Upload 1575 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 +33,42 @@ 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/images/background.png filter=lfs diff=lfs merge=lfs -text
+static/images/background1.png filter=lfs diff=lfs merge=lfs -text
+static/json/age_gender_model-shard1 filter=lfs diff=lfs merge=lfs -text
+static/json/face_expression_model-shard1 filter=lfs diff=lfs merge=lfs -text
+static/json/face_landmark_68_model-shard1 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
+static/json/tiny_face_detector_model-shard1 filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/chardet/__pycache__/langrussianmodel.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/distlib/t64-arm.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/distlib/t64.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/distlib/w64-arm.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/distlib/w64.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/idna/__pycache__/uts46data.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/pkg_resources/__pycache__/__init__.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/pyparsing/__pycache__/core.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/rich/__pycache__/_emoji_codes.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pip/_vendor/rich/__pycache__/console.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pkg_resources/__pycache__/__init__.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pkg_resources/_vendor/more_itertools/__pycache__/more.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/pkg_resources/_vendor/pyparsing/__pycache__/core.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/_vendor/__pycache__/typing_extensions.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/_vendor/more_itertools/__pycache__/more.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/_vendor/pyparsing/__pycache__/core.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/cli-arm64.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/command/__pycache__/easy_install.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/config/_validate_pyproject/__pycache__/fastjsonschema_validations.cpython-311.pyc filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/setuptools/gui-arm64.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/winpty/winpty-agent.exe filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/winpty/winpty.cp311-win_amd64.pyd filter=lfs diff=lfs merge=lfs -text
+venv/Lib/site-packages/winpty/winpty.dll filter=lfs diff=lfs merge=lfs -text
+venv/Scripts/pip.exe filter=lfs diff=lfs merge=lfs -text
+venv/Scripts/pip3.11.exe filter=lfs diff=lfs merge=lfs -text
+venv/Scripts/pip3.exe filter=lfs diff=lfs merge=lfs -text
+venv/Scripts/python.exe filter=lfs diff=lfs merge=lfs -text
+venv/Scripts/pythonw.exe filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,10 @@
+venv
+.vs
+.vscode
+models/*
+!models/Readme.txt
+uploaded_images/*
+!uploaded_images/Readme.txt
+uploaded_videos/*
+!uploaded_videos/Readme.txt
+

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"]

README.md

@@ -1,10 +1,83 @@
----
-title: Dep
-emoji: 🌍
-colorFrom: indigo
-colorTo: purple
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Deep fake detection Django Application
+## Requirements:
+
+**Note :** Nvidia GPU is mandatory to run the application.
+- CUDA version >= 10.0 for GPU
+- GPU Compute Capability > 3.0 
+
+
+You can find the list of requirements in [requirements.txt](https://github.com/abhijitjadhav1998/Deepfake_detection_using_deep_learning/blob/master/Django%20Application/requirements.txt). Main requirements are listed below:
+
+```
+Python >= v3.6
+Django >= v3.0
+```
+
+## Directory Structure
+
+- ml_app -> Directory containing code in views.py file
+- project_settings -> Contains Django settings and files to run in production
+- static -> Contains all css, js and json files (for face-api)
+- templates -> Template files for HTML
+
+<b>Note:</b> Before running the project make sure you have created directories namely <strong>models, uploaded_images, uploaded_videos</strong> in the project root and that you have proper permissions to access them.
+# Running application on Docker
+#### Step 1: Install docker desktop and start the Docker daemon
+
+#### Step 2: Run the deepfake detection docker docker image
+```
+docker run --rm --gpus all -v static_volume:/home/app/staticfiles/ -v media_volume:/app/uploaded_videos/ --name=deepfakeapplication abhijitjadhav1998/deefake-detection-20framemodel
+```
+#### Step 3: Run the Ngnix reverse proxy server docker image
+```
+docker run -p 80:80 --volumes-from deepfakeapplication -v static_volume:/home/app/staticfiles/ -v media_volume:/app/uploaded_videos/ abhijitjadhav1998/deepfake-nginx-proxyserver
+```
+#### Step 4: All set now launch up your application at [http://localhost:80](http://localhost:80)
+
+### Step 5: Star⭐ this repo 😉 on <a href="https://github.com/abhijitjadhav1998/Deepfake_detection_using_deep_learning" >  <img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white" /> </a> and   Star⭐ this image on <a href="https://hub.docker.com/r/abhijitjadhav1998/deefake-detection-20framemodel">  <img src="https://img.shields.io/badge/Docker-2CA5E0?style=for-the-badge&logo=docker&logoColor=white" /> </a>
+
+## We deserve a Coffee ☕ <a href="https://www.buymeacoffee.com/abhijitjadhav" target="_blank"><img src="https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: 35px !important;width: 174px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;" ></a>
+
+
+Please note that currently we have only pushed the image of 20 Frames model, If you can to create your own image of other frames model follow the steps given in the [blog](https://abhijithjadhav.medium.com/dockerise-deepfake-detection-django-application-using-nvidia-cuda-40cdda3b6d38).
+
+# Running application locally on your machine
+
+### Prerequisite
+1. Copy your trained model to the models folder.
+   - You can download our trained models from the [Google Drive](https://drive.google.com/drive/folders/1UX8jXUXyEjhLLZ38tcgOwGsZ6XFSLDJ-?usp=sharing) or you can train your models using the steps mentioned in Model Creation directory.
+
+#### Step 1 : Clone the repo and Navigate to Django Application
+
+`git clone https://github.com/abhijitjadhav1998/Deepfake_detection_using_deep_learning.git`
+
+#### Step 2: Create virtualenv (optional)
+
+`python -m venv venv`
+
+#### Step 3: Activate virtualenv (optional)
+
+`venv\Scripts\activate`
+
+#### Step 4: Install requirements
+
+`pip install -r requirements.txt`
+
+#### Step 5: Copy Models
+
+`Copy your trained model to the models folder i.e Django Application/models/`
+
+- You can download our trained models from [Google Drive](https://drive.google.com/drive/folders/1UX8jXUXyEjhLLZ38tcgOwGsZ6XFSLDJ-?usp=sharing)
+
+**Note :** The model name must be in specified format only i.e *model_84_acc_10_frames_final_data.pt*. Make sure that no of frames must be mentioned after certain 3 underscores `_` , in the above example the model is for 10 frames.
+
+
+### Step 6: Run project
+
+`python manage.py runserver`
+
+## Demo 
+### You can watch the [youtube video](https://www.youtube.com/watch?v=_q16aJTXVRE&t=823s) for demo
+<p align="center">
+  <img src="https://github.com/abhijitjadhav1998/Deepfake_detection_using_deep_learning/blob/master/github_assets/fakegif.gif" />
+</p>  

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">
+      <path class="st2" d="M9.8 282.4h-3L0 307.6h3z"/>
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+          <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"/>
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+      <path class="st2" d="M99.8 282.4h-3L90 307.6h3z"/>
+      <path class="st2" d="M109.8 282.4h-3l-6.8 25.2h3z"/>
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+      <g>
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+    <g id="cloudsAll">
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+    <g id="train">
+      <path fill="#b3dcdf" d="M344.5 248.5h507.2v37.8H344.5z"/>
+      <g id="wheels">
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+        <circle class="st6" cx="469.1" cy="285.6" r="15.1"/>
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+      </g>
+      <path id="bracefront" class="st7" d="M383.2 285.6h88.1"/>
+      <path id="braceback" class="st7" d="M733.2 285.6h88.1"/>
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+        <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 +1,392 @@
+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'
+about_template_name = "about.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))
+if torch.cuda.is_available():
+    device = 'gpu'
+else:
+    device = 'cpu'
+
+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('uint8'))
+    plt.show()
+
+
+def predict(model,img,path = './', video_file_name=""):
+  fmap,logits = model(img.to(device))
+  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(device))
+  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 model_path in list_models:
+        model_name.append(os.path.basename(model_path))
+
+    for model_filename in model_name:
+        try:
+            seq = model_filename.split("_")[3]
+            if int(seq) == sequence_length:
+                sequence_model.append(model_filename)
+        except IndexError:
+            pass  # Handle cases where the filename format doesn't match expected
+
+    if len(sequence_model) > 1:
+        accuracy = []
+        for filename in sequence_model:
+            acc = filename.split("_")[1]
+            accuracy.append(acc)  # Convert accuracy to float for proper comparison
+        max_index = accuracy.index(max(accuracy))
+        final_model = os.path.join(settings.PROJECT_DIR, "models", sequence_model[max_index])
+    elif len(sequence_model) == 1:
+        final_model = os.path.join(settings.PROJECT_DIR, "models", sequence_model[0])
+    else:
+        print("No model found for the specified sequence length.")  # Handle no models found case
+
+    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":
+        # Redirect to 'home' if 'file_name' is not in session
+        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 = os.path.basename(video_file)
+        video_file_name_only = os.path.splitext(video_file_name)[0]
+        # Production environment adjustments
+        if not settings.DEBUG:
+            production_video_name = os.path.join('/home/app/staticfiles/', video_file_name.split('/')[3])
+            print("Production file name", production_video_name)
+        else:
+            production_video_name = video_file_name
+
+        # Load validation dataset
+        video_dataset = validation_dataset(path_to_videos, sequence_length=sequence_length, transform=train_transforms)
+
+        # Load model
+        if(device == "gpu"):
+            model = Model(2).cuda()  # Adjust the model instantiation according to your model structure
+        else:
+            model = Model(2).cpu()  # Adjust the model instantiation according to your model structure
+        model_name = os.path.join(settings.PROJECT_DIR, 'models', get_accurate_model(sequence_length))
+        path_to_model = os.path.join(settings.PROJECT_DIR, model_name)
+        model.load_state_dict(torch.load(path_to_model, map_location=torch.device('cpu')))
+        model.eval()
+        start_time = time.time()
+        # Display 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:
+                frames.append(frame)
+            else:
+                break
+        cap.release()
+
+        print(f"Number of frames: {len(frames)}")
+        # Process each frame for preprocessing and face cropping
+        padding = 40
+        faces_found = 0
+        for i in range(sequence_length):
+            if i >= len(frames):
+                break
+            frame = frames[i]
+
+            # Convert BGR to RGB
+            rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+            # Save preprocessed image
+            image_name = f"{video_file_name_only}_preprocessed_{i+1}.png"
+            image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
+            img_rgb = pImage.fromarray(rgb_frame, 'RGB')
+            img_rgb.save(image_path)
+            preprocessed_images.append(image_name)
+
+            # Face detection and cropping
+            face_locations = face_recognition.face_locations(rgb_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]
+
+            # Convert cropped face image to RGB and save
+            rgb_face = cv2.cvtColor(frame_face, cv2.COLOR_BGR2RGB)
+            img_face_rgb = pImage.fromarray(rgb_face, 'RGB')
+            image_name = f"{video_file_name_only}_cropped_faces_{i+1}.png"
+            image_path = os.path.join(settings.PROJECT_DIR, 'uploaded_images', image_name)
+            img_face_rgb.save(image_path)
+            faces_found += 1
+            faces_cropped_images.append(image_name)
+
+        print("<=== | Videos Splitting and Face Cropping Done | ===>")
+        print("--- %s seconds ---" % (time.time() - start_time))
+
+        # No face detected
+        if faces_found == 0:
+            return render(request, 'predict_template_name.html', {"no_faces": True})
+
+        # Perform prediction
+        try:
+            heatmap_images = []
+            output = ""
+            confidence = 0.0
+
+            for i in range(len(path_to_videos)):
+                print("<=== | Started Prediction | ===>")
+                prediction = predict(model, video_dataset[i], './', video_file_name_only)
+                confidence = round(prediction[1], 1)
+                output = "REAL" if prediction[0] == 1 else "FAKE"
+                print("Prediction:", prediction[0], "==", output, "Confidence:", confidence)
+                print("<=== | Prediction Done | ===>")
+                print("--- %s seconds ---" % (time.time() - start_time))
+
+                # Uncomment if you want to create heat map images
+                # for j in range(sequence_length):
+                #     heatmap_images.append(plot_heat_map(j, model, video_dataset[i], './', video_file_name_only))
+
+            # Render results
+            context = {
+                'preprocessed_images': preprocessed_images,
+                'faces_cropped_images': faces_cropped_images,
+                'heatmap_images': heatmap_images,
+                'original_video': production_video_name,
+                'models_location': os.path.join(settings.PROJECT_DIR, 'models'),
+                'output': output,
+                'confidence': confidence
+            }
+
+            if settings.DEBUG:
+                return render(request, predict_template_name, context)
+            else:
+                return render(request, predict_template_name, context)
+
+        except Exception as e:
+            print(f"Exception occurred during prediction: {e}")
+            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_95_acc_40_frames_FF_data.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1a8484005389c1a9ae24625f4502b01e4a101c92ef7fbd775911b02f8da276c
+size 226547587

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 +1,101 @@
+altair==5.3.0
+asgiref==3.8.1
+astor==0.8.1
+attrs==23.2.0
+backcall==0.2.0
+base58==2.1.1
+bleach==6.1.0
+blinker==1.8.2
+cachetools==5.3.3
+certifi==2024.6.2
+chardet==5.2.0
+click==8.1.7
+cmake==3.29.6
+colorama==0.4.6
+cycler==0.12.1
+decorator==5.1.1
+defusedxml==0.7.1
+Django==5.0.6
+dlib==19.24.2
+docutils==0.21.2
+entrypoints==0.4
+enum-compat==0.0.3
+face-recognition==1.3.0
+face-recognition-models==0.3.0
+future==1.0.0
+google==3.0.0
+google-api-core==2.19.1
+google-api-python-client==2.134.0
+google-auth==2.30.0
+google-auth-httplib2==0.2.0
+googleapis-common-protos==1.63.2
+httplib2==0.22.0
+idna==3.7
+ipykernel==6.29.4
+ipython==8.12.3
+ipython-genutils==0.2.0
+ipywidgets==8.1.3
+jedi==0.16.0
+Jinja2==3.1.4
+jmespath==1.0.1
+json5==0.9.25
+jsonschema==4.22.0
+jupyter-client==8.6.2
+jupyter-core==5.7.2
+jupyterlab==4.2.2
+jupyterlab-server==2.27.2
+kiwisolver==1.4.5
+MarkupSafe==2.1.5
+matplotlib==3.9.0
+mistune==3.0.2
+nbconvert==7.16.4
+nbformat==5.10.4
+notebook==7.2.1
+numpy===1.26.4
+opencv-python==4.10.0.84
+packaging==24.1
+pandas==2.2.2
+pandocfilters==1.5.1
+parso==0.8.4
+pathtools==0.1.2
+pickleshare==0.7.5
+Pillow==10.3.0
+prometheus-client==0.20.0
+prompt-toolkit==3.0.47
+protobuf==5.27.2
+pyasn1==0.6.0
+pyasn1-modules==0.4.0
+pycodestyle==2.12.0
+pydeck==0.9.0b1
+Pygments==2.18.0
+pyparsing==3.1.2
+pyrsistent==0.20.0
+python-dateutil==2.9.0
+# pytz==2019.3 Commenting for production deployment
+# pywin32==227
+pywinpty==0.5.7
+PyYAML==6.0.1
+pyzmq==26.0.3
+requests==2.32.3
+rsa==4.9
+s3transfer==0.10.2
+Send2Trash==1.8.3
+six==1.16.0
+soupsieve==2.5
+sqlparse==0.5.0
+terminado==0.18.1
+testpath==0.6.0
+toml==0.10.2
+toolz==0.12.1
+torch==2.3.1
+torchvision==0.18.1
+tornado==6.4.1
+traitlets==5.14.3
+tzlocal==5.2
+uritemplate==4.1.1
+urllib3==2.2.2
+validators==0.28.3
+watchdog==4.0.1
+wcwidth==0.2.13
+webencodings==0.5.1
+widgetsnbextension==4.0.11