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@hamletbatista
hamletbatista / get_content_gaps.py
Created April 30, 2020 01:26
def get_content_gaps(content_formats):
content_gaps = dict()
SERP_features = content_formats["serp_features"]
content_gaps["url"] = content_formats["url"]
#Check if an image feature is necessary and the page doesn't have it
content_gaps["image"] = 0
@hamletbatista
hamletbatista / get_content_formats.py
Created April 30, 2020 01:17
import json
from jsonpath_ng import jsonpath, parse
def get_content_formats(filename):
content_formats = dict()
image = parse("$..image")
video = parse("$..embedUrl")
local_business = parse("$..address")
@hamletbatista
hamletbatista / get_feature_names.py
Created April 26, 2020 01:02
def get_feature_names(indices):
serp_index=["Instant answer", "Knowledge panel", "Carousel", "Local pack", "Top stories", "Image pack", "Site links", "Reviews", "Tweet", "Video", "Featured video", "Featured Snippet", "AMP", "Image", "AdWords top", "AdWords bottom", "Shopping ads", "Hotels Pack", "Jobs search", "Featured images", "Video Carousel", "People also ask"]
index_list = indices.split(",")
feature_names = list()
for i in index_list:
if len(i) > 0:
@hamletbatista
hamletbatista / get_serp_features.py
Created April 26, 2020 00:55
import requests
from urllib.parse import urlencode, urlparse, urlunparse, quote
import pandas as pd
#def get_seo_branded_data(brand, domain, database="us", export_columns="Ph,Po,Nq,Ur,Tg,Td,Ts", display_limit=10000, display_filter="+|Ph|Co|{brand}"):
#Found that SERP features is -> Fl in https://www.semrush.com/api-analytics/#columns
def get_serp_features(domain, database="us", export_columns="Ph,Fk", display_limit=100):
global key
@hamletbatista
hamletbatista / create_bert_model_definition_yaml.py
Created April 2, 2020 05:59
template="""
input_features:
-
name: tokens
type: text
encoder: bert
config_path: uncased_L-12_H-768_A-12/bert_config.json
checkpoint_path: uncased_L-12_H-768_A-12/bert_model.ckpt
reduce_output: null
preprocessing:
@hamletbatista
hamletbatista / fetchPrediction.js
Created April 2, 2020 05:09
function fetchPrediction(TEXT = "give me a flight from baltimore to newark"){
TEXT = encodeURI("tokens=BOS "+TEXT+" EOS");
console.log(TEXT);
//You need to replace this temporary URL
var url = "https://e769db6e.ngrok.io/predict";
var options = {
@hamletbatista
hamletbatista / create_model_definition_yaml.py
Created April 2, 2020 04:22
template="""
input_features:
-
name: tokens
type: text
level: word
encoder: rnn
cell_type: lstm
bidirectional: true
num_layers: 2
@hamletbatista
hamletbatista / predictive_prefetching.html
Last active February 4, 2020 09:11
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="X-UA-Compatible" content="ie=edge">
<title>Document</title>
<script src="index.js"></script>
<script>
@hamletbatista
hamletbatista / get_similarity_suggestions.py
Last active December 6, 2019 17:33
import heapq
TOP_N = 5
BEST_ONLY = False
THRESHOLD_PROBABILITY = 0.65
def get_similarity_suggestion(phrase, no_percentage=False):
graph = tf.Graph()
with tf.compat.v1.Session(graph = graph) as session:
embed = hub.Module(module_url)
@hamletbatista
hamletbatista / prepare_phrase_corpus.py
Last active August 18, 2020 02:16
# Here we combine both lists into a single set of unique phrases
messages = set(df_404s["phrase"].to_list() + df_canonicals["phrase"].to_list())
messages = list(messages)[:-1]
similarity_input_placeholder = tf.placeholder(tf.string, shape=(None))
similarity_message_encodings = embed(similarity_input_placeholder)
with tf.Session() as session:
session.run(tf.global_variables_initializer())