Flask

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Flask is a lightweight Python web framework for building web applications and HTTP request handlers.

Flask is a lightweight Python web framework for turning a trained model into a self-contained web deployment. It is useful for testing a model with beta clients before committing to public-cloud deployment. section 10.1

A practical Flask model deployment connects these pieces:

• A standalone Python server module loads the saved Keras model and defines view functions for the application’s pages. section 10.1.1 section 10.1.2
• The home page collects property characteristics such as location, rooms, and parking spaces; a prediction page displays the result. section 10.1.1
• JavaScript packages submitted values, while CSS controls page rendering and a YAML file supplies server parameters such as the model directory. section 10.1.1

Property-price prediction

A user opens localhost:5000, enters property details, and submits them to a Flask route. The view function reads query-string values, converts continuous inputs to floats, builds model tensors, invokes the loaded model, and passes a prediction to the results page. The demonstration hard-codes one bin value, leaving its calculation as an exercise. section 10.1.2

Figure 10.1 Overview of the web deployment

Use the deployment overview to trace how the trained model, Flask module, configuration, web pages, JavaScript, and CSS fit together. section 10.1.1

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes

Explains routes, view functions, model loading, and request handling, while stopping short of comprehensive Flask coverage.

Examples

Uses a property-price prediction interface with home and results pages, model-loading code, routes, query strings, and tensor conversion.

What it assumes

Assumes Python and a saved Keras model, while introducing web pages, routes, request values, and model-serving structure.

What you can do after

Build a local Flask demonstration that accepts web inputs, invokes a saved Keras model, and renders its prediction.

How it connects

Connects Python, HTML, JavaScript, CSS, YAML configuration, Keras models, and HTTP request handling.

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Also explained in

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  A Pythonic Adventure: From Python basics to a working web appFlask is a lightweight Python web application framework.
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  The Well-Grounded Python Developer: How the pros use Python and FlaskFlask is a lightweight web application framework for Python that handles HTTP requests and connects URL resources to application code.
• 

  liveBookmanning.com
  Coding with AIFlask is a lightweight Python web framework for building web applications and HTTP services.

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Related concepts

• frameworkkind of · A software framework is a reusable structure that provides components, conventions, services, or extension points for building applications or other software systems.
• Pythonuses · Python is a high-level, general-purpose, dynamically typed programming language with readable, indentation-based syntax and automatic memory management.
• Djangoalternative to · Django is an open-source Python web framework providing integrated components for developing structured, database-backed web applications and services.
• Jinjauses · Jinja is a Python templating engine that generates text by rendering templates containing static content, expressions, variables, and control structures with supplied data.
• templateuses · A template is a reusable structure with fixed elements and variable parts for creating or generating similar content, outputs, or artifacts.
• render_templatehas part · render_template is a Flask function that combines a named template with supplied context data and returns the resulting rendered response.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.