update(k8s): add storage and instance type

app/routes/think.py

@@ -0,0 +1,71 @@
+import os
+from fastapi import APIRouter, HTTPException
+from kubernetes import client, config
+from typing import List, Dict
+import requests
+
+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:
+        node_data = {
+            "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": "N/A",
+            "type": "N/A",
+            "namespaces": [ns.metadata.name for ns in v1.list_namespace().items()]
+        }
+        nodes.append(node_data)
+
+    # 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}