def predict():
form = PredictionForm()
if form.validate_on_submit():
iam_header = {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' + access_token
}
objects = [form.month.data, form.dayofweek.data, form.borough.data, form.min_humidity.data, form.max_humidity.data,
form.min_temp.data, form.max_temp.data, form.max_wind_speed.data, form.weather_description.data
]
userInput = []
userInput.append(objects)
payload_scoring = {"input_data": [{"fields": ["month", "dayofweek", "borough", "min_humidity", "max_humidity", "min_temp",
"max_temp", "max_wind_speed", "weather_description"],
"values": userInput }]}
predict_value = requests.post("",json = payload_scoring , headers = iam_header)
result = json.loads(predict_value.text)
return result
return render_template('index.html', form = form)
def predict():
form=PredictionForm()
if form.validate_on_submit():
post=Post()
if form.picture.data:
picture_file = save_picture(form.picture.data)
post.image_file = picture_file
picture_path = os.path.join(app.root_path, 'static/uploaded_pics', picture_file)
breed= predict_breed_transfer(picture_path)
if dog_detector(picture_path):
#flash('dog detected','success')
#flash('Breed: '+breed,'success')
post.title="Dog image detected"
post.content="The breed is "+breed
elif face_detector(picture_path):
#flash('face detected','success')
#flash('Breed: '+breed,'danger')
post.title="Human image detected"
post.content="The image resembles to "+breed
else:
flash('no face or dog detected','danger')
post.title="Unknown image detected"
post.content="It is neither a dog nor a human"
db.session.add(post)
db.session.commit()
return redirect(url_for('home'))
return render_template('prediction.html', title='Prediction', form=form)
def predict():
form = PredictionForm()
if form.validate_on_submit():
flash(f'Predicted House Price for given data is {form.crim.data}!',
'success')
return redirect(url_for('predict'))
return render_template('predict.html', title='Predict', form=form)
def predict():
form = PredictionForm()
if form.validate_on_submit():
sqft = request.form['sqft']
sqft_log = np.log(int(sqft))
baths_num = set_baths(request.form['baths_num'])
beds_num = set_beds(request.form['beds_num'])
story = set_story(request.form['story'])
age = set_age(request.form['age'])
schools_num = set_schools(request.form['schools_num'])
schools_8_up = check_select(request.form['schools_8_up'])
zipcode = int(request.form['zipcode'])
zipcode_expensive = set_zipcode(zipcode)
condo = check_select(request.form['condo'])
mobile = check_select(request.form['mobile'])
feature_names = [
'sqft_log', 'zipcode_expensive', 'zipcode', 'age', 'baths_num',
'schools_8_up', 'story', 'Condo', 'beds_num', 'schools_num',
'mobile'
]
work_features = [
sqft_log, zipcode_expensive, zipcode, age, baths_num, schools_8_up,
story, condo, beds_num, schools_num, mobile
]
x_test = pd.DataFrame([work_features], columns=feature_names)
x_test = scaler.transform(x_test)
price = predict_price(x_test)
flash(f'Прогнозируемая цена {price} $', 'success')
return render_template('predict.html', title='Predict', form=form)
def index_page():
"""
"""
global data, columns, dict_val, dataframe
form = PredictionForm()
if form.validate_on_submit():
# creating a dataframe with the input values
for val in form:
if val.id in columns:
# if the value categorical
if val.id in data:
# obtaining the labeled id
temp_val = data[val.id].index(val.data)
idx = columns.index(val.id)
dict_val[idx] = temp_val
else:
idx = columns.index(val.id)
dict_val[idx] = val.data
print(dict_val)
arr = [val for val in dict_val.values()]
arr = np.array([arr])
df = pd.DataFrame(arr, columns=columns)
dataframe = df
print(df)
flash(f"prediction completed!", 'success')
return redirect(url_for('prediction'))
return render_template('index.html', form=form)
def predict():
form = PredictionForm()
if form.validate_on_submit():
brand = request.form['mark']
bodyType = request.form['bodyType']
fuelType = request.form['fuelType']
productionDate = request.form['productionDate']
modelDate = request.form['modelDate']
numberOfDoors = request.form['numberOfDoors']
vehicleTransmission = request.form['vehicleTransmission']
engineDisplacement = request.form['engineDisplacement']
enginePower = define_power_category(request.form['enginePower'])
mileage = define_mileage_category(request.form['mileage'])
drive = request.form['drive']
owners = request.form['owners']
empty_features = [0] * 45
with open('features.list', 'r') as filehandle:
features = json.load(filehandle)
work_features = [
bodyType, brand, fuelType, modelDate, numberOfDoors,
productionDate, vehicleTransmission, engineDisplacement,
enginePower, mileage, drive, owners
]
df = pd.DataFrame([work_features + empty_features], columns=features)
price = predict_price(df)
write_logs(work_features, features[:12], price)
flash(f'Цена автомобиля {form.mark.data.upper()} {price} руб.',
'success')
return render_template('predict.html', title='Predict', form=form)
def data():
form = PredictionForm()
if form.validate_on_submit():
flower = Flower(sl=form.sl.data, sw=form.sw.data, pl=form.pl.data, pw=form.pw.data)
db.session.add(flower)
db.session.commit()
return redirect(url_for('predict'))
return render_template('data.html', form=form)
def data():
form = PredictionForm()
if form.validate_on_submit():
#flower = Flower(sl=form.sl.data, sw=form.sw.data, pl=form.pl.data, pw=form.pw.data)
promote = Promotion(dep=form.dep.data, reg=form.reg.data, edu=form.edu.data, gen=form.gen.data,
rec=form.rec.data, trn=form.trn.data, age=form.age.data, rat=form.rat.data,
srv=form.srv.data, kpi=form.kpi.data, awd=form.awd.data, scr=form.scr.data)
db.session.add(promote)
db.session.commit()
return redirect(url_for('predict'))
return render_template('data.html', form=form)
