527 skills found · Page 10 of 18
filippolauria / MfgMinimal FreeRADIUS GUI (MFG) is a web application written in Python 3 on top of Flask, SQLAlchemy and WTForms, which aims at managing an instance of FreeRADIUS (Version 3.x) with MySQL/MariaDB support.
DaviRP2018 / Courses Rest Apis With Flask And PythonAre you tired of boring, outdated, incomplete, or incorrect tutorials? I say no more to copy-pasting code that you don’t understand. Welcome to the bestselling REST API course on Udemy! I'm Jose. I'm a software engineer, here to help you truly understand and develop your skills in web and REST API development with Python and Flask. Production-ready REST APIs with Flask This course will guide you in creating simple, intermediate, and advanced REST APIs including authentication, deployments, databases, and much more. We'll start with a Python refresher that will take you from the very basics to some of the most advanced features of Python—that's all the Python you need to complete the course. Using Flask and popular extensions Flask-RESTful, Flask-JWT, and Flask-SQLAlchemy we will dive right into developing complete, solid, production-ready REST APIs. We will also look into essential technologies Git, Heroku, and nginx. You'll be able to... Create resource-based, production-ready REST APIs using Python, Flask, and popular Flask extensions; Handle secure user registration and authentication with Flask. Using SQLAlchemy and Flask-SQLAlchemy to easily and efficiently store resources to a database; and Understand the complex intricacies of deployments and the performance of Flask REST APIs. But what is a REST API anyway? A REST API is an application that accepts data from clients and returns data back. For example, a REST API could accept text data from the client, such as a username and password, and return whether that is a valid user in the database. When developing REST APIs, our clients are usually web apps or mobile apps. That's in contrast to when we make websites, where the clients are usually the users themselves. Together we'll develop a REST API that not only allows clients to authenticate but also to store and retrieve any data you want from a database. Learning this will help you develop any REST API that you need for your own projects! I pride myself on providing excellent support and feedback to every single student. I am always available to guide you and answer your questions. I'll see you on the inside. Take your first step towards REST API mastery! O que você aprenderá Connect web or mobile applications to databases and servers via REST APIs Create secure and reliable REST APIs which include authentication, logging, caching, and more Understand the different layers of a web server and how web applications interact with each other Handle seamless user authentication with advanced features like token refresh Handle log-outs and prevent abuse in your REST APIs with JWT blacklisting Develop professional-grade REST APIs with expert instruction Há algum requisito ou pré-requisito para o curso? Some prior programming experience in any programming language will help. The course includes a full Python refresher course. All software used in the course is provided, and completely free Complete beginners may wish to take a beginner Python course first, and then transition to this course afterwards Para quem é este curso: Students wanting to extend the capabilities of mobile and web applications by using server-side technologies Software developers looking to expand their skill-set by learning to develop professional grade REST APIs Those looking to learn Python while specifically catering to web services
abdsamadf / EaseFinanceStock-trading webiste
Tany303 / AI Powered Blog WriterA Python application that generates blog articles based on given topics using the OpenAI API. Includes a Flask-based web interface and supports export to Markdown.
bliutech / TaintMonkeyMIT IEEE URTC 2025. GSET 2025. Repository for "TaintMonkey: Dynamic Taint Analysis of Python Web Applications Using Monkey Patching". A Pytest plugin incorporating fuzzing and taint analysis to uncover security vulnerabilities in Flask web applications.
buseyaren / Facipa Facial Paralysis Diagnosis And DetectionFaCiPa is a mobile application study that detects facial paralysis disease. In this project, REST API is used to use image processing and mobile coding together. While Flask was preferred for API, Python programming language was preferred for image processing and Ionic was preferred for mobile programming.
Kianoush-h / Product SearcherThis project is a simple web application built with Python and Flask that allows users to upload a picture of a product, such as a pen, and then performs a Google image search to find stores that sell the product. The app then extracts and presents a list of stores along with their prices for the identified product.
workos / Python Flask Example ApplicationsExample Python Flask applications demonstrating how to use the WorkOS Python SDK to support SSO, Directory Sync, Admin Portal, and Magic Link.
mafzaldev / Quiz Webapp FlaskBrowser based Quiz application developed using Python (Flask) and Bootstrap.
HarunMbaabu / Flask And Docker Application DemoLux Academy & Data Science East Africa Python Boot Camp, Building and Deploying Flask Application Using Docker Demo App.
ema / Flask MoresqlA thin layer of glue between PostgreSQL stored procedures and Python/Flask web applications
Azure-Samples / Active Directory B2c Python Flask WebappDemonstrates how you can use a third party library to build a Python-Flask application that signs in users to Azure AD B2C.
InduprasadSR / Blockchain PythonA simple block chain application developed using python,flask and rest API
sujaybr / TaskerA simple single-screen python-Flask Application to create your own local to-do list, Diary with tags and a simple Calendar.
RalphGradien / HomeWorkoutRecommendationsPython-based web application, Flask platform, utilizes a powerful Content-Based Filtering Algorithm to provide personalized recommendations excercises
prakruthishekar / AI Content Generatorweb application, powered by Python Flask and OpenAI GPT-3, designed to generate exceptional AI-generated content for a wide range of applications, including product descriptions, job descriptions, tweets, cold emails, social media advertisements, business idea pitches, YouTube video ideas, and video descriptions.
rohanmistry231 / Ipynb To Py Converter AppA Python-based web application for converting Jupyter Notebook (.ipynb) files to Python (.py) scripts with a user-friendly interface. Built with Flask, it supports batch conversions and preserves code structure for seamless transitions.
eventmanagementproj / Event Management ProjectSource Code for the event management project at https://eventmanagement.pythonanywhere.com
Jai-Agarwal-04 / Sentiment Analysis With InsightsSentiment Analysis with Insights using NLP and Dash This project show the sentiment analysis of text data using NLP and Dash. I used Amazon reviews dataset to train the model and further scrap the reviews from Etsy.com in order to test my model. Prerequisites: Python3 Amazon Dataset (3.6GB) Anaconda How this project was made? This project has been built using Python3 to help predict the sentiments with the help of Machine Learning and an interactive dashboard to test reviews. To start, I downloaded the dataset and extracted the JSON file. Next, I took out a portion of 7,92,000 reviews equally distributed into chunks of 24000 reviews using pandas. The chunks were then combined into a single CSV file called balanced_reviews.csv. This balanced_reviews.csv served as the base for training my model which was filtered on the basis of review greater than 3 and less than 3. Further, this filtered data was vectorized using TF_IDF vectorizer. After training the model to a 90% accuracy, the reviews were scrapped from Etsy.com in order to test our model. Finally, I built a dashboard in which we can check the sentiments based on input given by the user or can check the sentiments of reviews scrapped from the website. What is CountVectorizer? CountVectorizer is a great tool provided by the scikit-learn library in Python. It is used to transform a given text into a vector on the basis of the frequency (count) of each word that occurs in the entire text. This is helpful when we have multiple such texts, and we wish to convert each word in each text into vectors (for using in further text analysis). CountVectorizer creates a matrix in which each unique word is represented by a column of the matrix, and each text sample from the document is a row in the matrix. The value of each cell is nothing but the count of the word in that particular text sample. What is TF-IDF Vectorizer? TF-IDF stands for Term Frequency - Inverse Document Frequency and is a statistic that aims to better define how important a word is for a document, while also taking into account the relation to other documents from the same corpus. This is performed by looking at how many times a word appears into a document while also paying attention to how many times the same word appears in other documents in the corpus. The rationale behind this is the following: a word that frequently appears in a document has more relevancy for that document, meaning that there is higher probability that the document is about or in relation to that specific word a word that frequently appears in more documents may prevent us from finding the right document in a collection; the word is relevant either for all documents or for none. Either way, it will not help us filter out a single document or a small subset of documents from the whole set. So then TF-IDF is a score which is applied to every word in every document in our dataset. And for every word, the TF-IDF value increases with every appearance of the word in a document, but is gradually decreased with every appearance in other documents. What is Plotly Dash? Dash is a productive Python framework for building web analytic applications. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python. Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready. Dash is an open source library, released under the permissive MIT license. Plotly develops Dash and offers a platform for managing Dash apps in an enterprise environment. What is Web Scrapping? Web scraping is a term used to describe the use of a program or algorithm to extract and process large amounts of data from the web. Running the project Step 1: Download the dataset and extract the JSON data in your project folder. Make a folder filtered_chunks and run the data_extraction.py file. This will extract data from the JSON file into equal sized chunks and then combine them into a single CSV file called balanced_reviews.csv. Step 2: Run the data_cleaning_preprocessing_and_vectorizing.py file. This will clean and filter out the data. Next the filtered data will be fed to the TF-IDF Vectorizer and then the model will be pickled in a trained_model.pkl file and the Vocabulary of the trained model will be stored as vocab.pkl. Keep these two files in a folder named model_files. Step 3: Now run the etsy_review_scrapper.py file. Adjust the range of pages and product to be scrapped as it might take a long long time to process. A small sized data is sufficient to check the accuracy of our model. The scrapped data will be stored in csv as well as db file. Step 4: Finally, run the app.py file that will start up the Dash server and we can check the working of our model either by typing or either by selecting the preloaded scrapped reviews.
syedsaadahmed / Python Flask CRUDPython-Flask Web Application CRUD Example with MySQL Database
