python - 从我的项目路径 django 下载 csv 文件

Question

我通过我的代码生成了一个csv文件，生成后，它转到我的项目的路径，我的项目名为sample，所以文件路径sample/output.csv..我添加了一个下载函数，并在我的主函数(home)中调用了这个函数，但是下载不起作用，我做错了什么？

def save_file(request):
   # data = open(os.path.join(settings.PROJECT_PATH,'data/table.csv'),'r').read()
    response = HttpResponse(content_type='text/csv')
    response['Content-Disposition'] = 'attachment; filename=output1.csv'
    return response


def home(request):
    if request.method=="POST":
        img = UploadForm(request.POST, request.FILES)
        if img.is_valid():
            logging.warning('Watch out!')  # will print a message to the console
           # paramFile = request.FILES['pic']
            paramFile =io.TextIOWrapper(request.FILES['pic'].file)
            portfolio1 = csv.DictReader(paramFile)
            print(type(paramFile))
            users = []
            # users = [row["BASE_NAME"] for row in csv_file]
            # users = zip(*csv_file)
            #  users = [row[0] for row in csv_file]

           # for row in portfolio1:
             #   users.append(row)
            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)
            # print(1-similarity)
            k = len(my_list)
            dist1 = np.subtract(np.ones((k, k), dtype=np.float), 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 = r'C:\users\A6B0SZZ\PycharmProjects\sample\media\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('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

            no_clus = 5
            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 = KMeans(n_clusters=5, init='k-means++')
                km = km.fit(arr_3d[i])
                # print km
                labels2 = km.labels_
                # error = km.inertia_
                print(labels2)

            labels = labels.tolist()
            labels2 = labels2.tolist()
            # new=list()


            csv_input = pd.read_csv(r'C:\users\A6B0SZZ\PycharmProjects\sample\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(r'C:\users\A6B0SZZ\PycharmProjects\sample\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('output1.csv', index=False)

           # my_list = portfolio
            save_file(request)
           # return HttpResponseRedirect(reverse('portfolio'))
            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 是:

<!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>
    {% for img in images %}
        {{forloop.counter}}.<a href="{{ img.pic.url }}">{{ img.pic.name }}</a>
        ({{img.upload_date}})<hr />
    {% endfor %}

</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

urlpatterns = [
    url(r'^upload/$', 'uploader.views.home', name='labels'),
    url(r'^admin/', admin.site.urls),
]+ static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

编辑，我现在的功能是:但仍然不起作用:

def save_file(request):
   # data = open(os.path.join(settings.PROJECT_PATH,'data/table.csv'),'r').read()
   # file_path = r'C:\users\A6B0SZZ\PycharmProjects\sample\output1.csv'
   # fsock = open(file_path, "r")
    fsock= pd.read_csv(r'C:\users\A6B0SZZ\PycharmProjects\sample\output1.csv', encoding='latin-1')
    response = HttpResponse(fsock, content_type='text/csv')
    response['Content-Disposition'] = 'attachment; filename=output1.csv'
    return response

Answer 1

对于无法下载的主要问题，请查看this answer因为 HttpResponse 的第一个参数应该是实际发送的数据。

现在，您还应该做的是查看与存储类的交互 & MEDIA_ROOT .

这将使您的项目能够在服务器上本地或远程工作。看看你发布的内容，我假设你的 settings.py包含类似 MEDIA_ROOT = 'C:\users\A6B0SZZ\PycharmProjects\sample\media' 的内容

您可能需要考虑 settings.py 中一些更通用、可重用的路径(取决于您的项目的结构，但这就是我所拥有的)；

SETTINGS_DIR = os.path.dirname(os.path.abspath(__file__))
PROJECT_DIR = os.path.abspath(os.path.join(SETTINGS_DIR, '../'))
BASE_DIR = os.path.abspath(os.path.join(PROJECT_DIR, '../'))

STATIC_ROOT = os.path.join(BASE_DIR, 'static-collection')
MEDIA_ROOT = os.path.join(BASE_DIR, 'media')

这里一些不错的读物是 Managing Files这将引导您了解创建文件和访问其路径的各种方法。