def tag_recommendation():
# Appeler les Inputs de la page HTML dashboard
question = request.form['question'] #request.args.get('question')
tags_text = ''
if question is not None:
question = str(question)
question_tag = preprocessing(question)
question_tag_df = pd.DataFrame([question_tag], columns=['question'])
test_input_df = pd.concat([question_tag_df, input_df_tags_500],
ignore_index=True)
question_input = test_input_df['question']
vectorizer = TfidfVectorizer(tokenizer=tokenize,
stop_words=stop_words,
max_features=355)
X_tfidf = vectorizer.fit_transform(question_input).toarray()
feature_names = vectorizer.get_feature_names()
X_test_question = pd.DataFrame(X_tfidf)
X_test_question = X_test_question.iloc[0:1, :]
X_test_question.columns = feature_names
tags_num = pipeline.predict(X_test_question)
mlb = MultiLabelBinarizer(classes=sorted(input_tags_500))
mlb.fit(input_tags_500)
tags_text = pd.concat(
[pd.Series(mlb.inverse_transform(tags_num), name='tags_num')],
axis=1)
tags_text = str(tags_text.values.tolist()).strip('[()]')
tags_text
return render_template('recommendation.html', tags=tags_text)
def update_graphs(_):
"""
Returns
-------
list
Lista de figuras que se actualizarán en la página
"""
figures = []
for ramo in ramos:
nrc, sigla, sec = ramo.values()
data, name_ = vacantes(semestre, nrc, sigla, sec)
df = preprocessing(data)
fig = px.sunburst(data_frame=df,
path=['T_Vacante', 'Escuela'],
values='Vacantes',
color='T_Vacante',
color_discrete_map={
'Disponible': 'Lavender',
'Ocupado': 'Firebrick'
})
fig.update_layout(margin=dict(t=30, b=30),
plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
font_color=colors['text'])
figures.append(fig)
return figures
##########################################################################################
# USER INITIALIZATIONS
##########################################################################################
question, options = 5, 5 # QUESTIONS AND CHOICES
myAnswers = [1, 2, 0, 4, 4] # Actual Answers
choiceArea = 1000 # Area of the circles
img_width, img_height = 550, 600
global contours
##########################################################################################
#########################################################################################
# PREPROCESSING THE IMAGE
#########################################################################################
image = cv.imread('OMR 1.jpg')
image = cv.resize(image, (img_width, img_height))
img_cont_2 = image.copy()
Canny, Erode = functions.preprocessing(image)
#############################################
try:
##########################################################################################
# CONTOURS
##########################################################################################
contours, img_cont = functions.get_contour(image, Canny, filters=4, draw=True, minArea=9000)
# contours contains (contours, area, peri, approx, len(approx), center, bbox)
# With respect to the given parameters
##########################################################################################
# Verification of the contours by drawing on img_cont_2
# Checking whether marking area is given or not
##########################################################################################
cv.drawContours(img_cont_2, contours[0][0], -1, (0, 0, 255), 5)
if len(contours) == 2:
cv.drawContours(img_cont_2, contours[1][0], -1, (255, 0, 0), 5)
from functions import preprocessing, gen_data, odeintI, get_params
from matplotlib import pyplot as plt
from matplotlib import gridspec
net = Net()
net.load_state_dict(torch.load("model"))
net.eval()
#VALEURS
params = get_params()
arange, brange, pop, n, T = params
#TEST SET
test_data, test_labels = gen_data(arange, brange, pop, n, T, 50)
p_test_data, p_test_labels = preprocessing(test_data, test_labels, params)
inputs, targets = torch.tensor(p_test_data).float(), torch.tensor(
p_test_labels).float()
fig = plt.figure(figsize=(15, 10))
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 2])
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax3 = plt.subplot(gs[2])
#A, B PREDICTION/LABEL GRAPHS
predictions = net(inputs).detach().numpy().reshape(len(targets), 2)
x1, y1 = [l[0] for l in targets], [l[1] for l in targets]
x2, y2 = [l[0] for l in predictions], [l[1] for l in predictions]
print("Net ready, creating training set")
epochs = 5000
train_size = 5000
val_size = 50
#VALEURS
params = get_params()
arange, brange, pop, n, T = params
#TRAINING SET
train_data, train_labels = gen_data(
arange, brange, pop, n, T,
train_size) #data,labels = infected curve(array), (a,b)
p_train_data, p_train_labels = preprocessing(
train_data, train_labels, params
) #p_data, p_labels = preprocesed data/labels : (max,max position,max delta,average) and normalized a and b
train_inputs, train_targets = torch.tensor(p_train_data).float(), torch.tensor(
p_train_labels).float(
) #inputs, labels = pytroch tensors for p_data and p_labels
print("Train set ready")
#VALIDATION SET
val_data, val_labels = gen_data(arange, brange, pop, n, T, val_size)
p_val_data, p_val_labels = preprocessing(val_data, val_labels, params)
val_inputs, val_targets = torch.tensor(p_val_data).float(), torch.tensor(
p_val_labels).float()
print("Validation set ready.")
criterion = torch.nn.MSELoss() #Loss function
def test_lowercase(self):
self.assertEqual(fs.preprocessing('HorSe.'), 'horse .')
self.assertEqual(
fs.preprocessing('This is a Test. UPPER CASE should not Exist.'),
'this is a test . upper case should not exist .')
def test_multiple_spaces(self):
self.assertEqual(
fs.preprocessing('Multiple spaces should disappear!'),
'multiple spaces should disappear !')
def test_space_before_punctuation(self):
self.assertEqual(
fs.preprocessing(''' Sometimes
Phrases are not finished with period
And got loose'''),
'sometimes . phrases are not finished with period . and got loose')
def test_possessive(self):
self.assertEqual(fs.preprocessing("This is Joe's book"),
'this is joe book')
def test_single_quotes(self):
self.assertEqual(
fs.preprocessing("This is an article with 'single' 'quotes'"),
'this is an article with single quotes')
def test_double_quotes(self):
self.assertEqual(
fs.preprocessing('This is an article with "double" "quotes"'),
'this is an article with double quotes')
def test_brackets(self):
self.assertEqual(
fs.preprocessing('This is an article with [text in brackets]'),
'this is an article with text in brackets')
def test_paranthesis(self):
self.assertEqual(
fs.preprocessing('This is an article with (text in parenthesis)'),
'this is an article with text in parenthesis')
def test_acronym(self):
self.assertEqual(fs.preprocessing('u.s.a.'), 'usa')
server = app.server
colors = {'background': '#250000', 'text': 'white'}
# Para usar en el Output de los callbacks
sigxsec = [f"{r['sigla']}-{r['sec']}" for r in ramos]
n_rows = len(ramos) // 2 if len(ramos) % 2 == 0 else len(ramos) // 2 + 1
rows = [[] for _ in range(n_rows)]
for ramo in ramos:
for row in rows:
if len(row) < 2:
pass
else:
continue
nrc, sigla, sec = ramo.values()
data, name_ = vacantes(semestre, nrc, sigla, sec)
df = preprocessing(data)
fig = px.sunburst(
data_frame=df,
path=['T_Vacante', 'Escuela'],
values='Vacantes',
color='T_Vacante',
color_discrete_map={
'Disponible': 'Lavender',
'Ocupado': 'Firebrick'
},
)
fig.update_layout(
margin=dict(t=30, b=30),
plot_bgcolor=colors['background'],
paper_bgcolor=colors['background'],
