the average loss of the examples in a mini-batch.
+
+     Args:
+      unused_predictions: a 2-d tensor storing the prediction scores, in which
+        each row represents a sample in the mini-batch and each column
+        represents a class.
+      unused_labels: a 2-d tensor storing the labels, which has the same shape
+        as the unused_predictions. The labels must be in the range of 0 and 1.
+      unused_params: loss specific parameters.
+
+    Returns:
+      A scalar loss tensor.
+    """
+    raise NotImplementedError()
+
+
+class CrossEntropyLoss(BaseLoss):
+  """Calculate the cross entropy loss between the predictions and labels."""
+
+  def calculate_loss(self,
+                     predictions,
+                     labels,
+                     label_weights=None,
+                     **unused_params):
+    with tf.name_scope("loss_xent"):
+      epsilon = 1e-5
+      float_labels = tf.cast(labels, tf.float32)
+      cross_entropy_loss = float_labels * tf.math.log(predictions + epsilon) + (
+          1 - float_labels) * tf.math.log(1 - predictions + epsilon)
+      cross_entropy_loss = tf.negative(cross_entropy_loss)
+      if label_weights is not None:
+        cross_entropy_loss *= label_weights
+      return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
+
+
+class HingeLoss(BaseLoss):
+  """Calculate the hinge loss between the predictions and labels.
+
+  Note the subgradient is used in the backpropagation, and thus the optimization
+  may converge slower. The predictions trained by the hinge loss are between -1
+  and +1.
+  """
+
+  def calculate_loss(self, predictions, labels, b=1.0, **unused_params):
+    with tf.name_scope("loss_hinge"):
+      float_labels = tf.cast(labels, tf.float32)
+      all_zeros = tf.zeros(tf.shape(float_labels), dtype=tf.float32)
+      all_ones = tf.ones(tf.shape(float_labels), dtype=tf.float32)
+      sign_labels = tf.subtract(tf.scalar_mul(2, float_labels), all_ones)
+      hinge_loss = tf.maximum(
+          all_zeros,
+          tf.scalar_mul(b, all_ones) - sign_labels * predictions)
+      return tf.reduce_mean(tf.reduce_sum(hinge_loss, 1))
+
+
+class SoftmaxLoss(BaseLoss):
+  """Calculate the softmax loss between the predictions and labels.
+
+  The function calculates the loss in the following way: first we feed the
+  predictions to the softmax activation function and then we calculate
+  the minus linear dot product between the logged softmax activations and the
+  normalized ground truth label.
+
+  It is an extension to the one-hot label. It allows for more than one positive
+  labels for each sample.
+  """
+
+  def calculate_loss(self, predictions, labels, **unused_params):
+    with tf.name_scope("loss_softmax"):
+      epsilon = 10e-8
+      float_labels = tf.cast(labels, tf.float32)
+      # l1 normalization (labels are no less than 0)
+      label_rowsum = tf.maximum(tf.reduce_sum(float_labels, 1, keep_dims=True),
+                                epsilon)
+      norm_float_labels = tf.div(float_labels, label_rowsum)
+      softmax_outputs = tf.nn.softmax(predictions)
+      softmax_loss = tf.negative(
+          tf.reduce_sum(tf.multiply(norm_float_labels, tf.log(softmax_outputs)),
+                        1))
+    return tf.reduce_mean(softmax_loss)

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Calculate the mean average precision.
+
+It provides an interface for calculating mean average precision
+for an entire list or the top-n ranked items.
+
+Example usages:
+We first call the function accumulate many times to process parts of the ranked
+list. After processing all the parts, we call peek_map_at_n
+to calculate the mean average precision.
+
+```
+import random
+
+p = np.array([[random.random() for _ in xrange(50)] for _ in xrange(1000)])
+a = np.array([[random.choice([0, 1]) for _ in xrange(50)]
+     for _ in xrange(1000)])
+
+# mean average precision for 50 classes.
+calculator = mean_average_precision_calculator.MeanAveragePrecisionCalculator(
+            num_class=50)
+calculator.accumulate(p, a)
+aps = calculator.peek_map_at_n()
+```
+"""
+
+import average_precision_calculator
+
+
+class MeanAveragePrecisionCalculator(object):
+  """This class is to calculate mean average precision."""
+
+  def __init__(self, num_class, filter_empty_classes=True, top_n=None):
+    """Construct a calculator to calculate the (macro) average precision.
+
+    Args:
+      num_class: A positive Integer specifying the number of classes.
+      filter_empty_classes: whether to filter classes without any positives.
+      top_n: A positive Integer specifying the average precision at n, or None
+        to use all provided data points.
+
+    Raises:
+      ValueError: An error occurred when num_class is not a positive integer;
+      or the top_n_array is not a list of positive integers.
+    """
+    if not isinstance(num_class, int) or num_class <= 1:
+      raise ValueError("num_class must be a positive integer.")
+
+    self._ap_calculators = []  # member of AveragePrecisionCalculator
+    self._num_class = num_class  # total number of classes
+    self._filter_empty_classes = filter_empty_classes
+    for _ in range(num_class):
+      self._ap_calculators.append(
+          average_precision_calculator.AveragePrecisionCalculator(top_n=top_n))
+
+  def accumulate(self, predictions, actuals, num_positives=None):
+    """Accumulate the predictions and their ground truth labels.
+
+    Args:
+      predictions: A list of lists storing the prediction scores. The outer
+        dimension corresponds to classes.
+      actuals: A list of lists storing the ground truth labels. The dimensions
+        should correspond to the predictions input. Any value larger than 0 will
+        be treated as positives, otherwise as negatives.
+      num_positives: If provided, it is a list of numbers representing the
+        number of true positives for each class. If not provided, the number of
+        true positives will be inferred from the 'actuals' array.
+
+    Raises:
+      ValueError: An error occurred when the shape of predictions and actuals
+      does not match.
+    """
+    if not num_positives:
+      num_positives = [None for i in range(self._num_class)]
+
+    calculators = self._ap_calculators
+    for i in range(self._num_class):
+      calculators[i].accumulate(predictions[i], actuals[i], num_positives[i])
+
+  def clear(self):
+    for calculator in self._ap_calculators:
+      calculator.clear()
+
+  def is_empty(self):
+    return ([calculator.heap_size for calculator in self._ap_calculators
+            ] == [0 for _ in range(self._num_class)])
+
+  def peek_map_at_n(self):
+    """Peek the non-interpolated mean average precision at n.
+
+    Returns:
+      An array of non-interpolated average precision at n (default 0) for each
+      class.
+    """
+    aps = []
+    for i in range(self._num_class):
+      if (not self._filter_empty_classes or
+          self._ap_calculators[i].num_accumulated_positives > 0):
+        ap = self._ap_calculators[i].peek_ap_at_n()
+        aps.append(ap)
+    return aps

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a collection of util functions for model construction."""
+import numpy
+import tensorflow as tf
+from tensorflow import logging
+from tensorflow import flags
+import tensorflow.contrib.slim as slim
+
+
+def SampleRandomSequence(model_input, num_frames, num_samples):
+  """Samples a random sequence of frames of size num_samples.
+
+  Args:
+    model_input: A tensor of size batch_size x max_frames x feature_size
+    num_frames: A tensor of size batch_size x 1
+    num_samples: A scalar
+
+  Returns:
+    `model_input`: A tensor of size batch_size x num_samples x feature_size
+  """
+
+  batch_size = tf.shape(model_input)[0]
+  frame_index_offset = tf.tile(tf.expand_dims(tf.range(num_samples), 0),
+                               [batch_size, 1])
+  max_start_frame_index = tf.maximum(num_frames - num_samples, 0)
+  start_frame_index = tf.cast(
+      tf.multiply(tf.random_uniform([batch_size, 1]),
+                  tf.cast(max_start_frame_index + 1, tf.float32)), tf.int32)
+  frame_index = tf.minimum(start_frame_index + frame_index_offset,
+                           tf.cast(num_frames - 1, tf.int32))
+  batch_index = tf.tile(tf.expand_dims(tf.range(batch_size), 1),
+                        [1, num_samples])
+  index = tf.stack([batch_index, frame_index], 2)
+  return tf.gather_nd(model_input, index)
+
+
+def SampleRandomFrames(model_input, num_frames, num_samples):
+  """Samples a random set of frames of size num_samples.
+
+  Args:
+    model_input: A tensor of size batch_size x max_frames x feature_size
+    num_frames: A tensor of size batch_size x 1
+    num_samples: A scalar
+
+  Returns:
+    `model_input`: A tensor of size batch_size x num_samples x feature_size
+  """
+  batch_size = tf.shape(model_input)[0]
+  frame_index = tf.cast(
+      tf.multiply(tf.random_uniform([batch_size, num_samples]),
+                  tf.tile(tf.cast(num_frames, tf.float32), [1, num_samples])),
+      tf.int32)
+  batch_index = tf.tile(tf.expand_dims(tf.range(batch_size), 1),
+                        [1, num_samples])
+  index = tf.stack([batch_index, frame_index], 2)
+  return tf.gather_nd(model_input, index)
+
+
+def FramePooling(frames, method, **unused_params):
+  """Pools over the frames of a video.
+
+  Args:
+    frames: A tensor with shape [batch_size, num_frames, feature_size].
+    method: "average", "max", "attention", or "none".
+
+  Returns:
+    A tensor with shape [batch_size, feature_size] for average, max, or
+    attention pooling. A tensor with shape [batch_size*num_frames, feature_size]
+    for none pooling.
+
+  Raises:
+    ValueError: if method is other than "average", "max", "attention", or
+    "none".
+  """
+  if method == "average":
+    return tf.reduce_mean(frames, 1)
+  elif method == "max":
+    return tf.reduce_max(frames, 1)
+  elif method == "none":
+    feature_size = frames.shape_as_list()[2]
+    return tf.reshape(frames, [-1, feature_size])
+  else:
+    raise ValueError("Unrecognized pooling method: %s" % method)

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains the base class for models."""
+
+
+class BaseModel(object):
+  """Inherit from this class when implementing new models."""
+
+  def create_model(self, unused_model_input, **unused_params):
+    raise NotImplementedError()

+"""Eval mAP@N metric from inference file."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl import app
+from absl import flags
+
+import mean_average_precision_calculator as map_calculator
+import numpy as np
+import tensorflow as tf
+
+flags.DEFINE_string(
+    "eval_data_pattern", "",
+    "File glob defining the evaluation dataset in tensorflow.SequenceExample "
+    "format. The SequenceExamples are expected to have an 'rgb' byte array "
+    "sequence feature as well as a 'labels' int64 context feature.")
+flags.DEFINE_string(
+    "label_cache", "",
+    "The path for the label cache file. Leave blank for not to cache.")
+flags.DEFINE_string("submission_file", "",
+                    "The segment submission file generated by inference.py.")
+flags.DEFINE_integer(
+    "top_n", 0,
+    "The cap per-class predictions by a maximum of N. Use 0 for not capping.")
+
+FLAGS = flags.FLAGS
+
+
+class Labels(object):
+  """Contains the class to hold label objects.
+
+  This class can serialize and de-serialize the groundtruths.
+  The ground truth is in a mapping from (segment_id, class_id) -> label_score.
+  """
+
+  def __init__(self, labels):
+    """__init__ method."""
+    self._labels = labels
+
+  @property
+  def labels(self):
+    """Return the ground truth mapping. See class docstring for details."""
+    return self._labels
+
+  def to_file(self, file_name):
+    """Materialize the GT mapping to file."""
+    with tf.gfile.Open(file_name, "w") as fobj:
+      for k, v in self._labels.items():
+        seg_id, label = k
+        line = "%s,%s,%s\n" % (seg_id, label, v)
+        fobj.write(line)
+
+  @classmethod
+  def from_file(cls, file_name):
+    """Read the GT mapping from cached file."""
+    labels = {}
+    with tf.gfile.Open(file_name) as fobj:
+      for line in fobj:
+        line = line.strip().strip("\n")
+        seg_id, label, score = line.split(",")
+        labels[(seg_id, int(label))] = float(score)
+    return cls(labels)
+
+
+def read_labels(data_pattern, cache_path=""):
+  """Read labels from TFRecords.
+
+  Args:
+    data_pattern: the data pattern to the TFRecords.
+    cache_path: the cache path for the label file.
+
+  Returns:
+    a Labels object.
+  """
+  if cache_path:
+    if tf.gfile.Exists(cache_path):
+      tf.logging.info("Reading cached labels from %s..." % cache_path)
+      return Labels.from_file(cache_path)
+  tf.enable_eager_execution()
+  data_paths = tf.gfile.Glob(data_pattern)
+  ds = tf.data.TFRecordDataset(data_paths, num_parallel_reads=50)
+  context_features = {
+      "id": tf.FixedLenFeature([], tf.string),
+      "segment_labels": tf.VarLenFeature(tf.int64),
+      "segment_start_times": tf.VarLenFeature(tf.int64),
+      "segment_scores": tf.VarLenFeature(tf.float32)
+  }
+
+  def _parse_se_func(sequence_example):
+    return tf.parse_single_sequence_example(sequence_example,
+                                            context_features=context_features)
+
+  ds = ds.map(_parse_se_func)
+  rated_labels = {}
+  tf.logging.info("Reading labels from TFRecords...")
+  last_batch = 0
+  batch_size = 5000
+  for cxt_feature_val, _ in ds:
+    video_id = cxt_feature_val["id"].numpy()
+    segment_labels = cxt_feature_val["segment_labels"].values.numpy()
+    segment_start_times = cxt_feature_val["segment_start_times"].values.numpy()
+    segment_scores = cxt_feature_val["segment_scores"].values.numpy()
+    for label, start_time, score in zip(segment_labels, segment_start_times,
+                                        segment_scores):
+      rated_labels[("%s:%d" % (video_id, start_time), label)] = score
+    batch_id = len(rated_labels) // batch_size
+    if batch_id != last_batch:
+      tf.logging.info("%d examples processed.", len(rated_labels))
+      last_batch = batch_id
+  tf.logging.info("Finish reading labels from TFRecords...")
+  labels_obj = Labels(rated_labels)
+  if cache_path:
+    tf.logging.info("Caching labels to %s..." % cache_path)
+    labels_obj.to_file(cache_path)
+  return labels_obj
+
+
+def read_segment_predictions(file_path, labels, top_n=None):
+  """Read segement predictions.
+
+  Args:
+    file_path: the submission file path.
+    labels: a Labels object containing the eval labels.
+    top_n: the per-class class capping.
+
+  Returns:
+    a segment prediction list for each classes.
+  """
+  cls_preds = {}  # A label_id to pred list mapping.
+  with tf.gfile.Open(file_path) as fobj:
+    tf.logging.info("Reading predictions from %s..." % file_path)
+    for line in fobj:
+      label_id, pred_ids_val = line.split(",")
+      pred_ids = pred_ids_val.split(" ")
+      if top_n:
+        pred_ids = pred_ids[:top_n]
+      pred_ids = [
+          pred_id for pred_id in pred_ids
+          if (pred_id, int(label_id)) in labels.labels
+      ]
+      cls_preds[int(label_id)] = pred_ids
+      if len(cls_preds) % 50 == 0:
+        tf.logging.info("Processed %d classes..." % len(cls_preds))
+    tf.logging.info("Finish reading predictions.")
+  return cls_preds
+
+
+def main(unused_argv):
+  """Entry function of the script."""
+  if not FLAGS.submission_file:
+    raise ValueError("You must input submission file.")
+  eval_labels = read_labels(FLAGS.eval_data_pattern,
+                            cache_path=FLAGS.label_cache)
+  tf.logging.info("Total rated segments: %d." % len(eval_labels.labels))
+  positive_counter = {}
+  for k, v in eval_labels.labels.items():
+    _, label_id = k
+    if v > 0:
+      positive_counter[label_id] = positive_counter.get(label_id, 0) + 1
+
+  seg_preds = read_segment_predictions(FLAGS.submission_file,
+                                       eval_labels,
+                                       top_n=FLAGS.top_n)
+  map_cal = map_calculator.MeanAveragePrecisionCalculator(len(seg_preds))
+  seg_labels = []
+  seg_scored_preds = []
+  num_positives = []
+  for label_id in sorted(seg_preds):
+    class_preds = seg_preds[label_id]
+    seg_label = [eval_labels.labels[(pred, label_id)] for pred in class_preds]
+    seg_labels.append(seg_label)
+    seg_scored_pred = []
+    if class_preds:
+      seg_scored_pred = [
+          float(x) / len(class_preds) for x in range(len(class_preds), 0, -1)
+      ]
+    seg_scored_preds.append(seg_scored_pred)
+    num_positives.append(positive_counter[label_id])
+  map_cal.accumulate(seg_scored_preds, seg_labels, num_positives)
+  map_at_n = np.mean(map_cal.peek_map_at_n())
+  tf.logging.info("Num classes: %d | mAP@%d: %.6f" %
+                  (len(seg_preds), FLAGS.top_n, map_at_n))
+
+
+if __name__ == "__main__":
+  app.run(main)

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+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a collection of util functions for training and evaluating."""
+
+import numpy
+import tensorflow as tf
+from tensorflow import logging
+
+try:
+  xrange  # Python 2
+except NameError:
+  xrange = range  # Python 3
+
+
+def Dequantize(feat_vector, max_quantized_value=2, min_quantized_value=-2):
+  """Dequantize the feature from the byte format to the float format.
+
+  Args:
+    feat_vector: the input 1-d vector.
+    max_quantized_value: the maximum of the quantized value.
+    min_quantized_value: the minimum of the quantized value.
+
+  Returns:
+    A float vector which has the same shape as feat_vector.
+  """
+  assert max_quantized_value > min_quantized_value
+  quantized_range = max_quantized_value - min_quantized_value
+  scalar = quantized_range / 255.0
+  bias = (quantized_range / 512.0) + min_quantized_value
+  return feat_vector * scalar + bias
+
+
+def MakeSummary(name, value):
+  """Creates a tf.Summary proto with the given name and value."""
+  summary = tf.Summary()
+  val = summary.value.add()
+  val.tag = str(name)
+  val.simple_value = float(value)
+  return summary
+
+
+def AddGlobalStepSummary(summary_writer,
+                         global_step_val,
+                         global_step_info_dict,
+                         summary_scope="Eval"):
+  """Add the global_step summary to the Tensorboard.
+
+  Args:
+    summary_writer: Tensorflow summary_writer.
+    global_step_val: a int value of the global step.
+    global_step_info_dict: a dictionary of the evaluation metrics calculated for
+      a mini-batch.
+    summary_scope: Train or Eval.
+
+  Returns:
+    A string of this global_step summary
+  """
+  this_hit_at_one = global_step_info_dict["hit_at_one"]
+  this_perr = global_step_info_dict["perr"]
+  this_loss = global_step_info_dict["loss"]
+  examples_per_second = global_step_info_dict.get("examples_per_second", -1)
+
+  summary_writer.add_summary(
+      MakeSummary("GlobalStep/" + summary_scope + "_Hit@1", this_hit_at_one),
+      global_step_val)
+  summary_writer.add_summary(
+      MakeSummary("GlobalStep/" + summary_scope + "_Perr", this_perr),
+      global_step_val)
+  summary_writer.add_summary(
+      MakeSummary("GlobalStep/" + summary_scope + "_Loss", this_loss),
+      global_step_val)
+
+  if examples_per_second != -1:
+    summary_writer.add_summary(
+        MakeSummary("GlobalStep/" + summary_scope + "_Example_Second",
+                    examples_per_second), global_step_val)
+
+  summary_writer.flush()
+  info = (
+      "global_step {0} | Batch Hit@1: {1:.3f} | Batch PERR: {2:.3f} | Batch "
+      "Loss: {3:.3f} | Examples_per_sec: {4:.3f}").format(
+          global_step_val, this_hit_at_one, this_perr, this_loss,
+          examples_per_second)
+  return info
+
+
+def AddEpochSummary(summary_writer,
+                    global_step_val,
+                    epoch_info_dict,
+                    summary_scope="Eval"):
+  """Add the epoch summary to the Tensorboard.
+
+  Args:
+    summary_writer: Tensorflow summary_writer.
+    global_step_val: a int value of the global step.
+    epoch_info_dict: a dictionary of the evaluation metrics calculated for the
+      whole epoch.
+    summary_scope: Train or Eval.
+
+  Returns:
+    A string of this global_step summary
+  """
+  epoch_id = epoch_info_dict["epoch_id"]
+  avg_hit_at_one = epoch_info_dict["avg_hit_at_one"]
+  avg_perr = epoch_info_dict["avg_perr"]
+  avg_loss = epoch_info_dict["avg_loss"]
+  aps = epoch_info_dict["aps"]
+  gap = epoch_info_dict["gap"]
+  mean_ap = numpy.mean(aps)
+
+  summary_writer.add_summary(
+      MakeSummary("Epoch/" + summary_scope + "_Avg_Hit@1", avg_hit_at_one),
+      global_step_val)
+  summary_writer.add_summary(
+      MakeSummary("Epoch/" + summary_scope + "_Avg_Perr", avg_perr),
+      global_step_val)
+  summary_writer.add_summary(
+      MakeSummary("Epoch/" + summary_scope + "_Avg_Loss", avg_loss),
+      global_step_val)
+  summary_writer.add_summary(
+      MakeSummary("Epoch/" + summary_scope + "_MAP", mean_ap), global_step_val)
+  summary_writer.add_summary(
+      MakeSummary("Epoch/" + summary_scope + "_GAP", gap), global_step_val)
+  summary_writer.flush()
+
+  info = ("epoch/eval number {0} | Avg_Hit@1: {1:.3f} | Avg_PERR: {2:.3f} "
+          "| MAP: {3:.3f} | GAP: {4:.3f} | Avg_Loss: {5:3f} | num_classes: {6}"
+         ).format(epoch_id, avg_hit_at_one, avg_perr, mean_ap, gap, avg_loss,
+                  len(aps))
+  return info
+
+
+def GetListOfFeatureNamesAndSizes(feature_names, feature_sizes):
+  """Extract the list of feature names and the dimensionality of each feature
+
+     from string of comma separated values.
+
+  Args:
+    feature_names: string containing comma separated list of feature names
+    feature_sizes: string containing comma separated list of feature sizes
+
+  Returns:
+    List of the feature names and list of the dimensionality of each feature.
+    Elements in the first/second list are strings/integers.
+  """
+  list_of_feature_names = [
+      feature_names.strip() for feature_names in feature_names.split(",")
+  ]
+  list_of_feature_sizes = [
+      int(feature_sizes) for feature_sizes in feature_sizes.split(",")
+  ]
+  if len(list_of_feature_names) != len(list_of_feature_sizes):
+    logging.error("length of the feature names (=" +
+                  str(len(list_of_feature_names)) + ") != length of feature "
+                  "sizes (=" + str(len(list_of_feature_sizes)) + ")")
+
+  return list_of_feature_names, list_of_feature_sizes
+
+
+def clip_gradient_norms(gradients_to_variables, max_norm):
+  """Clips the gradients by the given value.
+
+  Args:
+    gradients_to_variables: A list of gradient to variable pairs (tuples).
+    max_norm: the maximum norm value.
+
+  Returns:
+    A list of clipped gradient to variable pairs.
+  """
+  clipped_grads_and_vars = []
+  for grad, var in gradients_to_variables:
+    if grad is not None:
+      if isinstance(grad, tf.IndexedSlices):
+        tmp = tf.clip_by_norm(grad.values, max_norm)
+        grad = tf.IndexedSlices(tmp, grad.indices, grad.dense_shape)
+      else:
+        grad = tf.clip_by_norm(grad, max_norm)
+    clipped_grads_and_vars.append((grad, var))
+  return clipped_grads_and_vars
+
+
+def combine_gradients(tower_grads):
+  """Calculate the combined gradient for each shared variable across all towers.
+
+  Note that this function provides a synchronization point across all towers.
+
+  Args:
+    tower_grads: List of lists of (gradient, variable) tuples. The outer list is
+      over individual gradients. The inner list is over the gradient calculation
+      for each tower.
+
+  Returns:
+     List of pairs of (gradient, variable) where the gradient has been summed
+     across all towers.
+  """
+  filtered_grads = [
+      [x for x in grad_list if x[0] is not None] for grad_list in tower_grads
+  ]
+  final_grads = []
+  for i in xrange(len(filtered_grads[0])):
+    grads = [filtered_grads[t][i] for t in xrange(len(filtered_grads))]
+    grad = tf.stack([x[0] for x in grads], 0)
+    grad = tf.reduce_sum(grad, 0)
+    final_grads.append((
+        grad,
+        filtered_grads[0][i][1],
+    ))
+
+  return final_grads

+# Copyright 2016 Google Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS-IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains model definitions."""
+import math
+
+import models
+import tensorflow as tf
+import utils
+
+from tensorflow import flags
+import tensorflow.contrib.slim as slim
+
+FLAGS = flags.FLAGS
+flags.DEFINE_integer(
+    "moe_num_mixtures", 2,
+    "The number of mixtures (excluding the dummy 'expert') used for MoeModel.")
+
+
+class LogisticModel(models.BaseModel):
+  """Logistic model with L2 regularization."""
+
+  def create_model(self,
+                   model_input,
+                   vocab_size,
+                   l2_penalty=1e-8,
+                   **unused_params):
+    """Creates a logistic model.
+
+    Args:
+      model_input: 'batch' x 'num_features' matrix of input features.
+      vocab_size: The number of classes in the dataset.
+
+    Returns:
+      A dictionary with a tensor containing the probability predictions of the
+      model in the 'predictions' key. The dimensions of the tensor are
+      batch_size x num_classes.
+    """
+    output = slim.fully_connected(
+        model_input,
+        vocab_size,
+        activation_fn=tf.nn.sigmoid,
+        weights_regularizer=slim.l2_regularizer(l2_penalty))
+    return {"predictions": output}
+
+
+class MoeModel(models.BaseModel):
+  """A softmax over a mixture of logistic models (with L2 regularization)."""
+
+  def create_model(self,
+                   model_input,
+                   vocab_size,
+                   num_mixtures=None,
+                   l2_penalty=1e-8,
+                   **unused_params):
+    """Creates a Mixture of (Logistic) Experts model.
+
+     The model consists of a per-class softmax distribution over a
+     configurable number of logistic classifiers. One of the classifiers in the
+     mixture is not trained, and always predicts 0.
+
+    Args:
+      model_input: 'batch_size' x 'num_features' matrix of input features.
+      vocab_size: The number of classes in the dataset.
+      num_mixtures: The number of mixtures (excluding a dummy 'expert' that
+        always predicts the non-existence of an entity).
+      l2_penalty: How much to penalize the squared magnitudes of parameter
+        values.
+
+    Returns:
+      A dictionary with a tensor containing the probability predictions of the
+      model in the 'predictions' key. The dimensions of the tensor are
+      batch_size x num_classes.
+    """
+    num_mixtures = num_mixtures or FLAGS.moe_num_mixtures
+
+    gate_activations = slim.fully_connected(
+        model_input,
+        vocab_size * (num_mixtures + 1),
+        activation_fn=None,
+        biases_initializer=None,
+        weights_regularizer=slim.l2_regularizer(l2_penalty),
+        scope="gates")
+    expert_activations = slim.fully_connected(
+        model_input,
+        vocab_size * num_mixtures,
+        activation_fn=None,
+        weights_regularizer=slim.l2_regularizer(l2_penalty),
+        scope="experts")
+
+    gating_distribution = tf.nn.softmax(
+        tf.reshape(
+            gate_activations,
+            [-1, num_mixtures + 1]))  # (Batch * #Labels) x (num_mixtures + 1)
+    expert_distribution = tf.nn.sigmoid(
+        tf.reshape(expert_activations,
+                   [-1, num_mixtures]))  # (Batch * #Labels) x num_mixtures
+
+    final_probabilities_by_class_and_batch = tf.reduce_sum(
+        gating_distribution[:, :num_mixtures] * expert_distribution, 1)
+    final_probabilities = tf.reshape(final_probabilities_by_class_and_batch,
+                                     [-1, vocab_size])
+    return {"predictions": final_probabilities}

+> 1%
+last 2 versions
+not dead

+[*.{js,jsx,ts,tsx,vue}]
+indent_style = space
+indent_size = 2
+end_of_line = lf
+trim_trailing_whitespace = true
+insert_final_newline = true
+max_line_length = 200

+module.exports = {
+  root: true,
+  env: {
+    browser: true,
+    es6: true,
+    node: true,
+  },
+  extends: [
+    'plugin:vue/essential',
+    '@vue/airbnb',
+  ],
+  parserOptions: {
+    parser: 'babel-eslint',
+  },
+  rules: {
+    'no-console': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
+    'no-debugger': process.env.NODE_ENV === 'production' ? 'warn' : 'off',
+    'linebreak-style': ['error', 'unix'],
+    'max-len': ['error', { code: 200 }],
+  },
+};

+.DS_Store
+node_modules
+/dist
+
+# local env files
+.env.local
+.env.*.local
+
+# Log files
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Editor directories and files
+.idea
+.vscode
+*.suo
+*.ntvs*
+*.njsproj
+*.sln
+*.sw?

+# front
+
+## Project setup
+```
+yarn install
+```
+
+### Compiles and hot-reloads for development
+```
+yarn serve
+```
+
+### Compiles and minifies for production
+```
+yarn build
+```
+
+### Lints and fixes files
+```
+yarn lint
+```
+
+### Customize configuration
+See [Configuration Reference](https://cli.vuejs.org/config/).

+module.exports = {
+  presets: [
+    '@vue/cli-plugin-babel/preset',
+  ],
+};

+{
+  "name": "front",
+  "version": "0.1.0",
+  "private": true,
+  "scripts": {
+    "serve": "vue-cli-service serve",
+    "build": "vue-cli-service build",
+    "lint": "vue-cli-service lint"
+  },
+  "dependencies": {
+    "@mdi/font": "^3.6.95",
+    "axios": "^0.19.2",
+    "core-js": "^3.6.5",
+    "moment": "^2.24.0",
+    "roboto-fontface": "*",
+    "vue": "^2.6.11",
+    "vue-router": "^3.1.6",
+    "vuetify": "^2.2.11",
+    "vuex": "^3.1.3"
+  },
+  "devDependencies": {
+    "@vue/cli-plugin-babel": "~4.3.0",
+    "@vue/cli-plugin-eslint": "~4.3.0",
+    "@vue/cli-plugin-router": "~4.3.0",
+    "@vue/cli-plugin-vuex": "~4.3.0",
+    "@vue/cli-service": "~4.3.0",
+    "@vue/eslint-config-airbnb": "^5.0.2",
+    "babel-eslint": "^10.1.0",
+    "eslint": "^6.7.2",
+    "eslint-plugin-import": "^2.20.2",
+    "eslint-plugin-vue": "^6.2.2",
+    "node-sass": "^4.12.0",
+    "sass": "^1.19.0",
+    "sass-loader": "^8.0.2",
+    "vue-cli-plugin-vuetify": "~2.0.5",
+    "vue-template-compiler": "^2.6.11",
+    "vuetify-loader": "^1.3.0"
+  }
+}

+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width,initial-scale=1.0">
+    <link rel="icon" href="<%= BASE_URL %>favicon.ico">
+    <title>Profit-Hunter</title>
+  </head>
+  <body>
+    <noscript>
+      <strong>We're sorry but <%= htmlWebpackPlugin.options.title %> doesn't work properly without JavaScript enabled. Please enable it to continue.</strong>
+    </noscript>
+    <div id="app"></div>
+    <!-- built files will be auto injected -->
+  </body>
+</html>

+<template>
+  <v-app>
+    <v-app-bar app color="#ffffff" elevation="1" hide-on-scroll>
+      <v-icon size="35" class="mr-1" color="grey700">mdi-youtube</v-icon>
+      <div style="color: #343a40; font-size: 20px; font-weight: 500;">Youtube Auto Tagger</div>
+      <v-spacer></v-spacer>
+      <v-tooltip bottom>
+        <template v-slot:activator="{ on }">
+          <v-btn icon v-on="on" color="grey700" @click="clickInfo=true">
+            <v-icon size="30">mdi-information</v-icon>
+          </v-btn>
+        </template>
+        <span>Service Info</span>
+      </v-tooltip>
+    </v-app-bar>
+    <v-dialog v-model="clickInfo" max-width="600" class="pa-2">
+      <v-card elevation="0" outlined class="pa-4">
+        <v-row justify="center" class="mx-0 mb-8 mt-2">
+          <v-icon color="primary">mdi-power-on</v-icon>
+          <div
+            style="text-align: center; font-size: 22px; font-weight: 400; color: #343a40;"
+          >Information of This Service</div>
+          <v-icon color="primary">mdi-power-on</v-icon>
+        </v-row>
+        <v-btn color="primary" class="mt-2" block>Description of service</v-btn>
+        <v-btn color="primary" class="my-2" block>Term of this service</v-btn>
+        <v-btn color="primary" block>Used Opensource</v-btn>
+      </v-card>
+    </v-dialog>
+    <v-content>
+      <router-view />
+    </v-content>
+    <v-footer>
+      <v-row justify="center">
+        <v-avatar size="25" tile style="border-radius: 4px">
+          <v-img src="http://khuhub.khu.ac.kr/2020-1-capstone-design1/PKH_Project1/uploads/99f7d5c73e506d2c5c0072a21f362181/logo.69342704.png"></v-img>
+        </v-avatar>
+        <a href="http://khuhub.khu.ac.kr/2020-1-capstone-design1/PKH_Project1">
+          <div
+            style="margin-left: 4px; font-size: 16px; color: #5a5a5a; font-weight: 400"
+          >Profit-Hunter</div>
+        </a>
+      </v-row>
+    </v-footer>
+  </v-app>
+</template>
+<script>
+export default {
+  name: 'App',
+  data() {
+    return {
+      clickInfo: false,
+    };
+  },
+};
+</script>
+<style lang="scss">
+a:hover {
+  text-decoration: none;
+}
+a:link {
+  text-decoration: none;
+}
+a:visited {
+  text-decoration: none;
+}
+</style>

+<template>
+  <div></div>
+</template>
+
+<script>
+export default {
+
+};
+</script>
+
+<style>
+
+</style>

+<template>
+  <div></div>
+</template>
+
+<script>
+export default {
+
+};
+</script>
+
+<style>
+
+</style>

+<template>
+  <div></div>
+</template>
+
+<script>
+export default {
+
+};
+</script>
+
+<style>
+
+</style>

+import Vue from 'vue';
+import App from './App.vue';
+import router from './router';
+import store from './store';
+import vuetify from './plugins/vuetify';
+import 'roboto-fontface/css/roboto/roboto-fontface.css';
+import '@mdi/font/css/materialdesignicons.css';
+
+Vue.config.productionTip = false;
+
+new Vue({
+  router,
+  store,
+  vuetify,
+  render: (h) => h(App),
+}).$mount('#app');

+import Vue from 'vue';
+import Vuetify from 'vuetify/lib';
+
+Vue.use(Vuetify);
+
+export default new Vuetify({
+  theme: {
+    themes: {
+      light: {
+        primary: '#343a40',
+        secondary: '#506980',
+        accent: '#505B80',
+        error: '#FF5252',
+        info: '#2196F3',
+        blue: '#173f5f',
+        lightblue: '#72b1e4',
+        success: '#2779bd',
+        warning: '#12283a',
+        grey300: '#eceeef',
+        grey500: '#aaaaaa',
+        grey700: '#5a5a5a',
+        grey900: '#212529',
+      },
+      dark: {
+        primary: '#343a40',
+        secondary: '#506980',
+        accent: '#505B80',
+        error: '#FF5252',
+        info: '#2196F3',
+        blue: '#173f5f',
+        lightblue: '#72b1e4',
+        success: '#2779bd',
+        warning: '#12283a',
+        grey300: '#eceeef',
+        grey500: '#aaaaaa',
+        grey700: '#5a5a5a',
+        grey900: '#212529',
+      },
+    },
+  },
+});

+import Vue from 'vue';
+import VueRouter from 'vue-router';
+import axios from 'axios';
+import Home from '../views/Home.vue';
+
+Vue.prototype.$axios = axios;
+const apiRootPath = process.env.NODE_ENV !== 'production'
+  ? 'http://localhost:8000/api/'
+  : '/api/';
+Vue.prototype.$apiRootPath = apiRootPath;
+axios.defaults.baseURL = apiRootPath;
+
+Vue.use(VueRouter);
+
+const routes = [
+  {
+    path: '/',
+    name: 'Home',
+    component: Home,
+  },
+];
+
+const router = new VueRouter({
+  mode: 'history',
+  base: process.env.BASE_URL,
+  routes,
+});
+
+export default router;

+import Vue from 'vue';
+import Vuex from 'vuex';
+
+Vue.use(Vuex);
+
+export default new Vuex.Store({
+  state: {
+  },
+  mutations: {
+  },
+  actions: {
+  },
+  modules: {
+  },
+});

+<template>
+  <v-sheet>
+    <v-overlay v-model="loadingProcess">
+      <v-progress-circular :size="120" width="10" color="primary" indeterminate></v-progress-circular>
+      <div
+        style="color: #ffffff; font-size: 22px; margin-top: 20px; margin-left: -40px;"
+      >Analyzing Your Video...</div>
+    </v-overlay>
+    <v-layout justify-center>
+      <v-flex xs12 sm8 md6 lg4>
+        <v-row justify="center" class="mx-0 mt-12">
+          <div style="font-size: 34px; font-weight: 500; color: #343a40;">WELCOME</div>
+        </v-row>
+        <v-card elevation="0">
+          <!-- 데스크톱 화면 설명 요약 -->
+          <v-row justify="center" class="mx-0 mt-12" v-if="$vuetify.breakpoint.mdAndUp">
+            <v-flex md7 class="mt-1">
+              <div
+                style="font-size: 20px; font-weight: 300; color: #888;"
+              >This is Video auto tagging Service</div>
+              <div
+                style="font-size: 20px; font-weight: 300; color: #888;"
+              >Designed for Youtube Videos</div>
+              <div
+                style="font-size: 20px; font-weight: 300; color: #888;"
+              >It takes few minutes to analyze your Video!</div>
+            </v-flex>
+            <v-flex md5>
+              <v-card width="240" elevation="0" class="ml-5">
+                <v-img
+                  width="240"
+                  src="http://khuhub.khu.ac.kr/2020-1-capstone-design1/PKH_Project1/uploads/b70e4a173c2b7d5fa6ab73d48582dd6e/youtubelogoBlack.326653df.png"
+                ></v-img>
+              </v-card>
+            </v-flex>
+          </v-row>
+
+          <!-- 모바일 화면 설명 요약 -->
+          <v-card elevation="0" class="mt-8" v-else>
+            <div
+              style="font-size: 20px; font-weight: 300; color: #888; text-align: center"
+            >This is Video auto tagging Service</div>
+            <div
+              style="font-size: 20px; font-weight: 300; color: #888; text-align: center"
+            >Designed for Youtube Videos</div>
+            <div
+              style="font-size: 20px; font-weight: 300; color: #888; text-align: center"
+            >It takes few minutes to analyze your Video!</div>
+            <v-img
+              style="margin: auto; margin-top: 20px"
+              width="180"
+              src="http://khuhub.khu.ac.kr/2020-1-capstone-design1/PKH_Project1/uploads/b70e4a173c2b7d5fa6ab73d48582dd6e/youtubelogoBlack.326653df.png"
+            ></v-img>
+          </v-card>
+
+          <!-- Set Threshold -->
+          <div
+            class="mt-10"
+            style="font-size: 24px; text-align: center; font-weight: 400; color: #5a5a5a;"
+          >How To start this service</div>
+          <div
+            style="font-size: 20px; font-weight: 300; color: #888; text-align: center; margin-bottom: 15px"
+          >
+            <div>Set up Threshold of</div>
+            <div>Recommended Youtube link</div>
+          </div>
+          <v-row style="max-width: 300px; margin: auto">
+            <v-slider v-model="threshold" :thumb-size="20" thumb-label="always" :min="2" :max="15"></v-slider>
+          </v-row>
+
+          <!-- Upload Video -->
+          <div
+            style="font-size: 20px; font-weight: 300; color: #888; text-align: center"
+          >Then, Just Upload your Video</div>
+          <v-row justify="center" class="mx-0 mt-2">
+            <v-card
+              max-width="500"
+              outlined
+              height="120"
+              class="pa-9"
+              @dragover.prevent
+              @dragenter.prevent
+              @drop.prevent="onDrop"
+            >
+              <v-btn
+                style="text-transform: none"
+                @click="clickUploadButton"
+                text
+                large
+                color="primary"
+              >CLICK or DRAG & DROP</v-btn>
+              <input ref="fileInput" style="display: none" type="file" @change="onFileChange" />
+            </v-card>
+          </v-row>
+
+          <!-- 결과 화면 -->
+          <div
+            style="font-size: 24px; text-align: center; font-weight: 400; color: #5a5a5a;"
+            class="mt-10"
+          >The Results of Analyzed Video</div>
+          <v-card outlined class="pa-2 mx-5 mt-6" elevation="0" min-height="67">
+            <div
+              style="margin-left: 5px; margin-top: -18px; background-color: #fff; width: 110px; text-align: center;font-size: 14px; color: #5a5a5a; font-weight: 500"
+            >Generated Tags</div>
+            <v-chip-group column>
+              <v-chip color="secondary" v-for="(tag, index) in generatedTag" :key="index">{{ tag[0] }} : {{tag[1]}}</v-chip>
+            </v-chip-group>
+          </v-card>
+          <v-card outlined class="pa-3 mx-5 mt-8" elevation="0" min-height="67">
+            <div
+              style="margin-left: 5px; margin-top: -22px; margin-bottom: 5px; background-color: #fff; width: 140px; text-align: center;font-size: 14px; color: #5a5a5a; font-weight: 500"
+            >Related Youtube Link</div>
+            <v-flex style="margin-bottom: 2px" v-for="(url) in YoutubeUrl" :key="url.id">
+              <div>
+                <a style="color: #343a40; font-size: 16px; font-weight: 500" :href="url">{{url}}</a>
+              </div>
+            </v-flex>
+          </v-card>
+          <div
+            class="mt-3"
+            style="font-size: 20px; font-weight: 300; color: #888; text-align: center"
+          >If the Video is analyzed successfully,</div>
+          <div
+            class="mb-5"
+            style="font-size: 20px; font-weight: 300; color: #888; text-align: center"
+          >Result Show up in each of Boxes!</div>
+        </v-card>
+
+      </v-flex>
+    </v-layout>
+  </v-sheet>
+</template>
+<script>
+export default {
+  name: 'Home',
+  data() {
+    return {
+      videoFile: '',
+      YoutubeUrl: [],
+      generatedTag: [],
+      threshold: 5,
+      successDialog: false,
+      errorDialog: false,
+      loadingProcess: false,
+    };
+  },
+  created() {
+    // this.YoutubeUrl = [];
+    // this.generatedTag = [];
+  },
+  methods: {
+    loadVideoInfo() {},
+    uploadVideo(files) {
+      this.loadingProcess = true;
+      const formData = new FormData();
+      formData.append('file', files[0]);
+      formData.append('threshold', this.threshold);
+      console.log(files[0]);
+      this.$axios
+        .post('/upload', formData, {
+          headers: { 'Content-Type': 'multipart/form-data' },
+        })
+        .then((r) => {
+          const tag = r.data.tag_result;
+          const url = r.data.video_result;
+          url.forEach((element) => {
+            this.YoutubeUrl.push(element.video_url);
+          });
+          this.generatedTag = tag;
+          this.loadingProcess = false;
+          this.successDialog = true;
+          console.log(tag, url);
+        })
+        .catch((e) => {
+          this.errorDialog = true;
+          console.log(e.message);
+        });
+    },
+    onDrop(event) {
+      this.uploadVideo(event.dataTransfer.files);
+    },
+    clickUploadButton() {
+      this.$refs.fileInput.click();
+    },
+    onFileChange(event) {
+      this.uploadVideo(event.target.files);
+    },
+  },
+};
+</script>

+module.exports = {
+  transpileDependencies: [
+    'vuetify',
+  ],
+};