Flask Primer Memo

Aidemy　2020/10/4

Introduction

Hello, it is Yope! I am a liberal arts student, but I was interested in the possibilities of AI, so I went to the AI-specialized school "Aidemy" to study. I would like to share the knowledge gained here with you, and I am summarizing it on Qiita. I am very happy that many people have read the previous summary article. Thank you! This time, I will post an introduction to Flask memo. Nice to meet you.

• This article is a summary of what you learned in "Aidemy" "in your own words". It may contain mistakes and misunderstandings. Please note.

About Flask

What is Flask

-Flask is a Python web application framework. Similar to Rails in Ruby. -Use Flask as follows.

#Import Flask package
from flask import Flask
#Instantiate Flask class
app=Flask(__name__)
#Define the function when accessing the URL
@app.route('/')
def hello_world():
    return "Hello World"
#Only execute when the code is executed directly.
if __name__=='__main__':
    app.run()

[email protected] ("URL") __ is to execute the function defined after it when the URL is accessed. -The name will be described later.

What is name

-Name is a variable that is automatically defined for each file, and __the file name is stored. __ -In addition, __main is stored when the file is directly executed (when the file is executed by a command, etc.). __ (So, if you set name =='main', it will be the condition when the file is executed directly)

Implementation of handwriting character discrimination application

Code description

@app.route('/')
def hello_world():
    return render_template('index.html')

-When accessing the URL of @ app.route () with __render_template ('HTML file') __, HTML can be reflected. -The HTML file at this time must be put in the templates folder.

Rough flow

#First, list the classes to be classified
classes = ["0","1","2","3","4","5","6","7","8","9","10"]
#Specifying the image size
image_size = 28
#Specify the folder to save the uploaded image and specify the extension to allow uploading
UPLOAD_FOLDER = "uploads"
ALLOWED_EXTENSIONS = set(['png','jpg','jpeg'])

#Instantiation of Flask class
app = Flask(__name__)

#Judgment of extension of uploaded image
def allowed_file(filename):
    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS

#Model loading
model = load_model('./model.h5')

-The extension judgments "'.' In filename" and "filename.rsplit ('.', 1) [1] .lower () in ALLOWED_EXTENSIONS" are conditional expressions. The result of the correctness judgment of this is returned by return.

-".' In filename" has a "." In the file name, or "filename.rsplit ('.', 1) [1] .lower () in ALLOWED_EXTENSIONS" has a "." After the file name. Indicates whether it corresponds to any of "ALLOWED_EXTENSIONS". At this time, __ "rsplit" __ means that the character string is separated from the back.

Process the uploaded image file on the HTML side

-On the HTML side, I explained "・ Input form: __ \

@app.route('/', methods=['GET', 'POST'])
def upload_file():
    if request.method == 'POST':

#What to do if the file does not exist
  if 'file' not in request.files:
            flash('No file')
            return redirect(request.url)
        file = request.files['file']
#Processing when the file name is blank
        if file.filename == '':
            flash('No file')
            return redirect(request.url)

#Read the received image and convert it to np format
            img = image.load_img(filepath, grayscale=True, target_size=(image_size,image_size))
            img = image.img_to_array(img)
            data = np.array([img])
#Pass the transformed data to the model for prediction
            result = model.predict(data)[0]
            predicted = result.argmax()
            pred_answer = "this is" + classes[predicted] + "is"

  return render_template("index.html",answer=pred_answer)