Unified dashboard for training and testing

dashboard.html

@@ -242,6 +242,17 @@
         .toast-info { background: rgba(59,130,246,0.9); color: white; }
         @keyframes toastIn { from{opacity:0;transform:translateY(16px)} to{opacity:1;transform:translateY(0)} }
 
+        /* ── Tabs & Training ────────────────────────────────────── */
+        .tabs { display: flex; gap: 14px; margin-bottom: 24px; border-bottom: 1px solid var(--border); padding-bottom: 10px; }
+        .tab { font-size: 14px; font-weight: 600; color: var(--text-muted); cursor: pointer; padding: 10px 16px; border-radius: 8px; transition: 0.2s; }
+        .tab:hover { background: rgba(255,255,255,0.05); color: var(--text-primary); }
+        .tab.active { background: var(--bg-input); color: var(--accent-blue); border: 1px solid var(--border); }
+        .training-container { display: none; background: var(--bg-card); border: 1px solid var(--border); border-radius: 12px; padding: 24px; }
+        .training-container.active { display: block; }
+        .testing-container { display: none; }
+        .testing-container.active { display: block; }
+        .terminal { background: #000; color: #10b981; font-family: 'Courier New', monospace; padding: 16px; border-radius: 8px; height: 400px; overflow-y: auto; margin-top: 16px; font-size: 12px; border: 1px solid var(--border); line-height: 1.5; white-space: pre-wrap; }
+        
         @media (max-width:1200px) {
             .grid-stats { grid-template-columns: repeat(3, 1fr); }
             .grid-main, .grid-bottom { grid-template-columns: 1fr; }
@@ -269,6 +280,25 @@
         </div>
     </div>
 
+    <!-- ══════ Tabs ══════ -->
+    <div class="tabs">
+        <div class="tab active" id="tabLive" onclick="switchTab('live')">Live Testing</div>
+        <div class="tab" id="tabTrain" onclick="switchTab('train')">Agent Training</div>
+    </div>
+
+    <!-- ══════ Training View ══════ -->
+    <div class="training-container" id="trainingView">
+        <h3>Train RL Agent (PPO)</h3>
+        <p style="color:var(--text-muted); font-size:12px; margin-top:8px; margin-bottom:16px;">Launch background training across varying difficulty tasks.</p>
+        <div class="controls">
+            <span style="font-size:12px">Easy Episodes:</span>
+            <input type="number" id="trainEpisodes" value="300" style="padding:6px; border-radius:6px; border:1px solid var(--border); background:var(--bg-input); color:var(--text-primary); width:100px;">
+            <button class="btn btn-primary" onclick="startTraining()" id="trainBtn">▶ Start Training</button>
+        </div>
+        <div class="terminal" id="trainTerminal">Ready to train. Run PPO to update weights.</div>
+    </div>
+
+    <div class="testing-container active" id="testingView">
     <!-- ══════ Action Guide ══════ -->
     <div class="help-bar">
         <div class="help-item">
@@ -437,13 +467,14 @@
             </div>
         </div>
     </div>
+    </div> <!-- end testing-container -->
 </div>
 
 <script>
 // ═══════════════════════════════════════════════════════════════════
 // State
 // ═══════════════════════════════════════════════════════════════════
-const SERVER = 'https://tusharp2006-scaler-deployment.hf.space/';
+const SERVER = 'https://tusharp2006-scaler-deployment.hf.space';
 let currentAlerts = [];
 let episodeScores = [];
 let episodeRewards = [];
@@ -772,11 +803,61 @@ function showToast(msg, type='success') {
     setTimeout(() => el.remove(), 3500);
 }
 
+// ═══════════════════════════════════════════════════════════════════
+// UI & Tabs
+// ═══════════════════════════════════════════════════════════════════
+function switchTab(tab) {
+    document.getElementById('tabLive').className = tab === 'live' ? 'tab active' : 'tab';
+    document.getElementById('tabTrain').className = tab === 'train' ? 'tab active' : 'tab';
+    document.getElementById('testingView').className = tab === 'live' ? 'testing-container active' : 'testing-container';
+    document.getElementById('trainingView').className = tab === 'train' ? 'training-container active' : 'training-container';
+}
+
+// ═══════════════════════════════════════════════════════════════════
+// Training Webhooks
+// ═══════════════════════════════════════════════════════════════════
+let trainingInterval = null;
+
+async function startTraining() {
+    const ep = document.getElementById('trainEpisodes').value || 300;
+    try {
+        const r = await api('POST', `/train?episodes=${ep}`);
+        if(r.error) showToast(r.error, 'error');
+        else {
+            showToast('Training started in background!', 'success');
+            if(!trainingInterval) trainingInterval = setInterval(pollTraining, 2000);
+            document.getElementById('trainBtn').disabled = true;
+        }
+    } catch(e) { showToast('Trainer error', 'error'); }
+}
+
+async function pollTraining() {
+    try {
+        const r = await api('GET', '/train/status');
+        if (r.error) return;
+        
+        const term = document.getElementById('trainTerminal');
+        if (r.logs && r.logs.length) {
+            term.textContent = r.logs.join('\n');
+            term.scrollTop = term.scrollHeight; // Auto-scroll
+        }
+        
+        if (!r.is_running) {
+            if(trainingInterval) { clearInterval(trainingInterval); trainingInterval = null; }
+            document.getElementById('trainBtn').disabled = false;
+        } else {
+            document.getElementById('trainBtn').disabled = true;
+        }
+    } catch(e) {}
+}
+
 // ═══════════════════════════════════════════════════════════════════
 // Init
 // ═══════════════════════════════════════════════════════════════════
 pollHealth();
+pollTraining();
 setInterval(pollHealth, 5000);
+if(document.getElementById('tabTrain').className.includes('active')) setInterval(pollTraining, 2000);
 </script>
 </body>
 </html>

requirements.txt

@@ -20,4 +20,7 @@ openai>=1.0.0
 requests>=2.31.0
 
 # ── YAML parsing ─────────────────────────────────────────────────────────────
-pyyaml>=6.0
+pyyaml>=6.0
+
+# ── Auto-commit back to Space ─────────────────────────────────────────────────
+huggingface_hub>=0.20.0

src/adaptive_alert_triage/server.py

@@ -100,7 +100,7 @@ def _norm(raw: str) -> str:
 
 app = FastAPI(title="Adaptive Alert Triage RL Server", version="0.3.0")
 app.add_middleware(CORSMiddleware, allow_origins=["*"],
-                   allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
+                   allow_credentials=False, allow_methods=["*"], allow_headers=["*"])
 #Changes
 @app.middleware("http")
 async def log_requests(request, call_next):
@@ -551,6 +551,84 @@ async def root():
     }
 
 
+import threading
+import subprocess
+
+_training_proc = None
+_training_logs = []
+
+def _run_training(episodes: int):
+    global _training_proc, _training_logs, _ppo_agents
+    _training_logs = [f"Starting training with --episodes {episodes}..."]
+    try:
+        _training_proc = subprocess.Popen(
+            [sys.executable, "train_rl.py", "--episodes", str(episodes)],
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+            bufsize=1,
+            cwd=_project_root if _project_root else os.getcwd()
+        )
+        for line in iter(_training_proc.stdout.readline, ''):
+            if line:
+                _training_logs.append(line.rstrip('\n'))
+                if len(_training_logs) > 1000:
+                    _training_logs.pop(0)
+        _training_proc.wait()
+        _training_logs.append(f"Training finished with exit code {- _training_proc.returncode if _training_proc.returncode < 0 else _training_proc.returncode}")
+        
+        # Auto-reload PPO weights if training succeeded
+        if _training_proc.returncode == 0:
+            for tid in ("easy", "medium", "hard"):
+                agent = _load_ppo(tid)
+                if agent:
+                    _ppo_agents[tid] = agent
+            _training_logs.append("Successfully reloaded PPO weights for all tasks.")
+            
+            # Auto-save weights back to Hugging Face
+            try:
+                from huggingface_hub import HfApi
+                hf_token = os.environ.get("HF_TOKEN")
+                repo_id = os.environ.get("SPACE_ID", "tusharp2006/scaler-deployment")
+                
+                if hf_token:
+                    _training_logs.append(f"Pushing updated weights back to HF Hub ({repo_id})...")
+                    api = HfApi(token=hf_token)
+                    weights_dir = os.path.join(_project_root if _project_root else os.getcwd(), "weights")
+                    
+                    if os.path.exists(weights_dir):
+                        api.upload_folder(
+                            repo_id=repo_id,
+                            folder_path=weights_dir,
+                            path_in_repo="weights",
+                            repo_type="space",
+                            commit_message="Auto-sync weights after RL training"
+                        )
+                        _training_logs.append("Weights successfully pushed and persisted to Hugging Face!")
+                else:
+                    _training_logs.append("No HF_TOKEN found in environment. Skipping weight cloud-persistence.")
+            except ImportError:
+                _training_logs.append("huggingface_hub not installed. Skipping cloud backup.")
+            except Exception as e:
+                _training_logs.append(f"Failed to push weights to Hub: {e}")
+            
+    except Exception as e:
+        _training_logs.append(f"Error starting training: {e}")
+
+@app.post("/train")
+async def start_training(episodes: int = 300):
+    global _training_proc
+    if _training_proc is not None and _training_proc.poll() is None:
+        return {"status": "already running"}
+    threading.Thread(target=_run_training, args=(episodes,), daemon=True).start()
+    return {"status": "started"}
+
+@app.get("/train/status")
+async def get_training_status():
+    global _training_proc, _training_logs
+    is_running = _training_proc is not None and _training_proc.poll() is None
+    return {"is_running": is_running, "logs": _training_logs}
+
 @app.get("/web")
 async def web_ui():
     """