def start_test_server():
pywebio.enable_debug()
from flask import Flask, send_from_directory
from pywebio.platform.flask import webio_view, run_event_loop
from pywebio import STATIC_PATH
import threading
import logging
app = Flask(__name__)
app.add_url_rule('/io',
'webio_view',
webio_view(target, cdn=False),
methods=['GET', 'POST', 'OPTIONS'])
app.add_url_rule('/io2',
'webio_view_async_target',
webio_view(async_target, cdn=False),
methods=['GET', 'POST', 'OPTIONS'])
@app.route('/')
@app.route('/<path:static_file>')
def serve_static_file(static_file='index.html'):
return send_from_directory(STATIC_PATH, static_file)
threading.Thread(target=run_event_loop, daemon=True).start()
logging.getLogger('werkzeug').setLevel(logging.WARNING)
app.run(port=8080, host='127.0.0.1')
app = Flask(__name__)
def predict():
with popup("Face Match Classifier"):
put_text("Good to see you again")
img = file_upload("Select a image:", accept="images/*")
put_processbar('bar')
for i in range(1, 11):
set_processbar('bar', i / 10)
time.sleep(0.1)
content = img['content']
nparr = np.fromstring(content, np.uint8)
img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
pred = helper.main(img)
put_text('Predicted Person is : ', pred)
# put_markdown(pred)
put_image(content)
app.add_url_rule('/tool',
'webio_view',
webio_view(predict),
methods=['GET', 'POST', 'OPTIONS'])
app.run(host='localhost', port=80)
Seller_Type = select('Are you a dealer or an individual', ['Dealer', 'Individual'])
if (Seller_Type == 'Individual'):
Seller_Type = 106
else:
Seller_Type = 195
Transmission = select('Transmission Type', ['Manual Car', 'Automatic Car'])
if (Transmission == 'Manual Car'):
Transmission = 261
else:
Transmission = 40
prediction = model.predict([[Present_Price, Kms_Driven2, Fuel_Type, Seller_Type, Transmission, Owner, Year]])
output = round(prediction[0], 2)
if output < 0:
put_text("Sorry You can't sell this Car")
else:
put_text('You can sell this Car at price:',output)
app.add_url_rule('/tool', 'webio_view', webio_view(predict),
methods=['GET', 'POST', 'OPTIONS'])
#if __name__ == '__main__':
#predict()
app.run(host='localhost', port=80)
#visit http://localhost/tool to open the PyWebIO application.
if q2 == "The value of the gradient at extrema of a function is always zero":
c += 1
if q3 == "Decision trees are prone to be overfit":
c += 1
if q4 == "Normalize the data -> PCA -> training":
c += 1
if q5 == "Stop Word Removal":
c += 1
if c > 3:
put_markdown("""# you are passed""")
if c <= 3:
put_markdown("""# you are failed""")
app.add_url_rule('/tool',
'webio_view',
webio_view(main),
methods=['GET', 'POST', 'OPTIONS'])
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--port", type=int, default=8080)
args = parser.parse_args()
start_server(main, port=args.port)
style(put_text("2. Federal in form and unitary in spirit"),"color:SandyBrown")
style(put_text("3. Rajasthan"),"color:SandyBrown")
style(put_text("4. Madam Cama"),"color:SandyBrown")
style(put_text("5. Surpluses"),"color:SandyBrown")
style(put_text("6. Immune"),"color:SandyBrown")
style(put_text("7. Parliament"),"color:SandyBrown")
style(put_text("8. Banabhatta"),"color:SandyBrown")
style(put_text("9. Indonesia"),"color:SandyBrown")
style(put_text("10. Antartica\n\n\n"),"color:SandyBrown")
if c>=5:
style(put_text(name +", your score is: "+ str(c)),"color:MediumBlue")
style(put_text("Result : Passed"),"color:Turquoise")
else:
style(put_text(name +", your score is: "+ str(c)),"color:Maroon")
style(put_text("Result : failed"),"color:red")
style(put_text("Thank you for your participation.."),"font-family:Cursive,sans-serif")
app.add_url_rule('/','webio_view',webio_view(exam),methods=['GET','POST','OPTIONS'])
if __name__=="__main__":
port=os.environ.get('PORT',33507)
app.run(host='localhost',port=port,debug=False)
BMI = weight / (height / 100)**2
print(f"YOUR BMI = {BMI}")
top_status = [(16, 'Severely underweight'), (18.5, 'Underweight'),
(25, 'Normal'), (30, 'Overweight'), (35, 'Moderately obese'),
(float('inf'), 'Severely obese')]
for top, status in top_status:
if BMI <= top:
put_text('Your BMI: %.1f. Category: %s' % (BMI, status))
break
if __name__ == '__main__':
# bmi()
# webview.create_window('Hello world', pywebio.start_server(show_table, port=8888))
# webview.start()
# app = Flask(__name__)
# log = logging.getLogger('werkzeug')
# log.setLevel(logging.ERROR)
# log.disabled = True
app.add_url_rule('/tool',
'webio_view',
webio_view(layout_keys),
methods=['GET', 'POST', 'OPTIONS'])
# app.run(host='localhost', port=8089)
webview.create_window('Flask example', app)
webview.start()
defff_type = select('diferentiation scheme Type',
['forwrd', 'backwrd', 'central'])
if (defff_type == 'forwrd'):
defff_type = forwrd()
elif (defff_type == 'backwrd'):
defff_type = backwrd()
elif (defff_type == 'central'):
defff_type = central()
bmi = deff_type
app.add_url_rule('/tool',
'webio_view',
webio_view(bmi),
methods=['GET', 'POST',
'OPTIONS']) # need GET,POST and OPTIONS methods
# app.run(host='localhost', port=500)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--port", type=int, default=800)
args = parser.parse_args()
start_server(bmi, port=args.port)
if number < 0:
put_text("Number must be greater than zero")
number_getter()
number_list.append(number)
while number != 1:
if number % 2 == 0:
number = number // 2
number_list.append(number)
else:
number = 3 * number + 1
number_list.append(number)
else:
number_list.append(number)
put_text(
f"Seed number: {initial_number}.\nLargest number in sequence was {max(number_list)}\n"
f"It took {len(number_list)-2} iterations to reach the number 1\n\n"
)
put_html(make_graph(number_list))
number_getter()
except ValueError:
number_getter()
app.add_url_rule('/number',
'webio_view',
webio_view(number_getter),
methods=['GET', 'POST', 'OPTIONS'])
if __name__ == "__main__":
app.run(host="127.0.0.1", port=8080, debug=True)
}
IMG_SIZE = 224
MODEL_PATH = './models/resnet-model.h5'
model = load_model(MODEL_PATH)
def predict_img():
img_upload = file_upload(label='Upload image',
multiple=False,
accept='image/*',
placeholder='Upload an image of pokemon',
required=True)
file_path = img_upload.get('filename')
print(file_path)
img = image.load_img(file_path, target_size=(IMG_SIZE, IMG_SIZE))
x = image.img_to_array(img)
pred = model.predict(np.expand_dims(x, axis=0))
res = np.argmax(pred, axis=1)
im = open(file_path, 'rb').read()
put_image(im, width='400px', height='400px')
put_text("This pokemon is", IMG_CATEGORIES.get(int(res)))
app.add_url_rule('/tool',
'webio_view',
webio_view(predict_img),
methods=['GET', 'POST', 'OPTIONS'])
app.run(host='localhost', port=80)
# 自己程序
def expand_line(compressed_line):
refs = compressed_line.split(',')
for i, ref in enumerate(refs):
refs[i] = expand_ref(ref)
return ','.join(refs)
# 自己程序
def task_func():
set_env(title="Ref Expander")
put_markdown('本页面可以将压缩位号（U5-U10）转换为未压缩的位号（U5,U6,U7,U8,U9,U10）。')
str2process = textarea('输入压缩位号：', rows=15, placeholder='可多行，行内逗号分割。')
lines = str2process.split('\n')
for i, line in enumerate(lines):
lines[i] = expand_line(line)
expanded_lines = '\n'.join(lines)
put_markdown('未压缩的位号为：')
put_markdown(expanded_lines)
# Flask+WebIO框架
app = Flask(__name__)
# `task_func` is PyWebIO task function
app.add_url_rule('/',
'webio_view',
webio_view(task_func),
methods=['GET', 'POST',
'OPTIONS']) # need GET,POST and OPTIONS methods
selected_index = int(selected_index)
try:
row = dataset2.iloc[selected_index]
set_scope('BTV', -1, -1, 'clear')
clear('BTV')
put_markdown(r""" # How to reach""", lstrip=True, scope='BTV')
t = row['how to reach']
t = t.strip()
t = t.replace('-', '')
put_text(t, scope='BTV')
except Exception as ex:
put_text(ex)
app = Flask(__name__)
# app.add_url_rule('/', 'webio_view', webio_view(choices), methods=['GET', 'POST', 'OPTIONS'])
# app.run()
app.add_url_rule('/tool',
'webio_view',
webio_view(choices),
methods=['GET', 'POST', 'OPTIONS'])
# app.run(host='localhost', port=80)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-p", "--port", type=int, default=8080)
args = parser.parse_args()
start_server(choices, port=args.port)
def sentiment_analysis():
text=input("enter text to know whether the tweet ie positive or negative or neutral: ",type='text')
x=TextBlob(text)
sentiment_polarity=x.sentiment.polarity
#sentiment_polarity_output=[(0,["positive","negative","nuetral"])]
if sentiment_polarity>0:
put_text("The given tweet looks : positive")
elif sentiment_polarity<0:
put_text("The given tweet looks : negative")
elif sentiment_polarity==0:
put_text("The given tweet looks : neutral")
app.add_url_rule('/predict','webio_view',webio_view(sentiment_analysis),
methods=['GET','POST','OPTIONS'])
#app.run(host='localhost',port=80)
if __name__="__main__":
parser
