app_helper.py

import numpy as np
from sklearn.linear_model import LinearRegression
import datetime
import ast

def valid_time(a):
    delta = datetime.datetime.now() - a
    mini = delta.total_seconds()/60
    if mini >= 5:
        return False # pragma: no cover
    return True

def pre(delta, r):

    with open('demoResponse.txt', 'r') as values:
        data = ast.literal_eval(values.read())

    X = []
    Y = []

    pos = False
    if r > 0.5:
        # triggered only when news articles are negative
        pos = True # pragma: no cover

    for i, _ in enumerate(data):
        if pos and data[i][0] >= 0.5:
            X.append(round(data[i][0], 5)) # pragma: no cover
        else:
            X.append(round(data[i][0], 5))

    X = np.array(X).reshape(-1, 1)

    if delta == 1:
        for i, _ in enumerate(data):
            Y.append(round(data[i][1], 5))
    elif delta == 7:
        for i, _ in enumerate(data):
            Y.append(round(data[i][2], 5))
    elif delta == 15:
        for i, _ in enumerate(data):
            Y.append(round(data[i][3], 5))
    elif delta == 30:
        for i, _ in enumerate(data):
            Y.append(round(data[i][4], 5))
    reg = LinearRegression()
    classi = reg.fit(X, Y)

    return classi.predict(np.array(r).reshape(1, -1)).tolist()[0]*100