Damar Jati Damarcreative

## calculator.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>🧮 Calculator</title>
    <style>
        :root {
            --primary: #ff6b6b;
            --secondary: #4ecdc4;

## README.md

      
              4 files
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                Damarcreative
                / README.md
            
            
              Last active
              October 1, 2024 13:16
            
              
                Water_System_ESP
              
          
    Dokumentasi Sistem Kontrol Relay Air IoT

1. Fitur Manual On/Off Relay


Terdapat toggle switch untuk menghidupkan atau mematikan relay secara manual kapan saja, terlepas dari jadwal atau pengaturan mode yang telah diset sebelumnya.
Jika checkbox diaktifkan (checked), relay akan ON dan jika checkbox dimatikan (unchecked), relay akan OFF.
Saat toggle switch diaktifkan, relay akan bekerja sesuai durasi air yang diinputkan pada field "Water Duration".

Contoh: Jika input Water Duration diisi 30 detik, relay akan menyala selama 30 detik, setelah hitungan mundur mencapai 00:00, relay akan mati secara otomatis dan checkbox akan menjadi unchecked.
Jika Water Duration kosong atau bernilai 0, relay akan tetap menyala tanpa batas waktu (unlimited) sampai relay dimatikan secara manual dengan mematikan toggle (unchecked).


Toggle switch ini langsung mengirimkan status (ON/OFF) ke perangkat ESP tanpa perlu menekan tombol "SET".


## ChaosGame.py
import turtle
import random

# Setup the screen
screen = turtle.Screen()
screen.bgcolor("white")

# Create the turtle
t = turtle.Turtle()
t.speed(0)

## event_id.py
import requests
import json

# URL endpoint
url = "https://gradio.hf.space/queue/join?__theme=dark"

# Header sesuai dengan yang diberikan
headers = {
    'Accept': '*/*',
    'Accept-Encoding': 'gzip, deflate, br, zstd',

## yolo_image_crop.py
from PIL import Image
import os

def read_annotations(annotation_path):
    with open(annotation_path, 'r') as file:
        lines = file.readlines()

    annotations = []
    for line in lines:
        parts = line.strip().split()

## Fooocus.ipynb

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                Damarcreative
                / Fooocus.ipynb
            
            
              Created
              December 26, 2023 13:27
            
          
      Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## sentiment_analysis_example.py
# Dependency installation
!pip install transformers

# Import libraries
import torch
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

# Function to generate predictions using a model from Hugging Face
def predict_sentiment(model, tokenizer, text):
    inputs = tokenizer(text, return_tensors="pt")
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>🧮 Calculator</title>
	<style>
	:root {
	--primary: #ff6b6b;
	--secondary: #4ecdc4;
	import turtle
	import random

	# Setup the screen
	screen = turtle.Screen()
	screen.bgcolor("white")

	# Create the turtle
	t = turtle.Turtle()
	t.speed(0)
	import requests
	import json

	# URL endpoint
	url = "https://gradio.hf.space/queue/join?__theme=dark"

	# Header sesuai dengan yang diberikan
	headers = {
	'Accept': '/',
	'Accept-Encoding': 'gzip, deflate, br, zstd',
	from PIL import Image
	import os

	def read_annotations(annotation_path):
	with open(annotation_path, 'r') as file:
	lines = file.readlines()

	annotations = []
	for line in lines:
	parts = line.strip().split()
	# Dependency installation
	!pip install transformers

	# Import libraries
	import torch
	from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

	# Function to generate predictions using a model from Hugging Face
	def predict_sentiment(model, tokenizer, text):
	inputs = tokenizer(text, return_tensors="pt")