const { OpenAI } = require('openai');
const { GoogleGenerativeAI } = require('@google/generative-ai');
require('dotenv').config({ path: require('path').resolve(__dirname, '../.env') });

const groq = new OpenAI({
    baseURL: "https://api.groq.com/openai/v1",
    apiKey: process.env.GROQ_API_KEY || "dummy",
});

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY || "dummy");

// Define advisors (Updated to user preference)
const MODELS = [
    'openai/gpt-oss-120b',
    'llama-3.3-70b-versatile',
    'qwen/qwen3-32b',
    'gemini-2.5-flash'
];

async function callGroq(model, systemPrompt, userMessage) {
    try {
        const response = await groq.chat.completions.create({
            model: model,
            messages: [
                { role: "system", content: systemPrompt },
                { role: "user", content: userMessage }
            ],
            temperature: 0.7,
            max_tokens: 250,
        });
        return response.choices[0].message.content;
    } catch (error) {
        console.error(`Error calling Groq for model ${model}:`, error.message);
        return `[Model ${model} offline or encountered an error. Error: ${error.message}]`;
    }
}

async function callGeminiDebater(modelString, systemPrompt, userMessage) {
    try {
        const model = genAI.getGenerativeModel({ model: modelString, generationConfig: { temperature: 0.7 }});
        const result = await model.generateContent(`${systemPrompt}\n\n${userMessage}`);
        return result.response.text();
    } catch (error) {
        console.error(`Error calling Gemini Debater ${modelString}:`, error.message);
        return `[Model ${modelString} offline or encountered an error. Error: ${error.message}]`;
    }
}

async function callArchitect(systemPrompt, transcript) {
    if (!process.env.GEMINI_API_KEY || process.env.GEMINI_API_KEY === "dummy") {
        return "[System Error: No valid Gemini API Key found in .env. Please add it to see the Architect's verdict.]";
    }
    try {
        const model = genAI.getGenerativeModel({ model: "gemma-4-31b-it", generationConfig: { temperature: 0.5 }});
        const result = await model.generateContent(`${systemPrompt}\n\nTranscript of Debate:\n${transcript}`);
        return result.response.text();
    } catch (error) {
        console.error("Error calling Architect:", error.message);
        return `[Architect encountered an error. Error: ${error.message}]`;
    }
}

module.exports = {
    MODELS,
    callGroq,
    callGeminiDebater,
    callArchitect
};