A Python implementation of a Markov chain text generator that creates random text based on patterns learned from sample text. The generator uses bigrams (2-word sequences) to build a probabilistic model and generate coherent text.

• Markov Chain Implementation: Uses bigrams to build text generation model
• Multiple Input Options:
  • Pre-loaded sample texts
  • User-entered custom text
  • File input capability
• Text Processing: Cleans and normalizes input text for better results
• Configurable Generation: Adjustable text length and chain order
• Interactive CLI: User-friendly command-line interface

1. Text Analysis: Breaks input text into word sequences
2. Chain Building: Creates a dictionary mapping word pairs to possible next words
3. Generation: Randomly walks through the chain to create new text
4. Output: Produces coherent text based on learned patterns

python mini_markov_text_generator.py

1. Use Sample Text: Choose from pre-loaded examples
2. Enter Custom Text: Input your own text for generation
3. Load from File: Read text from an external file

# Sample input:
"Python is a great programming language. Python makes programming fun."

# Possible output:
"Python is a great programming fun and easy. Programming in Python makes programming language."

• Language: Python 3
• Dependencies: Standard library only (random, re, collections)
• Chain Order: 2 (bigrams)
• Default Output Length: 30 words

• clean_text(): Preprocesses input text
• build_markov_chain(): Creates the Markov chain dictionary
• generate_text(): Generates random text from the chain
• main(): Handles user interaction and program flow

Fixes #1020 - Mini Markov Text Generator

Contribution to 100LinesOfPythonCode repository