Add Rust workflow

.github/workflows/rust.yml

@@ -0,0 +1,42 @@
+on: [push, pull_request]
+
+name: CI
+
+jobs:
+  check:
+    name: Check
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: dtolnay/[email protected]
+      - run: cargo check
+        working-directory: web_api
+
+  test:
+    name: Tests
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: dtolnay/[email protected]
+      - run: cargo test
+        working-directory: web_api
+
+  fmt:
+    name: Rustfmt
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: dtolnay/[email protected]
+      - run: rustup component add rustfmt
+      - run: cargo fmt --all -- --check
+        working-directory: web_api
+
+  clippy:
+    name: Clippy
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: dtolnay/[email protected]
+      - run: rustup component add clippy
+      - run: cargo clippy -- -D warnings
+        working-directory: web_api

README.md

@@ -1,3 +1,5 @@
+![Build Status](https://github.com/deniskore/py-ml-to-rs/actions/workflows/rust.yml/badge.svg)
+
 # Intro
 A friend of mine asked me to demonstrate how to load a model trained in Python into a Rust service. In response, this repository showcases the entire process of training a machine learning model to distinguish between various text encodings, achieving around 98.5% validation accuracy, using data sourced from the English Wiktionary.
 Subsequently, the trained model is seamlessly integrated into a Rust-based microservice, utilizing the ntex-rs. This implementation is streamlined with minimal dependencies, ensuring a lightweight and efficient service.

web_api/src/main.rs

@@ -11,8 +11,12 @@ mod model;
 #[ntex::main]
 async fn main() -> std::io::Result<()> {
     let model = Arc::new(
-        model::load_model("../../model/detector".to_string(), "predict_input", "predict_output")
-            .map_err(|err| std::io::Error::new(std::io::ErrorKind::Other, err))?,
+        model::load_model(
+            "../../model/detector".to_string(),
+            "predict_input",
+            "predict_output",
+        )
+        .map_err(|err| std::io::Error::new(std::io::ErrorKind::Other, err))?,
     );
 
     web::server(move || {