Upload integration/mlBottleneckScanner.js

integration/mlBottleneckScanner.js

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+/**
+ * ALWAS ML-Enhanced Bottleneck Scanner
+ * Drop-in replacement for the existing node-cron bottleneck scanner.
+ * Uses ML models instead of simple 48h threshold.
+ * 
+ * Place in: server/cron/mlBottleneckScanner.js
+ * 
+ * Setup in server/app.js:
+ *   const cron = require('node-cron');
+ *   const mlScanner = require('./cron/mlBottleneckScanner');
+ *   cron.schedule('0 * * * *', () => mlScanner(io)); // hourly
+ */
+
+const ml = require('../utils/alwas-ml-client');
+const Block = require('../models/Block');
+const Notification = require('../models/Notification');
+const User = require('../models/User');
+
+async function mlBottleneckScanner(io) {
+  console.log('[ML Scanner] Starting hourly bottleneck scan...');
+  
+  try {
+    // Get all in-progress blocks
+    const blocks = await Block.find({ 
+      status: { $nin: ['Not Started', 'Completed'] } 
+    }).populate('assignedTo');
+
+    let highRisk = 0;
+    let mediumRisk = 0;
+    let alerts = [];
+
+    for (const block of blocks) {
+      try {
+        // Calculate days in current stage
+        const lastTransition = block.transitions?.[block.transitions.length - 1];
+        const daysSince = lastTransition 
+          ? (Date.now() - new Date(lastTransition.timestamp)) / (1000 * 60 * 60 * 24)
+          : 0;
+
+        const riskData = ml.constructor.formatForBottleneck(block, daysSince);
+        const risk = await ml.predictBottleneck(riskData);
+
+        if (risk.risk_level === 'High') {
+          highRisk++;
+          
+          // Create notification for assigned engineer
+          if (block.assignedTo) {
+            const notification = await Notification.create({
+              user: block.assignedTo._id || block.assignedTo,
+              type: 'stuck',
+              message: `⚠️ ML Alert: ${block.name} has HIGH bottleneck risk`,
+              data: {
+                blockId: block._id,
+                risk: risk.risk_level,
+                confidence: risk.confidence,
+                recommendations: risk.recommendations,
+                hours_over_budget: risk.hours_over_budget_ratio,
+              }
+            });
+
+            // Real-time socket notification
+            io.emit('newNotification', {
+              userId: block.assignedTo._id || block.assignedTo,
+              notification: notification,
+            });
+          }
+
+          // Also notify managers
+          const managers = await User.find({ role: 'manager' });
+          for (const manager of managers) {
+            await Notification.create({
+              user: manager._id,
+              type: 'stuck',
+              message: `🔴 ML Bottleneck Alert: ${block.name} (${block.status}) — ${risk.recommendations[0] || 'High risk detected'}`,
+              data: {
+                blockId: block._id,
+                risk: risk.risk_level,
+                confidence: risk.confidence,
+                recommendations: risk.recommendations,
+                engineer: block.assignedTo?.name || 'Unassigned',
+              }
+            });
+          }
+
+          alerts.push({
+            block: block.name,
+            stage: block.status,
+            risk: risk.risk_level,
+            confidence: risk.confidence,
+            reason: risk.recommendations[0] || 'High risk',
+          });
+
+        } else if (risk.risk_level === 'Medium') {
+          mediumRisk++;
+        }
+      } catch (blockError) {
+        console.error(`[ML Scanner] Error scanning block ${block._id}:`, blockError.message);
+      }
+    }
+
+    console.log(`[ML Scanner] Scan complete: ${blocks.length} blocks scanned, ${highRisk} high risk, ${mediumRisk} medium risk`);
+    
+    if (alerts.length > 0) {
+      console.log('[ML Scanner] High-risk blocks:');
+      alerts.forEach(a => console.log(`  - ${a.block} (${a.stage}): ${a.reason}`));
+    }
+
+    return { scanned: blocks.length, highRisk, mediumRisk, alerts };
+
+  } catch (error) {
+    console.error('[ML Scanner] Fatal error:', error.message);
+    return { error: error.message };
+  }
+}
+
+module.exports = mlBottleneckScanner;