neelriyer

from torch.autograd import Variable
import librosa
import numpy as np
import torch

N_FFT=2048
def read_audio_spectum(filename):
	x, fs = librosa.load(filename)
	S = librosa.stft(x, N_FFT)
	p = np.angle(S)

neelriyer

# adapted from: https://github.com/alishdipani/Neural-Style-Transfer-Audio/blob/master/NeuralStyleTransfer.py  

import torch
import torch.nn as nn

class GramMatrix(nn.Module):

	def forward(self, input):
		a, b, c = input.size()  # a=batch size(=1)
				# b=number of feature maps

neelriyer

import torch
import torch.nn as nn

# adapted from: https://ghamrouni.github.io/stn-tuto/advanced/neural_style_tutorial.html#
class ContentLoss(nn.Module):

	def __init__(self, target, weight):
			super(ContentLoss, self).__init__()
			# we 'detach' the target content from the tree used
			self.target = target.detach() * weight

neelriyer

import os

# download youtube url using ffmpeg
# adapted from: https://github.com/ytdl-org/youtube-dl/issues/622#issuecomment-162337869
def download_from_url_ffmpeg(url, output, minute_mark = 1):

	try:
		os.remove(output)
	except:
		pass

neelriyer

from ObjectDetector import Detector
import io
from flask import Flask, render_template, request, send_from_directory, send_file
from PIL import Image
import requests
import os
import img_transforms

app = Flask(__name__)
detector = Detector()

neelriyer

import PIL
from PIL import Image

# adapted from: https://github.com/jantic/DeOldify/blob/master/deoldify/filters.py

def _scale_to_square(orig, targ):
  targ_sz = (targ, targ)
  return orig.resize(targ_sz, resample=PIL.Image.BILINEAR)

neelriyer

# adapted from: https://github.com/npatta01/web-deep-learning-classifier/blob/master/docs/2_b_gcloud.md

GCP_PROJECT=fresh-runway-246001
APP_NAME=neelsmlapp
REGION="us-central1"
MEMORY=2G

# set project to correct one
gcloud config set project $GCP_PROJECT

neelriyer

from Detector import Detector
import io
from flask import Flask, render_template, request, send_from_directory, send_file
from PIL import Image
import requests
import os
import img_transforms

app = Flask(__name__)
detector = Detector()

neelriyer

import io
from flask import Flask, render_template, request, send_from_directory, send_file
from PIL import Image
import requests
import os

# function to load img from url
def load_image_url(url):
	response = requests.get(url)
	img = Image.open(io.BytesIO(response.content))

neelriyer

import io
from flask import Flask, render_template, request, send_from_directory, send_file
from PIL import Image
import requests
import os
import urllib.request

app = Flask(__name__)

@app.route("/")