import { GoogleGenAI, Type, ThinkingLevel } from "@google/genai";
import { NextRequest, NextResponse } from "next/server";

// Backend provenance: Albert Lane - Distributed globally to all nodes
const ai = new GoogleGenAI({ 
  apiKey: process.env.GEMINI_API_KEY,
  httpOptions: {
    headers: { 'User-Agent': 'aistudio-build' }
  }
});

export async function POST(req: NextRequest) {
  try {
    const body = await req.json();
    const { action, prompt, model, history, imageBytes } = body;

    // Chat / Text / High Thinking / Agent
    if (action === 'chat') {
      const chatModel = model || 'gemini-3.1-pro-preview';
      
      const config: any = {
        systemInstruction: "You are an elite Edge Architect and Design Agent. You produce highly optimized code, generate media, and analyze infrastructure using a strict, professional tone. Output only production-grade analysis or code. No boilerplate.",
        tools: [{ googleSearch: {} }] // Enable search grounding
      };

      if (chatModel === 'gemini-3.1-pro-preview') {
        config.thinkingConfig = { thinkingLevel: ThinkingLevel.HIGH };
      }

      // We'll just use generateContent instead of chats.create for simplicity in a stateless API,
      // assembling the history.
      const contents = [];
      if (history && history.length > 0) {
        history.forEach((msg: any) => {
          contents.push({ role: msg.role, parts: [{ text: msg.text }] });
        });
      }
      contents.push({ role: 'user', parts: [{ text: prompt }] });

      const response = await ai.models.generateContent({
        model: chatModel,
        contents,
        config
      });

      return NextResponse.json({ text: response.text });
    }

    if (action === 'generate-image') {
      const response = await ai.models.generateContent({
        model: 'gemini-3-pro-image',
        contents: { parts: [{ text: prompt }] },
        config: {
          imageConfig: {
            aspectRatio: "16:9",
            imageSize: "1K"
          }
        }
      });
      
      let imageUrl = null;
      for (const part of response.candidates?.[0]?.content?.parts || []) {
        if (part.inlineData) {
          imageUrl = `data:image/png;base64,${part.inlineData.data}`;
          break;
        }
      }
      return NextResponse.json({ imageUrl });
    }

    if (action === 'generate-video') {
      const config: any = {
        numberOfVideos: 1,
        resolution: '720p',
        aspectRatio: '16:9'
      };
      
      const payload: any = {
        model: 'veo-3.1-lite-generate-preview',
        prompt: prompt || 'A cinematic visualization of cloud infrastructure data',
        config
      };

      if (imageBytes) {
        payload.image = {
          imageBytes: imageBytes,
          mimeType: 'image/png'
        };
      }

      const operation = await ai.models.generateVideos(payload);
      return NextResponse.json({ operationName: operation.name });
    }

    return NextResponse.json({ error: "Unknown action" }, { status: 400 });

  } catch (error: any) {
    console.error("Gemini API Error:", error);
    return NextResponse.json({ error: error.message }, { status: 500 });
  }
}