import { createLLM, checkModelAvailability, isModelLoaded } from './llm';
import { zodResponseFormat } from 'openai/helpers/zod';
import { z } from 'zod';
import { RECIPE_DETECTION_PROMPT, RECIPE_EXTRACTION_PROMPT } from './prompts/recipe-extraction';
import { logError } from './utils/logger';
import type { ProgressCallback } from './extraction';

const RecipeSchema = z.object({
	name: z.string(),
	servings: z.number().nullable(),
	description: z.string().nullable(),
	ingredients: z
		.array(
			z.object({
				item: z.string(),
				amount: z.string(),
				unit: z.string()
			})
		)
		.nullable(),
	steps: z.array(z.string()).nullable(),
	image: z.string().nullable().optional()
});

export type Recipe = z.infer<typeof RecipeSchema>;

/**
 * Detect if the text contains a recipe using binary classification
 * @param text - The text to analyze
 * @returns True if a recipe is detected, false otherwise
 */
export async function detectRecipe(text: string): Promise<boolean> {
	try {
		const { client, model } = createLLM();

		console.log('[LLM] Starting recipe detection...');
		console.log('[LLM] Model:', model);
		console.log('[LLM] Text length:', text.length);

		const detectionResponse = await client.chat.completions.create({
			model,
			messages: [
				{
					role: 'system',
					content: RECIPE_DETECTION_PROMPT
				},
				{
					role: 'user',
					content: `Does this text contain a recipe?\n\n${text}`
				}
			],
			// 1024 gives thinking models room to reason before answering
			max_tokens: 1024,
			temperature: 0
		});

		const msg = detectionResponse.choices[0].message;
		// Some local models (e.g. Gemma thinking variants) return the answer in
		// reasoning_content instead of content when max_tokens is tight.
		// eslint-disable-next-line @typescript-eslint/no-explicit-any
		const reasoning: string = (msg as any).reasoning_content ?? '';
		const detectionResult = (msg.content ?? reasoning).toLowerCase();
		console.log('[LLM] Detection response:', detectionResult);

		return detectionResult.includes('yes');
	} catch (e) {
		logError('[LLM] Recipe detection error', e);

		// Check if this is a model-related error
		const errorMessage = (e as Error).message || '';
		const isModelError =
			errorMessage.includes('400') &&
			(errorMessage.toLowerCase().includes('model') || errorMessage.toLowerCase().includes('load'));

		if (isModelError) {
			const { model } = createLLM();
			const modelCheck = await checkModelAvailability(model);
			if (!modelCheck.available) {
				throw new Error(modelCheck.message || `Model "${model}" is not available`);
			}
		}

		throw new Error(`Failed to detect recipe: ${(e as Error).message}`);
	}
}

/**
 * Extract recipe data from text using LLM structured output
 * @param text - The text containing the recipe
 * @returns Parsed recipe object
 */
export async function parseRecipe(text: string): Promise<Recipe> {
	try {
		const { client, model } = createLLM();

		console.log('[LLM] Starting recipe parsing...');
		console.log('[LLM] Model:', model);

		const completion = await client.beta.chat.completions.parse({
			model,
			messages: [
				{
					role: 'system',
					content: RECIPE_EXTRACTION_PROMPT
				},
				{
					role: 'user',
					content: `Extract the recipe from this text:\n\n${text}`
				}
			],
			response_format: zodResponseFormat(RecipeSchema, 'recipe'),
			temperature: 0.3
		});

		const recipe = completion.choices[0].message.parsed;
		console.log('[LLM] Parse response:', recipe?.name);

		if (!recipe || !recipe.name) {
			throw new Error('Failed to extract recipe - missing name');
		}

		return recipe;
	} catch (e) {
		logError('[LLM] Recipe parsing error', e);

		// Check if this is a model-related error
		const errorMessage = (e as Error).message || '';
		const isModelError =
			errorMessage.includes('400') &&
			(errorMessage.toLowerCase().includes('model') || errorMessage.toLowerCase().includes('load'));

		if (isModelError) {
			const { model } = createLLM();
			const modelCheck = await checkModelAvailability(model);
			if (!modelCheck.available) {
				throw new Error(modelCheck.message || `Model "${model}" is not available`);
			}
		}

		// If structured output fails, try standard completion
		if (
			(e as any).message?.includes('response_format') ||
			(e as any).message?.includes('structured output')
		) {
			console.warn('[LLM] Falling back to standard completion');
			return await parseRecipeWithStandardCompletion(text);
		}

		throw new Error(`Failed to parse recipe: ${(e as Error).message}`);
	}
}

/**
 * Complete workflow: detect recipe and parse if found.
 *
 * Emits a `model_loading` progress event (if a callback is supplied) when the
 * configured llama-swap model is not yet warm — the first request after idle
 * blocks for several seconds while llama-swap loads the model into VRAM.
 *
 * @param text - The text to analyze
 * @param progressCallback - Optional callback for surfacing cold-load state
 * @returns Parsed recipe object if detected, null otherwise
 */
export async function extractRecipe(
	text: string,
	progressCallback?: ProgressCallback
): Promise<Recipe | null> {
	if (progressCallback) {
		const { model } = createLLM();
		const warm = await isModelLoaded(model);
		if (!warm) {
			progressCallback({
				type: 'model_loading',
				message: `Inference server cold — loading ${model} into VRAM (5–30s)...`,
				data: { model },
				timestamp: new Date().toISOString()
			});
		}
	}

	const isRecipe = await detectRecipe(text);

	if (!isRecipe) {
		return null;
	}

	return parseRecipe(text);
}

/**
 * Fallback parser using standard completion (no structured output)
 * Used when the model doesn't support beta.chat.completions.parse()
 */
async function parseRecipeWithStandardCompletion(text: string): Promise<Recipe> {
	const { client, model } = createLLM();

	console.log('[LLM] Using standard completion fallback');

	const completion = await client.chat.completions.create({
		model,
		messages: [
			{
				role: 'system',
				content: `You are a recipe extractor. Return ONLY valid JSON matching this schema:
{
  "name": "recipe name in Italian",
  "servings": number or null,
  "description": "description in Italian or null",
  "ingredients": [{"item": "ingredient name", "amount": "quantity", "unit": "SI unit"}],
  "steps": ["First step", "Second step", ...]
}

Convert all measurements to SI units (g, mL, °C).
Translate everything to Italian.
Extract ONLY what's in the text.`
			},
			{
				role: 'user',
				content: `Extract the recipe from this text:\n\n${text}`
			}
		],
		max_tokens: 2000,
		temperature: 0.3
	});

	const jsonResponse = completion.choices[0].message.content;
	if (!jsonResponse) {
		throw new Error('Empty response from LLM');
	}

	console.log('[LLM] Standard completion raw response:', jsonResponse.substring(0, 200));

	// Parse and validate JSON (remove code fences if present)
	const cleanedJson = jsonResponse.replace(/```json\n?|```\n?/g, '').trim();
	const parsedData = JSON.parse(cleanedJson);
	const recipe = RecipeSchema.parse(parsedData);

	console.log('[LLM] Standard completion parsed recipe:', recipe.name);

	return recipe;
}