export interface Message {
  role: 'system' | 'user' | 'assistant';
  content: string;
  image?: string;
}

export interface Chat {
  id: string;
  title: string;
  messages: Message[];
  createdAt: number;
  updatedAt: number;
  modelName: string;
}

export interface ModelConfig {
  name: string;
  path: string;
  supportsImages: boolean;
}

export interface GenerationParams {
  temperature: number;
  maxTokens: number;
  topP: number;
  topK: number;
}

/**
 * Available models for local inference
 * 
 * ⚠️ IMPORTANT: Your Turkish models are PEFT/LoRA adapters, not full models!
 * They currently fallback to BASE MODELS without Turkish fine-tuning.
 * 
 * To use your Turkish models:
 * 1. Merge LoRA adapters with base model (Python)
 * 2. Export merged model to ONNX format
 * 3. Upload to HuggingFace or host locally
 * 4. Update model paths here
 * 
 * See PEFT_TO_ONNX_GUIDE.md for complete instructions.
 * 
 * Current behavior:
 * - Chan-Y/TurkishReasoner-* → Uses base Gemma/Qwen/Llama (NO Turkish training)
 * - Base models will respond but WITHOUT your Turkish fine-tuning
 * - Merge & export ONNX to get Turkish responses
 */
export const MODELS: ModelConfig[] = [
  {
    name: "Gemma 3 1B Turkish Reasoning",
    path: "Chan-Y/TurkishReasoner-Gemma3-1B",
    supportsImages: false
  },
  {
    name: "Gemma 3 12B Turkish (Supports Images)",
    path: "Chan-Y/TurkishReasoner-Gemma3-12B",
    supportsImages: true
  },
  {
    name: "Qwen 2.5 3B Turkish Reasoning",
    path: "Chan-Y/TurkishReasoner-Qwen2.5-3B",
    supportsImages: false
  },
  {
    name: "Llama 3.1 8B Turkish Reasoning",
    path: "Chan-Y/TurkishReasoner-Llama3.1-8B",
    supportsImages: false
  }
];

export const DEFAULT_SYSTEM_PROMPT = `Sen kullanıcıların isteklerine Türkçe cevap veren bir asistansın ve sana bir problem verildi.
Problem hakkında düşün ve çalışmanı göster.
Çalışmanı <start_working_out> ve <end_working_out> arasına yerleştir.
Sonra, çözümünü <SOLUTION> ve </SOLUTION> arasına yerleştir.
Lütfen SADECE Türkçe kullan.`;