Spaces:
Sleeping
Sleeping
File size: 2,816 Bytes
ddd9009 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import time
import pandas as pd
import gradio as gr
from datetime import datetime
from enrichment import enrich_alert
from llm_classifier import classify_alert
from log_generator import generate_random_log
from playbook import run_playbook
stop_streaming = False
results_store = []
def stream_logs():
global stop_streaming, results_store
stop_streaming = False
results_store = []
base_time = datetime.now()
for i in range(10):
if stop_streaming:
break
log = generate_random_log(base_time, i)
enriched = enrich_alert(log.copy())
ai_result = classify_alert(enriched)
row = {
"Timestamp": enriched.get("timestamp"),
"Event": enriched.get("event"),
"Source IP": enriched.get("source_ip"),
"Reputation": enriched.get("ip_reputation"),
"Location": enriched.get("geo_location"),
"Category": ai_result.get("category"),
"Priority": ai_result.get("priority"),
"Action": ai_result.get("action"),
}
results_store.append(row)
yield pd.DataFrame(results_store)
time.sleep(3)
def stop_logs():
global stop_streaming
stop_streaming = True
return None
def export_alerts():
global results_store
if not results_store:
return None
df = pd.DataFrame(results_store)
export_path = "alerts_export.csv"
df.to_csv(export_path, index=False)
return export_path
def execute_playbooks():
global results_store
if not results_store:
return "No alerts to act on."
actions = [run_playbook(alert) for alert in results_store]
return "\n".join(actions)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("## 🛡️ Actionable : SOC Dashboard – Live Alert Prioritization & Triage")
with gr.Row():
start_btn = gr.Button("▶ Start Streaming", variant="primary")
stop_btn = gr.Button("⏹ Stop Streaming", variant="stop")
export_btn = gr.Button("💾 Export Alerts", variant="huggingface")
playbook_btn = gr.Button("⚡ Run Playbooks")
output_table = gr.Dataframe(
headers=["Timestamp", "Event", "Source IP", "Reputation", "Location", "Category", "Priority", "Action"],
wrap=True
)
download_file = gr.File(label="Download Exported Alerts")
playbook_output = gr.Textbox(label="Playbook Execution Log", lines=8)
start_btn.click(fn=stream_logs, outputs=output_table)
stop_btn.click(fn=stop_logs, outputs=None)
export_btn.click(fn=export_alerts, outputs=download_file)
playbook_btn.click(fn=execute_playbooks, outputs=playbook_output)
if __name__ == "__main__":
demo.queue().launch()
|