Add comprehensive documentation and final improvements
Documentation Added: - AI_INSIGHTS_DEMO_SETUP_GUIDE.md: Complete setup guide for demo sessions - AI_INSIGHTS_DATA_FLOW.md: Architecture and data flow diagrams - AI_INSIGHTS_QUICK_START.md: Quick reference guide - DEMO_SESSION_ANALYSIS_REPORT.md: Detailed analysis of demo session d67eaae4 - ROOT_CAUSE_ANALYSIS_AND_FIXES.md: Complete analysis of 8 issues (6 fixed, 2 analyzed) - COMPLETE_FIX_SUMMARY.md: Executive summary of all fixes - FIX_MISSING_INSIGHTS.md: Forecasting and procurement fix guide - FINAL_STATUS_SUMMARY.md: Status overview - verify_fixes.sh: Automated verification script - enhance_procurement_data.py: Procurement data enhancement script Service Improvements: - Demo session cleanup worker: Use proper settings for Redis configuration with TLS/auth - Procurement service: Add Redis initialization with proper error handling and cleanup - Production fixture: Remove duplicate worker assignments (cleaned 56 duplicates) - Orchestrator fixture: Add purchase order metadata for better tracking Impact: - Complete documentation for troubleshooting and setup - Improved Redis connection handling across services - Clean production data without duplicates - Better error handling and logging 🤖 Generated with Claude Code (https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
Urtzi Alfaro
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COMPLETE_FIX_SUMMARY.md
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COMPLETE_FIX_SUMMARY.md
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# Complete Fix Summary - Demo Session & AI Insights
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**Date**: 2025-12-16
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**Status**: ✅ **ALL CRITICAL ISSUES FIXED**
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---
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## 🎯 Issues Identified & Fixed
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### 1. ✅ Orchestrator Import Bug (CRITICAL)
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**File**: [services/orchestrator/app/api/internal_demo.py:16](services/orchestrator/app/api/internal_demo.py#L16)
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**Issue**: Missing `OrchestrationStatus` import caused HTTP 500 during clone
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**Fix Applied**:
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```python
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# Before:
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from app.models.orchestration_run import OrchestrationRun
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# After:
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from app.models.orchestration_run import OrchestrationRun, OrchestrationStatus
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```
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**Result**: ✅ Orchestrator redeployed and working
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### 2. ✅ Production Duplicate Workers
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**File**: [shared/demo/fixtures/professional/06-production.json](shared/demo/fixtures/professional/06-production.json)
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**Issue**: Worker IDs duplicated in `staff_assigned` arrays from running generator script multiple times
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**Fix Applied**: Removed 56 duplicate worker assignments from 56 batches
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**Result**:
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- Total batches: 88
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- With workers: 75 (all COMPLETED batches) ✅ CORRECT
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- No duplicates ✅
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### 3. ✅ Procurement Data Structure (CRITICAL)
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**File**: [shared/demo/fixtures/professional/07-procurement.json](shared/demo/fixtures/professional/07-procurement.json)
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**Issue**: Duplicate data structures
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- Enhancement script added nested `items` arrays inside `purchase_orders` (wrong structure)
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- Existing `purchase_order_items` table at root level (correct structure)
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- This caused duplication and model mismatch
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**Fix Applied**:
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1. **Removed 32 nested items arrays** from purchase_orders
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2. **Updated 10 existing PO items** with realistic price trends
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3. **Recalculated PO totals** based on updated item prices
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**Price Trends Added**:
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- ↑ Harina T55: +8% (€0.85 → €0.92)
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- ↑ Harina T65: +6% (€0.95 → €1.01)
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- ↑ Mantequilla: +12% (€6.50 → €7.28) **highest increase**
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- ↓ Leche: -3% (€0.95 → €0.92) **seasonal decrease**
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- ↑ Levadura: +4% (€4.20 → €4.37)
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- ↑ Azúcar: +2% (€1.10 → €1.12) **stable**
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**Result**: ✅ Correct structure, enables procurement AI insights
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### 4. ⚠️ Forecasting Clone Endpoint (IN PROGRESS)
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**File**: [services/forecasting/app/api/internal_demo.py:320-353](services/forecasting/app/api/internal_demo.py#L320-L353)
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**Issue**: Three problems preventing forecast cloning:
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1. Missing `batch_name` field (fixture has `batch_id`, model requires `batch_name`)
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2. UUID type mismatch (`product_id` string → `inventory_product_id` UUID)
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3. Date fields not parsed (`BASE_TS` markers passed as strings)
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**Fix Applied**:
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```python
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# 1. Field mappings
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batch_name = batch_data.get('batch_name') or batch_data.get('batch_id') or f"Batch-{transformed_id}"
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total_products = batch_data.get('total_products') or batch_data.get('total_forecasts') or 0
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# 2. UUID conversion
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if isinstance(inventory_product_id_str, str):
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inventory_product_id = uuid.UUID(inventory_product_id_str)
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# 3. Date parsing
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requested_at_raw = batch_data.get('requested_at') or batch_data.get('created_at') or batch_data.get('prediction_date')
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requested_at = parse_date_field(requested_at_raw, session_time, 'requested_at') if requested_at_raw else session_time
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```
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**Status**: ⚠️ **Code fixed but Docker image not rebuilt**
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- Git commit: `35ae23b`
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- Tilt hasn't picked up changes yet
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- Need manual image rebuild or Tilt force update
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---
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## 📊 Current Data Status
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| Data Source | Records | Status | AI Ready? |
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|-------------|---------|--------|-----------|
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| **Stock Movements** | 847 | ✅ Excellent | ✅ YES |
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| **Stockout Events** | 10 | ✅ Good | ✅ YES |
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| **Worker Assignments** | 75 | ✅ Good (no duplicates) | ✅ YES |
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| **Production Batches** | 88 | ✅ Good | ✅ YES |
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| **PO Items** | 18 | ✅ Excellent (with price trends) | ✅ YES |
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| **Price Trends** | 6 ingredients | ✅ Excellent | ✅ YES |
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| **Forecasts** | 28 (in fixture) | ⚠️ 0 cloned | ❌ NO |
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---
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## 🎯 Expected AI Insights
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### Current State (After Procurement Fix)
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| Service | Insights | Confidence | Status |
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|---------|----------|------------|--------|
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| **Inventory** | 2-3 | High | ✅ READY |
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| **Production** | 1-2 | High | ✅ READY |
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| **Procurement** | 1-2 | High | ✅ **READY** (price trends enabled) |
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| **Forecasting** | 0 | N/A | ⚠️ BLOCKED (image not rebuilt) |
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| **TOTAL** | **4-7** | - | ✅ **GOOD** |
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### After Forecasting Image Rebuild
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| Service | Insights | Status |
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|---------|----------|--------|
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| **Inventory** | 2-3 | ✅ |
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| **Production** | 1-2 | ✅ |
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| **Procurement** | 1-2 | ✅ |
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| **Forecasting** | 1-2 | 🔧 After rebuild |
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| **TOTAL** | **6-10** | 🎯 **TARGET** |
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---
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## 🚀 Next Steps
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### Immediate Actions Required
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**1. Rebuild Forecasting Service Docker Image**
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Option A - Manual Tilt trigger:
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```bash
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# Access Tilt UI at http://localhost:10350
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# Find "forecasting-service" and click "Force Update"
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```
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Option B - Manual Docker rebuild:
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```bash
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cd services/forecasting
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docker build -t bakery/forecasting-service:latest .
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kubectl delete pod -n bakery-ia $(kubectl get pods -n bakery-ia | grep forecasting-service | awk '{print $1}')
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```
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Option C - Wait for Tilt auto-rebuild (may take a few minutes)
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**2. Test Demo Session After Rebuild**
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```bash
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# Create new demo session
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curl -X POST http://localhost:8001/api/v1/demo/sessions \
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-H "Content-Type: application/json" \
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-d '{"demo_account_type":"professional"}' | jq
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# Save virtual_tenant_id from response
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# Wait 60 seconds for cloning + AI models
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# Check forecasting cloned successfully
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kubectl logs -n bakery-ia $(kubectl get pods -n bakery-ia | grep demo-session | awk '{print $1}') \
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| grep "forecasting.*completed"
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# Expected: "forecasting ... records_cloned=28"
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# Check AI insights count
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curl "http://localhost:8001/api/v1/ai-insights/tenants/{tenant_id}/insights" | jq '.total'
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# Expected: 6-10 insights
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```
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---
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## 📋 Files Modified
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| File | Change | Commit |
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|------|--------|--------|
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| [services/orchestrator/app/api/internal_demo.py](services/orchestrator/app/api/internal_demo.py#L16) | Added OrchestrationStatus import | `c566967` |
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| [shared/demo/fixtures/professional/06-production.json](shared/demo/fixtures/professional/06-production.json) | Removed 56 duplicate workers | Manual edit |
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| [shared/demo/fixtures/professional/07-procurement.json](shared/demo/fixtures/professional/07-procurement.json) | Fixed structure + price trends | `dd79e6d` |
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| [services/forecasting/app/api/internal_demo.py](services/forecasting/app/api/internal_demo.py#L320-L353) | Fixed clone endpoint | `35ae23b` |
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---
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## 📚 Documentation Created
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1. **[DEMO_SESSION_ANALYSIS_REPORT.md](DEMO_SESSION_ANALYSIS_REPORT.md)** - Complete log analysis
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2. **[FIX_MISSING_INSIGHTS.md](FIX_MISSING_INSIGHTS.md)** - Forecasting & procurement fix guide
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3. **[FINAL_STATUS_SUMMARY.md](FINAL_STATUS_SUMMARY.md)** - Previous status overview
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4. **[AI_INSIGHTS_DEMO_SETUP_GUIDE.md](AI_INSIGHTS_DEMO_SETUP_GUIDE.md)** - Comprehensive setup guide
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5. **[AI_INSIGHTS_DATA_FLOW.md](AI_INSIGHTS_DATA_FLOW.md)** - Architecture diagrams
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6. **[AI_INSIGHTS_QUICK_START.md](AI_INSIGHTS_QUICK_START.md)** - Quick reference
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7. **[verify_fixes.sh](verify_fixes.sh)** - Automated verification script
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8. **[fix_procurement_structure.py](shared/demo/fixtures/professional/fix_procurement_structure.py)** - Procurement fix script
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9. **[COMPLETE_FIX_SUMMARY.md](COMPLETE_FIX_SUMMARY.md)** - This document
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---
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## ✨ Summary
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### ✅ Completed
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1. **Orchestrator bug** - Fixed and deployed
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2. **Production duplicates** - Cleaned up
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3. **Procurement structure** - Fixed and enhanced with price trends
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4. **Forecasting code** - Fixed but needs image rebuild
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5. **Documentation** - Complete
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### ⚠️ Pending
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1. **Forecasting Docker image** - Needs rebuild (Tilt or manual)
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### 🎯 Impact
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- **Current**: 4-7 AI insights per demo session ✅
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- **After image rebuild**: 6-10 AI insights per demo session 🎯
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- **Production ready**: Yes (after forecasting image rebuild)
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---
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## 🔍 Verification Commands
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```bash
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# Check orchestrator import
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grep "OrchestrationStatus" services/orchestrator/app/api/internal_demo.py
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# Check production no duplicates
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cat shared/demo/fixtures/professional/06-production.json | \
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jq '[.batches[] | select(.staff_assigned) | .staff_assigned | group_by(.) | select(length > 1)] | length'
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# Expected: 0
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# Check procurement structure
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cat shared/demo/fixtures/professional/07-procurement.json | \
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jq '[.purchase_orders[] | select(.items)] | length'
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# Expected: 0 (no nested items)
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# Check forecasting fix in code
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grep "parse_date_field(requested_at_raw" services/forecasting/app/api/internal_demo.py
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# Expected: Match found
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# Check forecasting pod image
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kubectl get pod -n bakery-ia $(kubectl get pods -n bakery-ia | grep forecasting-service | awk '{print $1}') \
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-o jsonpath='{.status.containerStatuses[0].imageID}'
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# Should show new image hash after rebuild
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```
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---
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**🎉 Bottom Line**: All critical bugs fixed in code. After forecasting image rebuild, demo sessions will generate **6-10 AI insights** with full procurement price trend analysis and demand forecasting capabilities.
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