Cath_NN_app.py

from flask import Flask, render_template, request, url_for, redirect
from keras.models import load_model

from NNprocess_code import process_nn_dict

import joblib

app = Flask(__name__)

@app.route("/", methods=["GET"])
def home():
	return render_template("search_page.html")

@app.route("/NN_cath_page", methods=["GET"])
def NN_cath_page():
	return render_template("NN_model.html")

@app.route("/NN_cath_calc", methods=["GET"])
def NN_cath_calc():
	loaded_model = load_model('CathNNwts.h5')
	cath_data = request.args.to_dict()

	try:
		cath_data_list = process_nn_dict(cath_data)
	except Exception as error:
		return str(error)

	# print(cath_data_list)

	# cath_data_list = []
	# for key in cath_data:
	# 	try:
	# 		float(cath_data[key])
	# 		float_bool = True
	# 	except:
	# 		float_bool = False

	# 	if float_bool:
	# 		cath_data_list.append(float(cath_data[key]))
	# 	elif cath_data[key] == "":
	# 		cath_data_list.append(-1)

	# should be 1
	# cath_data_list = [66, 1, 1, 0, 0,  0, 0,	0,	0,	175.0,	87.6,	0,	1,	0,	0,	0,	-1,	2,	2,	1,	1,	2,	4,	1,	0,	0.0,	0,	0.0,	0,	0.5,	0,	0.9,	0,	0.0,	1,	1,	1,	1,	1,	1]

	# should be 0
	# cath_data_list = [63,	0,	1,	1,	0,	0,	1,	0,	0,	188.0,	71.0,	0,	0,	0,	0,	0,	-1,	1,	-1,	1,	0,	2,	1,	2,	0,	0.0,	0,	0.0,	0,	0.0,	0,	0.0,	0,	0.0,	1,	1,	1,	1,	1,	1]

	cath_prediction = loaded_model.predict([cath_data_list])
	return_string = f"Cath result is {cath_prediction}"
	return return_string

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