import path from "path";
import { mkdir, writeFile } from "fs/promises";
import { fileURLToPath } from "url";
import { callOllama } from "./ollamaClient.js";
import { compressPng } from "./compressPng.js";

const __dirname = path.dirname(fileURLToPath(import.meta.url));
const COMFYUI_URL = process.env.COMFYUI_URL || "http://localhost:8188";

// Drawing style prefix
const STYLE_PREFIX = `a high-contrast, black and white pen and ink drawing, hand-drawn sketch aesthetic, very low detail, extremely minimal, visible loose linework, expressive simple hatching for shadows, quick conceptual sketch, subtle color accent`;

// 1. Generate engineered visual prompt
async function generateVisualPrompt(flavor) {
  const rawPrompt = await callOllama(
    `You are a prompt engineer specializing in visual prompts for AI image generation (e.g., Stable Diffusion, ComfyUI). Given a piece of fantasy flavor text, your job is to extract and translate the visual elements into a highly descriptive image prompt.

Your output should be structured like a list of visual tags, not a story or paragraph. Focus on describing the environment, mood, architecture, lighting, materials, and color. Avoid abstract, emotional, and visual language. Be literal, specific, and visual only.

Rules:
- Do NOT repeat phrases or wording from the input.
- Only include things that could be seen in a single, still image.
- Use visual keywords, rich adjectives, and clear scene descriptors.
- Keep the prompt concise, 40-80 words.
- Maintain simplicity in descriptions. The image must be a minimal, hand drawn sketch aesthetic with low detail.
- Avoid characters or creatures unless clearly described.
- Avoid referencing rendering style, color technique, camera effects, or drawing medium — focus only on the visual content of the scene.
- Do NOT include phrases like “an image of” or “a scene showing”.

Input:
${flavor}

Output:`,
    "gemma3n:e4b", 3, "Generate Visual Prompt"
  );

  return `${STYLE_PREFIX}, ${rawPrompt.trim().replace(/\n/g, " ")}`;
}

// 2. Save image buffer
async function saveImage(buffer, filename) {
  const filepath = path.join(__dirname, filename);
  await mkdir(__dirname, { recursive: true });
  await writeFile(filepath, buffer);
  console.log(`✅ Saved image: ${filepath}`);
  return filepath;
}

// 3. Build workflow payload
function buildComfyWorkflow(promptText, negativeText = "") {
  return {
    "3": {
      "inputs": {
        "seed": Math.floor(Math.random() * 100000),
        "steps": 4,
        "cfg": 1,
        "sampler_name": "euler",
        "scheduler": "simple",
        "denoise": 1,
        "model": ["4", 0],
        "positive": ["6", 0],
        "negative": ["7", 0],
        "latent_image": ["5", 0]
      },
      "class_type": "KSampler"
    },
    "4": {
      "inputs": {
        "unet_name": "flux1-schnell-fp8.safetensors",
        "weight_dtype": "fp8_e4m3fn"
      },
      "class_type": "UNETLoader"
    },
    "5": {
      "inputs": {
        "width": 640,
        "height": 448,
        "batch_size": 1
      },
      "class_type": "EmptyLatentImage"
    },
    "6": {
      "inputs": {
        "text": promptText,
        "clip": ["10", 0]
      },
      "class_type": "CLIPTextEncode"
    },
    "7": {
      "inputs": {
        "text": negativeText,
        "clip": ["10", 0]
      },
      "class_type": "CLIPTextEncode"
    },
    "10": {
      "inputs": {
        "clip_name1": "clip_l.safetensors",
        "clip_name2": "t5xxl_fp8_e4m3fn.safetensors",
        "type": "flux"
      },
      "class_type": "DualCLIPLoader"
    },
    "11": {
      "inputs": {
        "vae_name": "ae.safetensors"
      },
      "class_type": "VAELoader"
    },
    "8": {
      "inputs": {
        "samples": ["3", 0],
        "vae": ["11", 0]
      },
      "class_type": "VAEDecode"
    },
    "9": {
      "inputs": {
        "filename_prefix": "ComfyUI_Flux",
        "images": ["8", 0]
      },
      "class_type": "SaveImage"
    }
  };
}


// 4a. Wait for ComfyUI to finish image generation
async function waitForImage(promptId, timeout = 900000) {
  const start = Date.now();

  while (Date.now() - start < timeout) {
    const res = await fetch(`${COMFYUI_URL}/history`);
    const data = await res.json();
    const historyEntry = data[promptId];

    if (historyEntry?.outputs) {
      const images = Object.values(historyEntry.outputs).flatMap(o => o.images || []);
      if (images.length > 0) return images.map(i => i.filename);
    }

    await new Promise(resolve => setTimeout(resolve, 1000));
  }

  throw new Error("Timed out waiting for ComfyUI image result.");
}

// 4b. Download image from ComfyUI server
async function downloadImage(filename, localFilename) {
  const url = `${COMFYUI_URL}/view?filename=${filename}`;
  const res = await fetch(url);

  if (!res.ok) throw new Error(`Failed to fetch image: ${res.statusText}`);
  const buffer = Buffer.from(await res.arrayBuffer());

  return await saveImage(buffer, localFilename);
}

// 4c. Submit prompt and handle full image pipeline
async function generateImageViaComfyUI(prompt, filename) {
  const workflow = buildComfyWorkflow(prompt, "photorealism, hyper-realistic, high detail, 3D render, CGI, ray tracing, glossy, polished, smooth shading, realistic lighting, digital painting, anime, cartoon, pixelated, noisy, cluttered, blurry, sharp focus, symmetrical, perfect perspective, detailed textures, high-resolution textures, high contrast lighting, lens flare, bokeh, camera artifacts, text, logo, signature, watermark, overexposed, underexposed, glowing edges");

  try {
    console.log("Submitting prompt to ComfyUI...");
    const res = await fetch(`${COMFYUI_URL}/prompt`, {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({ prompt: workflow })
    });

    if (!res.ok) {
      throw new Error(`ComfyUI error: ${res.statusText}`);
    }

    const { prompt_id } = await res.json();

    console.log("Waiting for image result...");
    const filenames = await waitForImage(prompt_id);
    if (filenames.length === 0) throw new Error("No image generated");

    const comfyFilename = filenames[0];

    console.log("Downloading image...");
    const filepath = await downloadImage(comfyFilename, filename);

    console.log("Compressing PNG...");
    await compressPng(filepath);
    return filepath;

  } catch (err) {
    console.error("Error generating image:", err.message);
    return null;
  }
}

// 5. Main export
export async function generateDungeonImages({ flavor }) {
  console.log("Generating dungeon image...");

  const finalPrompt = await generateVisualPrompt(flavor);
  console.log("Engineered visual prompt:\n", finalPrompt);

  const filename = `dungeon.png`;
  const filepath = await generateImageViaComfyUI(finalPrompt, filename);

  if (!filepath) {
    throw new Error("Failed to generate dungeon image.");
  }

  return filepath;
}