Roshan Raj roshanraj

## abc.py
{
	"src_path":"https://xavlabs-repository.s3.amazonaws.com/fnet/shafin/IMG_20190625_145657.jpg",
	"dst_path":"https://xavlabs-repository.s3.amazonaws.com/fnet/baseIdCard/IMG_6591.JPG"
}

## docker-compose.yaml
version: '2'
services:
  dbpostgres:
    image: postgres
    volumes:
      - /private/var/lib/postgresql:/var/lib/postgresql
    ports:
      - "5432:5432"
    environment:
      POSTGRES_USER: username

## maptotemplate.go
package main

import (
	"encoding/json"
	"fmt"
	"text/template"
	"os"
)

type StatusType struct {

## safari-reaper.html
<!DOCTYPE html>
<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
        <style>
        body {
            background: repeat url('data:image/jpeg;base64,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

## js - html santize
var entityMap = {
  '&': '&amp;',
  '<': '&lt;',
  '>': '&gt;',
  '"': '&quot;',
  "'": '&#39;',
  '/': '&#x2F;',
  '`': '&#x60;',
  '=': '&#x3D;'
};

## sentiment_classification.py
# You need to install scikit-learn:
# sudo pip install scikit-learn
#
# Dataset: Polarity dataset v2.0
# http://www.cs.cornell.edu/people/pabo/movie-review-data/
#
# Full discussion:
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn


## tensorport_minimal_distributed_tensorflow.py
# Notes:
# You need to have the tensorport package installed (pip install tensorport)
# Export logs to /logs, your data is in /data.
# See example here https://github.com/tensorport/mnist/blob/master/mnist_conv.py
# and documentation here

import tensorflow as tf
from tensorport import get_data_path, get_logs_path


## text_cnn.py
import tensorflow as tf
import numpy as np


class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(

## gist:c261b0753b4231c2c968968537cf2662
# Edit ~/.bash_profile

export GOPATH=/Users/username/go
export PATH=$GOPATH/bin:$PATH

# Reload profile : source ~/.bash_profile

## multipart_upload.go
package main

import (
	"bytes"
	"fmt"
	"io"
	"log"
	"mime/multipart"
	"net/http"
	"os"
	{
	"src_path":"https://xavlabs-repository.s3.amazonaws.com/fnet/shafin/IMG_20190625_145657.jpg",
	"dst_path":"https://xavlabs-repository.s3.amazonaws.com/fnet/baseIdCard/IMG_6591.JPG"
	}
	version: '2'
	services:
	dbpostgres:
	image: postgres
	volumes:
	- /private/var/lib/postgresql:/var/lib/postgresql
	ports:
	- "5432:5432"
	environment:
	POSTGRES_USER: username
	package main

	import (
	"encoding/json"
	"fmt"
	"text/template"
	"os"
	)

	type StatusType struct {
	<!DOCTYPE html>
	<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
	<style>
	body {
	background: repeat url('data:image/jpeg;base64,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
	var entityMap = {
	'&': '&',
	'<': '<',
	'>': '>',
	'"': '"',
	"'": ''',
	'/': '/',
	'`': '`',
	'=': '='
	};
	# You need to install scikit-learn:
	# sudo pip install scikit-learn
	#
	# Dataset: Polarity dataset v2.0
	# http://www.cs.cornell.edu/people/pabo/movie-review-data/
	#
	# Full discussion:
	# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn
	# Notes:
	# You need to have the tensorport package installed (pip install tensorport)
	# Export logs to /logs, your data is in /data.
	# See example here https://github.com/tensorport/mnist/blob/master/mnist_conv.py
	# and documentation here

	import tensorflow as tf
	from tensorport import get_data_path, get_logs_path
	import tensorflow as tf
	import numpy as np


	class TextCNN(object):
	"""
	A CNN for text classification.
	Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
	"""
	def __init__(
	# Edit ~/.bash_profile

	export GOPATH=/Users/username/go
	export PATH=$GOPATH/bin:$PATH

	# Reload profile : source ~/.bash_profile
	package main

	import (
	"bytes"
	"fmt"
	"io"
	"log"
	"mime/multipart"
	"net/http"
	"os"