gurusura

# To run you'll need some secrets:
# 1. SERPAPI_API_KEY secret in env var - get from https://serpapi.com/
# 2. OPENAI_API_KEY secret in env var - get from https://openai.com

import streamlit as st
import json, os
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from serpapi import GoogleSearch

gurusura

# Author: Kyle Kastner
# License: BSD 3-Clause
# Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/
# Visualization based on code from Gael Varoquaux gael.varoquaux@normalesup.org
# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

gurusura

# import the necessary packages
from scipy.spatial import distance as dist
import numpy as np
import imutils
from imutils import contours
from imutils import perspective
import cv2

# detect aruco marker
def findArucoMarkers(img, markerSize = 6, totalMarkers=100, draw=True):

gurusura

from __future__ import print_function
import os
import subprocess

root = "."

try:
   os.mkdir('compressed')
except FileExistsError:
   pass   

gurusura

"""
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Simple Hyperbolic tiling animation using taichi
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
import taichi as ti
ti.init(arch=ti.cpu)

# ----------------------------------------------
# global settings

gurusura

import sys
from requests import get
from bs4 import BeautifulSoup
# Put in your class details. It scans TRU bookstore to get ISBN
# It uses the ISBN to search Libgen to get MD5 
# It then downloads from Libgen using the MD5
def make_request(url):
    headers = {
    'Host': 'thebookstore.tru.ca',
    'Connection': 'keep-alive',

gurusura

import pandas as pd
import numpy as np
import datetime as dt
import math
import warnings

warnings.filterwarnings("ignore")

prices = pd.read_csv("adjclose.csv", index_col="Date", parse_dates=True)
volumechanges = pd.read_csv("volume.csv", index_col="Date", parse_dates=True).pct_change()*100

gurusura

#-*- coding:utf-8 - *-


def load_dataset():
    "Load the sample dataset."
    return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]


def createC1(dataset):
    "Create a list of candidate item sets of size one."