def buildCategories(self):
multiclass_exp = self.buildMulticlassClassifier()
train = self.multiclass_model.datasets.train_instances
test = self.multiclass_model.datasets.test_instances
all_instances = copy.deepcopy(test)
all_instances.union(train)
if test.numInstances() > 0:
predicted_families = self.multiclass_model.testing_monitoring.getPredictedLabels()
all_families = list(predicted_families) + train.families
predicted_proba = self.multiclass_model.testing_monitoring.getAllPredictedProba()
for family in train.families:
probas = [int(family == s) for s in self.multiclass_model.class_labels]
predicted_proba = np.vstack((predicted_proba, np.array(probas)))
else:
all_families = self.annotated_instances.families
predicted_proba = None
for family in self.annotated_instances.families:
probas = [int(family == s) for s in self.multiclass_model.class_labels]
if predicted_proba is None:
predicted_proba = np.array(probas)
else:
predicted_proba = np.vstack((predicted_proba, np.array(probas)))
labels_values = list(self.multiclass_model.class_labels)
assigned_categories = [labels_values.index(x) for x in all_families]
self.categories = Categories(multiclass_exp,
all_instances,
assigned_categories,
predicted_proba,
self.label,
self.multiclass_model.class_labels)
def get_category_by_id(self, cat_id): if cat_id == None: return None Category = Categories(self.db) category = Category.find_by_id(cat_id) if not category: return None return category
def remove_category():
'''Function used to remove a category from the txt database'''
# read the input and create a new python object
categoryToRemove = Category(input('What category you want to remove?\n'))
# remove the category
Categories.remove_category(categoryToRemove)
print("The category was removed.")
input(READ_ENTER_KEY)
def add_category():
'''Function used to display the jewlery menu in the console'''
# read the input and create a new python object
newCategory = Category(input('What category you want to add?\n'))
# load the new category in the txt database
Categories.add_category(newCategory)
print( str(newCategory) + ' added successfully')
input("\nPress enter key in order to continue\n")
def get_or_create_category_by_name(self, cat_name, gender=None, parent=None): if cat_name == None: return None if gender != None: gender = gender["id"] if parent != None: parent = parent["id"] Category = Categories(self.db) category = Category.find_by_name_and_gender(cat_name.strip(), gender) if not category: category = Category.save(cat_name.strip(), gender=gender, category=parent) if not category: return None return category
def setCategories(self, all_instances, assigned_categories, predicted_proba):
self.categories = Categories(self.iteration,
all_instances,
assigned_categories,
predicted_proba,
self.label,
self.multiclass_model.class_labels)
def __init__(self, nTrial, nStim):
self.nTrial = nTrial
self.nStim = nStim
self.categs = Categories(nTrial, nStim)
self.stims = self.categs.encDF
# self.trials = data.TrialHandler(self.stims,
# 1,
# method='sequential')
self.poslist = []
def uploader(self, updown):
while True:
images = updown.q.get()
if(images):
for image in images:
cat = Categories(image[2])
cat = cat.find_category()
desc = str(image[8]) + ", " + str(image[9])
url = self.base_url + image[1]
date = image[4]
lat = image[5]
lon = image[6]
description = Description(desc, url, date, cat, lat, lon)
print description.get_desc()
up = Upload(description=description, file_name=image[13])
up.perform()
self.db.update_image_uploaded(image[3])
time.sleep(120)
def __init__(self,nTrial,nStim):
'''
Parameters
----------
nTrial: number of runs for a subject
nStim: number of new images used in each phase in a trial
'''
self.log = logging.LogFile(f='imTaskLogFile')
self.nTrial = nTrial
self.nStim = nStim
self.categs = Categories(nTrial,nStim)
self.encDict = self.categs.encDF.to_dict(orient="index")
self.recDict = self.categs.recDF.to_dict(orient="index")
self.stimpos = {'0':(-250.0, 250.0),
'1':(250.0, 250.0),
'2':(250.0, -250.0),
'3':(-250.0, -250.0)}
self.outputlist = []
self.encInstStartText = '''\
Memorize the following images and
their location on screen.
Press space to start.\
'''.format()
self.recInstStartText = '''\
A series of {x} images will appear.
Indicate if shown image corresponds to a previously seen image
and in which quadrant it has appeared earlier.
Press SPACE to start\
'''.format(x=self.nStim+1)
self.recInstText1 = '''\
Have you seen this picture before?
If yes, press "y". If not, press "n".\
'''.format()
self.recInstText2 = '''\
Where have you seen it?
Press 0, 1, 2 or 3 to answer\
'''.format()
self.recInstPosText = '''\
Where have you seen it?
0 = upper-left, 1 = upper-right
2 = lower-left, 3 = lower-right\
'''.format()
self.ansSaveText = 'Answer saved!'
self.endText = 'Thank you for your time, goodbye!'
def display_categories():
print('The categories available in the jewlery shop are: \n')
# Get a list with the categories
categoriesList = Categories.load_categories()
# Display the categories retrieved in the list
for category in categoriesList:
print(category)
input(READ_ENTER_KEY)
def __init__(self, whichTrial, nTrial, nStim):
self.whichTrial = whichTrial
self.nTrial = nTrial
self.nStim = nStim
self.categs = Categories(nTrial, nStim)
self.encDict = self.categs.encDF.to_dict(orient="index")
self.thisEncTrial = self.encDict[self.whichTrial]
self.recDict = self.categs.recDF.to_dict(orient="index")
self.thisRecTrial = self.recDict[self.whichTrial]
self.thisTrialTarg = self.categs.Targs[self.whichTrial]
self.encStimlist = []
self.answerlist = []
self.stimpos = {
'0': (-250.0, 250.0),
'1': (250.0, 250.0),
'2': (250.0, -250.0),
'3': (-250.0, -250.0)
}
self.targetPos = self.getTargPos()
self.encInstStartText = '''\
Memorize the following images and
their location on screen.
Press space to start.\
'''.format()
self.recInstStartText = '''\
A series of {x} images will appear.
Indicate if shown image corresponds to a previously seen image
and in which quadrant it has appeared earlier.
Press SPACE to start\
'''.format(x=self.nStim + 1)
self.recInstText1 = '''\
Have you seen this picture before?
If yes, press "y". If not, press "n".\
'''.format()
self.recInstText2 = '''\
Where have you seen it?
Press 0, 1, 2 or 3 to answer\
'''.format()
self.recInstPosText = '''\
Where have you seen it?
0 = upper-left, 1 = upper-right
2 = lower-left, 3 = lower-right\
'''.format()
self.ansSaveText = 'Answer saved!'
self.endText = 'Thank you for your time, goodbye!'
def adminMenu():
print(" ~~ Administration MAIN MENUE ~~\n\n")
print(" 1- Manage Categories")
print(" 2- Manage Products")
print(" 3- Manage Customers\n")
manageGRP = input("Please choose your option [1-3] >>")
# admin select manage categories
if manageGRP[:1] == '1':
clear()
Category = Categories()
Category.main()
# admin select manage Products
elif manageGRP[:1] == '2':
clear()
product = Products()
product.main()
# admin select manage Customers
elif manageGRP[:1] == '3':
clear()
# Category = Categories()
# Category.main()
else:
print("Invalid Option")
def search_categories(categories, combobox):
"""
* Parameter: categories (etree element -Tag- 'Categories)
* Parameter: combobox (Qt Combobox object)
* Create Categories object -Note- Pass opt parameter Scan as True
* Iterate over category (etree element tag 'Category) in categories
* Get name of category (attrib 'Name')
* Execute add_category method for each category (Category Object)
"""
categories_object = Categories.add(categories.getparent(), combobox, True)
for category in categories:
name = category.attrib["Name"]
categories_object.add_category(name, True)
def check_categories(self):
"""
* Check if the categories checkbox is selected
* Add Categories object if it is checked
* Delete Categories object if it is not checked
* Update category fields
* Display error message if AttributeError occurs
"""
try:
status = self.categoryCheckbox.isChecked()
if status:
root = self.tree.getroot()
self.categories = Categories.add(root, self.category_box, False)
else:
self.categories.delete()
self.categories = None
set_software_tags(self)
update_category_fields(self, status)
except AttributeError:
self.statusBar().showMessage('No file selected.', 1500)
def __init__(self,nTrial,nStim):
self.nTrial = nTrial
self.nStim = nStim
self.categs = Categories(nTrial,nStim)
self.encResults = pd.read_csv(os.getcwd()+'\\stimDF.csv')
self.stims = self.categs.recDF
self.TargStims = self.categs.Targs
self.stimpos = {'0':np.array((-250.0, 250.0)),
'1':np.array((250.0, 250.0)),
'2':np.array((250.0, -250.0)),
'3':np.array((-250.0, -250.0))}
self.instStartText = '''\
... A series of {x} images will appear.
... Indicate if shown image corresponds to a previously seen image
... and in which quadrant it has appeared earlier.
... Press SPACE to start\
'''.format(x=len(self.stims)+1)
self.inst1Text = '''\
... Have you seen this picture before?
... If yes, press "y". If not, press "n".\
'''.format()
self.inst2Text = '''\
... Where have you seen it?
... Press 0, 1, 2 or 3 to answer\
'''.format()
self.instPosText = '''\
... Where have you seen it?
... 0 = upper-left, 1 = upper-right
... 2 = lower-left, 3 = lower-right\
'''.format()
self.ansSaveText = 'Answer saved!'
def get(self):
self.response.headers['Content-Type'] = 'text/plain'
action=self.request.get('action')
output='error'
if action == "login":
user = users.get_current_user()
if user:
output=self.createResponseXML('true')
else:
output=self.createResponseXML('false')
if action == "regtemp":
self.redirect(users.create_login_url("/loggedin.html"))
output = 'redirected'
if action =="remove":
user = users.get_current_user()
if user:
id=self.request.get('id')
deleteSession(id)
output=self.createResponseXML('true')
else:
output=self.createResponseXML('false')
if action == "save":
doc = xml.dom.minidom.parseString(self.request.get('urls'))
doc = doc.childNodes[0]
user = users.get_current_user()
if user:
output=self.createResponseXML('true')
name=doc.getAttribute("name")
categories_all = db.GqlQuery("SELECT * FROM Categories order by id desc limit 1")
curid=0
for cat in categories_all:
curid=cat.id+1;
for e in doc.childNodes:
category= Categories()
category.user=user.nickname()
category.name=name
category.id=curid
category.url=e.getAttribute('link')
category.put()
else:
output=self.createResponseXML('false')
if action == "load":
doc = xml.dom.minidom.Document()
urlsChild = doc.createElement("urls")
doc.appendChild(urlsChild)
urlsChild=doc.childNodes[0]
user = users.get_current_user()
if user:
id=self.request.get("id")
categories_all = db.GqlQuery("SELECT * FROM Categories where id="+id)
for cat in categories_all:
urlsChild.setAttribute('name',cat.name)
linkChild=doc.createElement("url")
linkChild.setAttribute('link',cat.url)
urlsChild.appendChild(linkChild)
output=doc.toxml()
else:
output=self.createResponseXML('false')
if action == "list":
doc = xml.dom.minidom.Document()
urlsChild = doc.createElement("categories")
doc.appendChild(urlsChild)
urlsChild=doc.childNodes[0]
user = users.get_current_user()
if user:
categories_all = db.GqlQuery("SELECT * FROM Categories where user='******'")
unique_results = []
for cat in categories_all:
if cat.id not in unique_results:
linkChild=doc.createElement("cat")
linkChild.setAttribute('name',cat.name)
linkChild.setAttribute('id',str(cat.id))
urlsChild.appendChild(linkChild)
unique_results.append(cat.id)
output=doc.toxml()
else:
output=self.createResponseXML('false')
if output == "error":
self.redirect('/tabinfo.html')
self.response.out.write(output)
class RareCategoryDetectionAnnotationQueries(AnnotationQueries):
def __init__(self, iteration, label, proba_min, proba_max,
multiclass_model = None,
multiclass_exp = None):
AnnotationQueries.__init__(self, iteration, label)
self.proba_min = proba_min
self.proba_max = proba_max
self.rare_category_detection_conf = self.iteration.experiment.conf.rare_category_detection_conf
self.multiclass_model = multiclass_model
self.multiclass_exp = multiclass_exp
def run(self, already_queried = None):
self.runModels(already_queried = already_queried)
start_time = time.time()
self.generateAnnotationQueries()
self.generate_queries_time = time.time() - start_time
self.exportAnnotationQueries()
self.generateClusteringVisualization()
def runModels(self, already_queried = None):
df = matrix_tools.extractRowsWithThresholds(self.predictions,
self.proba_min, self.proba_max, 'predicted_proba')
if already_queried is not None:
self.predicted_ids = list(set(df.index).difference(set(already_queried)))
else:
self.predicted_ids = list(df.index)
datasets = self.iteration.datasets
self.annotated_instances = datasets.getAnnotatedInstances(label = self.label)
self.families_analysis = self.familiesAnalysis()
if self.families_analysis:
self.annotations_type = 'families'
start_time = time.time()
self.buildCategories()
self.analysis_time = time.time() - start_time
self.categories.setLikelihood(self.iteration.iteration_number)
else:
self.annotations_type = 'individual'
self.categories = None
self.analysis_time = 0
def generateAnnotationQueries(self):
num_annotations = self.rare_category_detection_conf.num_annotations
if not self.families_analysis:
selected_instances = self.predicted_ids
if len(selected_instances) > num_annotations:
selected_instances = random.sample(selected_instances, num_annotations)
self.annotation_queries = []
for instance_id in selected_instances:
predicted_proba = self.predictions.loc[instance_id]['predicted_proba']
query = AnnotationQuery(instance_id, predicted_proba, self.label, None)
self.annotation_queries.append(query)
else:
self.categories.generateAnnotationQueries(self.rare_category_detection_conf)
def exportAnnotationQueries(self):
if not self.families_analysis:
AnnotationQueries.exportAnnotationQueries(self)
else:
filename = self.iteration.output_directory
filename += 'toannotate_' + self.label + '.json'
self.categories.exportAnnotationQueries(filename)
def annotateAuto(self):
if not self.families_analysis:
AnnotationQueries.annotateAuto(self)
else:
self.categories.annotateAuto(self.iteration)
def getManualAnnotations(self):
if not self.families_analysis:
AnnotationQueries.getManualAnnotations(self)
else:
self.categories.getManualAnnotations(self.iteration)
def checkAnnotationQueriesAnswered(self):
if not self.families_analysis:
return AnnotationQueries.checkAnnotationQueriesAnswered(self)
else:
return self.categories.checkAnnotationQueriesAnswered(self.iteration)
#######################
### Private methods ###
#######################
def createMulticlassExperiment(self):
conf = self.rare_category_detection_conf.classification_conf
exp = self.iteration.experiment
name = '-'.join(['AL' + str(exp.experiment_id),
'Iter' + str(self.iteration.iteration_number),
self.label,
'analysis'])
multiclass_exp = ClassificationExperiment(exp.project, exp.dataset, exp.session,
experiment_name = name,
labels_id = exp.labels_id,
parent = exp.experiment_id)
multiclass_exp.setFeaturesFilenames(exp.features_filenames)
multiclass_exp.setClassifierConf(conf)
multiclass_exp.createExperiment()
multiclass_exp.export()
return multiclass_exp
def buildCategories(self):
multiclass_exp = self.buildMulticlassClassifier()
train = self.multiclass_model.datasets.train_instances
test = self.multiclass_model.datasets.test_instances
all_instances = copy.deepcopy(test)
all_instances.union(train)
if test.numInstances() > 0:
predicted_families = self.multiclass_model.testing_monitoring.getPredictedLabels()
all_families = list(predicted_families) + train.families
predicted_proba = self.multiclass_model.testing_monitoring.getAllPredictedProba()
for family in train.families:
probas = [int(family == s) for s in self.multiclass_model.class_labels]
predicted_proba = np.vstack((predicted_proba, np.array(probas)))
else:
all_families = self.annotated_instances.families
predicted_proba = None
for family in self.annotated_instances.families:
probas = [int(family == s) for s in self.multiclass_model.class_labels]
if predicted_proba is None:
predicted_proba = np.array(probas)
else:
predicted_proba = np.vstack((predicted_proba, np.array(probas)))
labels_values = list(self.multiclass_model.class_labels)
assigned_categories = [labels_values.index(x) for x in all_families]
self.categories = Categories(multiclass_exp,
all_instances,
assigned_categories,
predicted_proba,
self.label,
self.multiclass_model.class_labels)
def buildMulticlassClassifier(self):
if self.multiclass_model is not None:
return self.multiclass_exp
multiclass_exp = self.createMulticlassExperiment()
datasets = self.iteration.datasets
predicted_instances = datasets.getInstancesFromIds(self.predicted_ids)
multiclass_datasets = ClassifierDatasets(multiclass_exp.classification_conf)
multiclass_datasets.train_instances = self.annotated_instances
multiclass_datasets.test_instances = predicted_instances
multiclass_datasets.setSampleWeights()
self.multiclass_model = multiclass_exp.classification_conf.model_class(
multiclass_exp.classification_conf,
multiclass_datasets,
cv_monitoring = True)
self.multiclass_model.run(multiclass_exp.getOutputDirectory(),
multiclass_exp)
return multiclass_exp
# A multi class supervised model is learned from the annotated instances if:
# - there are at most 2 families
# - the second most represented family has at least 2 instances
def familiesAnalysis(self):
num_families = len(self.annotated_instances.getFamiliesValues())
if num_families < 2:
return False
families_counts = self.annotated_instances.getFamiliesCount()
families_counts = [(k, x) for k, x in families_counts.iteritems()]
families_counts.sort(key=lambda tup: tup[1], reverse = True)
if families_counts[1][1] < 2:
return False
return True
def createClusteringExperiment(self):
conf = ClusteringConfiguration(self.categories.numCategories())
exp = self.iteration.experiment
name = '-'.join(['AL' + str(exp.experiment_id),
'Iter' + str(self.iteration.iteration_number),
self.label,
'clustering'])
clustering_exp = ClusteringExperiment(exp.project, exp.dataset, exp.session,
conf,
labels_id = exp.labels_id,
experiment_name = name,
parent = exp.experiment_id)
clustering_exp.setFeaturesFilenames(exp.features_filenames)
clustering_exp.createExperiment()
clustering_exp.export()
return clustering_exp
def generateClusteringVisualization(self):
if self.families_analysis:
self.clustering_exp = self.createClusteringExperiment()
clustering = Clustering(self.categories.instances,
self.categories.assigned_categories)
clustering.generateClustering(self.clustering_exp.getOutputDirectory(),
None, None)
else:
self.clustering_exp = None
def __init__(self, parent_exp, iteration, instances, assigned_categories,
assignment_proba, label, category_labels):
Categories.__init__(self, iteration, instances, assigned_categories, assignment_proba,
label, category_labels)
self.experiment = parent_exp
def get(self):
self.response.headers["Content-Type"] = "text/html"
user = users.get_current_user()
if user:
htmlUtils = HtmlUtils()
output = htmlUtils.generateStart("TabSender: My account")
output += htmlUtils.generateDefaultMenu()
output += htmlUtils.generateMenuEnd()
newShares = getNewShares(user.nickname())
type = self.request.get("type")
if not type or type == "browse":
output += '<h1><span class="colouredText">' + user.nickname() + "</span>'s shares on server:</h1><ul>"
if newShares == 0:
output += 'No new shares.<br />Return to <a href="/tabinfo.html">your account</a>.'
else:
output += "<ul>\n"
sharesForUser = db.GqlQuery("SELECT * FROM Share WHERE user='******'")
for share in sharesForUser:
output += (
'<li><a href="/share.html?type=details&id='
+ str(share.id)
+ '">'
+ getNameForID(share.sessionid)
+ '</a> by <span class="colouredText">'
+ share.creator
+ '</span><br /><span class="colouredText">Message</span>: '
+ share.message
+ "<br />\n"
)
output += (
'<a href="/share.html?type=accept&id='
+ str(share.id)
+ '"><img class="imageURL" src="/html/images/add.png" />Accept</a>\n'
)
output += (
'<a href="/share.html?type=cancel&id='
+ str(share.id)
+ '"><img class="imageURL" src="/html/images/cancel.png" />Cancel</a>\n'
)
output += "</li>\n"
output += "</ul>\n"
# if type == 'browsedb':
# sharesTable = db.GqlQuery("SELECT * FROM Share")
# for share in sharesTable:
# output += str(share.user) + ' ' + str(share.sessionid) + ' ' + str(share.message) + ' ' + str(share.creator) + '<br />'
elif type == "cancel":
id = self.request.get("id")
shareToCancel = db.GqlQuery("SELECT * FROM Share WHERE id=:1", int(id))
for share in shareToCancel:
share.delete()
self.redirect("/share.html")
elif type == "accept":
id = self.request.get("id")
sessionid = -1
shareToAccept = db.GqlQuery("SELECT * FROM Share WHERE id=:1", int(id))
for share in shareToAccept:
sessionid = share.sessionid
break
sessionToCopy = db.GqlQuery("SELECT * FROM Categories WHERE id=:1", sessionid)
categories_all = db.GqlQuery("SELECT * FROM Categories ORDER BY id DESC LIMIT 1")
curid = 0
for cat in categories_all:
curid = cat.id + 1
copiedSession = []
for sess in sessionToCopy:
copiedPart = Categories()
copiedPart.id = curid
copiedPart.name = sess.name
copiedPart.url = sess.url
copiedPart.user = user.nickname()
copiedPart.put()
for share in shareToAccept:
share.delete()
self.redirect("/tabinfo.html")
output += htmlUtils.generateMainPartEnd()
# if newShares == 0:
# output += 'You have no new shares.'
# else:
# output += 'TO FIX!!!'
output += htmlUtils.generateEnd()
self.response.out.write(output)
else:
self.redirect(users.create_login_url("/share.html"))
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 22 00:37:31 2019
@author: Francois
"""
from psychopy import core
from psychopy import data
from psychopy import event
from psychopy import visual
from Categories import Categories
from randSign import randSign
trials = data.TrialHandler(Categories(2, 8).trialslist, 1, method='sequential')
def runtask(trials):
win = visual.Window(size=(1000, 1000), color=(0, 0, 0), units='pix')
eachTrial = 0
while eachTrial <= len(trials.trialList) - 1:
def encodingphase(
win
): # Shows stimuli in each trial list in "trials"(also list)
#stimpos = [(250.0, 250.0),(-250.0, -250.0),(250.0, -250.0),(-250.0, -250.0)] #Possibly replacing randSign()
instructionStart = visual.TextStim(
win,
text=
'Memorize the following images and their location on screen. Press space to start.'
def __init__(self):
self.trials = data.TrialHandler(Categories(2, 5).trialslist,
1,
method='sequential')
def main(self):
product = Products()
self.clear()
print(" ***** Manage Products *****\n\n")
print(" 1- Add a new product")
print(" 2- Edit existing product")
print(" 3- Remove existing product\n")
productOperation = input("Please choose your option [1-3] >>")
if productOperation[:1] == '1':
category = Categories()
self.clear()
# Retrive real state data from database
print("Options:")
print(
"\n\n1- Choose a category from the existing categories to add your product to")
print("\n2- Add a new category to connect your product to")
option = input("\nPlease make a selection [1-2] >>")
if category.ValidateUserInputAsNumber(option):
option = int(option)
if(option == 1 or option == 2):
if(option == 1):
self.clear()
SQLStatment = category.selectStatment(
"*", "categories", "")
cursor = self.executeSQLStatment(SQLStatment)
rows = cursor.fetchall()
cursor.close()
print(
"-------------------------------------------------------------:")
print(
"---------------------- Categories List ----------------------")
#ProductCategory = Category()
if(rows):
counter = 0
for row in rows:
print(f"{counter+1} - {row[1]} ")
counter += 1
categoryNo = input(
"\n\nPlease enter a valid category from the list above>>")
if category.ValidateUserInputAsNumber(categoryNo):
categoryNo = int(categoryNo)
if(categoryNo > 0 and categoryNo <= counter and categoryNo is not None):
rowIndex = int(categoryNo)-1
category_id = int(rows[rowIndex][0])
print("\n\n ***New Product Information:***")
productName = input(
"Please enter the product name >> ")
productDescription = input(
"Please enter the product description >> ")
productPrice = input(
"Please enter the product price >> ")
self.addNewProduct(
category_id, productName, productDescription, productPrice)
else:
categoryName = input(
"Please enter the Category name that you want to add >>")
rows = category.addNewCategory(categoryName)
category_id = rows.fetchone()[0]
print("\n\n ***New Product Information:***")
productName = input(
"Please enter the product name >> ")
productDescription = input(
"Please enter the product description >> ")
productPrice = input(
"Please enter the product price >> ")
self.addNewProduct(
category_id, productName, productDescription, productPrice)
else:
print("invalid option")
elif productOperation[:1] == '2':
# self.EditProduct("Edit")
# self.editExistingProduct(
print("\n\n- Search Products by Name -")
productName = input(
"Please enter a keyword to search for Product Name >> ")
whereStatment = f"LOWER(product_name) like '%{productName}%' "
self.editExistingProduct(whereStatment)
elif productOperation[:1] == '3':
self.searchMainMenu("Delete")
else:
print("Invalid entry\n")