python - Django Biginner - How to connect my algorithm code in django -
this might simple, i'm quite new django. want build app make user upload csv file, run algorithm on it, , make output file available downloaded user. started uploading file thing , looks it's working, , have algorithm code ready, however, i'm not sure how connect algorithm code, not sure how make function include that, , need new url each uploading,running algorithm , downloading? 1 url displays 3 of them. here project files , app, model.py:
from django.db import models django.forms import modelform class upload(models.model): pic = models.filefield("csv file", upload_to="images/") upload_date=models.datetimefield(auto_now_add =true) # fileupload form class. class uploadform(modelform): class meta: model = upload fields = '__all__' # or list of fields want include in form view.py:
from __future__ import division django.shortcuts import render uploader.models import uploadform,upload io import textiowrapper io import stringio django.http import httpresponseredirect django.core.urlresolvers import reverse import csv import logging def home(request): if request.method=="post": img = uploadform(request.post, request.files) if img.is_valid(): img.save() # request.files['pic'].open("f") # portfolio = csv.dictreader(request.files['uploadfile'].file) #file =request.files['pic'] file1 = textiowrapper(request.files['pic'].file) # reader = stringio(file.read().decode()) reader = file1.read() file1.seek(0) portfolio = csv.dictreader(reader) print(portfolio) #with open(reader) f: my_list=[] import pdb;pdb.set_trace() # open(portfolio) f: my_list = [row["base_name"] row in portfolio] # row in portfolio: # my_list = [row["base_name"]] # print(my_list) logging.warning('watch out!') # print message console # row in reader: # print (row) #else: # print (img.errors) # print (request.files) # img.save() #file = request.files[pic] # open(file) f: # my_list = [row["base_name"] row in dictreader(f)] return httpresponseredirect(reverse('imageupload')) else: img=uploadform() images=upload.objects.all() return render(request,'home.html',{'form':img,'images':images}) my home.html file:
<!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 csv file</h1> <form action="#" method="post" enctype="multipart/form-data"> {% csrf_token %} {{form}} <input type="submit" value="upload" /> </form> {% img in images %} {{forloop.counter}}.<a href="{{ img.pic.url }}">{{ img.pic.name }}</a> ({{img.upload_date}})<hr /> {% endfor %} </div> </body> </html> and url.py
from django.conf.urls import url django.contrib import admin django.conf import settings django.conf.urls.static import static urlpatterns = [ url(r'^upload/$', 'uploader.views.home', name='imageupload'), url(r'^admin/', admin.site.urls), ]+ static(settings.media_url, document_root=settings.media_root) my algorithm code want implement is:
with open("export.csv") f: my_list = [row["base_name"] row in dictreader(f)] # codecsvmodel.import_from_file(file) 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 = np.subtract(np.ones((k, k), dtype=np.float), similarity) data2 = np.asarray(dist1) arr_3d = data2.reshape((1, k, k)) no_clus = 1500 in range(len(arr_3d)): km = agglomerativeclustering(n_clusters=no_clus, linkage='average').fit(arr_3d[i]) labels = km.labels_ csvfile = file csv_input = pd.read_csv(csvfile) csv_input['cluster_id'] = labels csv_input['base_name'] = my_list csv_input.to_csv('output.csv', index=false) clus_groups = list() j in range(no_clus): list_of_ints = ([my_list[i] 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)) no_clus = 5 in range(len(arr_3d)): km = kmeans(n_clusters=5, init='k-means++') km = km.fit(arr_3d[i]) labels2 = km.labels_ labels = labels.tolist() labels2 = labels2.tolist() csv_input = pd.read_csv('output.csv') labels1 = csv_input['cluster_id'] new_list = [] k in labels1: new_list.append(labels2[k]) # lookup value in list2 @ index given list1 csv_input = pd.read_csv('output.csv') 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) i tried in view.py make my_list , start, not sure if got my_list right since no print statements showing, want make function inside view implement algorithm above, , how link file got home function new function of algorithm, need function downloading thing, , not sure templates. i'm kinda lost , did uploading function example online. appreciate help.
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