def post(self,request,*args,**kwargs):
foundPersons=[]
givenTerm=request.POST.get('givenTerm')
searchBy=request.POST.get('searchBy')
if searchBy=='serial_number':
'''Search By Serial Number'''
try:
givenSN=int(givenTerm)
a=Person.objects.filter(person_id=givenSN)
foundPersons=[person for person in a]
except:pass
elif searchBy=='name':
'''Search By Name'''
try:
import nepali_roman as nr
foundPersons=[person for person in Person.objects.all() if nr.romanize_text(givenTerm).lower() in nr.romanize_text(person.full_name).lower()]
except:pass
elif searchBy=='batch_no':
'''Search By batch no'''
try:
givenBatchNo=int(givenTerm)
foundPersons=[person for person in Person.objects.filter(batch_no=givenBatchNo)]
except:pass
elif searchBy=='phone_number':
'''Search By phone_number'''
try:
foundPersons=[person for person in Person.objects.all() if givenTerm in person.contact_number]
except:pass
elif searchBy=='grandfather':
'''Search By Grandfather Name'''
try:
import nepali_roman as nr
foundPersons=[]
for person in Person.objects.all():
try:
if givenTerm.lower()==nr.romanize_text(person.father.father.full_name).lower():
foundPersons.append(person)
except:pass
except:pass
return render(request,peoples_table_template,{"persons":foundPersons})
def save(self, *args, **kwargs):
childUpdateOnly = False
suggestionUpdateOnly = False
if "childUpdateOnly" in args: childUpdateOnly = True
if "suggestionUpdateOnly" in args: suggestionUpdateOnly = True
self.full_name_romanized = nr.romanize_text(self.full_name).lower()
super(Person, self).save()
if (not childUpdateOnly and not suggestionUpdateOnly):
if self.father:
self.batch_no = self.father.batch_no + 1 if self.father.batch_no else 1
max_person_id = Person.objects.all().aggregate(
Max('person_id'))['person_id__max']
self.person_id = max_person_id if max_person_id > 0 else 0
self.same_vamsha = True
super(Person, self).save(
update_fields=["batch_no", "person_id", "same_vamsha"])
parent = self.father
elif self.mother:
self.batch_no = self.mother.batch_no + 1 if self.mother.batch_no else 1
self.same_vamsha = False
self.person_id = 0
super(Person,
self).save(update_fields=["person_id", "same_vamsha"])
parent = self.mother
else:
pass
'''Update the father -> children field with self id'''
try:
parent_children = parent.children if parent.children else {}
parent_children.update({self.id: self.full_name})
parent.children = parent_children
parent.save("childUpdateOnly", update_fields=["children"])
except Exception as e:
pass
if (suggestionUpdateOnly):
suggestion = None
person_suggestions = self.suggestions if self.suggestions else {}
for key, value in kwargs.items():
if key == "suggestion":
suggestion = value
if (suggestion):
person_suggestions.update(
{suggestion['suggester_name']: {
0: suggestion
}})
self.suggestions = person_suggestions
super(Person, self).save(update_fields=["suggestions"])
else:
pass
def accounts(num=5):
for _ in range(num):
city_name = nr.romanize_text(f.city_name())
street_name = nr.romanize_text(f.street_address())
postcode = f.postcode()
name = '{} {}'.format(
nr.romanize_text(f.first_name()).capitalize(),
nr.romanize_text(f.last_name()).capitalize())
email = name.replace(' ', '_') + '@gmail.com'
addr = '{}, {}-{}'.format(street_name, city_name, postcode)
phone_number = '+91' + str(f.random_number(10))
print(name, phone_number, email)
print(addr)
obj = CustomUser(
email=email,
password='******',
name=name,
mobile_number=phone_number,
job='NA',
address=addr,
)
obj.save()
def spacy_predicter(text):
return_arr = []
nlp = spacy.load(R".\output\model-best")
new_inp = text
new_data = nr.romanize_text(new_inp)
new_data = new_data.strip(" ")
filt_data = nepali_word_filter(new_data)
doc = nlp(filt_data)
entitities = doc.ents
for ent in doc.ents:
ent_dict = {
'text': ent.text,
'start_index': ent.start_char,
'end_index': ent.end_char,
'entity': ent.label_
}
return_arr.append(ent_dict)
def text_finder(link):
num_array = []
gh = link
new_text = nr.romanize_text(link)
new_text = nepali_word_filter(new_text)
top = new_text.split(" ")
threshold = 0.80
for key in new_arr:
for word in top:
try:
res = cosdis(word2vec(word), word2vec(key))
if res != False:
if res > threshold:
pattern = re.compile(rf'{key}[a-z]+')
if pattern.match(word):
new_text = re.sub(pattern, key, new_text)
except IndexError:
pass
for i in new_text.split(" "):
if i in new_arr:
res = [[ele.start(), ele.end() - 1]
for ele in re.finditer(rf"{i}", new_text)]
blo = [txt for txt in re.finditer(rf"{i}", new_text)]
if num_array == None:
num_array = res
else:
for j in res:
if j not in num_array:
num_array.append(j)
m = sorted(num_array)
word_value = word_indent(m)
if word_value != []:
app_arr = [new_text, word_value]
global_arr.append(app_arr)
return word_value