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# create weekly demand collection
database.create_collection("weekly_demand")

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result_1 = weekly_demand_collection.aggregate([
    ## stage 1
    {
        "$match" : {
            "center_id" : {
                "$eq" : 11 
            } 
        }
    },
    ## stage 2

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weekly_demand_collection.find_one()

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# add a sheet with 20 rows and 2 columns
sheet.add_worksheet(rows=20,cols=2,title='runs')

# get the instance of the second sheet
sheet_runs = sheet.get_worksheet(1)

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# define function to add the image in the html file with the class name
def get_picture_html(path, tag):
    image_html = """<p> {tag_name} </p> <picture> <img src= "../{path_name}"  height="300" width="400"> </picture>"""
    return image_html.format(tag_name=tag, path_name=path)

# define function to add the list element in the html file
def get_count_html(category, count):
    count_html = """<li> {category_name} : {count_} </li>"""
    return count_html.format(category_name = category, count_ = count)

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# get directory function in get images file
def get_directory(url):
    return "URL_" + str(url.replace("/","_"))

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# importing the required libaries
from flask import Flask, render_template, request, redirect, url_for
from get_images import get_images, get_path, get_directory
from get_prediction import get_prediction
from generate_html import generate_html
from torchvision import models
import json

app = Flask(__name__)

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# importing required libraries
import requests
from bs4 import BeautifulSoup
import os
import time

def get_path(url):
    return "static/URL_" + str(url.replace("/","_"))
  
headers = {

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# importing the required libraries
import json
import io
import glob
from PIL import Image
from torchvision import models
import torchvision.transforms as transforms

# Pass the parameter "pretrained" as "True" to use the pretrained weights:
model = models.densenet121(pretrained=True)

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# load model
dt_model = classification.load_model(model_name='decision_tree_1')