run.py

import json
import plotly
import pandas as pd

from tokenFile import tokenize,StartingVerbExtractor
from sklearn.base import BaseEstimator, TransformerMixin

from flask import Flask
from flask import render_template, request, jsonify
from plotly.graph_objs import Bar
#from sklearn.externals import joblib
import joblib
from sqlalchemy import create_engine
import re

db_name = 'postgresql://tqdqfmxgrgunzx:a6d3564a45d7148a5e09817cead82db91e8b431f7521be123af79c92afd0d92c@ec2-54-91-188-254.compute-1.amazonaws.com:5432/d7saa2toh2oscb'
url_regex = "http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"
app = Flask(__name__)

# load data
engine = create_engine(db_name)
conn = engine.connect()
df = pd.read_sql("select * from \"DisasterResponse\"",conn)

# load model
model = joblib.load("models/classifier2.pkl")


# index webpage displays cool visuals and receives user input text for model
@app.route('/')
@app.route('/index')
def index():
    
    # extract data needed for visuals
    genre_counts = df.groupby('genre').count()['message']
    genre_names = list(genre_counts.index)

    category_names = df.iloc[:,5:].columns
    category_boolean = (df.iloc[:,5:] != 0).sum().values
    
    # create visuals
    graphs = [
        {
            'data': [
                Bar(
                    x=genre_names,
                    y=genre_counts
                )
            ],

            'layout': {
                'title': 'Distribution of Message Genres',
                'yaxis': {
                    'title': "Count"
                },
                'xaxis': {
                    'title': "Genre"
                }
            }
        },

       {
            'data': [
                Bar(
                    x=category_names,
                    y=category_boolean
                )
            ],

            'layout': {
                'title': 'Distribution of Message Categories',
                'yaxis': {
                    'title': "Count"
                },
                'xaxis': {
                    'title': "Category",
                    'tickangle': 35
                }
            }
        }
    ]
    
    # 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[5:], 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=5000, debug=True)


if __name__ == '__main__':
    app.run()