import json

import plotly

import pandas as pd

import pickle

import re

from nltk.stem import WordNetLemmatizer

from nltk.tokenize import word_tokenize

from flask import Flask

from flask import render_template, request, jsonify

from plotly.graph_objs import Bar

from sqlalchemy import create_engine

app = Flask(__name__)

url_regex = 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+'

def tokenize(text):

'''

A function that tokennize text

Parameters

----------

text : String

The text string to tokenize

Returns

-------

clean_tokens : List

The tokenized text.

'''

detected_urls = re.findall(url_regex, text)

for url in detected_urls:

text = text.replace(url, "urlplaceholder")

tokens = word_tokenize(text)

lemmatizer = WordNetLemmatizer()

clean_tokens = []

for tok in tokens:

clean_tok = lemmatizer.lemmatize(tok).lower().strip()

clean_tokens.append(clean_tok)

return clean_tokens

# load data

# engine = create_engine('sqlite:///../data/YourDatabaseName.db')

# df = pd.read_sql_table('YourTableName', engine)

engine = create_engine('sqlite:///../data/DisasterResponse.db')

df = pd.read_sql_table('DisasterResponse', engine)

# load model

# model = joblib.load("../models/your_model_name.pkl")

model = pickle.load(open('../models/classifier.pkl','rb'))

# index webpage displays cool visuals and receives user input text for model

@app.route('/')

@app.route('/index')

def index():

# extract data needed for visuals

# TODO: Below is an example - modify to extract data for your own visuals

genre_counts = df.groupby('genre').count()['message']

genre_names = list(genre_counts.index)

# Get additional data

column_name = df.columns[4:]

Postive_percent = df[df.columns[4:]].mean().tolist()

Negative_percent = (1- df[df.columns[4:]].mean()).tolist()

# create visuals

# TODO: Below is an example - modify to create your own visuals

graphs = [

{

'data': [

Bar(

x=genre_names,

y=genre_counts

)

],

'layout': {

'title': 'Distribution of Message Genres',

'yaxis': {

'title': "Count"

},

'xaxis': {

'title': "Genre"

}

}

},

{

'data': [

Bar(name = 'Positive',

x=column_name,

y=Postive_percent

),

Bar(name = 'Negative',

x=column_name,

y=Negative_percent

)

],

'layout': {

'title': 'Categories',

'yaxis': {

'title': "Ratio"

},

'xaxis': {

'title': "Categories"

}

}

}

]

# encode plotly graphs in JSON

ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]

graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)

# render web page with plotly graphs

return render_template('master.html', ids=ids, graphJSON=graphJSON)

# web page that handles user query and displays model results

@app.route('/go')

def go():

# save user input in query

query = request.args.get('query', '')

# use model to predict classification for query

classification_labels = model.predict([query])[0]

classification_results = dict(zip(df.columns[4:], classification_labels))

# This will render the go.html Please see that file.

return render_template(

'go.html',

query=query,

classification_result=classification_results

)

def main():

app.run(port=3001, debug=True)

if __name__ == '__main__':

main()