Amaral Apps

Minute News - Flask

A fast and flexible news aggregation platform built using Flask, MongoDB, and web scraping techniques.

Real-Time News Aggregation Platform

Efficient news delivery through Flask framework
Automated updates with NewsAPI integration
Flexible database management using MongoDB
Optimized web scraping for real-time data and summaries

This project utilizes Flask and MongoDB to create a scalable news website, delivering updated content quickly and efficiently through integration with NewsAPI and custom web scraping tools.

Core Features

Flask Framework: Enables quick development and deployment of the web app
MongoDB: Used as the primary NoSQL database to store news articles
NewsAPI: Integrates to fetch the latest news from multiple sources
Web Scraping: Automates data extraction and uses online summarization tools to condense articles

The project allows for fast database updates and smooth news delivery, combining APIs with powerful scraping techniques to provide real-time news summaries from reliable sources.

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Train Your Classifier

A straightforward approach to train a machine learning model for image classification using Python and TensorFlow.

Image Classification Model Training

Setup development environment with Jupyter Notebook
Prepare datasets organized by categories (e.g., cats, dogs, birds)
Train a TensorFlow model using deep learning techniques
Enhance performance through transfer learning

This project empowers you to build an image classification model, enabling automated recognition of images in various categories such as animals and objects.

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Author: Miguel Angelo do Amaral Junior

COVID-19 Data Dashboard

An interactive application for real-time visualization and analysis of COVID-19 data, built using Streamlit, Pandas, and Plotly.

Real-Time COVID-19 Data Analysis

Country Selection: Analyze specific countries like Germany, Brazil, and the United States.
Interactive Data Visualization: Compare data with line, scatter, and bar charts.
Summary Statistics: View total cases, deaths, mortality rates, and daily averages.
Date Range Control: Customize analysis periods with a date selector.

This project uses Python 3.11 and Streamlit to provide a flexible tool for exploring COVID-19 trends across different countries and periods, with dynamic updates based on user inputs.

Core Features

Python 3.11 and Streamlit: Power the interactive user interface and data handling.
Pandas: Efficiently manages and processes COVID-19 data for analysis.
Plotly: Generates interactive charts for data visualization.
Requests: Retrieves real-time COVID-19 data from online sources.

The dashboard facilitates in-depth analysis of COVID-19 data, helping users understand the pandemic's impact over time and across different countries.

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Data Queue Keeper

Integrating messaging services with data storage using Docker Compose.

Overview of Data Queue Keeper

RabbitMQ: Manages message queues for efficient data handling
MongoDB: Provides persistent data storage
Python Application: Consumes messages and stores data

This project demonstrates a scalable system that integrates RabbitMQ and MongoDB for reliable data processing, showcasing modern practices like containerization with Docker Compose.

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Airflow Guide

Comprehensive guide for setting up Apache Airflow with Virtualenv and environment variables.

Setting Up Apache Airflow

Install dependencies: Using Virtualenv for isolation
Configure environment variables: Best practices for configuration
Understand key components: DAGs, Tasks, and Operators
Start Airflow locally: Run the database, web server, and scheduler

This guide provides a step-by-step approach to setting up Apache Airflow for local use, focusing on best practices for configuration and examples of DAGs.

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