import { createXai } from "@apicity/xai";

const xai = createXai({ apiKey: process.env.XAI_API_KEY! });

import { readFile } from "node:fs/promises";
import { createXai } from "@apicity/xai";

const xai = createXai({ apiKey: process.env.XAI_API_KEY! });

// 1. Load the image and inline it as a data URL. xAI also accepts
//    https:// URLs, but inlining keeps the call self-contained and
//    works against private hosts.
const image = await readFile("./portrait.jpg");
const base64 = image.toString("base64");

// 2. The system prompt enumerates the legal vocabulary for `shot` and
//    constrains `pose` to body geometry only. Combined with
//    `text.format.type: "json_object"` this gives Grok no room to drift
//    off-schema — temperature 0 keeps the result reproducible.
const SYSTEM_PROMPT = [
  "You are an expert image-to-prompt analyst.",
  "Return only a JSON object with keys prompt, shot, and pose.",
  "prompt: a single-paragraph reproduction-ready image prompt, 1900 characters or fewer, with no line breaks.",
  'shot: exactly "<size>, <angle>" where size is one of extreme close-up, close-up, medium close-up, medium shot, medium long shot, long shot, or extreme long shot, and angle is one of eye-level, low-angle, high-angle, overhead, or dutch.',
  "pose: only body geometry for human figures, with no clothing, hair, background, or lighting details.",
].join(" ");

// 3. Multimodal Responses request: system turn + a user turn whose
//    content is an array of `input_image` + `input_text` parts.
const result = await xai.post.v1.responses({
  model: "grok-4",
  input: [
    { role: "system", content: SYSTEM_PROMPT },
    {
      role: "user",
      content: [
        {
          type: "input_image",
          image_url: `data:image/jpeg;base64,${base64}`,
          detail: "high",
        },
        {
          type: "input_text",
          text: 'Analyze this image and produce a reproduction-ready JSON description with keys "prompt", "shot", and "pose".',
        },
      ],
    },
  ],
  text: { format: { type: "json_object" } },
  store: false,
  temperature: 0,
  max_output_tokens: 300,
});

// 4. The Responses API wraps output in a typed item array. Find the
//    assistant message, then the first `output_text` part inside it.
//    Discriminated unions narrow `item.type === "message"` so
//    `item.content` is statically typed.
const message = result.output.find((item) => item.type === "message");
const outputText =
  message?.type === "message"
    ? message.content.find((part) => part.type === "output_text")?.text
    : undefined;

if (!outputText) throw new Error("Grok did not return output_text");

const analysis = JSON.parse(outputText) as {
  prompt: string;
  shot: string;
  pose: string;
};

console.log(analysis.shot);
// → "medium close-up, eye-level"

console.log(analysis.pose);
// → "upright torso facing forward, head straight and centered, shoulders squared, arms relaxed downward (implied)"

// 5. Reasoning-token accounting. Grok-4 spent 623 of its 728 output
//    tokens reasoning before emitting the 105-token JSON answer —
//    surfaced in `usage.output_tokens_details.reasoning_tokens`.
console.log(result.usage);
// → {
//     input_tokens: 2684,
//     input_tokens_details: { cached_tokens: 679 },
//     output_tokens: 728,
//     output_tokens_details: { reasoning_tokens: 623 },
//     total_tokens: 3412,
//   }

import { createXai, withRetry } from "@apicity/xai";

const xai = createXai({ apiKey: process.env.XAI_API_KEY! });
const models = withRetry(xai.get.v1.models, { retries: 3 });

import {
  createXai,
  withRateLimit,
  withRetry,
  createRateLimiter,
  XAI_RATE_LIMITS,
} from "@apicity/xai";

const xai = createXai({ apiKey: process.env.XAI_API_KEY! });

// Use built-in tier presets (free, tier1, tier2, tier3, tier4)
const limiter = createRateLimiter(XAI_RATE_LIMITS.tier1);
// => { rpm: 60, concurrent: 10 }

const chat = withRateLimit(xai.post.v1.chat.completions, limiter);

const limiter = createRateLimiter({ rpm: 30, concurrent: 5 });
const chat = withRateLimit(xai.post.v1.chat.completions, limiter);

const limiter = createRateLimiter(XAI_RATE_LIMITS.tier2);

const chat = withRateLimit(xai.post.v1.chat.completions, limiter);
const responses = withRateLimit(xai.post.v1.responses, limiter);
const images = withRateLimit(xai.post.v1.images.generations, limiter);

const limiter = createRateLimiter(XAI_RATE_LIMITS.tier1);

const chat = withRetry(
  withRateLimit(xai.post.v1.chat.completions, limiter),
  { retries: 2 }
);

const limiter = createRateLimiter(XAI_RATE_LIMITS.tier1);
const chat = withRateLimit(xai.post.v1.chat.completions, limiter);

const results = await Promise.all(
  prompts.map((p) =>
    chat({
      model: "grok-3",
      messages: [{ role: "user", content: p }],
    })
  )
);