python - Passing string over urls django -

i'm having trouble in understanding error in code, first let me try , explain happening , i'm trying do.

my code designed load 45 separate text files array, including weight of each word/phrase , word phrase itself. has occur @ beginning, before description received.

second, once description received, parsed software words/phrases, compared words/phrases in array.

third, software provides top 3 classes, in rank order (first/second/third) number, along score each class.

i've made django application serve code, have form provide 2 parameters classes , description, this:

class trademarkclassifierform(forms.form):     """     todo: forms cover questions     initial classifier program     :returns: todo     """     classes = forms.charfield(max_length=10,                               label="test trademark classes? type 'yes' or else enter class tested ")     description = forms.charfield(widget=forms.textarea)      def __init__(self, *args, **kwargs):         super(trademarkclassifierform, self).__init__(*args, **kwargs)         self.helper = formhelper()         self.helper.add_input(submit('submit', 'submit')) 

then want pass 2 parameters in view on url this:

class trademarkclassifierresultview(formview):     """     todo: post should redirect it's on page get,     specify set values in query parameters,     ?classes=yes&name=nameoftrademarkclass     should visible on results page.     :param: classes     :param: description     :returns: todo - params     """     template_name = 'trademark.html'     form_class = trademarkclassifierform      def get(self, request, *args, **kwargs):         classes = str(self.request.get.get('classes'))         description = str(self.request.get.get('description'))         form = trademarkclassifierform(initial={'classes': classes, 'description': description})         context_data = self.get_context_data(classes, description, form=form)         return self.render_to_response(context_data)      def form_valid(self, form):         classes = form.cleaned_data['classes']         description = form.cleaned_data['description']         return redirect(self.get_success_url(classes, description))      def form_invalid(self, form):         messages.add_message(self.request, messages.error,                              "invalid data. please check fields.")         return self.render_to_response(             self.get_context_data(form=form)         )      def get_success_url(self, classes=none, description=none):         return reverse("classifier:trademark") + "?classes=" + str(classes) + "&description" + str(description)      def get_context_data(self, classes, description, **kwargs):         context = super(trademarkclassifierresultview, self).get_context_data(**kwargs)         context['classes'] = classes         context['description'] = description         context['trademark'] = classifymarkbased.control_program(classes, description)         return context 

now problem error:

environment:   request method: request url: http://127.0.0.1:8000/trademark/  django version: 1.11.2 python version: 2.7.12 installed applications: ['django.contrib.admin',  'django.contrib.auth',  'django.contrib.contenttypes',  'django.contrib.sessions',  'django.contrib.messages',  'django.contrib.staticfiles',  'django.contrib.sites',  'classifier',  'crispy_forms',  'allauth',  'allauth.account',  'allauth.socialaccount',  'widget_tweaks',  'debug_toolbar'] installed middleware: ['django.middleware.security.securitymiddleware',  'django.contrib.sessions.middleware.sessionmiddleware',  'django.middleware.common.commonmiddleware',  'django.middleware.csrf.csrfviewmiddleware',  'django.contrib.auth.middleware.authenticationmiddleware',  'django.contrib.messages.middleware.messagemiddleware',  'django.middleware.clickjacking.xframeoptionsmiddleware',  'debug_toolbar.middleware.debugtoolbarmiddleware']    traceback:  file "/home/petar/.virtualenvs/trademark/local/lib/python2.7/site-packages/django/core/handlers/exception.py" in inner   41.             response = get_response(request)  file "/home/petar/.virtualenvs/trademark/local/lib/python2.7/site-packages/django/core/handlers/base.py" in _get_response   187.                 response = self.process_exception_by_middleware(e, request)  file "/home/petar/.virtualenvs/trademark/local/lib/python2.7/site-packages/django/core/handlers/base.py" in _get_response   185.                 response = wrapped_callback(request, *callback_args, **callback_kwargs)  file "/home/petar/.virtualenvs/trademark/local/lib/python2.7/site-packages/django/views/generic/base.py" in view   68.             return self.dispatch(request, *args, **kwargs)  file "/home/petar/.virtualenvs/trademark/local/lib/python2.7/site-packages/django/views/generic/base.py" in dispatch   88.         return handler(request, *args, **kwargs)  file "/home/petar/documents/synergy/trademark/tm_base/classifier/views.py" in   60.         context_data = self.get_context_data(classes, description, form=form)  file "/home/petar/documents/synergy/trademark/tm_base/classifier/views.py" in get_context_data   82.         context['trademark'] = classifymarkbased.control_program(classes, description)  file "/home/petar/documents/synergy/trademark/tm_base/classifier/services/classify_mark_based.py" in control_program   89.             n = len(word_count_array_for_all_classes[i])  exception type: indexerror @ /trademark/ exception value: list index out of range 

this url: url(r'^trademark/', trademarkclassifierresultview.as_view(), name="trademark"),

and part of code should calculate trademark on 2 parameters:

import os import numpy np import re import requests   class trademarkservice(object):     # coding: utf-8      # in[5]:       # compare input string class     # words not found,look in dictionary - add text files trademark words       # in[6]:       # open each trademark class file , read words/frequency array     @staticmethod     def open_file_read_words(file_name):         unique_words_and_count_not_format = []          tm_word_count_array = []         my_list = []         all_possible_entries = 1          open(file_name) f:             lines = [line.strip() line in open(file_name)]         all_possible_entries = len(lines)          tm_word_count_array = [[0 x in range(2)] y in range(all_possible_entries)]         = 0         while < all_possible_entries:             tm_word_count_array[i] = lines[i].split(',', 1)              += 1          = 0         while < all_possible_entries:             tm_word_count_array[i][0] = int(tm_word_count_array[i][0])             += 1          return tm_word_count_array      # in[7]:       # section normalizes word frequency number of words x 1000     @staticmethod     def normalize_array(tm_word_count_array):         list_of_freqs = []         max_entries = len(tm_word_count_array)         list_of_freqs = [0 y in range(max_entries)]          = 0         while < max_entries:             list_of_freqs[i] = tm_word_count_array[i][0]             += 1          max_value = max(list_of_freqs)          = 0         while < max_entries:             tm_word_count_array[i][0] = ((float(tm_word_count_array[i][0])) / max_entries) * 1000             += 1          return tm_word_count_array      # in[8]:       # include list of not useful words here     @staticmethod     def find_not_useful_words(word):         not_useful_words = (         "about", "are", "upon", "-", " ", "up", "other", "or", "not", "namely", "more", "made", "in", "for", "except",         "but", "being", "all", "against", "was", "were", "will", "that", "its", "on", "it", "at", "was", "our", "your",         "ours", "yours", "their", "them", "other", "out", "having", "have", "has", "in", "be", "than", "use", "uses",         "using", "", "by", "and", "an", "a", "use", "used", "using", "for", "to", "of", "-)", "-]", "with", "as", "in",         "the", "from")         test_word in not_useful_words:             if word == test_word:                 return false         return true      # in[9]:       # clean phrases removing problematic characters     @staticmethod     def clean_up_phrases(data):         important_words = ''         word = data          c in word:             if 0 <= ord(c) <= 127:                 # ascii character.                 not_a_variable = 0             else:                  if ord(c) == 201:                     word = word.replace(c, "e")                 elif ord(c) == 241:                     word = word.replace(c, "n")                 elif ord(c) == 225:                     word = word.replace(c, "a")                 elif ord(c) == 251:                     word = word.replace(c, "u")                 elif ord(c) == 8206:                     word = word.replace(c, "")                 else:                     word = word.replace(c, "")                       # continue_yes=raw_input("do want continue?")          word = word.lower()          word = str(filter(lambda ch: ch not in "?.!/;:,'()[]", word))          # calls function above remove words found interfere classification          if data.find_not_useful_words(word):             if len(word) > 1:                 important_words += word          return important_words      # in[10]:       # find important words in string     @staticmethod     def find_important_words(data):          all_entries = len(data)          important_words = []          word in data.split():              c in word:                 if 0 <= ord(c) <= 127:                     # ascii character.                     not_a_variable = 0                 else:                      if ord(c) == 201:                         word = word.replace(c, "e")                     elif ord(c) == 241:                         word = word.replace(c, "n")                     elif ord(c) == 225:                         word = word.replace(c, "a")                     elif ord(c) == 251:                         word = word.replace(c, "u")                     elif ord(c) == 8206:                         word = word.replace(c, "")                     else:                         word = word.replace(c, "")              word = word.lower()              word = str(filter(lambda ch: ch not in " ?.!/;:,'()[]", word))              if word.endswith("-"):                 word = word[:-1]              if word.startswith("-"):                 word = word[:1]              if data.find_not_useful_words(word):                 if len(word) > 1:                     important_words.append(word)          return important_words      # in[11]:      @staticmethod     def analyze_each_line_test_data(test_sentence, n, normalized_tm_word_count_array):         # remove problematic characters , words, plus find important words/phrases         test_important_phrases = test_sentence.clean_up_phrases(test_sentence)         = 0         total_found = 0         total_tm_class_count = 0         total_tm_words_matched = []          # score trademark phrases in string         while < n:             count_phrases = 0             if len(normalized_tm_word_count_array[i][1].split()) > 1:                  if test_important_phrases.find(normalized_tm_word_count_array[i][1]) > -1:                     total_tm_words_matched.append(normalized_tm_word_count_array[i][1])                     total_tm_class_count += (normalized_tm_word_count_array[i][0])                     total_found += 1              += 1          # decompose string , remove extraneous words, score words in string         test_important_words = test_sentence.find_important_words(test_sentence)          = 0         while < n:             count_words = 0             if test_important_words.count(normalized_tm_word_count_array[i][1]) > 0:                 total_tm_words_matched.append(normalized_tm_word_count_array[i][1])                 count_words = test_important_words.count(normalized_tm_word_count_array[i][1])                 total_tm_class_count += (normalized_tm_word_count_array[i][0] * count_words)                 total_found += 1             += 1          = 0         normalized_tm_word_count_values = [0 y in range(n)]         normalized_tm_word_count_words = ['a' y in range(n)]         while < n:             normalized_tm_word_count_values[i] = normalized_tm_word_count_array[i][0]             normalized_tm_word_count_words[i] = normalized_tm_word_count_array[i][1]             += 1          total_words_to_match = len(test_important_words) + len(test_important_phrases)         not_found_words = list(set(test_important_words) - set(normalized_tm_word_count_words))         return total_found, total_tm_words_matched, not_found_words, total_tm_class_count      # in[12]:       @staticmethod     def open_class_file_read_words_to_array(file_name, file_name_class=none):         tm_word_count_array = []         tm_word_count_array = file_name.open_file_read_words(file_name_class)         return tm_word_count_array      # in[13]:       # create file trademark results     @staticmethod     def create_results_file(file_name, results_array, description):         unique_words_and_count_not_format = []         unique_words_and_count_to_write = []          open_file_name = open(file_name, 'a')         open_file_name.write("new trademark comparison")         open_file_name.write("\n")         open_file_name.write(description)         open_file_name.write("\n")          unique_words_and_count_to_write = np.array(results_array, dtype=object)          np.savetxt(open_file_name, unique_words_and_count_to_write, fmt='%s', delimiter=',')         open_file_name.write("\n")         open_file_name.write("\n")         open_file_name.write("\n")         open_file_name.close()      # in[14]:       # section controls program     @staticmethod     def control_the_program(classes, description):         description = []         word_count_array_for_all_classes = []         correct_class_set = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17',                              '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33',                              '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45']          # depending on answer, 1 class worth of trademark words loaded or else loaded         # test_all_classes = raw_input(         #     "test trademark classes? type 'yes' or else enter class tested ")         test_all_classes = classes          # test description of goods/services         # test_data_array = raw_input("provide description of goods or services ")         test_data_array = description         # file_name_data = raw_input("provide identifier results file ")          # file has output of classification engine, including top 3 results         # file_name_results = 'user_test_comparison_results_' + file_name_data + '.txt'          # call program open each file of trademark words in turn , read words array         if test_all_classes == 'yes':             = 1             number_classes_to_check = 45             word_count_array_for_all_classes = [[] z in range(46)]             temp_array = []             while <= 45:                 # opens each file trademark words                 file_name_class = 'counted_phrases_class' + str(i) + '.txt'                 temp_array = classes.open_class_file_read_words_to_array(file_name_class)                 # normalization used because classes have many words , have few words                 # words/phrases weighted according frequency                 word_count_array_for_all_classes[i] = classes.normalize_array(temp_array)                  += 1          else:             # print "you didn't enter yes"             pass          # length_test_data_array = len(test_data_array)         # open(file_name_results, 'a').close()         # start_writing_results = open(file_name_results, 'a')         # start_writing_results.write("the start of test")         # start_writing_results.write("\n")         # start_writing_results.write("total number of potential items match ")         # start_writing_results.write(str(length_test_data_array))         # start_writing_results.write("\n")         # start_writing_results.close()          top_result = [0 y in range(2)]         second_result = [0 y in range(2)]         third_result = [0 y in range(2)]         top_array_words_not_found = []         second_array_words_not_found = []         third_array_words_not_found = []          counter_for_9vs42 = 0         counter_for_data_errors = 0          top_result = [0 y in range(2)]         second_result = [0 y in range(2)]         third_result = [0 y in range(2)]         top_array_words_not_found = []         second_array_words_not_found = []         third_array_words_not_found = []          actual_class_results = [0 y in range(2)]         overall_array_results = [[0 x in range(3)] y in range(4)]         actual_class_words_not_found = []         = 1         while <= 45:             total_found = 0             total_tm_words_matched = 0             not_found_words = ['']             score = 0             n = len(word_count_array_for_all_classes[i])             total_found, total_tm_words_matched, not_found_words, score = classes.analyze_each_line_test_data(test_data_array, n,                                                                                                               word_count_array_for_all_classes[i])              if int(score) > 0:                 if int(score) > top_result[0]:                     third_result[0] = second_result[0]                     third_result[1] = second_result[1]                     third_array_words_not_found = second_array_words_not_found                     second_result[0] = top_result[0]                     second_result[1] = top_result[1]                     second_array_words_not_found = top_array_words_not_found                     top_result[0] = int(score)                     top_result[1] =                     top_array_words_not_found = ['']                     top_array_words_not_found = not_found_words                 elif int(score) > second_result[0]:                     third_result[0] = second_result[0]                     third_result[1] = second_result[1]                     third_array_words_not_found = second_array_words_not_found                     second_result[0] = int(score)                     second_result[1] =                     second_array_words_not_found = ['']                     second_array_words_not_found = not_found_words                  elif int(score) > third_result[0]:                     third_result[0] = int(score)                     third_result[1] =                     third_array_words_not_found = ['']                     third_array_words_not_found = not_found_words              += 1          overall_array_results[0][0] = top_result[0]         overall_array_results[0][1] = top_result[1]         overall_array_results[0][2] = top_array_words_not_found         overall_array_results[1][0] = second_result[0]         overall_array_results[1][1] = second_result[1]         overall_array_results[1][2] = second_array_words_not_found         overall_array_results[2][0] = third_result[0]         overall_array_results[2][1] = third_result[1]         overall_array_results[2][2] = third_array_words_not_found          # results - including first, second, third choices of engine , original description - written file         # create_results_file(file_name_results, overall_array_results, test_data_array)          # start_writing_results = open(file_name_results, 'a')         # start_writing_results.write("the end of test")         # start_writing_results.write("\n")         #         # start_writing_results.write("\n")         # start_writing_results.write("\n")         # start_writing_results.close()         # print "finished process" 

from code i've provided can see parameters provided on python raw_input , after calculation code creating file in can read result.

i've rewritten can serve on django application, parameters classes , description should overwrite raw_input , result displayed in template, this:

{{ trademark.overall_array_results.top_result }}<br> {{ trademark.overall_array_results.second_result }}<br> {{ trademark.overall_array_results.third_result }} 

i'm not sure if i'm doing write thing here, need understand better, can me on come error.

if classes not "yes", word_count_array_for_all_classes remains empty list.