Irfan Alghani Khalid khalidmeister

## import.r
library(readr)
confirmed <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
head(confirmed)

## transform-data.py
import numpy as np
import time
import copy
import os
import torch
import torch.optim as optim
import torch.nn as nn
import torchvision
import matplotlib.pyplot as plt
from torch.optim import lr_scheduler

## Train the model using several architectures
# Load the model (Download only at first time loading the model)
model_resnet = models.resnet18(pretrained=True)
model_vgg16 = models.vgg16(pretrained=True)
model_alexnet = models.alexnet(pretrained=True)

# Define the function to train the model
def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = copy.deepcopy(model.state_dict())

## test-model.py
# Show the images and the ground truth
dataiter = iter(dataloaders['test'])
images, labels = dataiter.next()
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ', '.join('%5s' % class_names[x] for x in labels))

# Predict the model
images = images.to(device)
labels = images.to(device)
output = model_resnet(images)

## save.model.py
# Save The Model
PATH = './fix_alexnet_net.pth'
torch.save(model_alexnet.state_dict(), PATH)

## load-model.py
# Declare the network
net = Net()

# Set the parameters
net.load_state_dict(torch.load(PATH))

## app.py
import io
import string
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torchvision import models
from flask import Flask, jsonify, request, render_template
from PIL import Image

app = Flask(__name__)

## layout.html
<!DOCTYPE HTML>
<html>
	<head>
		<!-- Required meta tags -->
	    <meta charset="utf-8">
	    <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

	    <!-- Bootstrap CSS -->
	    <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css" integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous">

## index.html
{% extends "layout.html" %}
{% block content %}
	<form class="form-signin" method=post enctype=multipart/form-data>
		<h1 class="h2 mb-3 font-weight-normal">Apple Leaf Disease Classifier</h1>
		<h2 class="h4 mb-3 font-weight-normal">Please Upload The Image</h2>
		<input type="file" name="file" class="form-control-file" id="inputfile"  onchange="preview_image(event)">
		<br />
		<img id="output-image" class="rounded mx-auto d-block"/>
		<button class="btn btn-lg btn-primary btn-block" type="submit">Upload</button>
		<p class="mt-5 mb-3 text-muted">Powered by PyTorch, Flask, and Bootstrap.</p>

## result.html
{% extends "layout.html" %}
{% block content %}
	<form class="form-signin" method=post enctype=multipart/form-data>
		{% if name == "healthy"%}
			<h1 class="h1 mb-3 font-weight-normal"> Your plant is healthy! </h1>
		{% else %}
			<h1 class="h1 mb-3 font-weight-normal"> Your leaf got a {{ name }} disease!</h5>
			<h3 class="h5 mb-3 font-weight-normal"> {{ description }} </p>
		{% endif %}
		<p class="mt-5 mb-3 text-muted">Powered by PyTorch, Flask, and Bootstrap.</p>
	library(readr)
	confirmed <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
	head(confirmed)
	import numpy as np
	import time
	import copy
	import os
	import torch
	import torch.optim as optim
	import torch.nn as nn
	import torchvision
	import matplotlib.pyplot as plt
	from torch.optim import lr_scheduler
	# Load the model (Download only at first time loading the model)
	model_resnet = models.resnet18(pretrained=True)
	model_vgg16 = models.vgg16(pretrained=True)
	model_alexnet = models.alexnet(pretrained=True)

	# Define the function to train the model
	def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
	since = time.time()

	best_model_wts = copy.deepcopy(model.state_dict())
	# Show the images and the ground truth
	dataiter = iter(dataloaders['test'])
	images, labels = dataiter.next()
	imshow(torchvision.utils.make_grid(images))
	print('GroundTruth: ', ', '.join('%5s' % class_names[x] for x in labels))

	# Predict the model
	images = images.to(device)
	labels = images.to(device)
	output = model_resnet(images)
	# Save The Model
	PATH = './fix_alexnet_net.pth'
	torch.save(model_alexnet.state_dict(), PATH)
	# Declare the network
	net = Net()

	# Set the parameters
	net.load_state_dict(torch.load(PATH))
	import io
	import string
	import torch
	import torch.nn as nn
	import torchvision.transforms as transforms
	from torchvision import models
	from flask import Flask, jsonify, request, render_template
	from PIL import Image

	app = Flask(__name__)
	<!DOCTYPE HTML>
	<html>
	<head>
	<!-- Required meta tags -->
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

	<!-- Bootstrap CSS -->
	<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.0/css/bootstrap.min.css" integrity="sha384-9aIt2nRpC12Uk9gS9baDl411NQApFmC26EwAOH8WgZl5MYYxFfc+NcPb1dKGj7Sk" crossorigin="anonymous">
	{% extends "layout.html" %}
	{% block content %}
	<form class="form-signin" method=post enctype=multipart/form-data>
	<h1 class="h2 mb-3 font-weight-normal">Apple Leaf Disease Classifier</h1>
	<h2 class="h4 mb-3 font-weight-normal">Please Upload The Image</h2>
	<input type="file" name="file" class="form-control-file" id="inputfile" onchange="preview_image(event)">
	<br />
	<img id="output-image" class="rounded mx-auto d-block"/>
	<button class="btn btn-lg btn-primary btn-block" type="submit">Upload</button>
	<p class="mt-5 mb-3 text-muted">Powered by PyTorch, Flask, and Bootstrap.</p>