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use hyper::Client as HyperClient;
use hyper::client::HttpConnector;
use hyper_openssl::HttpsConnector;
use crate::endpoints::{Response, ResponseError};
use crate::endpoints::request::Endpoint;
pub(crate) type HttpsHyperClient = HyperClient<HttpsConnector<HttpConnector>>;
#[derive(Debug)]
pub struct Client {
pub(crate) api_key: String,
pub(crate) https: HttpsHyperClient,
}
impl Client {
/// Returns a new response from the OpenAI API.
///
/// # Arguments
///
/// * `engine_id` - The engine id to use. Due to few endpoints this can be optional.
/// * `model` - The model to use. Each Model in the endpoints module is a corresponding model.
///
/// # Example
///
/// ```
/// use std::borrow::Cow;
/// use openai_rs::client::Client;
/// use openai_rs::endpoints::edits::Edit;
/// use openai_rs::endpoints::{Response, ResponseError};
/// use openai_rs::openai;
///
/// // Create the Client with your API key.
/// let client: Client = openai::new("api_key");
///
/// // Create the Edit struct with the input and instruction.
/// let edit = Edit {
/// input: Cow::Borrowed("What day of the wek is it?"),
/// instruction: Cow::Borrowed("Fix the spelling mistakes"),
/// ..Default::default()
/// };
///
/// // Send the request to the OpenAI API.
/// let response: Result<Response, ResponseError> = client.create(
/// Some("text-davinci-edit-001"), &edit
/// ).await;
/// ```
pub async fn create<T>(
&self,
engine_id: Option<&str>,
model: &T
) -> Result<Response, ResponseError>
where T: Endpoint {
match self.https.request(model.request(&*self.api_key, engine_id)).await {
Ok(response) => {
if response.status().is_success() {
let body = hyper::body::to_bytes(response.into_body()).await?;
let deserialized = serde_json::from_slice(&body)
.map_err(ResponseError::from)?;
trace!("Requesting: {:#?}", deserialized);
Ok(deserialized)
} else {
Err(ResponseError::ErrorCode(response.status()))
}
},
Err(error) => Err(error.into())
}
}
}