Lib.rs

SubSlay converts plain text into emoji sequences using semantic similarity with pre-embedded GloVe vectors and emoji keyword mappings — all bundled inside a pure Rust crate.

use subslay::EmojiStylist;

fn main() {
    let stylist = EmojiStylist::new().unwrap();
    let emojis = stylist.slay("Hello, world!");
    println!("{:?}", emojis); // ["👋", "🌍"]
}

🧾 What is SubSlay?

SubSlay is a Rust library that transforms ordinary text into emoji sequences that capture the semantic meaning of each word. It uses pre-trained GloVe word embeddings and maps them to emojis using embedded semantic vectors — all packed into the crate itself.

This enables fast, expressive emoji translation fully offline with zero API calls or external resources.

It’s ideal for:

• ✨ Chat apps
• 🤖 Discord bots
• 📱 Social media tools
• 💻 Terminal utilities

🚀 Features

• 🦀 Pure Rust — no runtime dependencies
• 📦 Embedded embeddings — GloVe + emoji vectors bundled as a single embeddings.bin file
• 🧠 Cosine similarity with emoji semantics for contextual matching
• 🆘 Fallback handling for unknown or out-of-vocab words
• 🌐 WASM-compatible — works in browser/Edge without filesystem or network access
• 💨 Blazing fast & offline — powered by bincode and quantized vector loading
• 🪶 Tiny footprint — uses f16 (half-precision) vectors for compact size

📦 Installation

Add SubSlay to your Cargo.toml:

[dependencies]
subslay = "0.1.9"

🛠️ Usage

use subslay::EmojiStylist;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let stylist = EmojiStylist::new()?;
    let emojis = stylist.slay("happy pizza");
    println!("{:?}", emojis); // e.g. ["😊", "🍕"]
    Ok(())
}

Example:

let stylist = EmojiStylist::new()?;
let sentence = "I love pizza and cats";
let emojis = stylist.slay(sentence);
println!("Emojis: {:?}", emojis);
// Possible output: ["❤️", "🍕", "😺"]

⚙️ How It Works (Under the Hood)

SubSlay embeds a pre-serialized binary file (embeddings.bin) using include_bytes!, which includes:

• 🔤 A HashMap<String, Vec<f16>> of words mapped to quantized 50D GloVe embeddings
• 😎 A HashMap<String, Vec<f16>> of emojis mapped to semantic emoji vectors

When .slay(&str) is called:

1. The input is cleaned (only letters and spaces).

2. Words are lowercased and split.

3. Each word:

   • Tries to fetch a GloVe embedding
   • Computes cosine similarity with all emoji vectors
   • Selects the closest emoji
   • Falls back to "@" if no match is found

Cosine similarity is calculated like this:

fn cosine_similarity(a: &[f16], b: &[f16]) -> f32 {
    let dot = a.iter().zip(b.iter()).map(|(x, y)| x.to_f32() * y.to_f32()).sum::<f32>();
    let norm_a = a.iter().map(|x| x.to_f32().powi(2)).sum::<f32>().sqrt();
    let norm_b = b.iter().map(|x| x.to_f32().powi(2)).sum::<f32>().sqrt();
    if norm_a == 0.0 || norm_b == 0.0 {
        return -1.0;
    }
    dot / (norm_a * norm_b)
}

🎯 API Overview

🧪 Testing

SubSlay includes unit tests for:

• ✅ Correct output for known words like "pizza"
• ✅ Full sentence translation
• ✅ Unknown/fallback behavior
• ✅ Empty input

Run tests:

cargo test

📁 Package Contents

• src/lib.rs — Main logic for translation and similarity
• data/embeddings.bin — Serialized, quantized GloVe and emoji vectors
• Cargo.toml — Dependency config
• README.md — You’re reading it

🧑‍💻 Developer Notes

• Embeddings are stored using quantized half-precision floats (f16) to reduce file size
• All data is serialized using bincode
• Pre-trained GloVe 50d embeddings are used for English vocabulary
• The emoji vectors are handcrafted or generated using centroid-like heuristics

💡 Future Plans

• Weighted phrase-to-emoji aggregation
• Fuzzy matching for slang and typos
• Custom user dictionaries or theme packs
• Parallel or batch .slay() calls
• Embedding compression + streaming load

📜 License

MIT © AndriaK GitHub Repository Live Demo