Mohd Sanad Zaki Rizvi mohdsanadzakirizvi

## .block
license: gpl-3.0
height: 700
width: 960
scrolling: no
border: no

## parts_of_speech.py
#dictionary to hold pos tags and their explanations
pos_dict = {
'CC': 'coordinating conjunction',
'CD': 'cardinal digit',
'DT': 'determiner',
'EX': 'existential there (like: \"there is\" ... think of it like \"there exists\")',
'FW': 'foreign word',
'IN':  'preposition/subordinating conjunction',
'JJ': 'adjective \'big\'',
'JJR': 'adjective, comparative \'bigger\'',

## lemma.py
import pandas as pd

def extract_lemma(doc):
    parsed_text = {'word':[], 'lemma':[]}
    for sent in doc.sentences:
        for wrd in sent.words:
            #extract text and lemma
            parsed_text['word'].append(wrd.text)
            parsed_text['lemma'].append(wrd.lemma)
    #return a dataframe

## corenlp_setup.py
from stanfordnlp.server import CoreNLPClient
# example text
print('---')
print('input text')
print('')
text = "Chris Manning is a nice person. Chris wrote a simple sentence. He also gives oranges to people."
print(text)
# set up the client
print('---')
print('starting up Java Stanford CoreNLP Server...')

## corenlp_depparse.py
    #get the dependency parse of the first sentence
    print('---')
    print('dependency parse of first sentence')
    dependency_parse = sentence.basicDependencies
    print(dependency_parse)
    # get the first token of the first sentence
    print('---')
    print('first token of first sentence')
    token = sentence.token[0]
    print(token)

## corenlp_ner.py
# get the named entity tag
    print('---')
    print('named entity tag of token')
    print(token.ner)
    # get an entity mention from the first sentence
    print('---')
    print('first entity mention in sentence')
    print(sentence.mentions[0])
    # access the coref chain
    print('---')

## corenlp_depparse.py
    #get the dependency parse of the first sentence
    print('---')
    print('dependency parse of first sentence')
    dependency_parse = sentence.basicDependencies
    print(dependency_parse)
    # get the first token of the first sentence
    print('---')
    print('first token of first sentence')
    token = sentence.token[0]
    print(token)

## actions.py
API_URL = "https://cricapi.com/api/"
API_KEY = ""

class ApiAction(Action):
	def name(self):
		return "action_match_news"

	def run(self, dispatcher, tracker, domain):
		res = requests.get(API_URL + "matches" + "?apikey=" + API_KEY)
		if res.status_code == 200:

## index.html
<!DOCTYPE html>
<html>
<head>
	<meta charset="UTF-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge">
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<!-- title of the page -->
	<title>image_classification</title>
	<!-- load processing library-->
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/p5.min.js"></script>

## draw.js
function draw() {
  image(video, 0, 0, width, height);

  // We can call both functions to draw all keypoints and the skeletons
  drawKeypoints();
  drawSkeleton();
}
	license: gpl-3.0
	height: 700
	width: 960
	scrolling: no
	border: no
	#dictionary to hold pos tags and their explanations
	pos_dict = {
	'CC': 'coordinating conjunction',
	'CD': 'cardinal digit',
	'DT': 'determiner',
	'EX': 'existential there (like: \"there is\" ... think of it like \"there exists\")',
	'FW': 'foreign word',
	'IN': 'preposition/subordinating conjunction',
	'JJ': 'adjective \'big\'',
	'JJR': 'adjective, comparative \'bigger\'',
	import pandas as pd

	def extract_lemma(doc):
	parsed_text = {'word':[], 'lemma':[]}
	for sent in doc.sentences:
	for wrd in sent.words:
	#extract text and lemma
	parsed_text['word'].append(wrd.text)
	parsed_text['lemma'].append(wrd.lemma)
	#return a dataframe
	from stanfordnlp.server import CoreNLPClient
	# example text
	print('---')
	print('input text')
	print('')
	text = "Chris Manning is a nice person. Chris wrote a simple sentence. He also gives oranges to people."
	print(text)
	# set up the client
	print('---')
	print('starting up Java Stanford CoreNLP Server...')
	#get the dependency parse of the first sentence
	print('---')
	print('dependency parse of first sentence')
	dependency_parse = sentence.basicDependencies
	print(dependency_parse)
	# get the first token of the first sentence
	print('---')
	print('first token of first sentence')
	token = sentence.token[0]
	print(token)
	# get the named entity tag
	print('---')
	print('named entity tag of token')
	print(token.ner)
	# get an entity mention from the first sentence
	print('---')
	print('first entity mention in sentence')
	print(sentence.mentions[0])
	# access the coref chain
	print('---')
	API_URL = "https://cricapi.com/api/"
	API_KEY = ""

	class ApiAction(Action):
	def name(self):
	return "action_match_news"

	def run(self, dispatcher, tracker, domain):
	res = requests.get(API_URL + "matches" + "?apikey=" + API_KEY)
	if res.status_code == 200:
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="UTF-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge">
	<meta name="viewport" content="width=device-width, initial-scale=1">
	<!-- title of the page -->
	<title>image_classification</title>
	<!-- load processing library-->
	<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.8.0/p5.min.js"></script>
	function draw() {
	image(video, 0, 0, width, height);

	// We can call both functions to draw all keypoints and the skeletons
	drawKeypoints();
	drawSkeleton();
	}