• File “/usr/local/lib/python3. 9/site-packages/flask/app. py”, line 1498, in __call__

   ) -> cabc. Iterable[bytes]:

   """The WSGI server calls the Flask application object as the

   WSGI application. This calls :meth:'wsgi_app', which can be

   wrapped to apply middleware. 

   """

   return self. wsgi_app(environ, start_response)

• File “/usr/local/lib/python3. 9/site-packages/flask/app. py”, line 1476, in wsgi_app

   try:

   ctx. push()

   response = self. full_dispatch_request()

   except Exception as e:

   error = e

   response = self. handle_exception(e)

   except: # noqa: B001

   error = sys. exc_info()[1]

   raise

   return response(environ, start_response)

   finally:

• File “/usr/local/lib/python3. 9/site-packages/flask/app. py”, line 1473, in wsgi_app

   ctx = self. request_context(environ)

   error: BaseException | None = None

   try:

   try:

   ctx. push()

   response = self. full_dispatch_request()

   except Exception as e:

   error = e

   response = self. handle_exception(e)

   except: # noqa: B001

   error = sys. exc_info()[1]

• File “/usr/local/lib/python3. 9/site-packages/flask/app. py”, line 882, in full_dispatch_request

   request_started. send(self, _async_wrapper=self. ensure_sync)

   rv = self. preprocess_request()

   if rv is None:

   rv = self. dispatch_request()

   except Exception as e:

   rv = self. handle_user_exception(e)

   return self. finalize_request(rv)

   def finalize_request(

   self,

   rv: ft. ResponseReturnValue | HTTPException,

• File “/usr/local/lib/python3. 9/site-packages/flask/app. py”, line 880, in full_dispatch_request

   try:

   request_started. send(self, _async_wrapper=self. ensure_sync)

   rv = self. preprocess_request()

   if rv is None:

   rv = self. dispatch_request()

   except Exception as e:

   rv = self. handle_user_exception(e)

   return self. finalize_request(rv)

   def finalize_request(

• File “/usr/local/lib/python3. 9/site-packages/flask/app. py”, line 865, in dispatch_request

   and req. method == "OPTIONS"

   ):

   return self. make_default_options_response()

   # otherwise dispatch to the handler for that endpoint

   view_args: dict[str, t. Any] = req. view_args # type: ignore[assignment]

   return self. ensure_sync(self. view_functions[rule. endpoint])(**view_args) # type: ignore[no-any-return]

   def full_dispatch_request(self) -> Response:

   """Dispatches the request and on top of that performs request

   pre and postprocessing as well as HTTP exception catching and

   error handling. 

• File “/nlpapps/app. py”, line 451, in process_summarytext

  # Find weighted frequency of occurrence

   stopwords = nltk. corpus. stopwords. words('english')

   word_frequencies = {}

   for word in nltk. word_tokenize(formatted_article_text):

   if word not in stopwords:

   if word not in word_frequencies. keys():

   word_frequencies[word] = 1

   else:

   word_frequencies[word] += 1

• File “/usr/local/lib/python3. 9/site-packages/nltk/tokenize/__init__. py”, line 142, in word_tokenize

   :param language: the model name in the Punkt corpus

   :type language: str

   :param preserve_line: A flag to decide whether to sentence tokenize the text or not. 

   :type preserve_line: bool

   """

   sentences = [text] if preserve_line else sent_tokenize(text, language)

   return [

   token for sent in sentences for token in _treebank_word_tokenizer. tokenize(sent)

   ]

• File “/usr/local/lib/python3. 9/site-packages/nltk/tokenize/__init__. py”, line 119, in sent_tokenize

   for the specified language). 

   :param text: text to split into sentences

   :param language: the model name in the Punkt corpus

   """

   tokenizer = _get_punkt_tokenizer(language)

   return tokenizer. tokenize(text)

  # Standard word tokenizer. 

  _treebank_word_tokenizer = NLTKWordTokenizer()

• File “/usr/local/lib/python3. 9/site-packages/nltk/tokenize/__init__. py”, line 105, in _get_punkt_tokenizer

   a lru cache for performance. 

   :param language: the model name in the Punkt corpus

   :type language: str

   """

   return PunktTokenizer(language)

  # Standard sentence tokenizer. 

  def sent_tokenize(text, language="english"):

   """

• File “/usr/local/lib/python3. 9/site-packages/nltk/tokenize/punkt. py”, line 1744, in __init__

   Punkt Sentence Tokenizer that loads/saves its parameters from/to data files

   """

   def __init__(self, lang="english"):

   PunktSentenceTokenizer. __init__(self)

   self. load_lang(lang)

   def load_lang(self, lang="english"):

   from nltk. data import find

   lang_dir = find(f"tokenizers/punkt_tab/{lang}/")

• File “/usr/local/lib/python3. 9/site-packages/nltk/tokenize/punkt. py”, line 1749, in load_lang

   self. load_lang(lang)

   def load_lang(self, lang="english"):

   from nltk. data import find

   lang_dir = find(f"tokenizers/punkt_tab/{lang}/")

   self. _params = load_punkt_params(lang_dir)

   self. _lang = lang

   def save_params(self):

   save_punkt_params(self. _params, dir=f"/tmp/{self. _lang}")

• File “/usr/local/lib/python3. 9/site-packages/nltk/data. py”, line 579, in find

   )

   msg += "
   Searched in:" + "". join("
   - %r" % d for d in paths)

   sep = "*" * 70

   resource_not_found = f"
  {sep}
  {msg}
  {sep}
  "

   raise LookupError(resource_not_found)

  def retrieve(resource_url, filename=None, verbose=True):

   """

   Copy the given resource to a local file. If no filename is

LookupError: ********************************************************************** Resource _[93mpunkt_tab_[0m not found. Please use the NLTK Downloader to obtain the resource: _[31m>>> import nltk >>> nltk. download(‘punkt_tab’) _[0m For more information see: https://www. nltk. org/data. html Attempted to load _[93mtokenizers/punkt_tab/english/_[0m Searched in: – ‘/root/nltk_data’ – ‘/usr/local/nltk_data’ – ‘/usr/local/share/nltk_data’ – ‘/usr/local/lib/nltk_data’ – ‘/usr/share/nltk_data’ – ‘/usr/local/share/nltk_data’ – ‘/usr/lib/nltk_data’ – ‘/usr/local/lib/nltk_data’

**********************************************************************