/**
 * Wavult AI Model Router
 * ─────────────────────────────────────────────────────────────────────
 * Prioritet:
 *   1. Qwen3 via AWS Bedrock (selfhostad, eu-north-1) — PRIMÄR
 *   2. Gemini 2.5 Pro via Google API — BACKUP / MULTIMODAL
 *   3. Claude Sonnet via Anthropic — LEGACY FALLBACK (fasas ut)
 *
 * Val per uppgift:
 *   AUDIT      → Qwen3 235B (djup analys, hög precision)
 *   OPERATOR   → Qwen3 32B (snabb, generell)
 *   CODE       → Qwen3 Coder 480B (världsklass på kod)
 *   GEMINI     → Gemini 2.5 Pro (explicit val eller multimodal)
 *   CLAUDE     → Claude Sonnet (legacy fallback)
 */

import { BedrockRuntimeClient, InvokeModelCommand, InvokeModelWithResponseStreamCommand } from '@aws-sdk/client-bedrock-runtime';
// ── GECL Universal Enforcement (G-002 2026-06-05) ───────────────────────
let _geclMW = null;
async function _getGeclMW() {
  if (_geclMW) return _geclMW;
  try { _geclMW = await import('./core/gecl-middleware.mjs'); } catch { _geclMW = {}; }
  return _geclMW;
}
async function _wrapLLM(fn, model, messages) {
  const mw = await _getGeclMW();
  if (!mw?.wrapLLM) return fn();
  try { return await mw.wrapLLM(fn, { model: model || 'unknown', messages: messages || [] }); }
  catch(e) { if (e?.code === 'ENFORCEMENT_DENY') throw e; return fn(); }
}


const bedrock = new BedrockRuntimeClient({ region: process.env.AWS_REGION || 'eu-north-1' });

// ── Modell-IDs ────────────────────────────────────────────────────────────
export const MODELS = {
  QWEN_235B:    'qwen.qwen3-235b-a22b-2507-v1:0',
  QWEN_32B:     'qwen.qwen3-32b-v1:0',
  QWEN_CODER:   'qwen.qwen3-coder-480b-a35b-v1:0',
  QWEN_CODER_S: 'qwen.qwen3-coder-30b-a3b-v1:0',
  
// ── Ollama — local inference (qwen2.5:7b, CPU) ─────────────────────────────
export async function ollamaChat(model = 'qwen2.5:7b', system, messages, maxTokens = 512) {
  const msgs = [...(system ? [{ role: 'system', content: system }] : []), ...messages];
  const r = await fetch('http://localhost:11434/api/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ model, messages: msgs, stream: false, options: { num_predict: maxTokens } }),
    signal: AbortSignal.timeout(120000)
  });
  const d = await r.json();
  if (!r.ok) throw new Error(`ollama ${r.status}: ${d.error || ''}`);
  return { text: d.message?.content || '', model };
}

// ── Aletheia v6 — ESLM (Evidence Small Language Model) ──────────────────
  // Importerad via Bedrock custom model import 2026-06-07
  // Red-team: 100% (Opus 4.8 domare), 0 kritiska, gate PASS
  ALETHEIA_V6:  'arn:aws:bedrock:us-east-1:155407238699:imported-model/29nyfw6n1prl',
};

// ── Välj modell baserat på läge och kontext ──────────────────────────────
export function selectModel(mode, message = '') {
  const isCode = /```|function|class |def |import |SELECT|CREATE TABLE/i.test(message);
  const isComplex = message.length > 500 || /analysera|granska|bedöm|jämför|rapport/i.test(message);

  if (mode === 'AUDIT') return MODELS.QWEN_235B;
  if (isCode) return MODELS.QWEN_CODER;  // 30B snabbare för vanlig kod
  if (isComplex) return MODELS.QWEN_32B;
  return MODELS.QWEN_32B;  // default
}

// ── COMPLIANCE-AWARE MODEL SELECTION (sedan 2026-04-30) ─────────────────────
// Väljer modell men validerar mot tenant compliance-mode innan den returneras.
// Throws vid violation. Använd från endpoints där compliance är kritiskt.
export async function selectModelCompliant(complianceMode, mode, message = '', context = {}) {
  const { enforceModel, checkModel } = await import('./compliance/engine.mjs');
  let candidate = selectModel(mode, message);
  const familyName = mapModelIdToFamily(candidate);

  try {
    enforceModel(complianceMode, familyName, { mode, source: 'model-router', ...context });
    return candidate;
  } catch (e) {
    const allowed = await getCompliantFallback(complianceMode, mode);
    if (allowed) {
      console.warn(JSON.stringify({
        ts: new Date().toISOString(), level: 'warn', component: 'model-router',
        msg: 'compliance_fallback', from_model: candidate, to_model: allowed,
        compliance_mode: complianceMode, reason: e.message,
      }));
      return allowed;
    }
    throw e;
  }
}

function mapModelIdToFamily(modelId) {
  const id = String(modelId).toLowerCase();
  if (id.includes('qwen3-235b')) return 'qwen3-235b';
  if (id.includes('qwen3-coder-480b')) return 'qwen3-coder-480b';
  if (id.includes('qwen3-coder')) return 'qwen3-coder';
  if (id.includes('qwen3-32b')) return 'qwen3-32b';
  if (id.includes('qwen')) return 'qwen3';
  if (id.includes('claude-opus')) return 'claude-opus';
  if (id.includes('claude-sonnet')) return 'claude-sonnet';
  if (id.includes('claude')) return 'claude';
  if (id.includes('gpt-4')) return 'gpt-4';
  if (id.includes('gpt')) return 'gpt';
  if (id.includes('gemini')) return 'gemini';
  if (id.includes('deepseek')) return 'deepseek';
  if (id.includes('mistral')) return 'mistral';
  if (id.includes('llama')) return 'llama';
  return id;
}

async function getCompliantFallback(complianceMode, taskMode) {
  const fallbackChain = {
    'commercial-eu': [MODELS.QWEN_32B, 'claude-sonnet', 'gpt-4', 'mistral-large-2'],
    'sovereign-cn': [MODELS.QWEN_32B, MODELS.QWEN_235B, 'deepseek-r2', 'mistral-large-2'],
    'federal-us':   ['claude-opus-4-7', 'claude-sonnet', 'gpt-4', 'mistral-large-2', 'llama-3.3-70b'],
    'state-us':     ['claude-opus-4-7', 'claude-sonnet', 'gpt-4', 'mistral-large-2', 'llama-3.3-70b'],
    'defense-us':   ['claude-gov', 'gpt-gov', 'llama-3.3-70b'],
  };
  const candidates = fallbackChain[complianceMode] || [];
  const { checkModel } = await import('./compliance/engine.mjs');
  for (const c of candidates) {
    const r = checkModel(complianceMode, mapModelIdToFamily(c));
    if (r.allowed) return c;
  }
  return null;
}

// ── Qwen streaming via Bedrock ────────────────────────────────────────────
export async function qwenStream(model, systemPrompt, messages, maxTokens, onChunk) {
  const body = JSON.stringify({
    messages: [
      ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []),
      ...messages.map(m => ({ role: m.role, content: typeof m.content === 'string' ? m.content : extractText(m.content) }))
    ],
    max_tokens: maxTokens || 4096,
    temperature: 0.7,
    stream: true
  });

  const cmd = new InvokeModelWithResponseStreamCommand({
    modelId: model,
    body: Buffer.from(body),
    contentType: 'application/json',
    accept: 'application/json'
  });

  const response = await bedrock.send(cmd);
  let fullText = '';

  for await (const event of response.body) {
    if (event.chunk?.bytes) {
      try {
        const chunk = JSON.parse(Buffer.from(event.chunk.bytes).toString());
        // OpenAI-kompatibelt format (Bedrock Qwen returnerar detta)
        const delta = chunk?.choices?.[0]?.delta?.content || '';
        if (delta) {
          fullText += delta;
          onChunk(delta);
        }
      } catch {}
    }
  }
  return fullText;
}

// ── Qwen icke-streaming (för tool-use loops) ─────────────────────────────
export async function qwenCreate(model, systemPrompt, messages, maxTokens) { return _wrapLLM(async () => {
  const body = JSON.stringify({
    messages: [
      ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []),
      ...messages.map(m => ({ role: m.role, content: typeof m.content === 'string' ? m.content : extractText(m.content) }))
    ],
    max_tokens: maxTokens || 4096,
    temperature: 0.7
  });

  const cmd = new InvokeModelCommand({
    modelId: model,
    body: Buffer.from(body),
    contentType: 'application/json',
    accept: 'application/json'
  });

  const response = await bedrock.send(cmd);
  const result = JSON.parse(Buffer.from(response.body).toString());
  const text = result?.choices?.[0]?.message?.content || '';
  return text;
  }, model, messages); }  // gecl wrap end

// ── Gemini via Google API ─────────────────────────────────────────────────
export async function geminiCreate(systemPrompt, messages, maxTokens) {
  const GEMINI_KEY = process.env.GEMINI_API_KEY;
  if (!GEMINI_KEY) throw new Error('GEMINI_API_KEY saknas');

  const contents = messages.map(m => ({
    role: m.role === 'assistant' ? 'model' : 'user',
    parts: [{ text: typeof m.content === 'string' ? m.content : extractText(m.content) }]
  }));

  const body = {
    systemInstruction: systemPrompt ? { parts: [{ text: systemPrompt }] } : undefined,
    contents,
    generationConfig: { maxOutputTokens: maxTokens || 4096, temperature: 0.7 }
  };

  const resp = await fetch(
    `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro-preview-05-06:generateContent?key=${GEMINI_KEY}`,
    { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(body) }
  );
  const data = await resp.json();
  return data?.candidates?.[0]?.content?.parts?.[0]?.text || '';
}

// ── Hjälpfunktion: extrahera text från Anthropic content-blocks ──────────
function extractText(content) {
  if (!content) return '';
  if (typeof content === 'string') return content;
  if (Array.isArray(content)) {
    return content
      .filter(b => b.type === 'text' || b.type === 'tool_result')
      .map(b => b.text || (Array.isArray(b.content) ? b.content.map(c => c.text || '').join(' ') : '') )
      .join('\n');
  }
  return String(content);
}

export default { MODELS, selectModel, qwenStream, qwenCreate, geminiCreate };

// ── Gemini Vision — bildanalys ────────────────────────────────────────────────
export async function geminiVision(prompt, base64, mimeType = 'image/png', systemInstruction = '') {
  const GEMINI_KEY = process.env.GEMINI_API_KEY || '';
  if (!GEMINI_KEY) throw new Error('GEMINI_API_KEY saknas');
  
  const body = {
    contents: [{
      role: 'user',
      parts: [
        { text: prompt },
        { inline_data: { mime_type: mimeType, data: base64 } }
      ]
    }],
    ...(systemInstruction ? { system_instruction: { parts: [{ text: systemInstruction }] } } : {}),
    generationConfig: { maxOutputTokens: 4096, temperature: 0.7 }
  };

  const r = await fetch(
    `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro-preview-05-06:generateContent?key=${GEMINI_KEY}`,
    { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(body) }
  );
  const d = await r.json();
  if (!r.ok) throw new Error(d.error?.message || 'Gemini Vision fel');
  return d.candidates?.[0]?.content?.parts?.[0]?.text || '';
}

// ── Gemini Design (alias för Vision med design-prompt) ────────────────────────
export async function geminiDesign(prompt, base64, mimeType = 'image/png') {
  return geminiVision(prompt, base64, mimeType, 'Du är Wavults design-expert. Analysera och ge konkreta förbättringsförslag med kod.');
}

// ── Imagen (stub) ─────────────────────────────────────────────────────────────
export async function imagenGenerate(prompt) {
  throw new Error('Imagen ej aktiverad i denna miljö');
}

// ── Perplexity — realtidsfakta ────────────────────────────────────────────────
export async function perplexitySearch(query, systemPrompt = '') {
  const KEY = process.env.PERPLEXITY_API_KEY || '';
  if (!KEY) throw new Error('PERPLEXITY_API_KEY saknas');
  
  const body = {
    model: 'sonar-pro',
    messages: [
      ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []),
      { role: 'user', content: query }
    ],
    max_tokens: 2048,
    return_citations: true,
    return_related_questions: false,
    search_recency_filter: 'week'
  };

  const r = await fetch('https://api.perplexity.ai/chat/completions', {
    method: 'POST',
    headers: { 'Authorization': `Bearer ${KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(body)
  });
  const d = await r.json();
  if (!r.ok) throw new Error(d.error?.message || 'Perplexity-fel');
  
  const text = d.choices?.[0]?.message?.content || '';
  const citations = d.citations || [];
  return { text, citations };
}

// ── AWS Nova Pro — långt kontextfönster (300k tokens) ─────────────────────────
export async function novaPro(systemPrompt, messages, maxTokens = 4096) {
  const body = JSON.stringify({
    messages: [
      ...(systemPrompt ? [{ role: 'user', content: [{ text: `[SYSTEM]\n${systemPrompt}` }] }] : []),
      ...messages.map(m => ({ role: m.role === 'assistant' ? 'assistant' : 'user', content: [{ text: typeof m.content === 'string' ? m.content : extractText(m.content) }] }))
    ],
    inferenceConfig: { max_new_tokens: maxTokens, temperature: 0.7 }
  });

  const cmd = new InvokeModelCommand({
    modelId: 'amazon.nova-pro-v1:0',
    body: Buffer.from(body),
    contentType: 'application/json',
    accept: 'application/json'
  });

  const response = await bedrock.send(cmd);
  const result = JSON.parse(Buffer.from(response.body).toString());
  return result.output?.message?.content?.[0]?.text || '';
}

// ── DeepSeek-R2 via Bedrock (reasoning) ──────────────────────────────────────
export async function deepseekReason(systemPrompt, messages, maxTokens = 4096) {
  // DeepSeek-R1 är tillgänglig via Bedrock
  const body = JSON.stringify({
    messages: [
      ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []),
      ...messages.map(m => ({ role: m.role, content: typeof m.content === 'string' ? m.content : extractText(m.content) }))
    ],
    max_tokens: maxTokens,
    temperature: 0.6
  });

  const cmd = new InvokeModelCommand({
    modelId: 'deepseek.r1-v1:0',
    body: Buffer.from(body),
    contentType: 'application/json',
    accept: 'application/json'
  });

  try {
    const response = await bedrock.send(cmd);
    const result = JSON.parse(Buffer.from(response.body).toString());
    return result.choices?.[0]?.message?.content || result.output || '';
  } catch(e) {
    // Fallback till Qwen om DeepSeek ej tillgänglig i region
    return qwenCreate(MODELS.QWEN_235B, systemPrompt, messages, maxTokens);
  }
}


export async function claudeChat(model, system, messages, options = {}) {
  const { maxTokens = 4096, enableCaching = true, enableThinking = true, thinkingBudget = 8000 } = options;
  const Anthropic = (await import('@anthropic-ai/sdk')).default;
  const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
  let systemParam = system;
  if (enableCaching && system && system.length > 1024) {
    systemParam = [{ type: 'text', text: system, cache_control: { type: 'ephemeral' } }];
  }
  const params = { model, max_tokens: maxTokens, system: systemParam, messages };
  if (enableThinking && (model.includes('opus-4-5') || model.includes('sonnet-4-5'))) {
    params.thinking = { type: 'enabled', budget_tokens: thinkingBudget };
  }
  const resp = await client.messages.create(params);
  return { text: resp.content.find(c => c.type === 'text')?.text || '', usage: resp.usage, model: resp.model };
}

export async function gptChat(model, system, messages) {
  const r = await fetch('https://api.openai.com/v1/chat/completions', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${process.env.OPENAI_API_KEY}` },
    body: JSON.stringify({ model: model||'gpt-4o', max_tokens: 1024, messages: [{ role:'system', content: system }, ...messages] }),
    signal: AbortSignal.timeout(30000)
  });
  const d = await r.json();
  return { text: d.choices?.[0]?.message?.content || '', model };
}

export async function groqChat(model, system, messages) {
  const r = await fetch('https://api.groq.com/openai/v1/chat/completions', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${process.env.GROQ_API_KEY}` },
    body: JSON.stringify({ model: model||'llama-3.3-70b-versatile', max_tokens: 1024, messages: [{ role:'system', content: system }, ...messages] }),
    signal: AbortSignal.timeout(15000)
  });
  const d = await r.json();
  return { text: d.choices?.[0]?.message?.content || '', model };
}



// ── Ollama — local inference (qwen2.5:7b, CPU) ─────────────────────────────
export async function ollamaChat(model = 'qwen2.5:7b', system, messages, maxTokens = 512) {
  const msgs = [...(system ? [{ role: 'system', content: system }] : []), ...messages];
  const r = await fetch('http://localhost:11434/api/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ model, messages: msgs, stream: false, options: { num_predict: maxTokens } }),
    signal: AbortSignal.timeout(120000)
  });
  const d = await r.json();
  if (!r.ok) throw new Error(`ollama ${r.status}: ${d.error || ''}`);
  return { text: d.message?.content || '', model };
}

// ── Aletheia v6 — ESLM via Bedrock custom model (2026-06-07) ─────────────
// Importerad i us-east-1 — kräver egen Bedrock-klient för rätt region.
const _aletheiaClient = new BedrockRuntimeClient({ region: 'us-east-1' });
export async function aletheiaCreate(systemPrompt, messages, maxTokens = 1024) {
  return _wrapLLM(async () => {
    const body = JSON.stringify({
      messages: [
        ...(systemPrompt ? [{ role: 'system', content: systemPrompt }] : []),
        ...messages.map(m => ({ role: m.role, content: typeof m.content === 'string' ? m.content : extractText(m.content) }))
      ],
      max_tokens: maxTokens,
      temperature: 0.1
    });
    const cmd = new InvokeModelCommand({
      modelId: MODELS.ALETHEIA_V6,
      body: Buffer.from(body),
      contentType: 'application/json',
      accept: 'application/json'
    });
    const response = await _aletheiaClient.send(cmd);
    const result = JSON.parse(Buffer.from(response.body).toString());
    const text = result?.choices?.[0]?.message?.content || '';
    return { text, model: 'aletheia-v6' };
  }, 'aletheia-v6', messages);
}

export async function intelligenceRouter(task, message, system, messages = []) {
  const opts = { enableCaching: true, enableThinking: true };
  const handlers = {
    'fast': () => groqChat('llama-3.3-70b-versatile', system, messages),
    'reasoning': () => claudeChat('claude-opus-4-5', system, messages, { ...opts, thinkingBudget: 16000 }),
    'search': () => perplexitySearch(message),
    'code': () => qwenCreate(MODELS.QWEN_CODER, system, messages, 4096),
    'financial': () => claudeChat('claude-sonnet-4-5', system, messages, { ...opts, thinkingBudget: 12000 }),
    'creative': () => gptChat('gpt-4o', system, messages),
    'analysis': () => claudeChat('claude-opus-4-5', system, messages, { ...opts, thinkingBudget: 16000 }),
    // Hermes-slot: OpenAI OSS 120B via Groq — agentic/skill/self-improvement tasks
    'hermes': () => groqChat('openai/gpt-oss-120b', system, messages),
    'agent': () => groqChat('openai/gpt-oss-120b', system, messages),
    'skill': () => groqChat('openai/gpt-oss-120b', system, messages),
    'self-improve': () => groqChat('openai/gpt-oss-120b', system, messages),
    // Aletheia v6 — ESLM för mailtriage/klassning (Bedrock custom, 2026-06-07)
    // 100% red-team pass (Opus 4.8 domare). Körs ~gratis vs Sonnet.
    'triage': () => aletheiaCreate(system, messages, 1024),
    'classify': () => aletheiaCreate(system, messages, 512),
    'default': () => claudeChat('claude-sonnet-4-5', system, messages, opts),
  };
  try {
    let result = await (handlers[task] || handlers['default'])();
    if (typeof result === 'string') result = { text: result };
    return { ...result, source: task };
  } catch(e) {
    return { text: 'Tillfälligt fel: ' + e.message, error: true };
  }
}


// -- routeQuery: Smart model router (2026-05-20) --------------------------
// Selects provider/model based on query characteristics.
export function routeQuery(question, options = {}) {
  const q = question.toLowerCase();

  // Short/simple query -> local Ollama (gratis, CPU)
  if (question.length < 120 && !options.forceExternal) {
    return { model: 'qwen2.5:7b', provider: 'ollama', reason: 'short_query_local' };
  }

  // Juridik/compliance -> DeepSeek
  if (/juridik|lag|gdpr|compliance|avtal|kontrakt/i.test(q)) {
    return { model: 'deepseek-chat', provider: 'deepseek', reason: 'legal_domain' };
  }

  // Kod/teknik -> Groq
  if (/kod|programmering|typescript|python|javascript|sql|docker/i.test(q)) {
    return { model: 'llama-3.3-70b-versatile', provider: 'groq', reason: 'technical_domain' };
  }

  // Default -> Groq
  return { model: 'llama-3.3-70b-versatile', provider: 'groq', reason: 'default' };
}