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Five tabs:
Live Ops β trigger replays, watch agent timeline, see live Grafana iframe
Incidents β browse past incident records + postmortems
Bench β comparison table (baseline vs grpo_v3)
Replays β one-click historical incident buttons
About β architecture + judging evidence
"""
import asyncio
import json
import os
import subprocess
from pathlib import Path
import gradio as gr
import requests
# ββ Config βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Keep runtime URLs environment-driven to avoid stale hardcoded infra endpoints.
GRAFANA_URL = os.getenv("GRAFANA_URL", "")
JAEGER_URL = os.getenv("JAEGER_URL", "")
ARGOCD_URL = os.getenv("ARGOCD_URL", "")
BOUTIQUE_URL = os.getenv("BOUTIQUE_URL", "")
COORDINATOR_URL = os.getenv("COORDINATOR_URL", "http://localhost:9099")
ROLE_ICONS = {"triage": "π΄", "diagnosis": "π", "remediation": "π§", "comms": "π£"}
PHASE_ICONS = {"tool_call": "β", "tool_result": "β", "conclusion": "β
", "thinking": "π"}
TRAJECTORIES_DIR = Path("data/trajectories")
RESULTS_DIR = Path("bench/results")
POSTMORTEM_DIR = Path("docs/postmortems")
CHAOS_DIR = Path("bench/chaos_manifests")
KUBECTL = os.getenv("KUBECTL_PATH",
"C:/Users/NSEIT/AppData/Local/Google/Cloud SDK/google-cloud-sdk/bin/kubectl.exe")
NAMED_REPLAYS = {
"Cloudflare 2019 β Regex CPU Storm": "named_replays/hist-cloudflare-2019",
"AWS S3 2017 β Accidental Scale-to-0": "named_replays/hist-aws-s3-2017",
"GitHub 2018 β DB Failover Loop": "named_replays/hist-github-2018",
"Datadog 2023 β DNS Failure Cascade": "named_replays/hist-datadog-2023",
"Discord 2022 β Cache Thundering Herd":"named_replays/hist-discord-2022",
"Fastly 2021 β Config Bug (VCL)": "named_replays/hist-fastly-2021",
"Facebook BGP 2021 β Route Withdraw": "named_replays/hist-facebook-bgp-2021",
"Slack 2022 β HTTP/2 Misconfig": "named_replays/hist-slack-2022",
"Azure DNS 2019 β Stale DNS": "named_replays/hist-azure-dns-2019",
"Knight Capital 2012 β Bad Deploy": "named_replays/hist-knight-capital-2012",
}
SINGLE_FAULT = {
"sf-001: cartservice pod-kill": "single_fault/sf-001",
"sf-002: paymentservice CPU hog": "single_fault/sf-002",
"sf-003: checkoutservice OOM": "single_fault/sf-003",
"sf-004: frontend 50% packet loss": "single_fault/sf-004",
"sf-005: Redis β cartservice partition": "single_fault/sf-005",
"sf-006: DNS failure on auth path": "single_fault/sf-006",
"sf-007: emailservice disk fill": "single_fault/sf-007",
"sf-008: paymentservice clock skew":"single_fault/sf-008",
}
# ββ Helpers βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def _kubectl(*args) -> str:
env = os.environ.copy()
env["USE_GKE_GCLOUD_AUTH_PLUGIN"] = "True"
r = subprocess.run([KUBECTL] + list(args), capture_output=True, text=True, env=env, timeout=15)
return r.stdout + (("\n[stderr] " + r.stderr) if r.returncode != 0 else "")
def _apply_chaos(scenario_path: str) -> str:
manifest = CHAOS_DIR / f"{scenario_path}.yaml"
if not manifest.exists():
return f"β Manifest not found: {manifest}"
env = os.environ.copy()
env["USE_GKE_GCLOUD_AUTH_PLUGIN"] = "True"
r = subprocess.run([KUBECTL, "apply", "-f", str(manifest)], capture_output=True, text=True, env=env)
return r.stdout if r.returncode == 0 else f"β {r.stderr}"
def _reset_chaos() -> str:
env = os.environ.copy()
env["USE_GKE_GCLOUD_AUTH_PLUGIN"] = "True"
r = subprocess.run(
[KUBECTL, "delete", "podchaos,networkchaos,stresschaos,dnschaos,iochaos,timechaos",
"--all", "-A", "--ignore-not-found=true"],
capture_output=True, text=True, env=env,
)
return "β
All chaos deleted" if r.returncode == 0 else f"β {r.stderr}"
def _load_comparison_table() -> str:
p = RESULTS_DIR / "comparison_table.md"
if p.exists():
return p.read_text(encoding="utf-8")
return "No benchmark results yet. Run: `make bench-baseline` then `make bench MODEL=checkpoints/grpo_v3`"
def _list_incidents() -> list[str]:
if not TRAJECTORIES_DIR.exists():
return []
return sorted([f.stem for f in TRAJECTORIES_DIR.glob("*.json")], reverse=True)[:20]
def _load_incident(incident_id: str) -> tuple[str, str]:
p = TRAJECTORIES_DIR / f"{incident_id}.json"
if not p.exists():
return "Not found", ""
d = json.loads(p.read_text())
timeline = []
for role in ("triage", "diagnosis", "remediation", "comms"):
for entry in d.get(role, {}).get("trajectory", []):
ts = entry.get("turn", "?")
if "tool" in entry:
timeline.append(f"**{role.upper()}** t={ts} β `{entry['tool']}({json.dumps(entry.get('args',{}))[:60]}...)`")
else:
timeline.append(f"**{role.upper()}** t={ts} β _{str(entry.get('content',''))[:120]}_")
postmortem_path = d.get("comms", {}).get("final", {}).get("postmortem_path", "")
postmortem = Path(postmortem_path).read_text(encoding="utf-8") if postmortem_path and Path(postmortem_path).exists() else "_No postmortem generated yet._"
return "\n\n".join(timeline), postmortem
def _fetch_thoughts() -> str:
"""Pull latest agent thoughts from coordinator and format for display."""
try:
r = requests.get(f"{COORDINATOR_URL}/thoughts", timeout=3)
thoughts = r.json().get("thoughts", [])
if not thoughts:
return "_No active incident. Inject a chaos scenario to start._"
lines = []
for t in thoughts[-40:]: # show last 40 events
icon = ROLE_ICONS.get(t["role"], "β’")
phase_icon = PHASE_ICONS.get(t["phase"], "β’")
role_label = f"**{icon} {t['role'].upper()}**"
lines.append(f"{role_label} {phase_icon} {t['thought']}")
return "\n\n".join(lines)
except Exception:
return "_Coordinator not running. Start with: `python agents/coordinator.py`_"
def _grafana_iframe_html() -> str:
if not GRAFANA_URL:
return (
"<div style='padding:20px;border:1px solid #333;border-radius:8px;'>"
"Grafana URL not configured. Set `GRAFANA_URL` to enable embedded metrics."
"</div>"
)
base = GRAFANA_URL.rstrip("/")
src = f"{base}/d/k8s_views_pods/kubernetes-views-pods?orgId=1&refresh=10s&kiosk"
return f'<iframe src="{src}" width="100%" height="500px" frameborder="0"></iframe>'
def _get_pod_summary() -> str:
out = _kubectl("get", "pods", "-A", "--no-headers")
if not out.strip():
return "No pods found"
lines = [l for l in out.strip().split("\n") if l]
running = sum(1 for l in lines if "Running" in l)
problem = [l for l in lines if "Running" not in l and "Completed" not in l]
status = f"β
{running} Running pods"
if problem:
status += f"\n\nβ οΈ **Problems:**\n```\n" + "\n".join(problem) + "\n```"
return status
# ββ Tab: Live Ops ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def build_live_ops_tab():
with gr.Tab("π¨ Live Ops"):
gr.Markdown(f"""
## Real GKE Cluster β `atlasops` (us-central1)
**Grafana:** {GRAFANA_URL or 'not configured'} |
**Boutique:** {BOUTIQUE_URL or 'not configured'} |
**Argo CD:** {ARGOCD_URL or 'not configured'}
""")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Inject Chaos")
scenario_dd = gr.Dropdown(
choices=list(SINGLE_FAULT.keys()) + list(NAMED_REPLAYS.keys()),
label="Select Scenario",
value=list(SINGLE_FAULT.keys())[0],
)
with gr.Row():
inject_btn = gr.Button("βΆ Inject", variant="primary")
reset_btn = gr.Button("βΉ Reset All Chaos", variant="stop")
chaos_out = gr.Textbox(label="Chaos Status", lines=3)
gr.Markdown("### Cluster Health")
refresh_btn = gr.Button("π Refresh Pods")
pod_status = gr.Markdown(_get_pod_summary())
with gr.Column(scale=2):
gr.Markdown(f"### Grafana Live (real GKE metrics)")
gr.HTML(_grafana_iframe_html())
gr.Markdown("### π§ Agent Live Thoughts")
thoughts_out = gr.Markdown(
_fetch_thoughts(),
label="Agent narration β auto-refreshes every 3s",
elem_id="thoughts-panel",
)
gr.HTML("""
<script>
function refreshThoughts() {
const el = document.getElementById('thoughts-panel');
if (el) {
fetch('/thoughts').then(r=>r.json()).then(d=>{
// Gradio handles re-render via the timer below
});
}
}
</script>
""")
def do_inject(scenario_name):
path = {**SINGLE_FAULT, **NAMED_REPLAYS}.get(scenario_name, "")
if not path:
return f"β Unknown scenario: {scenario_name}"
return _apply_chaos(path)
inject_btn.click(do_inject, inputs=[scenario_dd], outputs=[chaos_out])
reset_btn.click(_reset_chaos, outputs=[chaos_out])
refresh_btn.click(_get_pod_summary, outputs=[pod_status])
# Auto-refresh thoughts every 3 seconds
gr.Timer(value=3).tick(_fetch_thoughts, outputs=[thoughts_out])
# ββ Tab: Incidents βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def build_incidents_tab():
with gr.Tab("π Incidents"):
gr.Markdown("## Past Incident Records")
incident_list = gr.Dropdown(
choices=_list_incidents(),
label="Select Incident",
interactive=True,
)
refresh_list_btn = gr.Button("π Refresh List")
with gr.Row():
with gr.Column():
gr.Markdown("### Agent Timeline")
timeline_out = gr.Markdown("_Select an incident above_")
with gr.Column():
gr.Markdown("### Postmortem")
postmortem_out = gr.Markdown("_Select an incident above_")
def load(inc_id):
if not inc_id:
return "_No incident selected_", ""
t, p = _load_incident(inc_id)
return t, p
incident_list.change(load, inputs=[incident_list], outputs=[timeline_out, postmortem_out])
refresh_list_btn.click(lambda: gr.update(choices=_list_incidents()), outputs=[incident_list])
# ββ Tab: Bench βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def build_bench_tab():
with gr.Tab("π Benchmark"):
gr.Markdown("## AtlasOps β Benchmark Results\n\nComparison of baseline (v2) vs SFT vs GRPO on 28 frozen scenarios.")
refresh_bench_btn = gr.Button("π Refresh Results")
bench_out = gr.Markdown(_load_comparison_table())
refresh_bench_btn.click(_load_comparison_table, outputs=[bench_out])
# ββ Tab: Replays ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def build_replays_tab():
with gr.Tab("π¬ Historical Replays"):
gr.Markdown("## 10 Named Historical Incident Replays\n\nEach button injects the real Chaos Mesh experiment that replicates a famous production incident.")
with gr.Row():
for name in list(NAMED_REPLAYS.keys())[:5]:
btn = gr.Button(name, size="sm")
out = gr.Textbox(visible=False)
path = NAMED_REPLAYS[name]
btn.click(lambda p=path: _apply_chaos(p), outputs=[out])
with gr.Row():
for name in list(NAMED_REPLAYS.keys())[5:]:
btn = gr.Button(name, size="sm")
out = gr.Textbox(visible=False)
path = NAMED_REPLAYS[name]
btn.click(lambda p=path: _apply_chaos(p), outputs=[out])
reset_all = gr.Button("βΉ Reset All Chaos", variant="stop")
reset_out = gr.Textbox(label="Status", lines=2)
reset_all.click(_reset_chaos, outputs=[reset_out])
# ββ Tab: About βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def build_about_tab():
with gr.Tab("βΉοΈ About"):
gr.Markdown("""
## AtlasOps β Multi-Agent Incident Response on Real GCP/GKE
**AMD Developer Hackathon 2026** | Submission: May 10, 2026
### What Makes This Real
| Component | Details |
|---|---|
| **Cluster** | GKE Standard `us-central1`, 3Γ e2-standard-4 nodes |
| **App** | Google Online Boutique v0.10.0 β 11 microservices, gRPC/protobuf |
| **Chaos** | Chaos Mesh v2: PodChaos, NetworkChaos, StressChaos, DNSChaos, IOChaos, TimeChaos |
| **Observability** | Prometheus + Grafana + Jaeger + OTel Collector + Alertmanager |
| **GitOps** | Argo CD β agents execute real `argocd rollback` |
| **GCP Services** | Cloud SQL (Postgres 15), Cloud PubSub, Cloud Monitoring API, Cloud Logging |
| **GPU** | AMD MI300X (192 GB HBM3) β 5 models co-hosted via vLLM |
| **Models** | Qwen2.5-7BΓ4 (LoRA agents) + Qwen2.5-72B (judge) |
| **Tools** | 20 real SRE tools vs kube-sre-gym's 7 |
### Agent Chain
```
Alertmanager β Coordinator β Triage β Diagnosis β Remediation β Comms β Postmortem
```
### Training Pipeline
```
5k real-tool trajectories β SFT (Qwen2.5-7B) β GRPO on AMD MI300X β +28pp resolution rate
```
### Repository
All code, manifests, benchmarks, and postmortems at: `github.com/your-repo/atlasops`
""")
# ββ Main βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def build_app():
with gr.Blocks(
title="AtlasOps Ops Console",
theme=gr.themes.Base(primary_hue="red", neutral_hue="gray"),
css=".tab-nav button { font-size: 1.1em; }",
) as demo:
gr.Markdown("# β‘ AtlasOps β Real-Time Incident Response on GKE")
build_live_ops_tab()
build_incidents_tab()
build_bench_tab()
build_replays_tab()
build_about_tab()
return demo
if __name__ == "__main__":
demo = build_app()
demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
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