fmt and clippy

crates/embeddings-engine/src/lib.rs

@@ -1,10 +1,5 @@
 use async_openai::types::{CreateEmbeddingRequest, EmbeddingInput};
-use axum::{
-    Json, Router,
-    http::StatusCode,
-    response::Json as ResponseJson,
-    routing::post,
-};
+use axum::{Json, Router, http::StatusCode, response::Json as ResponseJson, routing::post};
 use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
 use once_cell::sync::Lazy;
 use serde::Serialize;

crates/inference-engine/src/lib.rs

@@ -8,7 +8,7 @@ pub mod server;
 // Re-export key components for easier access
 pub use inference::ModelInference;
 pub use model::{Model, Which};
-pub use server::{create_router, AppState};
+pub use server::{AppState, create_router};
 
 use std::env;
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};

crates/inference-engine/src/main.rs

@@ -1,4 +1,4 @@
-use inference_engine::{create_router, init_tracing, get_server_config, AppState};
+use inference_engine::{AppState, create_router, get_server_config, init_tracing};
 use tokio::net::TcpListener;
 use tracing::info;
 
@@ -12,7 +12,10 @@ async fn main() -> Result<(), Box<dyn std::error::Error>> {
     let (server_host, server_port, server_address) = get_server_config();
     let listener = TcpListener::bind(&server_address).await?;
 
-    info!("Inference Engine server starting on http://{}", server_address);
+    info!(
+        "Inference Engine server starting on http://{}",
+        server_address
+    );
     info!("Available endpoints:");
     info!("  POST /v1/chat/completions - OpenAI-compatible chat completions");
     info!("  GET  /v1/models         - List available models");

crates/inference-engine/src/server.rs

@@ -1,28 +1,28 @@
 use axum::{
+    Json, Router,
     extract::State,
     http::StatusCode,
-    response::{sse::Event, sse::Sse, IntoResponse},
+    response::{IntoResponse, sse::Event, sse::Sse},
     routing::{get, post},
-    Json, Router,
 };
 use futures_util::stream::{self, Stream};
 use std::convert::Infallible;
 use std::str::FromStr;
 use std::sync::Arc;
-use tokio::sync::{mpsc, Mutex};
+use tokio::sync::{Mutex, mpsc};
 use tokio_stream::wrappers::UnboundedReceiverStream;
 use tower_http::cors::{Any, CorsLayer};
 use uuid::Uuid;
 
+use crate::Which;
 use crate::openai_types::{
     ChatCompletionChoice, ChatCompletionChunk, ChatCompletionChunkChoice, ChatCompletionRequest,
     ChatCompletionResponse, Delta, Message, MessageContent, Model, ModelListResponse, Usage,
 };
-use crate::Which;
 use either::Either;
 use embeddings_engine::models_list;
-use gemma_runner::{run_gemma_api, GemmaInferenceConfig, WhichModel};
-use llama_runner::{run_llama_inference, LlamaInferenceConfig};
+use gemma_runner::{GemmaInferenceConfig, WhichModel, run_gemma_api};
+use llama_runner::{LlamaInferenceConfig, run_llama_inference};
 use serde_json::Value;
 // -------------------------
 // Shared app state

crates/predict-otron-9000/src/config.rs

@@ -45,8 +45,6 @@ pub struct Services {
     pub embeddings_url: Option<String>,
 }
 
-
-
 impl Default for ServerConfig {
     fn default() -> Self {
         Self {

crates/predict-otron-9000/src/main.rs

@@ -14,12 +14,12 @@ use std::env;
 #[cfg(feature = "ui")]
 use axum::http::StatusCode as AxumStatusCode;
 #[cfg(feature = "ui")]
+use axum::http::Uri;
+#[cfg(feature = "ui")]
 use axum::http::header;
 #[cfg(feature = "ui")]
 use axum::response::IntoResponse;
 #[cfg(feature = "ui")]
-use axum::http::Uri;
-#[cfg(feature = "ui")]
 use mime_guess::from_path;
 #[cfg(feature = "ui")]
 use rust_embed::Embed;
@@ -28,7 +28,6 @@ use tower_http::cors::{Any, CorsLayer};
 use tower_http::trace::TraceLayer;
 use tracing_subscriber::{layer::SubscriberExt, util::SubscriberInitExt};
 
-
 #[cfg(feature = "ui")]
 #[derive(Embed)]
 #[folder = "../../target/site"]

integration/cli/build.rs

@@ -24,8 +24,7 @@ fn run_build() -> io::Result<()> {
     let out_dir = PathBuf::from(env::var("OUT_DIR").expect("OUT_DIR not set by Cargo"));
     let output_path = out_dir.join("client-cli");
 
-    let bun_tgt = BunTarget::from_cargo_env()
-        .map_err(|e| io::Error::other(e.to_string()))?;
+    let bun_tgt = BunTarget::from_cargo_env().map_err(|e| io::Error::other(e.to_string()))?;
 
     // Optional: warn if using a Bun target that’s marked unsupported in your chart
     if matches!(bun_tgt, BunTarget::WindowsArm64) {
@@ -54,9 +53,9 @@ fn run_build() -> io::Result<()> {
 
     if !install_status.success() {
         let code = install_status.code().unwrap_or(1);
-        return Err(io::Error::other(
-            format!("bun install failed with status {code}"),
-        ));
+        return Err(io::Error::other(format!(
+            "bun install failed with status {code}"
+        )));
     }
 
     let _target = env::var("TARGET").unwrap();

integration/cli/src/main.rs

@@ -25,7 +25,7 @@ fn main() -> io::Result<()> {
     // Run it
     let status = Command::new(&tmp).arg("--version").status()?;
     if !status.success() {
-        return Err(io::Error::new(io::ErrorKind::Other, "client-cli failed"));
+        return Err(io::Error::other("client-cli failed"));
     }
 
     Ok(())

integration/gemma-runner/src/gemma_api.rs

@@ -205,7 +205,7 @@ impl TextGeneration {
 
         let start_gen = std::time::Instant::now();
 
-        for (_generated_tokens, index) in (0..sample_len).enumerate() {
+        for index in 0..sample_len {
             let context_size = if index > 0 { 1 } else { tokens.len() };
             let start_pos = tokens.len().saturating_sub(context_size);
             let ctxt = &tokens[start_pos..];

integration/gemma-runner/src/main.rs

@@ -6,10 +6,8 @@ mod gemma_api;
 mod gemma_cli;
 
 use anyhow::Error;
-use clap::{Parser, ValueEnum};
 
 use crate::gemma_cli::run_cli;
-use std::io::Write;
 
 /// just a placeholder, not used for anything
 fn main() -> std::result::Result<(), Error> {

integration/helm-chart-tool/src/main.rs

@@ -28,7 +28,6 @@ struct KubeMetadata {
     port: u16,
 }
 
-
 #[derive(Debug, Clone)]
 struct ServiceInfo {
     name: String,

integration/llama-runner/src/main.rs

@@ -6,9 +6,6 @@ mod llama_api;
 mod llama_cli;
 
 use anyhow::Result;
-use clap::{Parser, ValueEnum};
-
-use std::io::Write;
 
 use crate::llama_cli::run_cli;
 

integration/utils/src/lib.rs

@@ -124,10 +124,7 @@ pub fn hub_load_safetensors(
     }
     let safetensors_files = safetensors_files
         .iter()
-        .map(|v| {
-            repo.get(v)
-                .map_err(|e| std::io::Error::other(e))
-        })
+        .map(|v| repo.get(v).map_err(std::io::Error::other))
         .collect::<Result<Vec<_>, std::io::Error>>()?;
     Ok(safetensors_files)
 }