amn41

from rasa_dm.actions import Action
import requests

class ActionHTTPRequest(Action):
    def name(self):
        return "make_request"

    def run(self, dispatcher, tracker, domain):
        url = 'https://query.yahooapis.com/v1/public/yql?q=select%20*%20from%20weather.forecast%20where%20woeid%20in%20(select%20woeid%20from%20geo.places(1)%20where%20text%3D%22nome%2C%20ak%22)&format=json'
        result = requests.get(url).json()

amn41

from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
from builtins import str as text

import argparse
import io
import json

amn41

import sys, os
from mitie import *

trainer = text_categorizer_trainer("/path/to/total_word_feature_extractor.dat")

data = {} # same as before  - omitted for brevity

for label in training_examples.keys():
  for text in training_examples[label]["examples"]:
    tokens = tokenize(text)

amn41

def find_similar_words(embed,text,refs,thresh):

    C = np.zeros((len(refs),embed.W.shape[1]))

    for idx, term in enumerate(refs):
        if term in embed.vocab:
            C[idx,:] = embed.W[embed.vocab[term], :]


    tokens = text.split(' ')

amn41

import rinocloud as rino
import shutil, os
import subprocess
import hashlib

"""
persist = Persistor(config.rino_token,config.rino_dir)

def save_model_new(persist,model_file,score):
    temp_file="tmp_{0:06d}.txt".format(random.choice(range(10000)))                                                                                                                                                                                  

amn41

amn41

import sys, os
from mitie import *
sample = ner_training_instance(["I", "am", "looking", "for", "some", "cheap", "Mexican", "food", "."])

sample.add_entity(xrange(5,6), "pricerange")
sample.add_entity(xrange(6,7), "cuisine")

# And we add another training example
sample2 = ner_training_instance(["show", "me", "indian", "restaurants", "in", "the", "centre", "."])
sample2.add_entity(xrange(2,3), "cuisine")

amn41

\emph{hello}

amn41

import epic.models.{NerSelector, ParserSelector}
import epic.parser.ParserAnnotator
import epic.preprocess
import epic.preprocess.{TreebankTokenizer, MLSentenceSegmenter}
import epic.sequences.{SemiCRF, Segmenter}
import epic.slab.{EntityMention, Token, Sentence}
import epic.trees.{AnnotatedLabel, Tree}
import epic.util.SafeLogging

amn41

while ( not formData.is_complete() ):
    questionKey = formData.first_missing_field()
    ask(questions[questionKey])