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Setting up Python Flask server on internet via Port forwarding

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In this post we will cover topics; Starting a server using Python Flask. Accessing server in Local Network. Adding a Port Forwarding rule in NAT settings. Accessing server in Internet via public IP. Difference between static IP and dynamic IP. Step 1 :- As the first step, we need to setup a server on our system.A server exposes your code to a particular port.Some examples are Apache Tomcat, Spring boot (inbuilt), Python Django (inbuilt), Nginx. Here we have a simple server in Python Flask. For a GET request in root path the server returns a "Hello World!".That's it. Going to start the server on my local IP address(192.168.1.105) at default flask port(5000) using below commands, $export FLASK_APP=flaskTest.py $flask run  --host=192.168.1.105 Step 2 :- For security purpose all ports for external communication is closed by router's firewall by default. So no communication can be done to outside world. For same reason our server won't be
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Simultaneously deploy multiple functions in a single Azure function app

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 In this article, I will explain a method I used while working with multiple functions to manage multiple azure functions under an azure function app. Issue with VSCode : In Visual Studio Code, each time I create a python function, it will add a new project directory along with a separate virtual environment, host.json and other config files. When I try to deploy a function it will delete existing azure functions in the function app. Resolution : With Azure CLI, using 'func' command, multiple python functions are created with common virtual environment, requirements.txt, host.json and local.settings.json. Deploying from the parent directory will make sure all functions are deployed and are in the latest versions. Detailed Explanation: Starting with the basics, open command prompt/terminal/powershell and login to Azure CLI (Command: az login). A new tab will be opened in your browser and ask for login info. After successful login, you will be redirected to terminal and window wi
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Automate Blog Post creation using Blogger APIs and Python

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This article will discuss the following topics, Blogger's REST APIs. Google's OAuth 2.0 playground. Accessing spreadsheet in Python. Creating blogger posts via Python. An experiment to automate blog post creation when you have structured data. I was searching for IFSC codes for some bank transactions and came across Reserve Bank's website, which has a list of Excel files containing Bank Codes, Contact Info and related details of each bank's branch. This time I will be looking towards Bharat co-operative bank of Mumbai. Here is how the corresponding data looks like. In the above picture, each row represents lots of details about each branch of the Bank. Let's think of creating a list of blog posts where each post gives IFSC codes of a particular branch along with address and contact details. As the first step, I have created a blog in blogger. In the Blogger dashboard, please also note the parameter called blog ID which is a kind of unique identifier
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Supervised Learning

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In supervised learning, we will be having the set of sample inputs and outputs. Our job is to create an algorithm with given data based on different parameters (that influence the generation of outputs). Supervised learning problems can be classified again into Regression and Classification problems. Regression Problems In the problems which fall under this category, the algorithm will be predicting an approximate value for the output. For example, consider a problem where we need to predict share price of stock in future based on its past records. The possible parameters could be - company's import, export data, changes in consumer markets etc. Classification Problems In this category, based on different parameters we will classify data to discrete valued outputs. So there won't be a prediction of output values, Instead will map to an output group. For example, consider a simple spam filter which categories e-mail as spam or not based on the presence of few
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Unsupervised Learning

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In unsupervised learning, we are provided with a dataset and nothing else. There is no outputs, parameters or anything to distinguish the given data. Our aim is to let the machine to learn or create a grouping/classification on its own. Unsupervised learning is comparatively difficult with respect to supervised learning. Consider social networking sites, based on our actions they are filling our feed with posts/media that we would prefer, suggests people we want to follow and a lot of things. These kinds of solutions are done using unsupervised learning. In the figure, social media users are grouped by machine based on some factors which are unknown to the programmer. This could be a simple example of unsupervised learning.
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