Niyas Mohammed niazangels

## api-explosion.py
import requests
import json

url = "https://api.explosion.ai/displacy/ent/"
HEADER = {'Content-type': 'text/plain'}

while True:

    data = {'text':raw_input(), 'model':'en'}
    data = json.dumps(data)

## lesson-6.ipynb

      
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                niazangels
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              May 24, 2017 20:47
            
              
                3-char RNN model mostly always predicts a space
              
          
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## 3-char-model.ipynb

      
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                niazangels
                / 3-char-model.ipynb
            
            
              Created
              May 27, 2017 07:44
            
          
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## DRAGONS.txt
/*
                                                 ,  ,
                                               / \/ \
                                              (/ //_ \_
     .-._                                      \||  .  \
      \  '-._                            _,:__.-"/---\_ \
 ______/___  '.    .--------------------'~-'--.)__( , )\ \
`'--.___  _\  /    |             Here        ,'    \)|\ `\|
     /_.-' _\ \ _:,_          Be Dragons           " ||   (
   .'__ _.' \'-/,`-~`                                |/

## render-malaylam-text.py
from PIL import Image, ImageDraw, ImageFont
import matplotlib.pyplot as plt
import numpy as np

image = Image.new('RGB', (700, 40))

# Make sure the location to the font is correct
draw.text((40,5), 'നമ്മുടെ മലയാളം അടിപൊളിയായി ഇമേജിൽ വരുന്നു', font=ImageFont.truetype('./Manjari-Bold.ttf', 25))

plt.imshow(np.asarray(image))

## export-all-cypher.c
// Export entire database
CALL apoc.export.graphml.all('/tmp/complete-graph.graphml', {useTypes:true, storeNodeIds:false})

## import-cypher.c
// Import from GraphML
CALL apoc.import.graphml('/tmp/complete-graph.graphml', {batchSize: 10000, storeNodeIds: false})

## pdf-2-tiff-2-txt.sh

# $1 is the first argument
# remove result.txt
#rm output/result.txt

fname=`echo "$1" | awk -F "/" '{print $(NF)}'`
# convert the pdf to a group of tiffs

if [ ! -e extracted_tz_parliament/$fname.txt ];
   then

## qa-sample.json
{
    "query": "how much revenue did we generate in the last quarter from XYZ Corp?",
    "response": { ... }
}

## flask_stream_s3.py
import boto3
from boto3.s3.transfer import S3Transfer

from flask import Flask, Response

app = Flask(__name__)


class S3Storage:
    def __init__(self, bucket_name, pictures_path="", video_path=""):
	import requests
	import json

	url = "https://api.explosion.ai/displacy/ent/"
	HEADER = {'Content-type': 'text/plain'}

	while True:

	data = {'text':raw_input(), 'model':'en'}
	data = json.dumps(data)
	/*
	, ,
	/ \/ \
	(/ //_ \_
	.-._ \\|\| . \
	\ '-._ _,:__.-"/---\_ \
	______/___ '. .--------------------'~-'--.)__( , )\ \
	`'--.___ _\ / \| Here ,' \)\|\ `\\|
	/_.-' _\ \ _:,_ Be Dragons " \|\| (
	.'__ _.' \'-/,`-~` \|/
	from PIL import Image, ImageDraw, ImageFont
	import matplotlib.pyplot as plt
	import numpy as np

	image = Image.new('RGB', (700, 40))

	# Make sure the location to the font is correct
	draw.text((40,5), 'നമ്മുടെ മലയാളം അടിപൊളിയായി ഇമേജിൽ വരുന്നു', font=ImageFont.truetype('./Manjari-Bold.ttf', 25))

	plt.imshow(np.asarray(image))
	// Export entire database
	CALL apoc.export.graphml.all('/tmp/complete-graph.graphml', {useTypes:true, storeNodeIds:false})
	// Import from GraphML
	CALL apoc.import.graphml('/tmp/complete-graph.graphml', {batchSize: 10000, storeNodeIds: false})

	# $1 is the first argument
	# remove result.txt
	#rm output/result.txt

	fname=`echo "$1" \| awk -F "/" '{print $(NF)}'`
	# convert the pdf to a group of tiffs

	if [ ! -e extracted_tz_parliament/$fname.txt ];
	then
	{
	"query": "how much revenue did we generate in the last quarter from XYZ Corp?",
	"response": { ... }
	}
	import boto3
	from boto3.s3.transfer import S3Transfer

	from flask import Flask, Response

	app = Flask(__name__)


	class S3Storage:
	def __init__(self, bucket_name, pictures_path="", video_path=""):