https://github.com/nchinling/markov_sentence_constructor

Sentence generator app using Markov chain
https://github.com/nchinling/markov_sentence_constructor

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Sentence generator app using Markov chain

• Host: GitHub
• URL: https://github.com/nchinling/markov_sentence_constructor
• Owner: nchinling
• Created: 2024-02-10T14:45:34.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-16T09:49:30.000Z (12 months ago)
• Last Synced: 2024-11-11T18:09:56.137Z (3 months ago)
• Language: HTML
• Homepage:
• Size: 138 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#  Markov Sentence Generator app

## About the app

A Markov sentence generator is a type of algorithm used to generate sentences based on statistical patterns observed in a given corpus of text. The algorithm relies on Markov chains, which are mathematical models that describe a sequence of possible events in which the probability of each event depends on the state attained in the preceding event. A Markov chain can be used to model the likelihood of transitioning from one word to another based on the words that precede it in a given text.

The generator constructs a Markov chain by recording the frequency of word transitions. For example, if the word "cat" frequently follows the word "the" in the input text, the generator assigns a higher probability to that transition in the Markov chain.

To generate a sentence, the generator starts with a randomly selected word and uses the Markov chain to probabilistically select the next word based on the preceding word. This process continues until a predetermined stopping condition is met, such as reaching a provided sentence length. 

## Features

### 1. Train Model

- Users can provide paragraphs of text to train the text generation model.

- The paragraphs are preprocessed to remove unwanted characters, leading/trailing whitespace, and are converted to lowercase.

- The trained data is stored in a SQLite database.

### 2. Generate Text

- Users can generate text based on the trained model.

- The text generation process starts with a random word from the trained data and iteratively predicts the next word using a Markov Chain model until the desired length of the text is achieved.

- The generated text is capitalised and ends with a full-stop.

## Components

### 1. Flask Application

- The core of the application built using Flask, a Python web framework.

- Consists of routes for training the model, generating text, and serving HTML templates.

### 2. SQLite Database

- Stores the trained data in a relational database.

- The database schema includes tables for storing word frequencies.

### 3. Text Generation Logic

- Implemented using Markov Chain modeling.

- Chooses the next word based on the frequency of occurrence in the trained data.

## Usage

1. **Training the Model**

   - Navigate to the '/train' route.

   - Provide a paragraph of text in the input field and submit.

   - Trained data will be stored in the database.

2. **Generating Text**

   - Visit the '/generate' route.

   - Optionally specify the desired length of the generated text.

   - Click on the "Generate" button.

   - The app will generate text based on the trained model and display it on the page.

## Dependencies

- Flask: Web framework for building the application.

- SQLite3: Database management system for storing trained data.

- Random: Python module for selecting random words.

- Collections: Provides specialised datatypes.