akhalsa

{
  "url": "http://edops.com/",
  "seo_recommendations": [
    {
      "issue": "Missing Videos",
      "recommendation": "Insert relevant and engaging videos about educational operations to improve user engagement and time on page."
    },
    {
      "issue": "No CTA",
      "recommendation": "Include at least one clear and prominent CTA on each major section of the landing page, such as 'Contact Us', 'Learn More', or 'Get Started Today'."

akhalsa

{
  "body": {
    "seo_recommendations": [
      {
        "issue": "Missing Videos",
        "recommendation": "Insert relevant and engaging videos to explain your services and success stories. This will improve user engagement and time on page."
      },
      {
        "issue": "Poor Placement of CTAs",
        "recommendation": "Move the primary CTAs above the fold and ensure they are clearly visible and compelling. Specific CTAs like 'Learn More' or 'Contact Us' should be easily accessible."

akhalsa

var chai = require("chai");
var chaiHttp = require("chai-http");
var server = require("../server");
var db = require("../models");
// Configure chai
chai.use(chaiHttp);
chai.should();

describe("Basic Example API Test", function() {
  describe("GET ALL Examples", () => {

akhalsa

var chai = require("chai");
var chaiHttp = require("chai-http");
var server = require("../server");
var db = require("../models");
// Configure chai
chai.use(chaiHttp);
chai.should();

describe("Basic Example API Test", function() {
  describe("GET ALL Examples", () => {

akhalsa

query getConversationMessages($conversationId: ID!, $after: String, $first: Int) {
  allMessageConnection(conversationId: $conversationId, after: $after, first: $first) {
    __typename
    nextToken,
    messages {
      __typename
      id
      conversationId
      content
      createdAt

akhalsa

import numpy as np

def expSum(items):
    temp = np.copy(items)
    for x in np.nditer(temp, op_flags=['readwrite']):
        x[...] = np.exp( x)
    print "Using nditer and loop"
    print temp

    for row in items

akhalsa

import numpy as np

def expSum(items):
    temp = np.copy(items)
    for x in np.nditer(temp, op_flags=['readwrite']):
        x[...] = np.exp( x)
    print "Using nditer and loop"
    print temp

    print "transpose"

akhalsa

 x = np.array((9,5,8,6,4,11)).reshape(3,2)
>>> x
array([[ 9,  5],
       [ 8,  6],
       [ 4, 11]])
>>> x.min(axis=0)
array([4, 5])
>>> x.min(axis=1)
array([5, 6, 4])

akhalsa

def expSum(items):
    temp = np.copy(items)
    for x in np.nditer(temp, op_flags=['readwrite']):
        x[...] = np.exp( x)
    return np.sum(temp)

akhalsa

import numpy as np

# Create the following rank 2 array with shape (3, 4)
# [[ 1  2  3  4]
#  [ 5  6  7  8]
#  [ 9 10 11 12]]
a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

# Use slicing to pull out the subarray consisting of the first 2 rows
# and columns 1 and 2; b is the following array of shape (2, 2):