Quorafind

"use client"

import * as React from "react"
import { buttonVariants } from "@/components/ui/button"
import { ScrollArea } from "@/components/ui/scroll-area"
import { Select, SelectContent, SelectItem, SelectTrigger, SelectValue } from "@/components/ui/select"
import { cn } from "@/lib/utils"
import { ChevronLeft, ChevronRight } from "lucide-react"
import { DayPicker, DropdownProps } from "react-day-picker"

Quorafind

module.exports = {"Copy Chat History": copyChatWithHistory, "Copy Last Answer": copyLastAnswer};

const ExportMD = (function () {
	if (!window.TurndownService) return;
	const hljsREG = /^.*(hljs).*(language-[a-z0-9]+).*$/i;
	const turndownService = new window.TurndownService({
		headingStyle: "atx",
		hr: "---",
		bulletListMarker: "-",
		codeBlockStyle: "fenced",

Quorafind

// Version 0.0.1
module.exports = askChatWithPrompt;

function askChat(str, app) {
	const prompt = encodeURI(str);

	const chatViewEl = app.workspace.getLeavesOfType("surfing-view").find((item)=>{ return /chat\.openai\.com/.test(item?.view?.currentUrl)}).view.webviewEl;
	chatViewEl.executeJavaScript(`
		var htmlElement = document.querySelector("textarea");
		htmlElement.value = decodeURI("${ prompt }");

Quorafind

Import data json below to dynamic highlight config.

{
  "light-time-8-5": {
    "class": "light-time-8-5",
    "color": "#E45858",
    "regex": true,
    "query": "(?<=\\s)(0?8|20):[3-5][0-9](:[0-5][0-9])?(?=(\\s|$))",
    "mark": [

Quorafind

export default class mapForNotePlugin extends Plugin {
    //...
    public activeMapForNoteView: MapForNote = null;

    async onload(): Promise<void> {
    //...
        this.registerEventListeners();
    }

    //...

Quorafind

import tensorflow as tf
import numpy as np
from PIL import Image
 
FILEPATH = path_to_img  # 检测文件地址
MODELPATH = path_to_pb_file  # pb文件
SIZE = [100, 100]  # 检测图片规格
DIC = {0: 'aaa', 1: 'bbb'}  # 定义字典，保存标签和类别的映射关系
 
# 图片转化为数组

Quorafind

def load_pb(path_to_pb):
    with tf.gfile.GFile(path_to_pb, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def, name='')
        return graph
    
#你可以检索感兴趣的张量
input = graph.get_tensor_by_name('input:0')