Upload 84 files

.dockerignore

@@ -0,0 +1,37 @@
+# Ignore a file or directory in the context root named "modules"
+venv
+
+# Ignore any files or directories within the subdirectory named "modules" 
+# in the context root
+venv/*
+
+# Ignore any files or directories in the context root beginning with "modules"
+venv*
+
+# Ignore any files or directories one level down from the context root named
+# "modules"
+*/venv
+
+# Ignore any files or directories at any level, including the context root, 
+# named modules
+**/venv
+
+# Git
+.git
+.gitignore
+
+# Docker
+.docker
+
+# Python
+__pycache__
+app/__pycache__/
+app/*/__pycache__/
+app/*/*/__pycache__/
+app/*/*/*/__pycache__/
+.env/
+.venv/
+venv/
+
+# Local PostgreSQL data
+data/

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+static/json/face_recognition_model-shard1 filter=lfs diff=lfs merge=lfs -text
+static/json/face_recognition_model-shard2 filter=lfs diff=lfs merge=lfs -text
+static/json/mtcnn_model-shard1 filter=lfs diff=lfs merge=lfs -text
+static/json/ssd_mobilenetv1_model-shard1 filter=lfs diff=lfs merge=lfs -text
+static/json/ssd_mobilenetv1_model-shard2 filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,9 @@
+venv
+.vs
+.vscode
+!models/Readme.txt
+uploaded_images/*
+!uploaded_images/Readme.txt
+uploaded_videos/*
+!uploaded_videos/Readme.txt
+

.python-version

@@ -0,0 +1 @@
+3.6.8

Dockerfile

@@ -0,0 +1,22 @@
+#pull the nvidia cuda GPU docker image
+FROM nvidia/cuda
+
+#pull python 3.6.8 docker image
+FROM python:3.6.8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+#create a directory to serve static files 
+RUN mkdir -p /home/app/staticfiles/app/uploaded_videos/
+WORKDIR /app
+COPY ./requirements.txt /app/requirements.txt
+RUN python -m pip install --upgrade pip
+RUN pip install cmake
+RUN pip install opencv-python==4.2.0.32
+RUN pip install -r requirements.txt
+COPY . /app
+RUN python manage.py collectstatic --noinput
+RUN pip install gunicorn
+RUN mkdir -p /app/uploaded_videos /app/uploaded_videos/
+
+VOLUME /app/run/
+ENTRYPOINT ["/app/bin/gunicorn_start.sh"]

bin/gunicorn_start.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+NAME="project_settings"                                  # Name of the application
+DJANGODIR=/app             # Django project directory
+SOCKFILE=/app/run/gunicorn.sock  # we will communicte using this unix socket
+NUM_WORKERS=3                                     # how many worker processes should Gunicorn spawn
+DJANGO_SETTINGS_MODULE=project_settings.settings             # which settings file should Django use
+DJANGO_WSGI_MODULE=project_settings.wsgi                     # WSGI module name
+
+echo "Starting $NAME as `whoami`"
+
+# Create the run directory if it doesn't exist
+RUNDIR=$(dirname $SOCKFILE)
+test -d $RUNDIR || mkdir -p $RUNDIR
+
+# Start your Django Gunicorn
+ gunicorn project_settings.wsgi:application --bind=unix:$SOCKFILE --workers $NUM_WORKERS --timeout 600

manage.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'project_settings.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()

ml_app/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

ml_app/apps.py

@@ -0,0 +1,5 @@
+from django.apps import AppConfig
+
+
+class MlAppConfig(AppConfig):
+    name = 'ml_app'

ml_app/forms.py

@@ -0,0 +1,6 @@
+from django import forms
+
+class VideoUploadForm(forms.Form):
+
+    upload_video_file = forms.FileField(label="Select Video", required=True,widget=forms.FileInput(attrs={"accept": "video/*"}))
+    sequence_length = forms.IntegerField(label="Sequence Length", required=True)

ml_app/models.py

@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.

ml_app/templates/404.html

@@ -0,0 +1,422 @@
+{% extends 'base.html' %}
+{%load static%}
+{%block content%}
+<style>
+    .main{
+  margin-top: 15%;
+}
+
+.st0{fill:#fff}
+.st2{fill:#5d89af}
+.st3{fill:#709abf}
+.st4,.st6{
+  fill:#fff;
+  stroke:#b3dcdf;
+  stroke-miterlimit:10
+}
+.st6{
+  stroke:#5d89af;
+  stroke-width:2
+}
+.st7,.st8,.st9{
+  stroke:#709abf;
+  stroke-miterlimit:10
+}
+
+.st7{
+  stroke-width:5;
+  stroke-linecap:round;
+  fill:none
+}
+.st8,.st9{
+  fill:#fff
+}
+.st9{
+  fill:none
+}
+.st10{
+  
+}
+
+#cloud1{
+  animation: cloud003 15s linear infinite;
+}
+
+#cloud2{
+  animation: cloud002 25s linear infinite;
+}
+
+#cloud3{
+  animation: cloud003 20s linear infinite;
+}
+
+#cloud4{
+  animation: float 4s linear infinite;
+}
+
+#cloud5{
+  animation: float 8s linear infinite;
+}
+
+#cloud7{
+  animation: float 5s linear infinite;
+}
+
+#tracks{
+  animation: slide 650ms linear infinite;
+}
+
+#bumps{
+  animation: land 10000ms linear infinite;
+}
+
+@keyframes jig {
+    0% { transform: translateY(0px); }
+    50% { transform: translateY(1px); }
+    100% { transform: translateY(0px); }
+}
+
+#car-layers{
+  animation: jig 0.35s linear infinite;
+}
+
+@keyframes land {
+    from { transform: translateX(0); }
+    to { transform: translateX(1000px); }
+}
+
+
+@keyframes slide {
+    from { transform: translateX(0px); }
+    to { transform: translateX(100px); }
+}
+
+/* @keyframes cloudFloat {
+  0% { transform: translateX(0) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+} */
+
+@keyframes cloud001 {
+  0% { transform: translateX(-1000px) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+}
+
+@keyframes cloud002 {
+  0% { transform: translateX(-1000px) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+}
+
+@keyframes cloud003 {
+  0% { transform: translateX(-1000px) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+}
+
+@keyframes float {
+    0% { transform: translateY(0px) translateX(0); }
+    50% { transform: translateY(8px) translateX(5px); }
+    100% { transform: translateY(0px) translateX(0); }
+}
+
+#bracefront, #braceback{
+  animation: braces 1s linear infinite;
+}
+
+@keyframes braces {
+    0% { transform: translateX(-2px); }
+  25% { transform: translateX(3px); }
+    50% { transform: translateX(-2px); }
+    75% { transform: translateX(3px); }
+  100% { transform: translateX(-2px); }
+}
+</style>
+<div class="main">
+    <div>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1000 355">
+    <g id="ocean">
+      <path id="sky" class="st0" d="M0 0h1000v203.1H0z"/>
+      <linearGradient id="water_1_" gradientUnits="userSpaceOnUse" x1="500" y1="354" x2="500" y2="200.667">
+        <stop offset="0" stop-color="#fff"/>
+        <stop offset="1" stop-color="#b3dcdf"/>
+      </linearGradient>
+      <path id="water" fill="url(#water_1_)" d="M0 200.7h1000V354H0z"/>
+      <path id="land" class="st0" d="M0 273.4h1000V354H0z"/>
+      <g id="bumps">
+        <path class="st0" d="M0 275.2s83.8-28 180-28 197 28 197 28H0z"/>
+      <path class="st0" d="M377 275.2s54.7-28 117.5-28 128.6 28 128.6 28H377z"/>
+      <path class="st0" d="M623.2 275.2s83.7-28 179.9-28 196.9 28 196.9 28H623.2z"/>
+        <path class="st0" d="M-998 275.2s83.8-28 180-28 197 28 197 28h-377z"/>
+        <path class="st0" d="M-621 275.2s54.7-28 117.5-28 128.6 28 128.6 28H-621z"/>
+        <path class="st0" d="M-374.8 275.2s83.7-28 179.9-28S2 275.2 2 275.2h-376.8z"/>
+      </g>
+    </g>
+    <g id="tracks">
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+        <path class="st2" d="M150 282.4h-3l-6.8 25.2h3z"/>
+        <path class="st2" d="M140 282.4h-3l-6.8 25.2h3z"/>
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+        <path class="st2" d="M60 282.4h-3l-6.8 25.2h3z"/>
+      </g>
+      <path class="st2" d="M550 282.4h-3l-6.8 25.2h3z"/>
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+      <path class="st2" d="M540 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M530 282.4h-3l-6.8 25.2h3z"/>
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+      <path class="st2" d="M510 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st3" d="M-499.5 300.2H1000v5.1H-499.5z"/>
+      <path class="st3" d="M-499.5 283.8H1000v2.8H-499.5z"/>
+    </g>
+    <g id="cloudsAll">
+      <path id="cloud1" class="st4" d="M19.5 69.7s-21.3.5-25-12.2c0 0-4.3-21.3 16-21.8 0 0-2.1-12.2 12.2-14.9 0 0 15-3.2 21.3 6.9 0 0 3.6-20.7 17.8-22.3 0 0 24-3 26.6 13.1 0 0 .1 9.5-2.8 13.5 0 0 9.5-15 26.5-4.8 0 0 12.1 7.9 7 20.2 0 0 16 4.8 10.1 18.1 0 0-10.2 8.5-17.1-1.1 0 0-5.5 16-32.5 16 0 0-19.1 2.1-27-13.3 0 0 .5 10.1-13.3 10.6-.1 0-20.3 3.2-19.8-8z"/>
+      <path id="cloud3" class="st4" d="M836 132s-18.3 2.1-22.2-4.9c0 0-4.9-11.8 12.5-13.8 0 0-2.5-6.8 9.7-9.6 0 0 12.7-3.1 18.7 2.1 0 0 2-12.2 14-14.3 0 0 16.6-3.3 23.7 2.1 0 0 4.8 3.9 2.4 6.5 0 0 3.1-4.8 18.4-.4 0 0 10.9 3.5 7.2 11 0 0 13.8-1.5 9.7 9.5 0 0-4.1 10.8-15.5 4.8 0 0-3.1 5.6-26.4 7.9 0 0-16.3 2.8-24-5.3 0 0 1 5.7-10.8 7.2-.1.1-17.2 3.6-17.4-2.8z"/>
+      <path id="cloud2" class="st4" d="M19.3 159.5s-15.9.6-18.8-5.1c0 0-3.4-9.5 11.7-10.1 0 0-1.7-5.5 9-6.9 0 0 11.2-1.7 16 2.8 0 0 2.5-9.4 13.1-10.3 0 0 17.9-1.8 20 5.4 0 0 .2 4.3-2 6.1 0 0 6.9-6.9 19.8-2.6 0 0 9.1 3.4 5.5 9 0 0 6.5 0 4.5 6.7 0 0-2.6 5.6-9.6 1 0 0-4 7.3-24.2 7.7 0 0-14.2 1.3-20.4-5.5 0 0 .5 4.5-9.8 5 0 .1-15 1.8-14.8-3.2z"/>
+      <path id="cloud4" class="st4" d="M370.3 109.5s15.9.6 18.8-5.1c0 0 3.4-9.5-11.7-10.1 0 0 1.7-5.5-9-6.9 0 0-11.2-1.7-16 2.8 0 0-2.5-9.4-13.1-10.3 0 0-17.9-1.8-20 5.4 0 0-.2 4.3 2 6.1 0 0-6.9-6.9-19.8-2.6 0 0-9.1 3.4-5.5 9 0 0-12 1.9-7.7 8 0 0 7.5 4 12.8-.2 0 0 4 7.3 24.2 7.7 0 0 14.2 1.3 20.4-5.5 0 0-.5 4.5 9.8 5 0 0 15.1 1.7 14.8-3.3z"/>
+      <path id="cloud5" class="st4" d="M511.7 12.4s-21.3-.3-25 7c0 0-4.3 12.2 16 12.5 0 0-2.1 7 12.2 8.6 0 0 15 1.8 21.3-4 0 0 3.6 11.9 17.8 12.8 0 0 19.5 1.6 27-4.4 0 0 5-4.4 2.1-6.7 0 0 4.1 4.4 21.2-1.5 0 0 12.1-4.6 7-11.6 0 0 16-2.8 10.1-10.4 0 0-10.2-4.9-17.1.6 0 0-5.5-9.2-32.5-9.2 0 0-19.1-1.2-27 7.6 0 0 .5-5.8-13.3-6.1-.1.2-20.3-1.6-19.8 4.8z"/>
+    </g>
+    <g id="train">
+      <path fill="#b3dcdf" d="M344.5 248.5h507.2v37.8H344.5z"/>
+      <g id="wheels">
+        <circle class="st6" cx="384.1" cy="285.6" r="15.1"/>
+        <path class="st2" d="M384.1 295.7c-5.6 0-10.1-4.5-10.1-10.1s4.5-10.1 10.1-10.1 10.1 4.5 10.1 10.1c0 5.5-4.6 10.1-10.1 10.1z"/>
+        <circle class="st6" cx="416.1" cy="285.6" r="15.1"/>
+        <path class="st2" d="M416.1 295.7c-5.6 0-10.1-4.5-10.1-10.1s4.5-10.1 10.1-10.1 10.1 4.5 10.1 10.1c0 5.5-4.6 10.1-10.1 10.1z"/>
+        <circle class="st6" cx="469.1" cy="285.6" r="15.1"/>
+        <path class="st2" d="M469.1 295.7c-5.6 0-10.1-4.5-10.1-10.1s4.5-10.1 10.1-10.1 10.1 4.5 10.1 10.1c0 5.5-4.6 10.1-10.1 10.1z"/>
+        <circle class="st6" cx="734.1" cy="285.6" r="15.1"/>
+        <path class="st2" d="M734.1 295.7c-5.6 0-10.1-4.5-10.1-10.1s4.5-10.1 10.1-10.1 10.1 4.5 10.1 10.1c0 5.5-4.6 10.1-10.1 10.1z"/>
+        <circle class="st6" cx="766.1" cy="285.6" r="15.1"/>
+        <path class="st2" d="M766.1 295.7c-5.6 0-10.1-4.5-10.1-10.1s4.5-10.1 10.1-10.1 10.1 4.5 10.1 10.1c0 5.5-4.6 10.1-10.1 10.1z"/>
+        <circle class="st6" cx="821.1" cy="285.6" r="15.1"/>
+        <path class="st2" d="M821.1 295.7c-5.6 0-10.1-4.5-10.1-10.1s4.5-10.1 10.1-10.1 10.1 4.5 10.1 10.1c0 5.5-4.6 10.1-10.1 10.1z"/>
+      </g>
+      <path id="bracefront" class="st7" d="M383.2 285.6h88.1"/>
+      <path id="braceback" class="st7" d="M733.2 285.6h88.1"/>
+      <g id="car-layers">
+        <path id="car" class="st8" d="M321.8 300.7v-32.4s1.2.7-1.5-2.4v-29.1s3.1-11.6 10.7-21.1c0 0 7.6-12 15.5-17.5h1.3s10.2-4.9 30.9-28h.6s-.9-1.4 0-2.7c0 0 10.1-10.5 21-12.3 0 0 9.4-1.8 20.2-1.8h47.7V151H492v-1.1h10.1v1.1h19v2.2s8.2.9 19.2-4.2c0 0 1.4-1.1 28.8-1.1h291.5v6.8h7.5v2.2s12.2-.6 12.2 9.8V177l-10-.1v57.9s14.9-.5 14.9 10.2c0 0 1 9-14.9 8.9v3.8H719.5s-2.4.1-4.3 3l-15 29s-2.9 5.1-10.8 5.1H504.3s-2.9.1-6.1-5l-13.1-25s-4.5-7.1-11.8-7.1H369v2.4s-3.2 1.3-7.1 8.7L351.4 289s-2.9 6.3-6.9 6.4h-17.8l-4.9 5.3z"/>
+        <path id="streamline-outine" class="st8" d="M320.3 236.6s1.4-6.8 4.4-11.3c0 0 .1-2.3 23.2-6.3l78-16.6s103.3-21.1 134.9-26.1c0 0 93.3-16 120.5-17.9 0 0 57.6-4.3 100-4.1h88.9v63.4s-10.3 5.4-17.1 5.3c0 0-305.6 4.9-366.3 8.1 0 0-100.3 4.8-119.1 6.8 0-.1-46.6 1.2-47.4-1.3z"/>
+        <g id="window-grate">
+          <path class="st9" d="M739.5 182.6H854"/>
+          <path class="st9" d="M739.5 177.6H854"/>
+          <path class="st9" d="M739.5 172.6H854"/>
+          <path class="st9" d="M739.5 167.6H854"/>
+          <path class="st9" d="M739.5 161.4H854v26.1H739.5z"/>
+        </g>
+        <path class="st9" d="M320.3 257.8h549.9"/>
+        <g id="Text">
+          <text transform="translate(377.037 230.025)" class="st8 st10" font-size="21">
+            404
+          </text>
+          <text transform="translate(659.5 213.994)" class="st8 st10" font-size="24.025">
+          Page not found.
+          </text>
+        </g>
+        <g id="ladders">
+          <g id="ladder-f">
+            <path id="front-ladder" class="st8" d="M433.8 258.4h17.8v34.8h-17.8z"/>
+            <path id="fb-rung" class="st9" d="M433.8 281.1h17.7"/>
+            <path id="ft-rung" class="st9" d="M433.8 268.6h17.7"/>
+          </g>
+          <g id="ladder-b">
+            <path id="ladder-back" class="st8" d="M851.8 257.8h17.8v34.8h-17.8z"/>
+            <path id="bt-rung" class="st9" d="M851.8 268.6h17.7"/>
+            <path id="bb-rung" class="st9" d="M851.8 281.1h17.7"/>
+          </g>
+        </g>
+        <path id="window-front" class="st8" d="M350.5 196.4s-.4 3.9 15.2 4.3l32.3-30.3s-18.2 1.1-19-.8l-28.5 26.8z"/>
+      </g>
+    </g>
+  </svg>
+    </div>
+  </div>
+
+
+{%endblock%}

ml_app/templates/about.html

@@ -0,0 +1,4 @@
+{% extends 'base.html' %}
+{% load static %}
+{%block content%}
+{%endblock%}

ml_app/templates/cuda_full.html

@@ -0,0 +1,422 @@
+{% extends 'base.html' %}
+{%load static%}
+{%block content%}
+<style>
+    .main{
+  margin-top: 15%;
+}
+
+.st0{fill:#fff}
+.st2{fill:#5d89af}
+.st3{fill:#709abf}
+.st4,.st6{
+  fill:#fff;
+  stroke:#b3dcdf;
+  stroke-miterlimit:10
+}
+.st6{
+  stroke:#5d89af;
+  stroke-width:2
+}
+.st7,.st8,.st9{
+  stroke:#709abf;
+  stroke-miterlimit:10
+}
+
+.st7{
+  stroke-width:5;
+  stroke-linecap:round;
+  fill:none
+}
+.st8,.st9{
+  fill:#fff
+}
+.st9{
+  fill:none
+}
+.st10{
+  
+}
+
+#cloud1{
+  animation: cloud003 15s linear infinite;
+}
+
+#cloud2{
+  animation: cloud002 25s linear infinite;
+}
+
+#cloud3{
+  animation: cloud003 20s linear infinite;
+}
+
+#cloud4{
+  animation: float 4s linear infinite;
+}
+
+#cloud5{
+  animation: float 8s linear infinite;
+}
+
+#cloud7{
+  animation: float 5s linear infinite;
+}
+
+#tracks{
+  animation: slide 650ms linear infinite;
+}
+
+#bumps{
+  animation: land 10000ms linear infinite;
+}
+
+@keyframes jig {
+    0% { transform: translateY(0px); }
+    50% { transform: translateY(1px); }
+    100% { transform: translateY(0px); }
+}
+
+#car-layers{
+  animation: jig 0.35s linear infinite;
+}
+
+@keyframes land {
+    from { transform: translateX(0); }
+    to { transform: translateX(1000px); }
+}
+
+
+@keyframes slide {
+    from { transform: translateX(0px); }
+    to { transform: translateX(100px); }
+}
+
+/* @keyframes cloudFloat {
+  0% { transform: translateX(0) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+} */
+
+@keyframes cloud001 {
+  0% { transform: translateX(-1000px) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+}
+
+@keyframes cloud002 {
+  0% { transform: translateX(-1000px) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+}
+
+@keyframes cloud003 {
+  0% { transform: translateX(-1000px) translateY(3px); }
+  100% { transform: translateX(1000px) translateY(0); }
+}
+
+@keyframes float {
+    0% { transform: translateY(0px) translateX(0); }
+    50% { transform: translateY(8px) translateX(5px); }
+    100% { transform: translateY(0px) translateX(0); }
+}
+
+#bracefront, #braceback{
+  animation: braces 1s linear infinite;
+}
+
+@keyframes braces {
+    0% { transform: translateX(-2px); }
+  25% { transform: translateX(3px); }
+    50% { transform: translateX(-2px); }
+    75% { transform: translateX(3px); }
+  100% { transform: translateX(-2px); }
+}
+</style>
+<div class="main">
+    <div>
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1000 355">
+    <g id="ocean">
+      <path id="sky" class="st0" d="M0 0h1000v203.1H0z"/>
+      <linearGradient id="water_1_" gradientUnits="userSpaceOnUse" x1="500" y1="354" x2="500" y2="200.667">
+        <stop offset="0" stop-color="#fff"/>
+        <stop offset="1" stop-color="#b3dcdf"/>
+      </linearGradient>
+      <path id="water" fill="url(#water_1_)" d="M0 200.7h1000V354H0z"/>
+      <path id="land" class="st0" d="M0 273.4h1000V354H0z"/>
+      <g id="bumps">
+        <path class="st0" d="M0 275.2s83.8-28 180-28 197 28 197 28H0z"/>
+      <path class="st0" d="M377 275.2s54.7-28 117.5-28 128.6 28 128.6 28H377z"/>
+      <path class="st0" d="M623.2 275.2s83.7-28 179.9-28 196.9 28 196.9 28H623.2z"/>
+        <path class="st0" d="M-998 275.2s83.8-28 180-28 197 28 197 28h-377z"/>
+        <path class="st0" d="M-621 275.2s54.7-28 117.5-28 128.6 28 128.6 28H-621z"/>
+        <path class="st0" d="M-374.8 275.2s83.7-28 179.9-28S2 275.2 2 275.2h-376.8z"/>
+      </g>
+    </g>
+    <g id="tracks">
+      <path class="st2" d="M9.8 282.4h-3L0 307.6h3z"/>
+      <path class="st2" d="M19.8 282.4h-3L10 307.6h3z"/>
+      <path class="st2" d="M29.8 282.4h-3L20 307.6h3z"/>
+      <path class="st2" d="M39.8 282.4h-3L30 307.6h3z"/>
+      <path class="st2" d="M49.8 282.4h-3L40 307.6h3z"/>
+      <path class="st2" d="M59.8 282.4h-3L50 307.6h3z"/>
+      <path class="st2" d="M69.8 282.4h-3L60 307.6h3z"/>
+      <path class="st2" d="M79.8 282.4h-3L70 307.6h3z"/>
+      <path class="st2" d="M89.8 282.4h-3L80 307.6h3z"/>
+      <path class="st2" d="M99.8 282.4h-3L90 307.6h3z"/>
+      <path class="st2" d="M109.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M119.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M129.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M139.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M149.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M159.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M169.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M179.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M189.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M199.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M209.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M219.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M229.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M239.8 282.4h-3l-6.8 25.2h3z"/>
+      <path class="st2" d="M249.8 282.4h-3l-6.8 25.2h3z"/>
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+        <path id="car" class="st8" d="M321.8 300.7v-32.4s1.2.7-1.5-2.4v-29.1s3.1-11.6 10.7-21.1c0 0 7.6-12 15.5-17.5h1.3s10.2-4.9 30.9-28h.6s-.9-1.4 0-2.7c0 0 10.1-10.5 21-12.3 0 0 9.4-1.8 20.2-1.8h47.7V151H492v-1.1h10.1v1.1h19v2.2s8.2.9 19.2-4.2c0 0 1.4-1.1 28.8-1.1h291.5v6.8h7.5v2.2s12.2-.6 12.2 9.8V177l-10-.1v57.9s14.9-.5 14.9 10.2c0 0 1 9-14.9 8.9v3.8H719.5s-2.4.1-4.3 3l-15 29s-2.9 5.1-10.8 5.1H504.3s-2.9.1-6.1-5l-13.1-25s-4.5-7.1-11.8-7.1H369v2.4s-3.2 1.3-7.1 8.7L351.4 289s-2.9 6.3-6.9 6.4h-17.8l-4.9 5.3z"/>
+        <path id="streamline-outine" class="st8" d="M320.3 236.6s1.4-6.8 4.4-11.3c0 0 .1-2.3 23.2-6.3l78-16.6s103.3-21.1 134.9-26.1c0 0 93.3-16 120.5-17.9 0 0 57.6-4.3 100-4.1h88.9v63.4s-10.3 5.4-17.1 5.3c0 0-305.6 4.9-366.3 8.1 0 0-100.3 4.8-119.1 6.8 0-.1-46.6 1.2-47.4-1.3z"/>
+        <g id="window-grate">
+          <path class="st9" d="M739.5 182.6H854"/>
+          <path class="st9" d="M739.5 177.6H854"/>
+          <path class="st9" d="M739.5 172.6H854"/>
+          <path class="st9" d="M739.5 167.6H854"/>
+          <path class="st9" d="M739.5 161.4H854v26.1H739.5z"/>
+        </g>
+        <path class="st9" d="M320.3 257.8h549.9"/>
+        <g id="Text">
+          <text transform="translate(377.037 230.025)" class="st8 st10" font-size="21">
+            Server is out of GPU memory.
+          </text>
+          <text transform="translate(659.5 213.994)" class="st8 st10" font-size="24.025">
+          Please try again.
+          </text>
+        </g>
+        <g id="ladders">
+          <g id="ladder-f">
+            <path id="front-ladder" class="st8" d="M433.8 258.4h17.8v34.8h-17.8z"/>
+            <path id="fb-rung" class="st9" d="M433.8 281.1h17.7"/>
+            <path id="ft-rung" class="st9" d="M433.8 268.6h17.7"/>
+          </g>
+          <g id="ladder-b">
+            <path id="ladder-back" class="st8" d="M851.8 257.8h17.8v34.8h-17.8z"/>
+            <path id="bt-rung" class="st9" d="M851.8 268.6h17.7"/>
+            <path id="bb-rung" class="st9" d="M851.8 281.1h17.7"/>
+          </g>
+        </g>
+        <path id="window-front" class="st8" d="M350.5 196.4s-.4 3.9 15.2 4.3l32.3-30.3s-18.2 1.1-19-.8l-28.5 26.8z"/>
+      </g>
+    </g>
+  </svg>
+    </div>
+  </div>
+
+
+{%endblock%}

ml_app/templates/index.html

@@ -0,0 +1,71 @@
+{% extends 'base.html' %}
+{%load static%}
+{%block content%}
+<div class="bg" >
+<div class="container">
+    
+    <div class="row align-items-center justify-content-center">
+        <div class="col-12 my-auto">
+            <div class="logo text-center mb-3"><img src="{% static 'images/logo1.png'%}" alt="Logo" ></div>
+            <div class="width-400">
+                <video width="100%" controls id="videos">
+                    <source src="" id="video_source">
+                    Your browser does not support HTML5 video.
+                </video>
+                <form class="form" method="POST" enctype="multipart/form-data" name="video-upload" id="video-upload"
+                    class="text-center mt-3">
+                    {%csrf_token%}
+                    <div class="form-group">
+                        <label>{{form.upload_video_file.widget}}</label>
+                        {{form.upload_video_file}}
+                        <!-- <input type="file" id="{{form.upload_video_file.id_for_label}}" name="{{form.upload_video_file.name}}" /> -->
+                        {%if form.upload_video_file.errors%}
+                        {%for each_error in form.upload_video_file.errors%}
+                        <div class="alert alert-danger mt-1 {{form.upload_video_file.id_for_label}}">
+                            {{each_error}}
+                        </div>
+                        {%endfor%}
+                        {%endif%}
+                    </div>
+                    <div class="form-group">
+                        <label for="{{form.sequence_length.id_for_label}}">{{form.sequence_length.label}}: </label><span
+                            id="slider-value"></span>
+                        <input type="number" hidden="hidden" id="{{form.sequence_length.id_for_label}}"
+                            name="{{form.sequence_length.name}}"></input>
+                        <div id='slider'></div>
+                        {%if form.sequence_length.errors%}
+                        {%for each_error in form.sequence_length.errors%}
+                        <div class="alert alert-danger mt-1 {{form.sequence_length.id_for_label}}">
+                            {{each_error}}
+                        </div>
+                        {%endfor%}
+                        {%endif%}
+                    </div>
+                    <button id="videoUpload" type="submit" name="submit" class="btn btn-success mt-3 btn-block">Upload</button>
+                </form>
+            </div>
+        </div>
+    </div>
+</div>
+</div>
+{%endblock%}
+{%block js_cripts%}
+<script src="{%static 'js/script.js'%}"></script>
+<script>
+    $(function () {
+        var sliderSequenceNumbers = [10,20,40,60,80,100];
+        var slider = $("div#slider").slider({
+            value: 1,
+            min: 0,
+            max: sliderSequenceNumbers.length-1,
+            slide: function (event, ui) {
+                $('#{{form.sequence_length.id_for_label}}').val(sliderSequenceNumbers[ui.value]);
+                $('#{{form.sequence_length.id_for_label}}').val(sliderSequenceNumbers[ui.value]);
+                $('#slider-value').html(sliderSequenceNumbers[ui.value]);
+            }
+        });
+        $("#{{form.sequence_length.id_for_label}}").val(sliderSequenceNumbers[$("#slider").slider("value")]);
+        $('#slider-value').html(sliderSequenceNumbers[$("#slider").slider("value")]);
+    });
+</script>
+{%endblock%}

ml_app/templates/predict.html

@@ -0,0 +1,110 @@
+{% extends 'base.html' %}
+{% load static %}
+{%block content%}
+{%if no_faces%}
+<div class="container">
+  <div class="logo text-center mb-3"><img src="{% static 'images/logo1.png'%}" alt="Logo" ></div>
+  <hr />
+  <div class="alert alert-danger">
+    No faces detected. Cannot process the video.
+  </div>
+</div>
+{%else%}
+<div class="container">
+  <div class="logo text-center mb-3"><img src="{% static 'images/logo1.png'%}" alt="Logo" ></div>
+  <hr />
+
+  <h3>Frames Split</h3>
+  <div id="preprocessed_images" class="col-12 mt-4 mb-2">
+    {% for each_image in preprocessed_images %}
+    <img src="{%static each_image%}" class="preprocess" width=auto height="250" />
+    {%endfor%}
+  </div>
+
+
+  <h3>Face Cropped Frames</h3>
+  <div id="faces_images" class="col-12 mb-2">
+    {% for each_image in faces_cropped_images %}
+    <img src="{%static each_image%}" class="faces" width=auto height="150" />
+    {%endfor%}
+  </div>
+
+  <div class="result text-center">
+    <h3>Play to see Result</h3>
+    <video height="320" width="640" id="predict-media" controls>
+      <source src="{{MEDIA_URL}}{{original_video}}" type="video/mp4" codecs="avc1.4d002a" />
+    </video>
+    {%if output == "REAL" %}
+    <h4 class="mx-auto">Result: <span style="color:green">{{output}}</span>
+      <img src="{% static 'images/thumpup.png'%}" alt="real" height="100px" width=auto>
+      {%else%}
+      <h4 class="mx-auto">Result: <span style="color:red">{{output}}</span>
+        <img src="{% static 'images/thumpdown.png'%}" alt="fake" height="100px" width=auto >
+      {%endif%}
+  </div>
+<!--
+  <h3>Heat Maps</h3>
+  <div id="heatmap_images" class="col-12 mb-4 ">
+    {% for each_heatmap_image in heatmap_images %}
+    <img src="{%static each_heatmap_image%}" class="heat-map" width="100" height="150" />
+    {%endfor%}
+  </div>
+</div>
+-->
+{%endif%}
+{%endblock%}
+{%block js_cripts%}
+<script src="{%static 'js/face-api.min.js'%}"></script>
+<script>
+  $(document).ready(function () {
+    const video = document.getElementById("predict-media");
+
+    Promise.all([
+    faceapi.nets.ssdMobilenetv1.loadFromUri('/static/json'),
+      faceapi.nets.tinyFaceDetector.loadFromUri("/static/json")
+      
+    ])
+
+    var detectionTimeout;
+    video.addEventListener("playing", () => {
+      var canvas;
+      if ($('canvas').length < 1) {
+        canvas = faceapi.createCanvasFromMedia(video);
+        canvas.style.top = video.offsetTop + "px";
+        canvas.style.left = video.offsetLeft + "px";
+        document.body.append(canvas);
+      }
+      /* In order to be able to pause the video */
+      const displaySize = { width: video.width, height: video.height - 60 };
+      faceapi.matchDimensions(canvas, displaySize);
+
+      detectionTimeout = setInterval(async () => {
+        const detections = await faceapi.detectAllFaces(video);
+        const resizedDetections = faceapi.resizeResults(detections, displaySize);
+        canvas.getContext("2d").clearRect(0, 0, canvas.width, canvas.height);
+        canvas.style.top = video.offsetTop + "px";
+        canvas.style.left = video.offsetLeft + "px";
+        resizedDetections.forEach((result, i) => {
+          console.log(resizedDetections[i].box);
+          var result = '{{output}}';
+          var confidence = '{{confidence}}';
+          var drawOptions = {label: result.concat("  " , confidence , "%")};
+          if (result == 'REAL'){
+            drawOptions["boxColor"] = "#0f0";
+          }
+          else if (result == 'FAKE'){
+            drawOptions["boxColor"] = "#f00";
+          }
+          var box = { x: resizedDetections[i].box.x, y: resizedDetections[i].box.y, height: 100, width: 100 };
+          const drawBox = new faceapi.draw.DrawBox(box, drawOptions);
+          drawBox.draw(canvas);
+        });
+      }, 1);
+    });
+
+    video.addEventListener("paused", () => {
+      clearTimeout(detectionTimeout);
+    });
+  })
+</script>
+{%endblock%}

ml_app/tests.py

@@ -0,0 +1,3 @@
+from django.test import TestCase
+
+# Create your tests here.

ml_app/urls.py

@@ -0,0 +1,16 @@
+"""project_settings URL Configuration
+"""
+from django.contrib import admin
+from django.urls import path, include
+from . import views
+from .views import about, index, predict_page,cuda_full
+
+app_name = 'ml_app'
+handler404 = views.handler404
+
+urlpatterns = [
+    path('', index, name='home'),
+    path('about/', about, name='about'),
+    path('predict/', predict_page, name='predict'),
+    path('cuda_full/',cuda_full,name='cuda_full'),
+]

ml_app/views.py

@@ -0,0 +1,366 @@
+from django.shortcuts import render, redirect
+import torch
+import torchvision
+from torchvision import transforms, models
+from torch.utils.data import DataLoader
+from torch.utils.data.dataset import Dataset
+import os
+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+import face_recognition
+from torch.autograd import Variable
+import time
+import sys
+from torch import nn
+import json
+import glob
+import copy
+from torchvision import models
+import shutil
+from PIL import Image as pImage
+import time
+from django.conf import settings
+from .forms import VideoUploadForm
+
+index_template_name = 'index.html'
+predict_template_name = 'predict.html'
+
+im_size = 112
+mean=[0.485, 0.456, 0.406]
+std=[0.229, 0.224, 0.225]
+sm = nn.Softmax()
+inv_normalize =  transforms.Normalize(mean=-1*np.divide(mean,std),std=np.divide([1,1,1],std))
+
+train_transforms = transforms.Compose([
+                                        transforms.ToPILImage(),
+                                        transforms.Resize((im_size,im_size)),
+                                        transforms.ToTensor(),
+                                        transforms.Normalize(mean,std)])
+
+class Model(nn.Module):
+
+    def __init__(self, num_classes,latent_dim= 2048, lstm_layers=1 , hidden_dim = 2048, bidirectional = False):
+        super(Model, self).__init__()
+        model = models.resnext50_32x4d(pretrained = True)
+        self.model = nn.Sequential(*list(model.children())[:-2])
+        self.lstm = nn.LSTM(latent_dim,hidden_dim, lstm_layers,  bidirectional)
+        self.relu = nn.LeakyReLU()
+        self.dp = nn.Dropout(0.4)
+        self.linear1 = nn.Linear(2048,num_classes)
+        self.avgpool = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x):
+        batch_size,seq_length, c, h, w = x.shape
+        x = x.view(batch_size * seq_length, c, h, w)
+        fmap = self.model(x)
+        x = self.avgpool(fmap)
+        x = x.view(batch_size,seq_length,2048)
+        x_lstm,_ = self.lstm(x,None)
+        return fmap,self.dp(self.linear1(x_lstm[:,-1,:]))
+
+
+class validation_dataset(Dataset):
+    def __init__(self,video_names,sequence_length=60,transform = None):
+        self.video_names = video_names
+        self.transform = transform
+        self.count = sequence_length
+
+    def __len__(self):
+        return len(self.video_names)
+
+    def __getitem__(self,idx):
+        video_path = self.video_names[idx]
+        frames = []
+        a = int(100/self.count)
+        first_frame = np.random.randint(0,a)
+        for i,frame in enumerate(self.frame_extract(video_path)):
+            #if(i % a == first_frame):
+            faces = face_recognition.face_locations(frame)
+            try:
+              top,right,bottom,left = faces[0]
+              frame = frame[top:bottom,left:right,:]
+            except:
+              pass
+            frames.append(self.transform(frame))
+            if(len(frames) == self.count):
+                break
+        """
+        for i,frame in enumerate(self.frame_extract(video_path)):
+            if(i % a == first_frame):
+                frames.append(self.transform(frame))
+        """        
+        # if(len(frames)<self.count):
+        #   for i in range(self.count-len(frames)):
+        #         frames.append(self.transform(frame))
+        #print("no of frames", self.count)
+        frames = torch.stack(frames)
+        frames = frames[:self.count]
+        return frames.unsqueeze(0)
+    
+    def frame_extract(self,path):
+      vidObj = cv2.VideoCapture(path) 
+      success = 1
+      while success:
+          success, image = vidObj.read()
+          if success:
+              yield image
+
+def im_convert(tensor, video_file_name):
+    """ Display a tensor as an image. """
+    image = tensor.to("cpu").clone().detach()
+    image = image.squeeze()
+    image = inv_normalize(image)
+    image = image.numpy()
+    image = image.transpose(1,2,0)
+    image = image.clip(0, 1)
+    # This image is not used
+    # cv2.imwrite(os.path.join(settings.PROJECT_DIR, 'uploaded_images', video_file_name+'_convert_2.png'),image*255)
+    return image
+
+def im_plot(tensor):
+    image = tensor.cpu().numpy().transpose(1,2,0)
+    b,g,r = cv2.split(image)
+    image = cv2.merge((r,g,b))
+    image = image*[0.22803, 0.22145, 0.216989] +  [0.43216, 0.394666, 0.37645]
+    image = image*255.0
+    plt.imshow(image.astype(int))
+    plt.show()
+
+
+def predict(model,img,path = './', video_file_name=""):
+  fmap,logits = model(img.to('cuda'))
+  img = im_convert(img[:,-1,:,:,:], video_file_name)
+  params = list(model.parameters())
+  weight_softmax = model.linear1.weight.detach().cpu().numpy()
+  logits = sm(logits)
+  _,prediction = torch.max(logits,1)
+  confidence = logits[:,int(prediction.item())].item()*100
+  print('confidence of prediction:',logits[:,int(prediction.item())].item()*100)  
+  return [int(prediction.item()),confidence]
+
+def plot_heat_map(i, model, img, path = './', video_file_name=''):
+  fmap,logits = model(img.to('cuda'))
+  params = list(model.parameters())
+  weight_softmax = model.linear1.weight.detach().cpu().numpy()
+  logits = sm(logits)
+  _,prediction = torch.max(logits,1)
+  idx = np.argmax(logits.detach().cpu().numpy())
+  bz, nc, h, w = fmap.shape
+  #out = np.dot(fmap[-1].detach().cpu().numpy().reshape((nc, h*w)).T,weight_softmax[idx,:].T)
+  out = np.dot(fmap[i].detach().cpu().numpy().reshape((nc, h*w)).T,weight_softmax[idx,:].T)
+  predict = out.reshape(h,w)
+  predict = predict - np.min(predict)
+  predict_img = predict / np.max(predict)
+  predict_img = np.uint8(255*predict_img)
+  out = cv2.resize(predict_img, (im_size,im_size))
+  heatmap = cv2.applyColorMap(out, cv2.COLORMAP_JET)
+  img = im_convert(img[:,-1,:,:,:], video_file_name)
+  result = heatmap * 0.5 + img*0.8*255
+  # Saving heatmap - Start
+  heatmap_name = video_file_name+"_heatmap_"+str(i)+".png"
+  image_name = os.path.join(settings.PROJECT_DIR, 'uploaded_images', heatmap_name)
+  cv2.imwrite(image_name,result)
+  # Saving heatmap - End
+  result1 = heatmap * 0.5/255 + img*0.8
+  r,g,b = cv2.split(result1)
+  result1 = cv2.merge((r,g,b))
+  return image_name
+
+# Model Selection
+def get_accurate_model(sequence_length):
+    model_name = []
+    sequence_model = []
+    final_model = ""
+    list_models = glob.glob(os.path.join(settings.PROJECT_DIR, "models", "*.pt"))
+    for i in list_models:
+        model_name.append(i.split("\\")[-1])
+    for i in model_name:
+        try:
+            seq = i.split("_")[3]
+            if (int(seq) == sequence_length):
+                sequence_model.append(i)
+        except:
+            pass
+
+    if len(sequence_model) > 1:
+        accuracy = []
+        for i in sequence_model:
+            acc = i.split("_")[1]
+            accuracy.append(acc)
+        max_index = accuracy.index(max(accuracy))
+        final_model = sequence_model[max_index]
+    else:
+        final_model = sequence_model[0]
+    return final_model
+
+ALLOWED_VIDEO_EXTENSIONS = set(['mp4','gif','webm','avi','3gp','wmv','flv','mkv'])
+
+def allowed_video_file(filename):
+    #print("filename" ,filename.rsplit('.',1)[1].lower())
+    if (filename.rsplit('.',1)[1].lower() in ALLOWED_VIDEO_EXTENSIONS):
+        return True
+    else: 
+        return False
+def index(request):
+    if request.method == 'GET':
+        video_upload_form = VideoUploadForm()
+        if 'file_name' in request.session:
+            del request.session['file_name']
+        if 'preprocessed_images' in request.session:
+            del request.session['preprocessed_images']
+        if 'faces_cropped_images' in request.session:
+            del request.session['faces_cropped_images']
+        return render(request, index_template_name, {"form": video_upload_form})
+    else:
+        video_upload_form = VideoUploadForm(request.POST, request.FILES)
+        if video_upload_form.is_valid():
+            video_file = video_upload_form.cleaned_data['upload_video_file']
+            video_file_ext = video_file.name.split('.')[-1]
+            sequence_length = video_upload_form.cleaned_data['sequence_length']
+            video_content_type = video_file.content_type.split('/')[0]
+            if video_content_type in settings.CONTENT_TYPES:
+                if video_file.size > int(settings.MAX_UPLOAD_SIZE):
+                    video_upload_form.add_error("upload_video_file", "Maximum file size 100 MB")
+                    return render(request, index_template_name, {"form": video_upload_form})
+
+            if sequence_length <= 0:
+                video_upload_form.add_error("sequence_length", "Sequence Length must be greater than 0")
+                return render(request, index_template_name, {"form": video_upload_form})
+            
+            if allowed_video_file(video_file.name) == False:
+                video_upload_form.add_error("upload_video_file","Only video files are allowed ")
+                return render(request, index_template_name, {"form": video_upload_form})
+            
+            saved_video_file = 'uploaded_file_'+str(int(time.time()))+"."+video_file_ext
+            if settings.DEBUG:
+                with open(os.path.join(settings.PROJECT_DIR, 'uploaded_videos', saved_video_file), 'wb') as vFile:
+                    shutil.copyfileobj(video_file, vFile)
+                request.session['file_name'] = os.path.join(settings.PROJECT_DIR, 'uploaded_videos', saved_video_file)
+            else:
+                with open(os.path.join(settings.PROJECT_DIR, 'uploaded_videos','app','uploaded_videos', saved_video_file), 'wb') as vFile:
+                    shutil.copyfileobj(video_file, vFile)
+                request.session['file_name'] = os.path.join(settings.PROJECT_DIR, 'uploaded_videos','app','uploaded_videos', saved_video_file)
+            request.session['sequence_length'] = sequence_length
+            return redirect('ml_app:predict')
+        else:
+            return render(request, index_template_name, {"form": video_upload_form})
+
+def predict_page(request):
+    if request.method == "GET":
+        if 'file_name' not in request.session:
+            return redirect("ml_app:home")
+        if 'file_name' in request.session:
+            video_file = request.session['file_name']
+        if 'sequence_length' in request.session:
+            sequence_length = request.session['sequence_length']
+        path_to_videos = [video_file]
+        video_file_name = video_file.split('\\')[-1]
+        if settings.DEBUG == False:
+            production_video_name = video_file_name.split('/')[3:]
+            production_video_name = '/'.join([str(elem) for elem in production_video_name])
+            print("Production file name",production_video_name)
+        video_file_name_only = video_file_name.split('.')[0]
+        video_dataset = validation_dataset(path_to_videos, sequence_length=sequence_length,transform= train_transforms)
+        model = Model(2).cuda()
+        model_name = os.path.join(settings.PROJECT_DIR,'models', get_accurate_model(sequence_length))
+        models_location = os.path.join(settings.PROJECT_DIR,'models')
+        path_to_model = os.path.join(settings.PROJECT_DIR, model_name)
+        model.load_state_dict(torch.load(path_to_model))
+        model.eval()
+        start_time = time.time()
+        # Start: Displaying preprocessing images
+        print("<=== | Started Videos Splitting | ===>")
+        preprocessed_images = []
+        faces_cropped_images = []
+        cap = cv2.VideoCapture(video_file)
+
+        frames = []
+        while(cap.isOpened()):
+            ret, frame = cap.read()
+            if ret==True:
+                frames.append(frame)
+                if cv2.waitKey(1) & 0xFF == ord('q'):
+                    break
+            else:
+                break
+        cap.release()
+
+        for i in range(1, sequence_length+1):
+            frame = frames[i]
+            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            img = pImage.fromarray(image, 'RGB')
+            image_name = video_file_name_only+"_preprocessed_"+str(i)+'.png'
+            if settings.DEBUG:
+                image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
+            else:
+                print("image_name",image_name)
+                image_path = "/home/app/staticfiles" + image_name
+            img.save(image_path)
+            preprocessed_images.append(image_name)
+        print("<=== | Videos Splitting Done | ===>")
+        print("--- %s seconds ---" % (time.time() - start_time))
+        # End: Displaying preprocessing images
+
+
+        # Start: Displaying Faces Cropped Images
+        print("<=== | Started Face Cropping Each Frame | ===>")
+        padding = 40
+        faces_found = 0
+        for i in range(1, sequence_length+1):
+            frame = frames[i]
+            #fig, ax = plt.subplots(1,1, figsize=(5, 5))
+            face_locations = face_recognition.face_locations(frame)
+            if len(face_locations) == 0:
+                continue
+            top, right, bottom, left = face_locations[0]
+            frame_face = frame[top-padding:bottom+padding, left-padding:right+padding]
+            image = cv2.cvtColor(frame_face, cv2.COLOR_BGR2RGB)
+
+            img = pImage.fromarray(image, 'RGB')
+            image_name = video_file_name_only+"_cropped_faces_"+str(i)+'.png'
+            if settings.DEBUG:
+                image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', video_file_name_only+"_cropped_faces_"+str(i)+'.png')
+            else:
+                image_path = "/home/app/staticfiles" + image_name
+            img.save(image_path)
+            faces_found = faces_found + 1
+            faces_cropped_images.append(image_name)
+        print("<=== | Face Cropping Each Frame Done | ===>")
+        print("--- %s seconds ---" % (time.time() - start_time))
+
+        # No face is detected
+        if faces_found == 0:
+            return render(request, predict_template_name, {"no_faces": True})
+
+        # End: Displaying Faces Cropped Images
+        try:
+            heatmap_images = []
+            for i in range(0, len(path_to_videos)):
+                output = ""
+                print("<=== | Started Predicition | ===>")
+                prediction = predict(model, video_dataset[i], './', video_file_name_only)
+                confidence = round(prediction[1], 1)
+                print("<=== |  Predicition Done | ===>")
+                # print("<=== | Heat map creation started | ===>")
+                # for j in range(0, sequence_length):
+                #     heatmap_images.append(plot_heat_map(j, model, video_dataset[i], './', video_file_name_only))
+                if prediction[0] == 1:
+                    output = "REAL"
+                else:
+                    output = "FAKE"
+                print("Prediction : " , prediction[0],"==",output ,"Confidence : " , confidence)
+                print("--- %s seconds ---" % (time.time() - start_time))
+            if settings.DEBUG:
+                return render(request, predict_template_name, {'preprocessed_images': preprocessed_images, 'heatmap_images': heatmap_images, "faces_cropped_images": faces_cropped_images, "original_video": video_file_name, "models_location": models_location, "output": output, "confidence": confidence})
+            else:
+                return render(request, predict_template_name, {'preprocessed_images': preprocessed_images, 'heatmap_images': heatmap_images, "faces_cropped_images": faces_cropped_images, "original_video": production_video_name, "models_location": models_location, "output": output, "confidence": confidence})
+        except:
+            return render(request, 'cuda_full.html')
+def about(request):
+    return render(request, about_template_name)
+
+def handler404(request,exception):
+    return render(request, '404.html', status=404)
+def cuda_full(request):
+    return render(request, 'cuda_full.html')

models/model_90_acc_20_frames_FF_data.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30d72a8c5f6751e70d86895d1dcf09fe416a9f6f46c517bb565c4677f0688112
+size 226547581

nginx/Dockerfile

@@ -0,0 +1,5 @@
+FROM nginx
+WORKDIR /etc/nginx/
+RUN rm /etc/nginx/conf.d/default.conf
+COPY nginx.conf /etc/nginx/conf.d
+EXPOSE 80

nginx/nginx.conf

@@ -0,0 +1,31 @@
+upstream project_settings {
+    server unix:/app/run/gunicorn.sock 
+    fail_timeout=0;
+}
+
+server {
+
+    listen 80;
+    # disable any limits to avoid HTTP 413 for large image uploads
+    client_max_body_size 0;
+
+    location / {
+        if (!-f $request_filename) {
+            proxy_pass http://project_settings;
+            break;
+        }
+        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+        proxy_set_header Host $host;
+        proxy_redirect off;
+    }
+    #static file directory
+    location /static/ {
+        alias /home/app/staticfiles/;
+    }
+    #media file directory
+    location /media/ {
+        alias   /app/uploaded_videos/;
+    }
+}
+
+

project_settings/asgi.py

@@ -0,0 +1,16 @@
+"""
+ASGI config for project_settings 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', 'project_settings.settings')
+
+application = get_asgi_application()

project_settings/settings.py

@@ -0,0 +1,135 @@
+"""
+Django settings for project_settings project.
+"""
+
+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__)))
+
+# Build paths inside the project like this: os.path.join(PROJECT_DIR, ...)
+PROJECT_DIR = os.path.abspath(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 = '@)0qp0!&-vht7k0wyuihr+nk-b8zrvb5j^1d@vl84cd1%)f=dz'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+# Change and set this to correct IP/Domain
+ALLOWED_HOSTS = ["*"]
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'ml_app.apps.MlAppConfig'
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'project_settings.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [os.path.join(PROJECT_DIR, 'templates')],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.messages.context_processors.messages',
+                'django.template.context_processors.media'
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'project_settings.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/3.0/ref/settings/#databases
+
+DATABASES = {
+    "default": {
+        "ENGINE": "django.db.backends.sqlite3",
+        "NAME": os.path.join(PROJECT_DIR, 'db.sqlite3'),
+    }
+}
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/3.0/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = False
+
+USE_L10N = False
+
+USE_TZ = False
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/3.0/howto/static-files/
+
+#used in production to serve static files
+STATIC_ROOT = "/home/app/staticfiles/"
+
+#url for static files
+STATIC_URL = '/static/'
+
+STATICFILES_DIRS = [
+    os.path.join(PROJECT_DIR, 'uploaded_images'),
+    os.path.join(PROJECT_DIR, 'static'),
+    os.path.join(PROJECT_DIR, 'models'),
+]
+
+CONTENT_TYPES = ['video']
+MAX_UPLOAD_SIZE = "104857600"
+
+MEDIA_URL = "/media/"
+
+MEDIA_ROOT = os.path.join(PROJECT_DIR, 'uploaded_videos')
+
+#for extra logging in production environment
+if DEBUG == False:
+    LOGGING = {
+        'version': 1,
+        'disable_existing_loggers': False,
+        'handlers': {
+            'console': {
+                'class': 'logging.StreamHandler',
+            },
+        'file': {
+            'level': 'DEBUG',
+            'class': 'logging.FileHandler',
+            'filename': 'log.django',
+        },
+        },
+        'loggers': {
+            'django': {
+                'handlers': ['console','file'],
+                'level': os.getenv('DJANGO_LOG_LEVEL', 'DEBUG'),
+            },
+        },
+    }

project_settings/urls.py

@@ -0,0 +1,11 @@
+"""project_settings URL Configuration
+"""
+from django.contrib import admin
+from django.urls import path, include
+
+from django.conf import settings
+from django.conf.urls.static import static
+
+urlpatterns = [
+    path('', include('ml_app.urls')),
+] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

project_settings/wsgi.py

@@ -0,0 +1,16 @@
+"""
+WSGI config for project_settings 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', 'project_settings.settings')
+
+application = get_wsgi_application()

requirements.txt

@@ -0,0 +1,100 @@
+altair
+asgiref
+astor
+attrs
+backcall
+base58
+bleach
+blinker
+cachetools
+certifi
+chardet
+click
+cmake
+colorama
+cycler
+decorator
+defusedxml
+Django
+dlib
+docutils
+entrypoints
+enum-compat
+face-recognition
+face-recognition-models
+future
+google
+google-api-core
+google-api-python-client
+google-auth
+google-auth-httplib2
+googleapis-common-protos
+httplib2
+idna
+ipykernel
+ipython
+ipython-genutils
+ipywidgets
+jedi
+Jinja2
+jmespath
+json5
+jsonschema
+jupyter-client
+jupyter-core
+jupyterlab
+jupyterlab-server
+kiwisolver
+MarkupSafe
+matplotlib
+mistune
+nbconvert
+nbformat
+notebook
+numpy
+opencv-python
+packaging
+pandas
+pandocfilters
+parso
+pathtools
+pickleshare
+Pillow
+prometheus-client
+prompt-toolkit
+protobuf
+pyasn1
+pyasn1-modules
+pycodestyle
+pydeck
+Pygments
+pyparsing
+pyrsistent
+python-dateutil
+pytz
+pywinpty
+PyYAML
+pyzmq
+requests
+rsa
+s3transfer
+Send2Trash
+six
+soupsieve
+sqlparse
+terminado
+testpath
+toml
+toolz
+torch
+torchvision
+tornado
+traitlets
+tzlocal
+uritemplate
+urllib3
+validators
+watchdog
+wcwidth
+webencodings
+widgetsnbextension