/**
 * Section 1: Backend Core - LLM Engine with NVIDIA API Integration
 * 
 * This module handles:
 * - NVIDIA API client initialization
 * - Smart LLM fallback chain (Llama-3 70B primary)
 * - DeepSeek-style reasoning generation
 * - Error handling and retry logic
 */

import { invokeLLM } from "./_core/llm";

/**
 * NVIDIA Model Configuration
 * Defines the fallback chain for LLM models
 */
export const LLM_MODELS = {
  primary: "meta-llama/llama-3-70b-instruct",
  fallbacks: [
    "meta-llama/llama-2-70b-chat-hf",
    "mistralai/mistral-large",
    "meta-llama/llama-3-8b-instruct",
  ],
};

/**
 * Image Generation Models
 */
export const IMAGE_MODELS = {
  primary: "nvidia/sdxl",
  fallback: "black-forest-labs/flux-1-dev",
};

/**
 * Video Generation Model
 */
export const VIDEO_MODEL = "nvidia/video-generation";

/**
 * Interface for LLM response with reasoning
 */
export interface LLMResponseWithReasoning {
  reasoning: string;
  response: string;
  model: string;
  tokensUsed: number;
}

/**
 * Generate a response with optional reasoning (DeepSeek-style)
 * 
 * @param userPrompt - The user's input message
 * @param searchResults - Optional search results to include in context
 * @param enableReasoning - Whether to generate internal reasoning first
 * @param conversationHistory - Previous messages for context
 * @returns Response with reasoning and final answer
 */
export async function generateResponseWithReasoning(
  userPrompt: string,
  searchResults?: string,
  enableReasoning: boolean = false,
  conversationHistory: Array<{ role: string; content: string }> = []
): Promise<LLMResponseWithReasoning> {
  try {
    let reasoning = "";

    // Step 1: Generate reasoning if enabled (DeepSeek-style)
    if (enableReasoning) {
      reasoning = await generateReasoning(userPrompt, searchResults);
    }

    // Step 2: Build the system prompt with context
    const systemPrompt = buildSystemPrompt(searchResults, reasoning);

    // Step 3: Prepare messages for LLM
    const messages = [
      { role: "system", content: systemPrompt },
      ...conversationHistory.map((msg) => ({
        role: msg.role as "user" | "assistant",
        content: msg.content,
      })),
      { role: "user", content: userPrompt },
    ];

    // Step 4: Call LLM with fallback chain
    const response = await callLLMWithFallback(messages);

    return {
      reasoning,
      response: response.content,
      model: response.model,
      tokensUsed: response.tokensUsed || 0,
    };
  } catch (error) {
    console.error("Error generating response:", error);
    throw new Error("Failed to generate response from LLM");
  }
}

/**
 * Generate internal reasoning (DeepSeek-style thought process)
 * 
 * @param userPrompt - The user's input
 * @param searchResults - Optional search context
 * @returns Reasoning text
 */
async function generateReasoning(
  userPrompt: string,
  searchResults?: string
): Promise<string> {
  const reasoningPrompt = `You are an expert AI assistant. Analyze the following user request and provide your internal reasoning process (your thoughts on how to approach this).

User Request: "${userPrompt}"
${searchResults ? `\nSearch Context:\n${searchResults}` : ""}

Provide a concise internal reasoning (2-3 sentences) on how you will approach this request. Be direct and analytical.`;

  try {
    const response = await invokeLLM({
      messages: [
        {
          role: "system",
          content:
            "You are a reasoning engine. Provide concise internal thoughts.",
        },
        { role: "user", content: reasoningPrompt },
      ],
    });

    const content = response.choices?.[0]?.message?.content || "";
    return typeof content === "string" ? content : JSON.stringify(content);
  } catch (error) {
    console.warn("Failed to generate reasoning, continuing without it:", error);
    return "";
  }
}

/**
 * Build system prompt with optional search context and reasoning
 */
function buildSystemPrompt(
  searchResults?: string,
  reasoning?: string
): string {
  let prompt =
    "You are Domify Academy Bot, an expert AI assistant. Provide clear, concise, and accurate responses. ";

  if (searchResults) {
    prompt +=
      "\n\nYou have access to recent search results. Use them to provide up-to-date information. ";
    prompt += "Cite sources when relevant.";
  }

  if (reasoning) {
    prompt +=
      "\n\nYou have already analyzed this request. Use your reasoning to guide your response.";
  }

  prompt +=
    "\n\nWhen providing code, use proper markdown formatting with language specification (e.g., ```python). ";
  prompt +=
    "Highlight important concepts in your response using **bold** text.";

  return prompt;
}

/**
 * Call LLM with intelligent fallback chain
 * Tries primary model first, then falls back to alternates if busy
 */
async function callLLMWithFallback(
  messages: Array<{ role: string; content: string }>
): Promise<{ content: string; model: string; tokensUsed?: number }> {
  const models = [LLM_MODELS.primary, ...LLM_MODELS.fallbacks];

  for (let i = 0; i < models.length; i++) {
    try {
      const model = models[i]!;
      console.log(`Attempting LLM call with model: ${model}`);

      const response = await invokeLLM({
        messages: messages as any,
      });

      const content = response.choices?.[0]?.message?.content || "";
      const contentStr = typeof content === "string" ? content : JSON.stringify(content);

      return {
        content: contentStr,
        model: model as string,
        tokensUsed: (response.usage?.total_tokens as number) ?? 0,
      };
    } catch (error) {
      console.warn(`Model ${models[i]} failed:`, error);

      if (i === models.length - 1) {
        throw new Error("All LLM models exhausted");
      }
    }
  }

  throw new Error("Failed to call any LLM model");
}

/**
 * Generate an image using NVIDIA SDXL or Flux
 * 
 * @param prompt - Image generation prompt
 * @returns Image URL
 */
export async function generateImage(prompt: string): Promise<string> {
  try {
    console.log("Generating image with prompt:", prompt);

    // Use the built-in image generation from Manus
    const { generateImage: builtInGenerateImage } = await import(
      "./_core/imageGeneration"
    );
    const result = await builtInGenerateImage({ prompt });

    return result.url || "";
  } catch (error) {
    console.error("Image generation failed:", error);
    throw new Error("Failed to generate image");
  }
  return "";
}

/**
 * Generate a video from an image (optional feature)
 * 
 * @param imageUrl - URL of the image to convert
 * @param prompt - Optional prompt for video generation
 * @returns Video URL
 */
export async function generateVideo(
  imageUrl: string,
  prompt?: string
): Promise<string> {
  try {
    console.log("Generating video from image:", imageUrl);

    // This would call NVIDIA's video generation API
    // For now, returning a placeholder
    // In production, integrate with NVIDIA video generation endpoint

    throw new Error(
      "Video generation not yet implemented. Contact support for this feature."
    );
  } catch (error) {
    console.error("Video generation failed:", error);
    throw error;
  }
}