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import time
from flask import Flask, jsonify
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
@fredgido
fredgido / LITDIRT.user.js
Last active April 11, 2023 02:45
LITDIRT
// ==UserScript==
// @name I see cute girls online
// @namespace ISCG
// @version 0.000000001
// @description ...
// @author made by fredgy-kun
// @match https://*.aibooru.online/posts*
// @match https://*.aibooru.online/media_assets*
// @match https://*.aibooru.online/
// @exclude https://*.aibooru.online/posts/*
import discord
from PIL import Image
import io
import json
import httpx
bot = discord.Bot()
@bot.slash_command()
@fredgido
fredgido / LITDAI.user.js
Last active October 2, 2022 13:41
Look Into The Deep ai
// ==UserScript==
// @name Look Into The Deep ai
// @namespace LITD
// @version 1.0
// @description ...
// @author made by fredgy-kun
// @match https://aibooru.online/uploads/*
// @match https://aibooru.online/posts/*
// @connect deepdanbooru.donmai.us
// @grant GM.xmlHttpRequest
@fredgido
fredgido / app.py
Last active September 16, 2021 17:07
import uuid
from apscheduler.events import EVENT_JOB_EXECUTED, EVENT_JOB_ERROR
from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
from flask import Flask
from flask_apscheduler import APScheduler
app = Flask(__name__)
import time
from io import BytesIO
import pyvips
from PIL import Image # Pillow-SIMD
def figure_size(ih, iw, oh, ow):
if oh / ih >= ow / oh:
return int(iw * (oh / ih)), oh
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta http-equiv="X-UA-Compatible" content="ie=edge">
<title>8Ball Pool by Fred & Rui</title>
<link rel="stylesheet" href="./css/main.css">
<style>
article > div > div.css-1dbjc4n.r-16y2uox.r-1wbh5a2.r-1ny4l3l.r-1udh08x.r-1j3t67a > div > div.css-1dbjc4n.r-18u37iz > div.css-1dbjc4n.r-1iusvr4.r-16y2uox.r-1777fci.r-1mi0q7o > div:nth-child(2) > div:nth-child(2) > div > div > div > div > a > div > div.r-1p0dtai.r-1pi2tsx.r-1d2f490.r-u8s1d.r-ipm5af.r-13qz1uu > div > img{
position: static !important;
opacity: unset !important;
}
article > div > div.css-1dbjc4n.r-16y2uox.r-1wbh5a2.r-1ny4l3l.r-1udh08x.r-1j3t67a > div > div.css-1dbjc4n.r-18u37iz > div.css-1dbjc4n.r-1iusvr4.r-16y2uox.r-1777fci.r-1mi0q7o > div:nth-child(2) > div:nth-child(2) > div > div > div > div > a > div > div.r-1p0dtai.r-1pi2tsx.r-1d2f490.r-u8s1d.r-ipm5af.r-13qz1uu > div > div{
display: none !important;
}
article > div > div.css-1dbjc4n.r-16y2uox.r-1wbh5a2.r-1ny4l3l.r-1udh08x.r-1j3t67a > div > div.css-1dbjc4n.r-18u37iz > div.css-1dbjc4n.r-1iusvr4.r-16y2uox.r-1777fci.r-1mi0q7o > div:nth-child(2) > div:nth-child(2) > div > div > div > div > a > div > div.r-1p0dtai.r-1pi2tsx.r-1d2f4
import pprint
import tensorflow as tf
import keras
import tensorflow_hub as hub
import numpy as np
import PIL.Image as Image
model_url = "https://tfhub.dev/google/imagenet/nasnet_mobile/classification/4"
IMAGE_SHAPE = (224, 224)
classifier = tf.keras.Sequential([hub.KerasLayer(model_url, input_shape=IMAGE_SHAPE + (3,))])
import tensorflow as tf
from typing import Any, Union
import six
import math
import numpy as np
from skimage.transform import AffineTransform
from skimage import transform
import pprint
model = tf.keras.models.load_model("C:\\deepdanbooru-v3-20200101-sgd-e30\\model-resnet_custom_v3.h5")