greed2411

from starlette.applications import Starlette
from starlette.responses import PlainTextResponse
import uvicorn

app = Starlette()


@app.route('/')
async def homepage(request):
    return PlainTextResponse('')

greed2411

def dice_loss(input, target):
    smooth = 1.

    iflat = input.view(-1)
    tflat = target.view(-1)
    intersection = (iflat * tflat).sum()
    
    return 1 - ((2. * intersection + smooth) /
              (iflat.sum() + tflat.sum() + smooth))

greed2411

from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical

from keras.models import Sequential
from keras.layers.embeddings import Embedding
from keras.layers.core import Dense, Dropout
from keras.layers import LSTM
from keras.regularizers import l2
from keras.optimizers import Adam

greed2411

from vibora import Vibora, Response

app = Vibora()

@app.route('/')
async def index():
    return Response(b'', headers={'content-type': 'html'})

@app.route("/user/<int:id>", methods=['GET'])
async def user_info(id):

greed2411

greed2411

# onehot2labels()

def onehot2labels(y):
  labels = []
  for row_element in y:
    labels.append(np.argmax(row_element))
  return np.array(labels)

greed2411

import zipfile
zip_ref = zipfile.ZipFile(path_to_zip_file, 'r')
zip_ref.extractall(directory_to_extract_to)
zip_ref.close()

greed2411

import os
import img2pdf

# takes all the images in the current working directory and puts as one centered image per page
with open("output.pdf", "wb") as f:
    f.write(img2pdf.convert([i for i in os.listdir(os.getcwd()) if i.endswith(".jpg")]))

greed2411

import base64, uuid

def cookie_secret():
    return base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes)

if __name__ == "__main__":
    print(base64.b64encode(uuid.uuid4().bytes + uuid.uuid4().bytes))

greed2411

import torch
import torchvision
from torchvision import datasets, transforms, models
import torch.nn as nn
from torch.autograd import Variable
from PIL import Image
import numpy as np

# >>> torch.__version__
# '0.2.0_4'