This repository showcases a Java implementation of a simple Markov-chain text generator. It ingests a plain-text corpus, learns word transitions (using a first‑order Markov model), and generates whimsical new sentences that statistically resemble the source text. For sample I/O traces and additional information, visit the project page:
https://www.rsiddiq.com/software-design.html

Why it matters: Markov chains are a foundational concept for probabilistic language modeling. This project demonstrates data ingestion, frequency‑based modeling, and random sampling; core ideas that generalize to modern NLP workflows.

• Clean Java implementation of a first‑order Markov chain for text generation
• Corpus‑driven modeling: preserves frequency by storing duplicate successors
• Sentence bootstrapping: seeds generation from a special BEGINS_SENTENCE key
• Robust parsing: ignores blank lines, handles punctuation end‑markers
• Deterministic API surface: addFromFile, addLine, addWord, getSentence, randomWord, endsWithPunctuation

├── Markov-Text-Generation/
│   ├── app/
│   │   ├── src/
│   │   │   ├── main/
│   │   │   │   ├── java/
│   │   │   │   │   ├── Main.java
│   │   │   │   │   └── Markov.java
│   │   │   ├── test/
│   │   │   │   ├── java/
│   │   │   │   │   └── MarkovTest.java
│   │   ├── build.gradle
│   │   ├── azkaban.txt
│   │   ├── cloudy.txt
│   │   ├── hamlet.txt
│   │   ├── phrases.txt
│   │   └── spam.txt
│   ├── gradle/
│   │   ├── wrapper/
│   │   │   ├── gradle-wrapper.jar
│   │   │   └── gradle-wrapper.properties
│   │   └── libs.versions.toml
│   ├── .gitattributes
│   ├── .gitignore
│   ├── gradle.properties
│   ├── gradlew
│   ├── gradlew.bat
│   └── settings.gradle

1. Compile

   javac Markov-Text-Generation/app/src/main/java/Main.java Markov-Text-Generation/app/src/main/java/Markov.java Markov-Text-Generation/app/src/test/java/MarkovTest.java

2. Run (example driver)

   java Main

Tip: Try different corpora (e.g., spam.txt, hamlet.txt) to see how style and vocabulary affect generated sentences.

• State: HashMap<String, ArrayList<String>> words maps each token to the multiset of followers.
• Frequency proxy: Duplicates are stored to preserve empirical transition probabilities.
• Sentence logic: Start from BEGINS_SENTENCE; stop when a token ends with punctuation in PUNCTUATION_MARKS (e.g., . ! ?).
• Error handling: File I/O guarded; punctuation checks isolate into endsWithPunctuation(String) for testability.

This mirrors a classic teaching pattern: simple data structures + clear APIs + randomized outputs for quick visual feedback.

• Verify constructor initializes words with the BEGINS_SENTENCE key
• Unit-test addFromFile on tiny corpora (e.g., Hello World.)
• Assert getSentence() returns a non-empty string ending with punctuation
• Validate endsWithPunctuation for positive and negative cases

• Ability to translate a written specification into a maintainable Java implementation
• Competence with collections (HashMap, ArrayList) and file I/O
• Sensible API design enabling unit testing and reuse
• Clear documentation and repository hygiene

Feel free to use, modify, and extend for learning and portfolio purposes.