def comment_add(req, blog_id):
if not req.user.is_authenticated:
return redirect(reverse_lazy("blog:index"))
template = "comment_form.html"
comment = Comment()
comment.author = req.user
comment.blog = Blog.objects.get(id=blog_id)
if req.method == "POST":
form = CommentForm(req.POST, instance=comment)
if form.is_valid():
form.save()
return redirect(reverse_lazy("blog:view_blog", args=[blog_id]))
else:
res(form)
context = {
"page_title": "Add New Comment",
"comment_form": CommentForm(instance=comment)
}
return render(req, template, context)
def download_file(request, file_name):
fl_path = 'file'
filename = file_name + '.txt'
fl = open(fl_path, 'r')
mime_type, _ = mimetypes.guess_type(fl_path)
response = res(fl, content_type=mime_type)
response['Content-Disposition'] = "attachment; filename=%s" % filename
return response
def blog_delete(req, blog_id):
if not req.user.is_authenticated:
return redirect(reverse_lazy("blog:index"))
blog = Blog.objects.get(id=blog_id)
blog.delete()
return redirect(reverse_lazy("blog:index"))
return res("Delete blog")
def register(request):
if request.method == 'POST':
form = FORMS.formRegister(request.POST)
if form.is_valid():
data = form.cleaned_data
user = User(name=data['name'],
phone=data['phone'],
email=data['email'])
##################3
sizePublickey = len(PublicKey.objects.all()) + 1
sizeUser = len(User.objects.all()) + 1
n, e, d = generateKey()
userid = generateId(sizeUser)
idpublickey = generateId(sizePublickey)
publicKey = PublicKey(idPublicKey=str(idpublickey),
publicKeyE=str(e),
publicKeyN=str(n))
user = User(userId=str(userid),
name=data['name'],
phone=data['phone'],
email=data['email'])
#Id + id khoa cong khai
CI = str(encode(userid, n, d)) + '#' + str(idpublickey)
C = int(str(CI).split('#')[0])
P = decode(C, n, e)
# return res(P == int.from_bytes(sha512(str(userid).encode()).digest(),byteorder='big'))
try:
u = User.objects.filter(email=user.email).get().email
if (u == user.name):
return res(
"<script>alert('tai khoan da ton tai')</script>")
except User.DoesNotExist:
user.save()
publicKey.save()
return res(CI)
return HttpResponseRedirect(reverse('app:index'))
return
form = FORMS.formRegister()
return render(request, "app/register.html", {"form": form})
def index(request):
return res("Hello da den voi trang ban hang")
def show(request, ID):
return res(f"id cua ban la {ID}")
def show(request):
info = {'name': 'arpit', 'age': 30}
return res(json.dumps(info))
def del_todo(req, todo_id):
obj = models.Todo.objects.using(todoDB).get(id=todo_id)
obj.delete()
return res('delete')
def add_todo(req, name):
obj = models.Todo(content=name)
obj.save(using=todoDB)
return res('save')
def todo_list(req):
flds = ['content']
# obj = serialise(getTodoList(), flds)
obj1 = simple_serialise(getTodoList(), flds)
# listJson = {'data': obj}
return res(obj1, content_type=jsonType)
def hello(request):
#最简单的定义url例子
return res("hello world")
def home(request, text, keyword):
print(keyword)
movie_user_likes = text
tic = time.time()
df = pd.read_csv("./dataset/movie_dataset.csv")
def combine_features(row):
return row['keywords'] + " " + row['genres']
features = ['keywords', 'genres']
for feature in features:
df[feature] = df[feature].fillna(
'') # filling all NaNs with blank string
df['combined_features'] = df.apply(combine_features, axis=1)
if len(df[df['id'] == text]) > 0:
print("success")
cv = CountVectorizer()
count_matrix = cv.fit_transform(df['combined_features'])
cosine_sim = cosine_similarity(count_matrix)
def get_id_from_index(index):
return df[df.index == index]['id'].values[0]
def get_index_from_id(id):
return df[df.id == id]['index'].values[0]
movie_index = get_index_from_id(movie_user_likes)
print(movie_index)
similar_movies = list(enumerate(cosine_sim[movie_index]))
# accessing the row corresponding to given movie to find all the similarity scores for that movie and then enumerating over it
similar_sorted = sorted(similar_movies,
key=lambda x: x[1],
reverse=True)[1:]
similar_movies = similar_sorted[0:10]
tac = time.time()
print("read time" + str(((tac - tic) * 1000)) + "ms")
list_names = []
for names in similar_movies:
# list_names.append(df[df.id==get_id_from_index(names[0])]['title'].values[0])
temp = {
'id':
str(get_id_from_index(names[0])),
'movie':
df[df.id == get_id_from_index(names[0])]['title'].values[0]
}
list_names.append(temp)
result = {'result': list_names}
# list.append(text)
# print(list)
return res(result)
else:
print("failed")
df2 = pd.DataFrame({
"index": [len(df['index'])],
"id": [text],
"combined_features": [keyword]
})
df = df.append(df2, ignore_index=True)
cv = CountVectorizer()
count_matrix = cv.fit_transform(df['combined_features'])
cosine_sim = cosine_similarity(count_matrix)
def get_id_from_index(index):
return df[df.index == index]['id'].values[0]
def get_index_from_id(id):
return df[df.id == id]['index'].values[0]
movie_index = get_index_from_id(movie_user_likes)
print(movie_index)
similar_movies = list(enumerate(cosine_sim[movie_index]))
# accessing the row corresponding to given movie to find all the similarity scores for that movie and then enumerating over it
similar_sorted = sorted(similar_movies,
key=lambda x: x[1],
reverse=True)[1:]
similar_movies = similar_sorted[0:10]
tac = time.time()
print("read time" + str(((tac - tic) * 1000)) + "ms")
list_names = []
for names in similar_movies:
# list_names.append(df[df.id==get_id_from_index(names[0])]['title'].values[0])
temp = {
'id':
str(get_id_from_index(names[0])),
'movie':
df[df.id == get_id_from_index(names[0])]['title'].values[0]
}
list_names.append(temp)
result = {'result': list_names}
# list.append(text)
# print(list)
return res(result)
def about(request):
return res("<h2>About</h2")
def hello(request):
#最简单的定义url例子
return res("hello world")
def success(request):
return res("dang ky ok ")
def home(request):
return res("dang nhap thanh cong")