python-web-app

OPUS Converter é uma aplicação web que permite converter arquivos de áudio do formato OPUS para outros formatos populares, como MP3, WAV, FLAC e AAC. Esta ferramenta foi desenvolvida para facilitar a conversão de múltiplos arquivos simultaneamente, oferecendo uma interface simples e intuitiva.

A simple web app to convert JPG images to PDF and Word files using Flask.

A Python-based, human-centered workflow system to manage job-search intentionality and reduce cognitive load. (v0.2.0)

Posterfy is a Python Flask-based web app that lets users search for movies and download posters in various resolutions. It integrates the OMDb API for fetching movie data and supports a smooth, intuitive UI for film enthusiasts.

Flask-based CRUD web application for managing employee records. Built using Flask and SQLAlchemy with a SQLite database, it allows users to create, view, update, and delete employee entries. Demonstrates MVC architecture, form handling, routing, and ORM-based database operations in a clean, dynamic web interface

An intelligent Applicant Tracking System (ATS) resume auditor powered by Google's Gemini 3.5 Flash API. Parses PDF resumes, performs comparative gap analyses against job descriptions, and provides actionable improvement suggestions in a modern glassmorphic dashboard.

A simple and efficient URL shortener built with Django. This project allows users to shorten long URLs into compact, shareable links. Hosted on Google Cloud Run with a NeonDB PostgreSQL database, it ensures scalability and reliability.

A modern, full-stack Furniture House Management & POS web application built with Python (Flask), SQLite, and a premium SaaS Light Theme UI. Developed by Muzammal Ikhlaq.

A backend system for tracking student attendance with role-based access, validated status handling (present, absent, tardy), bulk and single-entry support, and reporting tools. Built with Flask, SQLAlchemy, and SQLite.

Building a scalable object detection pipeline using Python, OpenCV, and the Ultralytics YOLO framework..

An end-to-end machine learning project predicting diabetes risk. Compares SVC, Random Forest, and Logistic Regression on the Pima Indians dataset, selecting the top-performing SVC model (73.4% test accuracy). Deployed via an interactive Streamlit dashboard featuring robust input scaling and real-time classification results.