#!/usr/bin/env bun
/**
 * persona-based-adwriter
 * Generates persona-specific ad copy with channel adaptations using OpenAI.
 */

import { parseArgs } from "util";
import { existsSync, mkdirSync, appendFileSync } from "fs";
import { join, dirname } from "path";

type OutputFormat = "markdown" | "json";

interface SkillOptions {
  brief: string;
  personas: string[];
  channels: string[];
  offers?: string[];
  tone?: string;
  format: OutputFormat;
  model: string;
  output?: string;
}

interface OpenAIChatResponse {
  choices?: Array<{
    message?: {
      content?: string | null;
    };
  }>;
  error?: {
    message?: string;
  };
}

const SKILL_SLUG = "persona-based-adwriter";

function ensureDir(path: string) {
  if (!existsSync(path)) {
    mkdirSync(path, { recursive: true });
  }
}

function getPaths() {
  const sessionStamp = new Date().toISOString().replace(/[:.]/g, "_").replace(/-/g, "_");
  const exportsRoot = process.env.SKILLS_EXPORTS_DIR || join(process.cwd(), ".skills", "exports");
  const logsRoot = process.env.SKILLS_LOGS_DIR || join(process.cwd(), ".skills", "logs");

  const skillExportsDir = join(exportsRoot, SKILL_SLUG);
  const skillLogsDir = join(logsRoot, SKILL_SLUG);

  ensureDir(skillExportsDir);
  ensureDir(skillLogsDir);

  return {
    sessionStamp,
    skillExportsDir,
    skillLogsDir,
  };
}

function createLogger(logDir: string, sessionStamp: string) {
  const logFile = join(logDir, `log_${sessionStamp}.txt`);

  function write(level: "info" | "success" | "error", message: string) {
    const timestamp = new Date().toISOString();
    const entry = `[${timestamp}] [${level.toUpperCase()}] ${message}\n`;
    appendFileSync(logFile, entry);
    const prefix = level === "success" ? "✅" : level === "error" ? "❌" : "ℹ️";
    console.log(`${prefix} ${message}`);
  }

  return {
    info: (message: string) => write("info", message),
    success: (message: string) => write("success", message),
    error: (message: string) => write("error", message),
    logFile,
  };
}

function slugify(value: string): string {
  return value
    .toLowerCase()
    .replace(/[^a-z0-9]+/g, "-")
    .replace(/^-+|-+$/g, "")
    .slice(0, 40) || "persona-ads";
}

function parseOptions(): SkillOptions {
  if (Bun.argv.includes("--help") || Bun.argv.includes("-h")) {
    console.log(`persona-based-adwriter

Usage:
  skills run persona-based-adwriter -- <brief> [options]

Options:
  --brief <text>        Product or offer brief
  --personas <list>     Semicolon-separated target personas
  --channels <list>     Comma-separated channels
  --offers <list>       Comma-separated offers
  --tone <tone>         Copy tone
  --format <format>     markdown or json
  --model <model>       OpenAI model
  --output <path>       Output file path`);
    process.exit(0);
  }

  const { values, positionals } = parseArgs({
    args: Bun.argv.slice(2),
    options: {
      brief: { type: "string" },
      personas: { type: "string" },
      channels: { type: "string" },
      offers: { type: "string" },
      tone: { type: "string" },
      format: { type: "string", default: "markdown" },
      model: { type: "string", default: "gpt-4o-mini" },
      output: { type: "string" },
      help: { type: "boolean", short: "h" },
    },
    allowPositionals: true,
  });

  const brief = values.brief || positionals.join(" ").trim();
  if (!brief) {
    throw new Error("Provide a product/offer brief via positional text or --brief.");
  }

  const personas = values.personas
    ? values.personas.split(";").map(p => p.trim()).filter(Boolean)
    : ["Primary Persona"];
  const channels = values.channels
    ? values.channels.split(",").map(c => c.trim()).filter(Boolean)
    : ["LinkedIn", "Facebook"];
  const offers = values.offers
    ? values.offers.split(",").map(o => o.trim()).filter(Boolean)
    : undefined;

  const format: OutputFormat =
    values.format === "json" ? "json" : values.format === "markdown" ? "markdown" : "markdown";

  return {
    brief,
    personas,
    channels,
    offers,
    tone: values.tone,
    format,
    model: values.model,
    output: values.output,
  };
}

function buildPrompt(options: SkillOptions) {
  const system = `You are a performance marketing strategist and copy chief.
Create persona-based ad copy for:
- Product/Offer: ${options.brief}
- Personas: ${options.personas.join(" | ")}
- Channels: ${options.channels.join(", ")}
- Offers: ${options.offers?.join(", ") || "core CTA"}
- Tone: ${options.tone || "confident, solution-oriented"}

For each persona include:
- Persona snapshot (pain points, motivators, objections).
- Message pillars mapped to each channel.
- For each channel: 3 ad variants with headline, primary copy, CTA, creative direction notes (visual guidance).
- Social proof/proof points to highlight.
- Compliance/sensitivity considerations.
- Metrics to monitor (CTR, CPL, conversion).

Also provide:
- Cross-persona testing ideas (what to experiment with).
- Personalization tokens/dynamic fields suggestions.`;

  const instructions =
    options.format === "json"
      ? "Respond in JSON with keys: personas, testing, personalization. Personas array entries should include name, snapshot, channels (array with name, variants[{headline, copy, cta, creative}]), proof_points, compliance, metrics."
      : "Respond in polished Markdown. Start with an executive summary blockquote, include sections per persona with tables of channel variants, bullet lists for proof/compliance/metrics, and finish with testing and personalization recommendations.";

  const userPayload = {
    brief: options.brief,
    personas: options.personas,
    channels: options.channels,
    offers: options.offers,
    tone: options.tone,
    format: options.format,
  };

  const user = `${instructions}\n\n${JSON.stringify(userPayload, null, 2)}`;

  return { system, user };
}

async function callOpenAI(options: SkillOptions, system: string, user: string): Promise<string> {
  const apiKey = process.env.OPENAI_API_KEY;

  if (!apiKey) {
    throw new Error("OPENAI_API_KEY environment variable is required.");
  }

  const body = {
    model: options.model,
    messages: [
      { role: "system", content: system },
      { role: "user", content: user },
    ],
    temperature: 0.62,
    max_tokens: options.format === "json" ? 2400 : 2100,
  };

  const response = await fetch("https://api.openai.com/v1/chat/completions", {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${apiKey}`,
    },
    body: JSON.stringify(body),
  });

  const data: OpenAIChatResponse = await response.json();

  if (!response.ok) {
    throw new Error(data.error?.message || `OpenAI API error (${response.status})`);
  }

  const content = data.choices?.[0]?.message?.content;

  if (!content) {
    throw new Error("OpenAI response did not include content.");
  }

  return content.trim();
}

async function writeExport(path: string, content: string) {
  ensureDir(dirname(path));
  await Bun.write(path, content);
}

async function run() {
  const options = parseOptions();
  const { sessionStamp, skillExportsDir, skillLogsDir } = getPaths();
  const logger = createLogger(skillLogsDir, sessionStamp);

  try {
    logger.info("Generating persona-based ad copy.");
    logger.info(`Format: ${options.format.toUpperCase()}, Model: ${options.model}`);

    const { system, user } = buildPrompt(options);
    const content = await callOpenAI(options, system, user);

    const slugBase = options.personas[0] || options.brief.split(/\s+/).slice(0, 4).join("-");
    const personaSlug = slugify(slugBase);
    const extension = options.format === "json" ? "json" : "md";
    const defaultPath = join(skillExportsDir, `persona-ads-${personaSlug}-${sessionStamp}.${extension}`);
    const targetPath = options.output ? options.output : defaultPath;

    let finalContent = content;

    if (options.format === "json") {
      try {
        finalContent = JSON.stringify(JSON.parse(content), null, 2);
      } catch {
        logger.error("Model response was not valid JSON. Wrapping raw response.");
        finalContent = JSON.stringify({ raw: content }, null, 2);
      }
    }

    await writeExport(targetPath, finalContent);

    logger.success("Persona-based ad package generated successfully.");
    console.log("\n=== Persona Ad Preview ===\n");
    console.log(finalContent.slice(0, 1500));
    if (finalContent.length > 1500) {
      console.log("\n… (truncated)");
    }
    console.log(`\nExport saved to: ${targetPath}`);
    console.log(`Logs written to: ${skillLogsDir}`);
  } catch (error) {
    const message = error instanceof Error ? error.message : String(error);
    logger.error(message);
    process.exit(1);
  }
}

run();