我有两件事我想解决，我是django的新手和造型：首先，我有这个算法，我在上传文件上运行，加载需要时间，所以我想要显示加载消息，直到生成output.csv并准备下载（执行主页功能后）。我对造型和模板很陌生，任何人都可以帮我解决这个问题吗？
我的文件是这样的：
view.py:
from __future__ import division
from django.shortcuts import render
from uploader.models import UploadForm,Upload
from uploader.models import Book
import io
from io import TextIOWrapper
import csv
from csv import DictReader
from io import StringIO
from django.http import HttpResponseRedirect
from django.core.urlresolvers import reverse
import csv
import logging
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
import csv
from csv import DictReader
import numpy
from numpy.random import *
import numpy as np
import random
from functools import partial
from sklearn.metrics import pairwise_distances_argmin_min
from scipy.spatial.distance import pdist, squareform
import pandas as pd
import functools
from scipy import spatial
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from math import pi, acos
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.metrics import jaccard_similarity_score
import pprint
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import Normalizer
from sklearn import metrics
from sklearn.cluster import KMeans, MiniBatchKMeans
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn.datasets.samples_generator import make_blobs
from django.core.files.storage import default_storage
import codecs
from django.utils.encoding import smart_text
from django.http import HttpResponse
from django.conf import settings #or from my_project import settings
from django.http import StreamingHttpResponse
from wsgiref.util import FileWrapper
from django.utils.encoding import smart_str
def send_file(request):
filename = settings.MEDIA_ROOT +'/'+ 'output1.csv'
#filename= r"C:\Users\A6B0SZZ\PycharmProjects\sample\media\output1.csv"
download_name ="output1.csv"
wrapper = FileWrapper(open(filename))
response = HttpResponse(wrapper,content_type='text/csv')
response['Content-Disposition'] = "attachment; filename=%s"%download_name
return response
def home(request):
if request.method=="POST":
img = UploadForm(request.POST, request.FILES)
if img.is_valid():
# paramFile = request.FILES['pic']
paramFile =io.TextIOWrapper(request.FILES['pic'].file)
portfolio1 = csv.DictReader(paramFile)
print(type(paramFile))
users = [row["BASE_NAME"] for row in portfolio1]
print(len(users))
my_list = users
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(my_list)
lsa = TruncatedSVD(n_components=100)
dtm_lsa = lsa.fit_transform(dtm)
dtm_lsa = Normalizer(copy=False).fit_transform(dtm_lsa)
similarity = np.asarray(numpy.asmatrix(dtm_lsa) * numpy.asmatrix(dtm_lsa).T)
k = len(my_list)
dist1 = (1- similarity)
# dist1=similarity
# dist1.astype(float)
#print(dist1)
# print(cosine_similarity(tfidf_matrix[3:4], tfidf_matrix))
# float dist = 1 - similarity;
data2 = np.asarray(dist1)
arr_3d = data2.reshape((1, k, k))
# arr_3d= 1- arr_3d
#print(arr_3d)
no_clus = 40
for i in range(len(arr_3d)):
# print (i+1910)
# km = AgglomerativeClustering(n_clusters=no_clus, linkage='ward').fit(arr_3d[i])
km = AgglomerativeClustering(n_clusters=no_clus, linkage='average').fit(arr_3d[i])
# km = AgglomerativeClustering(n_clusters=no_clus, linkage='complete').fit(arr_3d[i])
# km = MeanShift()
# km = KMeans(n_clusters=no_clus, init='k-means++')
# km = MeanShift()
# km = km.fit(arr_3d[i])
# print km
labels = km.labels_
csvfile = settings.MEDIA_ROOT +'\\'+ 'images\\export.csv'
csv_input = pd.read_csv(csvfile, encoding='latin-1')
csv_input['cluster_ID'] = labels
csv_input['BASE_NAME'] = my_list
csv_input.to_csv(settings.MEDIA_ROOT +'/'+ 'output.csv', index=False)
clus_groups = list()
for j in range(no_clus):
# print(" cluster no %i:%s" % (j, [my_list[i] for i, x in enumerate(labels) if x == j]))
list_of_ints = ([my_list[i] for i, x in enumerate(labels) if x == j])
clus_groups.append(' '.join(list_of_ints))
vectorizer = CountVectorizer()
dtm = vectorizer.fit_transform(my_list)
lsa = TruncatedSVD(n_components=100)
dtm_lsa = lsa.fit_transform(dtm)
dtm_lsa = Normalizer(copy=False).fit_transform(dtm_lsa)
similarity = np.asarray(numpy.asmatrix(dtm_lsa) * numpy.asmatrix(dtm_lsa).T)
k = len(my_list)
dist1 = 1 - similarity
data2 = np.asarray(dist1)
arr_3d = data2.reshape((1, k, k))
# arr_3d= 1- arr_3d
###########################################here#######################
no_clus = 5
for i in range(len(arr_3d)):
km = KMeans(n_clusters=no_clus, init='k-means++')
km = km.fit(arr_3d[i])
labels2 = km.labels_
print(labels2)
labels = labels.tolist()
labels2 = labels2.tolist()
# new=list()
csv_input = pd.read_csv(settings.MEDIA_ROOT +'/'+ 'output.csv',encoding='latin-1')
labels1 = csv_input['cluster_ID']
new_list = []
for k in labels1:
new_list.append(labels2[k]) # lookup the value in list2 at the index given by list1
print(new_list)
print(len(new_list))
csv_input = pd.read_csv(settings.MEDIA_ROOT +'/'+ 'output.csv',encoding='latin-1')
csv_input['cluster_ID'] = labels
csv_input['BASE_NAME'] = my_list
csv_input['User_Map'] = new_list
csv_input.to_csv(settings.MEDIA_ROOT + '/' + 'output1.csv', index=False)
filename= settings.MEDIA_ROOT +'/'+ 'output.csv'
send_file(request)
# my_list = portfolio
#save_file('output1.csv')
# csv(request)
# return HttpResponseRedirect(reverse('labels'))
return render(request, 'home.html', {'labels': labels})
else:
img=UploadForm()
images=Upload.objects.all()
return render(request,'home.html',{'form':img,'images':images})
我的home.html：
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Title</title>
</head>
<body>
<div style="padding:40px;margin:40px;border:1px solid #ccc">
<h1>Upload the CSV File to Run the Algorithm on:</h1>
<form action="#" method="post" enctype="multipart/form-data">
{% csrf_token %} {{form}}
<input type="submit" value="Upload" />
</form>
<a href="/media/output1.csv">Download CSV File</a>
</div>
</body>
</html>
url.py:
from django.conf.urls import url
from django.contrib import admin
from django.conf import settings
from django.conf.urls.static import static
from uploader import views
urlpatterns = [
url(r'^upload/$', 'uploader.views.home', name='labels'),
url(r'^media/output1.csv', 'uploader.views.send_file'),
url(r'^admin/', admin.site.urls),
]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
我假设我需要将人员添加到我的html文件或一般的模板目录中。我很感激帮助。 第二件事是我需要一个盒子，以便用户确定输入的簇数，现在我将它定义为5，就在我提到“##### here #####”之前。我怎样才能做到这一点？我很感谢代码的帮助。
One more这个问题，我怎样才能在download csv file函数执行时显示home的链接？
修改
我试着开发数字输入框，它应该是get.request我认为：
我的form.py
from django import forms
class NameForm(forms.Form):
your_name = forms.CharField(label='Your name', max_length=100)
View.py
from .forms import NameForm
def get_name(request):
# if this is a POST request we need to process the form data
if request.method == 'GET':
# create a form instance and populate it with data from the request:
form = NameForm(request.GET)
# check whether it's valid:
if form.is_valid():
# process the data in form.cleaned_data as required
# ...
# redirect to a new URL:
return render_to_response('new.html')
# if a GET (or any other method) we'll create a blank form
else:
form = NameForm()
return render(request, 'new.html', {'form': form})
稍后在view.py内home function：
no_clus=get_name(request)
for i in range(len(arr_3d)):
km = KMeans(n_clusters=no_clus, init='k-means++')
km = km.fit(arr_3d[i])
labels2 = km.labels_
# error = km.inertia_
print(labels2)
new.html
<form method="get">
<label for="your_name">Number of Users: </label>
<input id="your_name" type="text" name="your_name" value="{{ current_name }}">
<input type="submit" style="display:none;">
</form>
Error:
unorderable types: int() < HttpResponse()
Edit:
想要可选输入框，如果用户不输入任何内容，则应该执行else之后的默认功能，我尝试了以下但是没有工作：
view.py：
def number_cluster(request, data_size):
if request.method == 'GET':
img1 = Book(request.GET)
if img1.is_valid():
no_cluster = int(request.POST.get('num_clusters1'))
return render(int(no_cluster), {'form': img1})
else:
no_cluster = data_size / 2.34
return (int(no_cluster))
型号：
class Book(models.Model):
description = models.TextField(blank=True)
**`New.html`**
可选：请插入群集数量
答案 0 :(得分：1)
不是最终解决方案 - 但要提出一个想法： 正如Rajesh在评论中提到的那样，这可以通过客户端（使用JavaScript / jQuery）来解决。因此，您可以在提交表单时添加所需的消息。
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Title</title>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.12.2/jquery.min.js"></script>
<style>
#loading {
background: red;
position: fixed;
top: 0;
width: 100%;
height: 100%;
}
</style>
</head>
<body>
<div style="padding:40px;margin:40px;border:1px solid #ccc">
<h1>Upload the CSV File to Run the Algorithm on:</h1>
<form action="#" method="post" enctype="multipart/form-data">
{% csrf_token %} {{form}}
<input type="submit" value="Upload" />
</form>
<a href="/media/output1.csv">Download CSV File</a>
</div>
<script>
$(function(){
$('form').on('submit', function(e){
$('body').append('<div id="loading">Loading...</div>')
});
})
</script>
</body>
</html>
在你的模板中：
<!-- template.html -->
<form ...>
...
<input type="number" name="num_clusters" min="1" max="500">
...
在您看来：
# view.py
def home(request):
if request.method=="POST":
num_clusters = request.POST.get('num_clusters', 10) # 10 as default