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():
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():
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)
def home(request):
context = {}
if request.user.is_anonymous():
now = datetime.datetime.now()
blog_date = blog_title = blog_content = None
competition = Competition.objects\
.filter(start_date__lt=now,
close_date__gt=now)\
.order_by('start_date')
if competition:
competition = competition[0]
predictions = Prediction.objects\
.filter(competition=competition)\
.all()[:10]
else:
competition = None
predictions = None
context['blog_date'] = blog_date
context['blog_title'] = blog_title
context['blog_content'] = blog_content
context['competition'] = competition
context['predictions'] = predictions
if request.method == "POST":
# login attempt
if request.POST.get('login', ''):
username = request.POST['email']
password = request.POST['password']
user = authenticate(username=username, password=password)
if user and not user.is_anonymous():
login(request, user)
return redirect(reverse('home'))
else:
error = "Sorry, your details weren't recognised"
context = {'error': error}
return redirect(addToQueryString(reverse('home'), context))
else:
# signup attempt
form = PredictionForm(request.POST, request.FILES)
if form.is_valid():
prediction = form.save()
request.session['prediction'] = prediction
request.session['competition'] = competition
return redirect(reverse('signup'))
else:
context['form'] = form
else:
# default homepage
context['form'] = PredictionForm()
return render_with_context(request, 'home.html', context)
else:
return redirect(reverse('logged_in'))
def predict():
form = PredictionForm()
select_options = [(key, value) for key, value in form_select.items()]
form.team.choices = select_options
form.opp.choices = select_options
return render_template("prediction_model.html", form=form)
def prediction_submit():
"""Provide HTML form to submit a prediction."""
Trending = get_TT()
form = PredictionForm(request.form)
if request.method == 'POST' and form.validate():
# whatever we do with text etc
text = form.text.data
return redirect(url_for('prediction_made', text=text))
TT_nohas = []
for i in range(0, 10):
if ("#" in Trending[i]):
TT_nohas.append(Trending[i].strip('#'))
else:
TT_nohas.append(Trending[i])
return render_template('prediction/submit.html',
form=form,
TT=Trending,
TTno=TT_nohas)
def index():
form = PredictionForm()
return render_template('index.html', form = form)
def predict():
form = PredictionForm()
if form.submit():
# flash("Working",'success')
header = {
'Content-Type': 'application/json',
'Authorization': 'Bearer ' +
"eyJraWQiOiIyMDIwMDYyNDE4MzAiLCJhbGciOiJSUzI1NiJ9.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.1d60axMyD9AZrZge3p_WHQsdfBH2EAqQs1k4B56uA8Gnn2VXJ3qnZS3cLCpBku2gj_Z2XY4FSlLa3TH3Aa98GZzM_FPsKzr-AVHvdA4Xa_sHq4w7Zg-MCniKE-l6_5_mHMJHNcpeVvhKOX2UqRUIzGe_79_DwOC5V24C9UXUb81SuYRO87u6jLu1JizEGkMtAU_QMZxo38gRuIOpWEZnJm5wIc4QIR4DOT0S2k6fvVypybE6UGwf3Oawr7OUogx_o8XgQ7cgY4rYyFTHo6YFTEk0ZmMIy39FkG_srof9jpn5CiNBEh6mp03L9kNVy5pe2LqM1qO2iw2hxG5A1S0OMw",
"refresh_token":
"OKA_gdpog54U7jbh9N2oMTpdDUCAeTkE4WvhLbql0QDT1NHn-5It5d3dSzxjeHBSsfur1_q_FR40-mi6NEIWPlHhley7C6LfeHtYNn4fzepHpb51RifLBjsEYMFTXNjZMf2p0DmcT_zTh_maXRWThssNwZNIuNw4_7L8Kv_bqbL7bgzKBNXULk8QjPF-5bZ51S_vcLtUudJlbL62_FMbWKP9zSBAYoMBy3o208AXgdU8ikofjQXpttEseVNriWHw6Iz0P21OIzU4l21Psymwnol5VmierRM1IaxTddTkWH8IwmdIBM6R3coWbbblp4VNfRXSGxA3lUF2C6mqFdDoUviHxY5DoNVuLWzYVJOBhgek5EURHooSZ3TEce_30YQ6xVIdp8kO9wEM9wA8Iv--H0Bighr2ppiV-ENMBlIxyMsUfamTSRzdIsJaV9fJ46sAgNfHdGJDR1dT0zKAnh-2DFnyY0X0-BaiwLUjSeVEQ4X3OjYwoMGrqhpWWX9uSItDhvzUP8oUMtoB8PzJyCUGusDw6drUGOryW3JT08jHyC7jVh5ErjRtaC3toA2hP9XcwqAcllWSTykbte6YkO_10Iz-lbf9fi5r9dbGOsFVfaYTvMgI3dRQLsX64dghEDlmo5okifitDag1QHKk72cUfnxWNaYaaiCa30DtIs8wmSsIb_LUUAzpEerMXCgqJHAllKek2bL96hn9ytkzZdc_tvkPdAS_6U_uNoozjXHExGNySEcozdnfIU4DrXlkRfUIQ3qS3cHUkrkbSp-49ohqUfwiyNox35ElhbCpC9ArP5jdDXdNMIRqDxWqfayvyneViEDCUaV_sQ1yk7MidDy0jmq-73jejWMlEuiaMbxC74WLDKj5HYj0K8c_rsgLtTzSiX2WktAQvXAdR0uL3l-d6IG-YWxnDxbHMNIcu1U0WAlzrCQzQHFYCuKsKOPclc5K2rvU88M-LsrvM60QPkbywx4_oAunkP7ArimKHmAbWfM5dj4pMTp51-VfaULhWydJvy4r3dBDEMrWbVRQuUikfW4p0DHx_ehQI6aBUquxTcFe7A",
'ML-Instance-ID': "28607ee8-f59c-42a8-87e7-5941b3198461 "
}
if (form.country.data == None):
python_object = []
else:
python_object = [
form.country.data, form.year.data, form.status.data,
form.adult_mortality.data, form.infant_deaths.data,
form.alcohol.data, form.percentage_expenditure.data,
form.hepatitis_b.data, form.measles.data, form.bmi.data,
form.under_five_deaths.data, form.polio.data,
form.total_expenditure.data, form.diphtheria.data,
form.hiv.data, form.gdp.data, form.population.data,
form.thinness_1_to_19_years.data,
form.thinness_5_to_9_years.data, form.income.data,
form.schooling.data
]
#Transform python objects to Json
userInput = []
userInput.append(python_object)
# NOTE: manually define and pass the array(s) of values to be scored in the next line
payload_scoring = {
"input_data": [{
"fields": [
"Country", "Year", "Status", "Adult Mortality",
"infant deaths", "Alcohol", "percentage expenditure",
"Hepatitis B", "Measles ", " BMI ", "under-five deaths ",
"Polio", "Total expenditure", "Diphtheria ", " HIV/AIDS",
"GDP", "Population", " thinness 1-19 years",
" thinness 5-9 years", "Income composition of resources",
"Schooling"
],
"values":
userInput
}]
}
response_scoring = requests.post(
'https://eu-gb.ml.cloud.ibm.com/v4/deployments/0447057f-d7e7-47b0-a45e-3c33baedd315/predictions',
json=payload_scoring,
headers=header)
print("Scoring response")
print(json.loads(response_scoring.text))
response_scoring = requests.post(
"https://eu-gb.ml.cloud.ibm.com/v4/deployments/0447057f-d7e7-47b0-a45e-3c33baedd315/predictions",
json=payload_scoring,
headers=header)
output = json.loads(response_scoring.text)
print(output)
for key in output:
ab = output[key]
print(ab)
for key in ab[0]:
bc = ab[0][key]
print(bc)
roundedExpectancy = round(bc[0][0], 2)
print(roundedExpectancy)
form.abc = roundedExpectancy # this returns the response back to the front page
return render_template('predictorForm.html', form=form)
def startApp():
form = PredictionForm()
return render_template('predictorForm.html', form=form)
def make_prediction(request, competition=None):
if not competition:
competition = Competition.objects\
.get(pk=settings.CURRENT_COMPETITION_ID)
competition = Competition.objects.get(pk=competition)
has_password = request.user.has_usable_password()
current_prediction = Prediction.objects\
.filter(competition=competition,
user=request.user)\
.order_by('-created_date')
current_prediction = current_prediction.count()\
and current_prediction[0] or None
now = datetime.datetime.now()
top_predictions = Prediction.objects\
.filter(competition=competition)
if current_prediction:
top_predictions = top_predictions\
.exclude(pk=current_prediction.pk)
top_predictions = top_predictions[:3]
if not competition.is_open():
error = "This competition is now closed"
if request.method == "POST":
if has_password:
form = PredictionForm(request.POST,
request.FILES,
default_table=current_prediction)
else:
form = PredictionPasswordForm(request.POST,
request.FILES,
default_table=current_prediction)
if form.is_valid():
this_year = datetime.datetime(settings.CURRENT_SEASON, 1, 1)
prediction = form.save()
saving = request.POST.get('save', '')
prediction_obj = Prediction.objects.get_or_create(
user=request.user,
name=request.user.email,
competition=competition)[0]
prediction_obj.teams.clear()
for t_id in prediction:
prediction_obj.teams.add(Team.objects.get(pk=t_id))
prediction_obj.edited_date = now
prediction_obj.save()
score = prediction_obj.calculateScore(force=True)
goaldiff = prediction_obj.calculateGoalDiff(force=True)
position = prediction_obj.calculatePosition()
prediction_obj.position = position
if not has_password:
request.user.set_password(form.cleaned_data['password1'])
request.user.save()
if saving:
transaction.commit()
return redirect(
reverse('logged_in') + '#comp-%s' % competition.pk)
else:
transaction.rollback()
else:
if has_password:
form = PredictionForm(default_table=current_prediction)
else:
form = PredictionPasswordForm(default_table=current_prediction)
return locals()
