sciencelee

import requests

post_data = [{
                "0":5,
                "1":8,
                "2":15
            }]

#url = 'https://square-flask-api.herokuapp.com/square/'
url = ' http://127.0.0.1:5000/square/'

sciencelee

from flask import Flask, jsonify, request

# create app
app = Flask(__name__, static_folder='static')

# routes
@app.route('/square/', methods=['POST'])
def square():
    # get data
    data = request.get_json()[0]

sciencelee

from flask import Flask, jsonify, request, make_response
import flask_cors

# create app
app = Flask(__name__, static_folder='static')
cors = flask_cors.CORS(app, resources={r"/api/*": {"origins": "*"}})

# routes
@app.route('/square/', methods=['POST', 'OPTIONS'])
@flask_cors.cross_origin()

sciencelee

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# create my NN model
model = Sequential()

embedding_size = 128
model.add(Embedding(len(total_vocabulary), embedding_size))
model.add(LSTM(25, return_sequences=True))

sciencelee

from keras.preprocessing.sequence import pad_sequences
from keras.layers import Input, Dense, LSTM, Embedding
from keras.layers import Dropout, Activation, Bidirectional, GlobalMaxPool1D
from keras.models import Sequential
from keras import initializers, regularizers, constraints, optimizers, layers
from keras.preprocessing import text, sequence

# set the emotion/sentiment as our target
target = df['emotion']

sciencelee

import pandas as pd
from nltk import word_tokenize

df = pd.read_csv('judge-1377884607_tweet_product_company.csv') 
df = df.rename(columns={"is_there_an_emotion_directed_at_a_brand_or_product": "emotion"})
df = df.dropna(subset=['emotion', 'tweet_text'])

data = df['tweet_text'].map(word_tokenize).values
total_vocabulary = set(word.lower() for tweet in data for word in tweet)  # set created from nested comprehension 
print('There are {} unique words in the dataset.'.format(len(total_vocabulary)))

sciencelee

print(location.raw)
print(location.address)
print((location.latitude, location.longitude))

sciencelee

import plotly.express as px

df['size'] = 1 # just add a size col to please the mapbox function.  Sizes are relative. All 1 means all same size

fig = px.scatter_mapbox(df, 
                        lat="lat", 
                        lon="lon",
                        zoom=10,
                        size='size',
                        size_max=10, # manually set largest size

sciencelee

import pandas as pd

top5 = ['Walter Payton College Prep',
        'northside college preparatory high school',
        'Jones College Prep',
        'Whitney Young High',
        'Lane Tech High School',
       ]

df = pd.DataFrame(top5, columns=['school'])

sciencelee

import pandas as pd

top5 = ['Walter Payton College Prep',
        'northside college preparatory high school',
        'Jones College Prep',
        'Whitney Young High',
        'Lane Tech High School',
       ]

df = pd.DataFrame(top5, columns=['school'])