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run.py
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run.py
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import sys
import json
import plotly
from flask import Flask
from flask import render_template, request
from plotly.graph_objects import Heatmap, Bar
from sklearn.externals import joblib
from sqlalchemy import create_engine
sys.path.append("common")
from common.nlp_common_utils import *
if len(sys.argv) == 1:
sys.argv.append('./data/DisasterResponse.db')
sys.argv.append('./models/classifier.pkl')
# this requires for joblib and pickle
def tokenize(text):
"""
Used a common utility functions for tokenize text in to cleaned token list.
INPUT:
text - raw message
OUTPUT:
clean_tokens -- cleaned tokenized list
"""
return tokenize_text(text)
# create a flask app
app = Flask(__name__, template_folder='app/templates')
#
database_file_location, model_location = sys.argv[1:]
# load data
engine = create_engine('sqlite:///{}'.format(database_file_location))
df = pd.read_sql_table('DisasterResponse', engine)
# category df
df_categories = df.iloc[:, 4:]
# load model
model = joblib.load(model_location)
def generate_graph_with_template(data, title, yaxis_title, xaxi_title):
"""
This common layout can be used to create Plotly graph layout.
INPUT:
data - a graph required JSON data i.e list
title - a tile of the chart
yaxis_title - Y title
xaxix_title - X title
OUTPUT:
layout for particular graph.
"""
return {
'data': [data],
'layout': {
'title': title,
'yaxis': {
'title': yaxis_title
},
'xaxis': {
'title': xaxi_title
}
}
}
def generate_message_genres_bar_chart():
"""
create a graph using extracted data for `genre`
"""
# extract data needed for visuals
genre_counts = df.groupby('genre').count()['message']
genre_names = list(genre_counts.index)
data = Bar(x=genre_names, y=genre_counts)
title = 'Distribution of Message Genres'
y_title = 'Count'
x_title = 'Genre'
return generate_graph_with_template(data, title, y_title, x_title)
def generate_message_categories_distribution_bar_chart():
"""
create a graph for distribution of the messages.
"""
data = Bar(x=df_categories.columns,
y=list(df_categories.sum().sort_values(ascending=False)))
title = 'Distribution of Message Categories'
y_title = 'Count'
x_title = 'Category'
return generate_graph_with_template(data, title, y_title, x_title)
def generate_two_cat_relation_heat_map():
"""
A correlation matrix for categories
"""
data = Heatmap(
z=df_categories.corr(),
y=df_categories.columns,
x=df_categories.columns)
title = 'Correlation Distribution of Categories'
y_title = 'Category'
x_title = 'Category'
return generate_graph_with_template(data, title, y_title, x_title)
def generate_graphs():
# create visuals
graphs = [generate_message_genres_bar_chart(),
generate_message_categories_distribution_bar_chart(),
generate_two_cat_relation_heat_map()]
return graphs
# index webpage displays cool visuals and receives user input text for model
@app.route('/')
@app.route('/index')
def index():
graphs = generate_graphs()
# encode plotly graphs in JSON
ids = ["graph-{}".format(i) for i, _ in enumerate(graphs)]
graph_json = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
# render web page with plotly graphs
return render_template('master.html', ids=ids, graphJSON=graph_json)
# 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(host='0.0.0.0', port=3001, debug=True)
if __name__ == '__main__':
main()