from fastapi import APIRouter, HTTPException
from kubernetes import client, config
from typing import List, Dict
import requests
import os

router = APIRouter()

def fetch_ai_summary(cluster_name: str, nodes: List[Dict], api_url: str, auth_token: str) -> str:
    """
    Sends node data to an external AI API endpoint and retrieves a summary response.
    """
    payload = {
        "data": {
            "cluster_name": cluster_name,
            "nodes": nodes
        }
    }

    headers = {
        "Authorization": f"Bearer {auth_token}",
        "Content-Type": "application/json"
    }

    try:
        response = requests.post(api_url, json=payload, headers=headers)
        if response.status_code == 200:
            result = response.json()
            if result.get("success"):
                return result.get("result", "No result provided by the API.")
            else:
                return "API responded with success=false."
        else:
            return f"API request failed with status code {response.status_code}: {response.text}"
    except requests.RequestException as e:
        return f"An error occurred while contacting the API: {str(e)}"

@router.get("/think/k8s")
def think_k8s():
    """
    Fetch Kubernetes data, send it to the AI API, and return the AI-generated summary.
    """
    config.load_incluster_config()
    v1 = client.CoreV1Api()

    cluster_name = "k8s-cluster"

    # Format node data
    nodes = []
    for node in v1.list_node().items:
        # Fetch ephemeral-storage and instance type
        ephemeral_storage = node.status.capacity.get("ephemeral-storage", "0")
        instance_type = node.metadata.labels.get("beta.kubernetes.io/instance-type", "N/A")

        nodes.append({
            "name": node.metadata.name,
            "cpu": f"{node.status.capacity.get('cpu')} cores",
            "memory": f"{round(int(node.status.capacity.get('memory').replace('Ki', '')) / 1024 / 1024, 2)} GB",
            "storage": f"{round(int(ephemeral_storage.replace('Ki', '')) / (1024 ** 2), 2)} GB",
            "type": instance_type,
            "namespaces": [ns.metadata.name for ns in v1.list_namespace().items()]
        })

    # Fetch token from environment
    api_url = os.getenv("AI_API_URL")
    auth_token = os.getenv("AI_API_TOKEN")

    if not auth_token:
        raise HTTPException(status_code=500, detail="AI API token is not set. Please set the AI_API_TOKEN environment variable.")

    # Call AI API
    summary = fetch_ai_summary(cluster_name, nodes, api_url, auth_token)

    return {"summary": summary}