update(k8s): add storage and instance type

app/extras/pseudographic.py

@@ -87,19 +87,27 @@ def display_kubernetes_nodes():
     table.add_column("Node Name", style="white")
     table.add_column("CPU", justify="right", style="yellow")
     table.add_column("Memory (MiB)", justify="right", style="cyan")
-    table.add_column("Pods Allocatable", justify="right", style="green")
+    table.add_column("Storage", style="green")
+    table.add_column("Instance Type", style="blue")
+    table.add_column("Pods Allocatable", justify="right", style="magenta")
 
     nodes = v1.list_node()
     for node in nodes.items:
+        storage = node.metadata.annotations.get("node.kubernetes.io/storage", "N/A")
+        instance_type = node.metadata.labels.get("beta.kubernetes.io/instance-type", "N/A")
+
         table.add_row(
             node.metadata.name,
             node.status.capacity.get("cpu"),
             f"{round(convert_memory_to_mib(node.status.capacity.get('memory')), 2)}",
+            storage,
+            instance_type,
             node.status.allocatable.get("pods")
         )
 
     console.print(table)
 
+
 def display_namespace_usage():
     console = Console()
     config.load_incluster_config()
@@ -143,5 +151,3 @@ if __name__ == "__main__":
     display_virtual_machines()
     display_kubernetes_nodes()
     display_namespace_usage()
-    console.print("\n🚀 [bold cyan]Dashboard rendering complete![/bold cyan] 🚀")
-

app/routes/export.py

@@ -85,19 +85,14 @@ def fetch_k8s_data_with_usage():
 # Export endpoint
 @router.get("/export")
 def export_data(format: str = "yaml"):
-    """
-    API endpoint to export data in YAML or JSON format.
-    """
     data = {
         "metal_nodes": fetch_all("metal_nodes"),
         "virtual_machines": fetch_all("virtual_machines"),
         "kubernetes": fetch_k8s_data_with_usage(),
     }
 
-    logging.info(f"Exporting data: {data}")
-
     if format.lower() == "yaml":
         yaml_data = yaml.safe_dump(data, sort_keys=False)
         return Response(content=yaml_data, media_type="text/yaml")
-    
+
     return Response(content=json.dumps(data, indent=2), media_type="application/json")

app/routes/k8s.py

@@ -9,12 +9,20 @@ def fetch_k8s_data_with_usage():
     metrics_client = client.CustomObjectsApi()
 
     # Fetch nodes
-    nodes = [{
-        "node_name": node.metadata.name,
-        "cpu": node.status.capacity.get("cpu"),
-        "memory": round(convert_memory_to_mib(node.status.capacity.get("memory")), 2),  # Convert to MiB
-        "pods_allocatable": node.status.allocatable.get("pods"),
-    } for node in v1.list_node().items]
+    nodes = []
+    for node in v1.list_node().items:
+        # Extract storage and instance type from labels or annotations
+        storage = node.metadata.annotations.get("node.kubernetes.io/storage", "N/A")
+        instance_type = node.metadata.labels.get("beta.kubernetes.io/instance-type", "N/A")
+
+        nodes.append({
+            "node_name": node.metadata.name,
+            "cpu": node.status.capacity.get("cpu"),
+            "memory": round(convert_memory_to_mib(node.status.capacity.get("memory")), 2),  # Convert to MiB
+            "storage": storage,
+            "instance_type": instance_type,
+            "pods_allocatable": node.status.allocatable.get("pods"),
+        })
 
     # Fetch namespaces
     namespaces = [ns.metadata.name for ns in v1.list_namespace().items]
@@ -50,24 +58,15 @@ def fetch_k8s_data_with_usage():
 
 
 def convert_cpu_to_cores(cpu):
-    """
-    Convert CPU usage to cores for human-readable format.
-    Handles units: n (nano), u (micro), m (milli), or none (cores).
-    Returns float values for cores, rounded appropriately.
-    """
-    if "n" in cpu:  # Nanocores to cores
+    if "n" in cpu:
         return round(int(cpu.replace("n", "")) / 1e9, 4)
-    elif "u" in cpu:  # Microcores to cores
+    elif "u" in cpu:
         return round(int(cpu.replace("u", "")) / 1e6, 4)
-    elif "m" in cpu:  # Millicores to cores
+    elif "m" in cpu:
         return round(int(cpu.replace("m", "")) / 1000, 4)
-    return float(cpu)  # Already in cores
+    return float(cpu)
 
 def convert_memory_to_mib(memory):
-    """
-    Convert memory to MiB (mebibytes).
-    Handles units: Ki (kibibytes), Mi (mebibytes), Gi (gibibytes).
-    """
     if "Ki" in memory:
         return int(memory.replace("Ki", "")) / 1024
     elif "Mi" in memory:
@@ -78,8 +77,4 @@ def convert_memory_to_mib(memory):
 
 @router.get("/k8s/data")
 def get_k8s_data():
-    """
-    API endpoint to fetch Kubernetes data including nodes, namespaces,
-    and namespace resource usage.
-    """
     return fetch_k8s_data_with_usage()

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}