[Flask 공부]
https://github.com/Turtlefromocean/Flask_01
Turtlefromocean/Flask_01
Contribute to Turtlefromocean/Flask_01 development by creating an account on GitHub.
github.com
1. 기본 틀
from flask import Flask
# init app
app = Flask(__name__)
# Route
@app.route('/')
def index():
return 'Hello Data Science Optimizers'
if __name__ == '__main__':
app.run(debug=True)
2. route & html 파일 render_template
from flask import Flask, render_template
# Route
@app.route('/')
def index():
return 'Hello Data Science Optimizers'
# HTML 파일 추가
@app.route('/home')
def home():
return render_template('home.html')
if __name__ == '__main__':
app.run(debug=True)
3. GET, POST & Jinja
- home.html (index.html)
👉<form> 태그: POST , action -> /predict
<!DOCTYPE html>
<html>
<head>
<title>Home Page</title>
</head>
<body>
<div>
<h2>Home Page for ML Apps</h2>
</div>
<div>
<form method="POST" action="{{ url_for('predict') }}"> <!-- action= "/preict" -->
<input type="text" name="firstname" placeholder="FirstName">
<input type="text" name="lastname" placeholder="LastName">
<button type="reset">Clear</button>
<button type="submit">Submit</button>
</form>
</div>
<div>
<p>당신의 이름은 {{firstname}} 이고 성은 {{lastname}} 입니다</p>
</div>
</body>
</html>- app.py
from flask import Flask, render_template, request, url_for
# init app
app = Flask(__name__)
# Route
@app.route('/')
def index():
return 'Hello Data Science Optimizers'
# HTML 파일 추가
@app.route('/home')
def home():
return render_template('home.html')
#
@app.route('/predict', methods=['GET', 'POST'])
def predict():
if request.method == 'POST':
firstname = request.form['firstname']
lastname = request.form['lastname']
single_user = User(firstname=firstname, lastname=lastname)
db.session.add(single_user)
db.session.commit()
return render_template('home.html', firstname=firstname.upper(), lastname=lastname)
# Templating , Jinja
@app.route('/about')
def about():
mission = "Optimizing Data and ML Models with Python"
return render_template('about.html', mission_frontend=mission)
if __name__ == '__main__':
app.run(debug=True)
- about. html
👉Jinja: {{ }} 또는 {% %}
<!DOCTYPE html>
<html>
<head>
<title>About Page</title>
</head>
<body>
<h2>About Us</h2>
<div>
{{mission_frontend}}
</div>
</body>
</html>4. Database => flask_sqlalchemy
- db 설정
- sql types of columns
from flask import Flask, render_template, request, url_for
from flask_sqlalchemy import SQLAlchemy
# init app
app = Flask(__name__)
# DB
db = SQLAlchemy(app)
app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///static/database/users.db' # /// 3개 !!
# model schema
class User(db.Model):
id = db.Column(db.Integer, primary_key=True)
firstname = db.Column(db.String(50))
lastname = db.Column(db.String(50))
# Route
@app.route('/')
def index():
return 'Hello Data Science Optimizers'
# HTML 파일 추가
@app.route('/home')
def home():
return render_template('home.html')
#
@app.route('/predict', methods=['GET', 'POST'])
def predict():
if request.method == 'POST':
firstname = request.form['firstname']
lastname = request.form['lastname']
single_user = User(firstname=firstname, lastname=lastname)
db.session.add(single_user)
db.session.commit()
return render_template('home.html', firstname=firstname.upper(), lastname=lastname)
@app.route('/allusers')
def allusers():
userslist = User.query.all()
print(userslist)
return render_template('results.html', userslist=userslist)
@app.route('/profile/<firstname>')
def profile(firstname):
user = User.query.filter_by(firstname=firstname).first()
return render_template('profile.html', user=user)
# Templating , Jinja
@app.route('/about')
def about():
mission = "Optimizing Data and ML Models with Python"
return render_template('about.html', mission_frontend=mission)
if __name__ == '__main__':
app.run(debug=True)
[YOLO v3 Webcam with Flask] 성공🥳
Github 🔗: https://github.com/Turtlefromocean/flask-yolo-web
Turtlefromocean/flask-yolo-web
Contribute to Turtlefromocean/flask-yolo-web development by creating an account on GitHub.
github.com
*requirements.txt 가 없어서 만들었다
$ pipenv shell
$ pip install -r requirements.txt*yolo.weights는 크기가 커서 따로 다운받은 후 h5 파일로 변환
$ wget https://pjreddie.com/media/files/yolov3-tiny.weights
$ python convert.py yolov3-tiny.cfg yolov3-tiny.weights model_data/yolo.h5*실행
$ python3 server.py
* 페이지가 너무 허전해서 bootstrap 적용
* 물체인식해주는 상자가 너무 별로여서 색이랑 두께 조정
* 기분탓인지 모르겠지만 뭔가,, predict 값이 잘 안나온다
참조) https://github.com/zxqcreations/YOLOv3-withWebServer
[모델 배포]
https://bcho.tistory.com/1191?category=555440
얼굴 인식 모델을 만들어보자 #6 - CloudML을 이용하여 예측하기
CloudML을 이용하여 예측하기 조대협 (http://bcho.tistory.com) 지난글 (http://bcho.tistory.com/1189) 에서 학습된 모델을 *.pb 파일 포맷으로 Export 하였다. 그러면 이 Export 된 모델을 이용하여 예측 (pred..
bcho.tistory.com
ML 프레임 워크
Streamlit — The fastest way to build custom ML tools
Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful data apps in hours, not weeks. All in pure Python. All for free.
www.streamlit.io
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