Namaz vakit verilerini hesaplar, API ile ücretsizi sınırsız sunar.

Mawaqit integration - salat time and nearest mosque - in Home Assistant

Namaz Vakti Web Uygulaması

Seamlessly access precise prayer times with our Flutter package tailored to your location. Enhance your namaz routine effortlessly.

Namaz gem: AlAdhan.com wrapper in ruby to use REST API that is open for public use. The API only supports the GET method and returns JSON.

How to pray namaz (salat)

Javascript Kullanarak bir namaz vakit apisi

Prayer times using JavaScipr and real API

Simple Namaz Application based on React Native.

This is a small study py file to create a Telegram Chatbot that will give prayer times and link to Google maps with closest mosques

Shows صلاة time depending on locations

Productive Muslim is a productivity app for the adherents of the Islamic faith made as the Lab project for CSE 4508: RDBMS Programming Lab.

Diyanet namaz vakitleri için alternatif frontend sitesi.

Faithful Path is an Islamic website designed to provide comprehensive and authentic resources about Islam. Built with modern web technologies like React.js, React Router, and Tailwind CSS, this project aims to offer a user-friendly experience to those seeking knowledge about the Quran, Hadith, Dua, Sunnah, Salat, Ramadan, and more.

This project is all about the meaning of Salah (Salat) in Bangla.

Smart Prayer App is an innovative mobile application designed to enhance the practice of Namaz (Islamic prayer) by integrating modern technology with traditional religious practices. This app leverages machine learning, specifically TensorFlow, to provide users with an interactive and personalized experience, ensuring that their prayer practices.

The code showing the prayer times parsed from the islam.uz site

Beautiful & Responsive multipage Namaz related website with prayer times and Tasbeeh counter using HTML, CSS, JavaScript, and Bootstrap.

Diyanet İşleri Başkanlığı'ndan aylık veya haftalık olarak namaz vakitlerini alır.

This project is focused on predicting prayer postures using a custom-trained YOLOv11 model. The model is trained to detect specific prayer postures from input images.