MLWhiz

import pandas as pd
import random
import datetime

def random_dt_bw(start_date,end_date):
    days_between = (end_date - start_date).days
    random_num_days = random.randrange(days_between)
    random_dt = start_date + datetime.timedelta(days=random_num_days)
    return random_dt

MLWhiz

import pandas as pd
import random
import datetime

def random_dt_bw(start_date,end_date):
    days_between = (end_date - start_date).days
    random_num_days = random.randrange(days_between)
    random_dt = start_date + datetime.timedelta(days=random_num_days)
    return random_dt

MLWhiz

# Lists to keep track of progress/Losses
img_list = []
G_losses = []
D_losses = []
iters = 0

# Number of training epochs
num_epochs = 50
# Batch size during training
batch_size = 128

MLWhiz

# Initialize BCELoss function
criterion = nn.BCELoss()

# Create batch of latent vectors that we will use to visualize
# the progression of the generator
fixed_noise = torch.randn(64, nz, 1, 1, device=device)

# Establish convention for real and fake labels during training
real_label = 1.
fake_label = 0.

MLWhiz

# Number of channels in the training images. For color images this is 3
nc = 3
# Size of feature maps in discriminator
ndf = 64

class Discriminator(nn.Module):
    def __init__(self, ngpu):
        super(Discriminator, self).__init__()
        self.ngpu = ngpu
        self.main = nn.Sequential(

MLWhiz

# Size of feature maps in generator
ngf = 64
# Number of channels in the training images. For color images this is 3
nc = 3
# Size of z latent vector (i.e. size of generator input noise)
nz = 100

class Generator(nn.Module):
    def __init__(self, ngpu):
        super(Generator, self).__init__()

MLWhiz

# Root directory for dataset
dataroot = "anime_images/"
# Number of workers for dataloader
workers = 2
# Batch size during training
batch_size = 128
# Spatial size of training images. All images will be resized to this size using a transformer.
image_size = 64
# Number of channels in the training images. For color images this is 3
nc = 3

MLWhiz

import streamlit as st
import base64
import io
import requests,json
from PIL import Image
import cv2
import numpy as np
import matplotlib.pyplot as plt
import requests
import random

MLWhiz

from PIL import Image
import numpy as np
import cv2
import matplotlib.pyplot as plt

def PILImage_to_cv2(img):
    return np.asarray(img)

def drawboundingbox(img, boxes,pred_cls, rect_th=2, text_size=1, text_th=2):
    img = PILImage_to_cv2(img)

MLWhiz

from fastapi import FastAPI
from pydantic import BaseModel
import torchvision
from torchvision import transforms
import torch
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from PIL import Image
import numpy as np
import cv2
import io, json