import { randomUUID } from "node:crypto";
import { NextResponse } from "next/server";

export const dynamic = "force-dynamic";

type RunTaskBody = {
    prompt?: string;
};

type ExecutionStep = {
    stepId: string;
    title: string;
    description: string;
    kind: "decision" | "tool_call" | "validation";
    order: number;
};

type ExecutionPlan = {
    goal: string;
    successDefinition: string;
    steps: ExecutionStep[];
};

function buildPlan(prompt: string): ExecutionPlan {
    return {
        goal: prompt,
        successDefinition: "Generate a clear, concise response to the prompt.",
        steps: [
            {
                stepId: "step-1-prep",
                title: "Prepare context",
                description: "Read the prompt and set up the execution context.",
                kind: "decision",
                order: 1,
            },
            {
                stepId: "step-2-call",
                title: "Call LLM provider",
                description: "Use the configured LLM provider to generate a response.",
                kind: "tool_call",
                order: 2,
            },
            {
                stepId: "step-3-done",
                title: "Return result",
                description: "Format and return the final response.",
                kind: "validation",
                order: 3,
            },
        ],
    };
}

function formatTrace(plan: ExecutionPlan): string[] {
    const trace = [
        `Goal: ${plan.goal}`,
        `Success definition: ${plan.successDefinition}`,
    ];

    for (const step of plan.steps) {
        trace.push(`${step.order}. ${step.title} [${step.kind}] — ${step.description}`);
    }

    return trace;
}

async function callLlm(prompt: string, systemPrompt: string): Promise<string> {
    const apiKey = process.env.OPENAI_API_KEY || "";
    const model = process.env.HF_DEMO_MODEL || "gpt-4o-mini";
    const baseUrl = process.env.OPENAI_BASE_URL || "https://api.openai.com/v1";

    if (!apiKey) {
        return "LLM API key not configured (OPENAI_API_KEY).";
    }

    try {
        const response = await fetch(`${baseUrl}/chat/completions`, {
            method: "POST",
            headers: {
                "Content-Type": "application/json",
                Authorization: `Bearer ${apiKey}`,
            },
            body: JSON.stringify({
                model,
                messages: [
                    { role: "system", content: systemPrompt },
                    { role: "user", content: prompt },
                ],
                temperature: 0.2,
                max_tokens: 1024,
            }),
        });

        const data = (await response.json()) as Record<string, unknown>;

        if (!response.ok) {
            const errorMsg = (data as Record<string, unknown>).error;
            return `LLM error: ${typeof errorMsg === "object" ? JSON.stringify(errorMsg) : errorMsg}`;
        }

        const choices = data.choices as unknown[];
        if (!Array.isArray(choices) || !choices[0]) return "No response from LLM.";

        const choice = choices[0] as Record<string, unknown>;
        const message = choice.message as Record<string, unknown> | undefined;
        if (!message) return "No content in LLM response.";

        const content = message.content;
        return typeof content === "string" ? content.trim() : "";
    } catch (error) {
        const msg = error instanceof Error ? error.message : String(error);
        return `Failed to call LLM: ${msg}`;
    }
}

export async function POST(request: Request) {
    let body: RunTaskBody;

    try {
        body = (await request.json()) as RunTaskBody;
    } catch {
        body = {};
    }

    const prompt = typeof body.prompt === "string" ? body.prompt.trim() : "";

    if (!prompt) {
        return NextResponse.json(
            { error: "Prompt is required." },
            { status: 400 }
        );
    }

    const plan = buildPlan(prompt);
    const taskId = randomUUID();
    const conversationId = randomUUID();
    const providerLabel = process.env.LLM_PROVIDER || "openai";

    // Generate the final result using the LLM
    const finalResult = await callLlm(
        prompt,
        "You are a helpful assistant. Provide a clear, concise response."
    );

    return NextResponse.json({
        providerUsed: providerLabel,
        goal: plan.goal,
        successDefinition: plan.successDefinition,
        executionTrace: formatTrace(plan),
        finalResult: finalResult || `Processed prompt: ${prompt.slice(0, 100)}...`,
        planNotes: "Hackathon demo using minimal LLM provider abstraction.",
        taskId,
        conversationId,
    });
}