You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: serve.rs
    path of file: ./repos/modelfox/crates/cli
    
    the code of the file until where you have to start completion: //! This module runs an HTTP server for making predictions with a modelfox model.
//!
//! Start the server with a `.modelfox` file:
//! ```not-rust
//! $ modelfox serve --model heart_disease.modelfox
//! ```
//!
//! Make a request:
//! ```not-rust
//! $ curl -X POST http://localhost:8080/predict -H 'Content-Type: application/json' -d '{ "inputs": [{"age": 63.0,"gender": "male","chest_pain": "typical angina","resting_blood_pressure": 145.0,"cholesterol": 233.0,"fasting_blood_sugar_greater_than_120": "true","resting_ecg_result": "probable or definite left ventricular hyper
    
	async fn test_predict_default_options() {
		let payload = json!({ "inputs": [{
			"age": 63.0,
			"gender": "male",
			"chest_pain": "typical angina",
			"resting_blood_pressure": 145.0,
			"cholesterol": 233.0,
			"fasting_blood_sugar_greater_than_120": "true",
			"resting_ecg_result": "probable or definite left ventricular hypertrophy",
			"exercise_max_heart_rate": 150.0,
			"exercise_induced_angina": "no",
			"exercise_st_depression": 2.3,
			"exercise_st_slope": "downsloping",
			"fluoroscopy_vessels_colored": "0",
			"thallium_stress_test": "fixed defect"
		}]});

		let mut request = hyper::Request::builder()
			.method(http::Method::POST)
			.uri("/predict")
			.header(http::header::CONTENT_TYPE, "application/json")
			.body(hyper::Body::from(payload.to_string()))
			.unwrap();

		let context = Arc::new(test_model());
		request.extensions_mut().insert(Arc::clone(&context));
		let response = handle(request).await;

		assert_eq!(response.status(), http::status::StatusCode::OK);

		let body = hyper::body::to_bytes(response.into_body()).await.unwrap();
		let body: Value = serde_json::from_slice(&body).unwrap();
		insta::assert_json_snapshot!(body, @r###"
  [
    {
      "class_name": "Positive",
      "feature_contributions": null,
      "probability": 0.56037986,
      "type": "binary_classification"
    }
  ]
  "###);
	}