anuj2110

package main

import (
	"flag"
	"log"
	"net/http"
	"net/url"
	"strings"
	"sync"
	"time"

anuj2110

package main

import (
	"flag"
	"log"
	"net/http"
	"net/url"
	"strings"
	"sync"
	"time"

anuj2110

from fastapi import FastAPI,Depends
from .database import engine,SessionLocal,get_db
from sqlalchemy.orm import Session
import psycopg2
from psycopg2.extras import RealDictCursor

app = FastAPI()

models.Base.metadata.create_all(bind=engine)

anuj2110

def printbinaryStringsWithoutConsecutive1s(n,i,osf,lastDigit):
    if i==n:
        print(osf)
        return
    if lastDigit==0:
        printbinaryStringsWithoutConsecutive1s(n,i+1,osf+'0',0)
        printbinaryStringsWithoutConsecutive1s(n,i+1,osf+'1',1)
    else:
        printbinaryStringsWithoutConsecutive1s(n,i+1,osf+'0',0)
printbinaryStringsWithoutConsecutive1s(4,0,'',0)

anuj2110

def printStringSubsets(n,i,osf,char,string):
    if(i==n):
        print(f'"{osf}"')
        return
    printStringSubsets(n,i+1,osf+originalString[i-1],originalString[i-1],string)
    printStringSubsets(n,i+1,osf,originalString[i-1],string)

originalString = 'xyza'
printStringSubsets(len(originalString)+1,1,'',originalString[0],originalString)

anuj2110

import JSZip from 'jszip';
import { saveAs } from 'file-saver';

// TODO: Initialize the firebase project in the client side

const storage = firebase.storage().ref('<Folder_Name')

const getFileUrls = async (dest_storage_path:string) => {
    const files = await (await storage.listAll()).items
    const jszip = new JSZip();

anuj2110

# making the imports  ----------------------------------------------------
import os
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input
from tensorflow.keras.layers import Dense,Flatten
from tensorflow.keras.models import Model, Sequential
# import section end ------------------------------------------------------
from PIL import ImageFile

anuj2110

def stackImages(scale,imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range ( 0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:

anuj2110

def convert_to_sequences(tokens:list):
    '''
    Function to convert the sequences to tokens.
    params:
        tokens(list): The text converted to a list of cleaned tokens.
    returns:
        sequnces(list): The list of sequences formed from tokens.abs
    '''
    length = 50 + 1
    sequences = list()

anuj2110

import string
import numpy as np 
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.layers import Dense,LSTM,Dropout,Embedding
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical

def load_file(path:str):
    '''
    Function to load the text file for training the language model.