app.py

import argparse
from flask import Flask, jsonify, request
from flask import render_template, send_from_directory
import os
import re
import joblib
import socket
import json
import numpy as np
import pandas as pd


# import model specific functions and variables
from my_modules.model import model_train, model_load, model_predict, MODEL_VERSION, MODEL_VERSION_NOTE
from my_modules.logger import LOGS_DIR

app = Flask(__name__)

@app.route("/")
def landing():
	return render_template('index.html')

@app.route('/index')
def index():
	return render_template('index.html')

@app.route('/dashboard')
def dashboard():
	return render_template('dashboard.html')

@app.route('/running', methods=['POST'])
def running():
	return render_template('running.html')

@app.route('/predict', methods=['GET','POST'])
def predict():
	"""
	basic predict function for the API
	"""
	
	## input checking
	if not request.json:
		print("ERROR: API (predict): did not receive request data")
		return jsonify([])

	if 'query' not in request.json:
		print("ERROR API (predict): received request, but no 'query' found within")
		return jsonify([])

	if 'type' not in request.json:
		print("WARNING API (predict): received request, but no 'type' was found assuming 'numpy'")
		query_type = 'numpy'

	## set the test flag
	test = False
	if 'mode' in request.json and request.json['mode'] == 'test':
		test = True

	## extract the query
	query = request.json['query']
		
	if request.json['type'] == 'dict':
		pass
	else:
		print("ERROR API (predict): only dict data types have been implemented")
		return jsonify([])

		
	## load model
	all_data, all_models = model_load(test=test)
	
	if not all_models:
		print("ERROR: all_models is not available")
		return jsonify([])

	_result = model_predict(query['country'], query['year'], query['month'], query['day'], all_data, all_models, test=test)
	result = {}
	
	## convert numpy objects to ensure they are serializable
	for key,item in _result.items():
		if isinstance(item,np.ndarray):
			result[key] = item.tolist()
		else:
			result[key] = item
	
	return(jsonify(result))

@app.route('/train', methods=['GET','POST'])
def train():
	"""
	basic predict function for the API

	the 'mode' flag provides the ability to toggle between a test version and a 
	production verion of training
	"""
	
	## check for request data
	if not request.json:
		print("ERROR: API (train): did not receive request data")
		return jsonify(False)

	## set the test flag
	test = False
	if 'mode' in request.json and request.json['mode'] == 'test':
		test = True

	print("... training model")
	model = model_train(test=test)
	print("... training complete")

	return(jsonify(True))
		
@app.route('/logs/<filename>', methods=['GET'])
def logs(filename):
	"""
	API endpoint to get logs
	"""

	if not re.search(".log", filename):
		print("ERROR: API (log): file requested was not a log file: {}".format(filename))
		return jsonify([])

	if not os.path.isdir(LOGS_DIR):
		print("ERROR: API (log): cannot find log dir")
		return jsonify([])

	file_path = os.path.join(LOGS_DIR, filename)
	if not os.path.exists(file_path):
		print("ERROR: API (log): file requested could not be found: {}".format(filename))
		return jsonify([])
	
	return send_from_directory(LOGS_DIR, filename, as_attachment=True)

if __name__ == '__main__':

	## parse arguments for debug mode
	ap = argparse.ArgumentParser()
	ap.add_argument("-d", "--debug", action="store_true", help="debug flask")
	args = vars(ap.parse_args())

	if args["debug"]:
		app.run(debug=True, port=8080)
	else:
		app.run(host='0.0.0.0', threaded=True, port=8080)