ANUJ TREHAN anuj2110

## README-Template.md

      
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                anuj2110
                / README-Template.md
            
            
              Created
              May 31, 2020 17:31
                — forked from PurpleBooth/README-Template.md
            
              
                A template to make good README.md
              
          
    Project Title

One Paragraph of project description goes here
Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Prerequisites


## app_upload.py
# we make our imports
from flask import Flask,render_template,redirect,request,send_from_directory
from tensorflow.keras.models import load_model
import os
from PIL import Image
import numpy as np

# we load the model before starting the app
model_file = "model.h5"
model = load_model(model_file)

## app.py
# we make our imports
from flask import Flask,render_template,redirect,request,send_from_directory
from tensorflow.keras.models import load_model
import os
from PIL import Image
import numpy as np

# we load the model before starting the app
model_file = "model.h5"
model = load_model(model_file)

## home.html
{% extends 'base.html' %}

{% block content%}
<center><img src="{{ url_for('static', filename= filename) }}" alt="" style="width: 300px; height:300px;"></center>
<br>
<br>
{% if show==True %}
<center>

    {% if message=="healthy" %}

## base.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Pneumonia Detection Project</title>
    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css" integrity="sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh" crossorigin="anonymous">
</head>
<body>
    <nav class="navbar navbar-dark bg-dark">

## app.py
# we make our imports
from flask import Flask,render_template,redirect,request,send_from_directory
from tensorflow.keras.models import load_model
import os
from PIL import Image
import numpy as np

# we load the model before starting the app
model_file = "model.h5"
model = load_model(model_file)

## train.py
# making the imports
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input,Conv2D,MaxPooling2D,Dropout,Flatten,Dense,Activation,BatchNormalization,add
from tensorflow.keras.models  import Model,Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import plot_model
from tensorflow.keras.applications.vgg16 import VGG16,preprocess_input
import os

## checkDataset.py
# Making the imports
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
from imutils import build_montages

def plot_distribution(paths:list,portion:str):
    """
    This function is used to plot the distribution of train and test images among NORMAL and PNEUMONIA labels

## PredictOnImages.py
import cv2
from tensorflow.keras.models import load_model
import matplotlib.pyplot as plt
import numpy as np
import os
from imutils import build_montages

haar_cascade = "haarcascade_frontalface_default.xml"
imgs = os.listdir(path_to_images_dir)
model_name = path_to_model

## LiveRecognition.py
import cv2
from tensorflow.keras.models import load_model
import numpy as np

cascade_file = "haarcascade_frontalface_default.xml"
model_file = "fermodel.h5"
emotions = ["angry","disgust","scared", "happy", "sad", "surprised","neutral"]

face_detection = cv2.CascadeClassifier(cascade_file)
emotion_classifier = load_model(model_file, compile=False)
	# we make our imports
	from flask import Flask,render_template,redirect,request,send_from_directory
	from tensorflow.keras.models import load_model
	import os
	from PIL import Image
	import numpy as np

	# we load the model before starting the app
	model_file = "model.h5"
	model = load_model(model_file)
	{% extends 'base.html' %}

	{% block content%}
	<center><img src="{{ url_for('static', filename= filename) }}" alt="" style="width: 300px; height:300px;"></center>
	<br>
	<br>
	{% if show==True %}
	<center>

	{% if message=="healthy" %}
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Pneumonia Detection Project</title>
	<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css" integrity="sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh" crossorigin="anonymous">
	</head>
	<body>
	<nav class="navbar navbar-dark bg-dark">
	# making the imports
	import numpy as np
	import matplotlib.pyplot as plt
	from tensorflow.keras.layers import Input,Conv2D,MaxPooling2D,Dropout,Flatten,Dense,Activation,BatchNormalization,add
	from tensorflow.keras.models import Model,Sequential
	from tensorflow.keras.preprocessing.image import ImageDataGenerator
	from tensorflow.keras.utils import plot_model
	from tensorflow.keras.applications.vgg16 import VGG16,preprocess_input
	import os
	import cv2
	from tensorflow.keras.models import load_model
	import matplotlib.pyplot as plt
	import numpy as np
	import os
	from imutils import build_montages

	haar_cascade = "haarcascade_frontalface_default.xml"
	imgs = os.listdir(path_to_images_dir)
	model_name = path_to_model