import { z } from "zod";

const SYSTEM_PROMPT = `You are a film research assistant for an international audience.
You MUST respond in English with valid JSON only — no markdown fences, no commentary.
Use a clear, neutral tone. If uncertain about a scene or timestamp, state uncertainty and set confidence to "low".
Do not invent scenes, quotes, or timestamps. Base answers on the movie context provided.`;

export async function generateStructuredResponse<T extends z.ZodType>(
  schema: T,
  userPrompt: string,
): Promise<z.infer<T>> {
  const apiKey = process.env.LLM_API_KEY;
  if (!apiKey) {
    throw new Error("LLM_API_KEY is not set");
  }

  const baseUrl = process.env.LLM_BASE_URL ?? "https://api.openai.com/v1";
  const model = process.env.LLM_MODEL ?? "gpt-4o-mini";

  const headers: Record<string, string> = {
    Authorization: `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  };

  if (process.env.LLM_HTTP_REFERER) {
    headers["HTTP-Referer"] = process.env.LLM_HTTP_REFERER;
  }

  if (process.env.LLM_APP_TITLE) {
    headers["X-Title"] = process.env.LLM_APP_TITLE;
  }

  const response = await fetch(`${baseUrl}/chat/completions`, {
    method: "POST",
    headers,
    body: JSON.stringify({
      model,
      temperature: 0.2,
      response_format: { type: "json_object" },
      messages: [
        { role: "system", content: SYSTEM_PROMPT },
        { role: "user", content: userPrompt },
      ],
    }),
  });

  if (!response.ok) {
    const errorText = await response.text();
    throw new Error(`LLM request failed: ${response.status} ${errorText}`);
  }

  const data = (await response.json()) as {
    choices: Array<{ message: { content: string } }>;
  };

  const content = data.choices[0]?.message?.content;
  if (!content) {
    throw new Error("LLM returned empty content");
  }

  let parsed: unknown;
  try {
    parsed = JSON.parse(content);
  } catch {
    throw new Error("LLM returned invalid JSON");
  }

  return schema.parse(parsed);
}