import { Observable } from "rxjs";

/**
 * Sends a request to the inference server to generate a response based on the recent messages.
 * @param recentMessages - An array of recent messages to use as context for the inference.
 * @param engine - The engine settings to use for the inference.
 * @param model - The model to use for the inference.
 * @returns An Observable that emits the generated response as a string.
 */
export function requestInference(
  recentMessages: any[],
  engine: EngineSettings,
  model: OpenAIModel,
  controller?: AbortController
): Observable<string> {
  return new Observable((subscriber) => {
    let model_id: string = model.id;
    if (engine.full_url.includes(OPENAI_DOMAIN)) {
      model_id = engine.full_url.split("/")[5];
    }
    const requestBody = JSON.stringify({
      messages: recentMessages,
      stream: true,
      model: model_id,
      ...model.parameters,
    });
    fetch(`${engine.full_url}`, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Accept: model.parameters.stream
          ? "text/event-stream"
          : "application/json",
        "Access-Control-Allow-Origin": "*",
        Authorization: `Bearer ${engine.api_key}`,
        "api-key": `${engine.api_key}`,
      },
      body: requestBody,
      signal: controller?.signal,
    })
      .then(async (response) => {
        if (!response.ok) {
          subscriber.next(
            (await response.json()).error?.message ?? "Error occured"
          );
          subscriber.complete();
          return;
        }
        if (model.parameters.stream === false) {
          const data = await response.json();
          subscriber.next(data.choices[0]?.message?.content ?? "");
        } else {
          const stream = response.body;
          const decoder = new TextDecoder("utf-8");
          const reader = stream?.getReader();
          let content = "";

          while (true && reader) {
            const { done, value } = await reader.read();
            if (done) {
              break;
            }
            const text = decoder.decode(value);
            const lines = text.trim().split("\n");
            for (const line of lines) {
              if (line.startsWith("data: ") && !line.includes("data: [DONE]")) {
                const data = JSON.parse(line.replace("data: ", ""));
                content += data.choices[0]?.delta?.content ?? "";
                if (content.startsWith("assistant: ")) {
                  content = content.replace("assistant: ", "");
                }
                subscriber.next(content);
              }
            }
          }
        }
        subscriber.complete();
      })
      .catch((err) => subscriber.error(err));
  });
}