hm_ai_qc_report_tool/DEPLOYMENT_CHECKLIST.md

Unified HM QC Platform - Deployment Checklist

Prerequisites

1. System Requirements

• Python 3.9 or higher

• ffmpeg (required for video processing)

  # macOS
  brew install ffmpeg
  
  # Ubuntu/Debian
  sudo apt-get install ffmpeg
  
  # Windows
  # Download from https://ffmpeg.org/download.html
  

• chromaprint (optional, for audio fingerprinting)

  # macOS
  brew install chromaprint
  
  # Ubuntu/Debian
  sudo apt-get install libchromaprint-tools
  

2. Environment Setup

Step 1: Recreate Virtual Environment

cd /Users/nickviljoen/Desktop/HM_QC_Bitbucket/hm_ai_qc_report_tool

# Remove old venv if exists
rm -rf venv

# Create new venv
python3 -m venv venv

# Activate venv
source venv/bin/activate  # macOS/Linux
# OR
venv\Scripts\activate     # Windows

Step 2: Install Dependencies

pip install --upgrade pip
pip install -r requirements.txt

Step 3: Configure Environment Variables

Create/update .env file:

# Flask Configuration
SECRET_KEY=<generate-random-key>
FLASK_ENV=development  # or production
HOST=0.0.0.0
PORT=7183
DEBUG=True  # Set to False in production

# Azure AD Authentication
AZURE_TENANT_ID=e519c2e6-bc6d-4fdf-8d9c-923c2f002385
AZURE_CLIENT_ID=9079054c-9620-4757-a256-23413042f1ef

# Box.com Configuration
BOX_CONFIG_PATH=config/box_config.json
BOX_REPORT_FOLDER_ID=133295752718

# Database
DATABASE_PATH=database/qc_platform.db

# LLM Providers (NO HARDCODED KEYS - all from environment)
OPENAI_API_KEY=<your-openai-api-key>
ANTHROPIC_API_KEY=<your-anthropic-api-key>
# Optional: Azure OpenAI
AZURE_OPENAI_API_KEY=<your-azure-key>
AZURE_OPENAI_ENDPOINT=<your-endpoint>
# Optional: Google Gemini
GOOGLE_API_KEY=<your-google-key>

Important: Never commit .env to version control!

3. Directory Structure Verification

Ensure these directories exist:

mkdir -p database
mkdir -p uploads/hm_qc
mkdir -p uploads/video_qc
mkdir -p uploads/video_master
mkdir -p storage/reports/hm_qc
mkdir -p storage/reports/consolidated
mkdir -p data/fingerprints
mkdir -p data/masters

4. Database Initialization

# Initialize database
python3 -c "from app import create_app; from core.models.database import db; app = create_app(); app.app_context().push(); db.create_all(); print('Database initialized')"

Integration Testing

Test 1: Application Startup

python3 test_integration.py

Expected output:

• ✓ App created successfully
• ✓ All 4 blueprints registered (reporting, hm_qc, video_qc, video_master)
• ✓ Database connectivity
• ✓ Core services available
• ✓ Routes accessible

Test 2: Manual Startup

python3 app.py

Expected output:

INFO:__main__:Database initialized
INFO:__main__:Authentication initialized
INFO:__main__:Reporting blueprint registered at /reporting
INFO:__main__:HM QC blueprint registered at /hm-qc
INFO:__main__:Video QC blueprint (BETA) registered at /video-qc
INFO:__main__:Video Master blueprint (BETA) registered at /video-master
INFO:__main__:Application initialized successfully
 * Running on http://0.0.0.0:7183

Test 3: Web Interface Access

Open browser to: http://localhost:7183

Test each tab:

1. ✅ Reporting Tab (/reporting)

   • Search interface loads
   • Job number search works
   • Box.com reports displayed
   • Dashboard view works

2. ✅ HM QC Tab (/hm-qc)

   • Landing page displays
   • Upload interface works
   • File upload successful
   • Configure profile selection
   • Execute QC and view progress
   • Results with scores displayed

3. ✅ Video QC (BETA) Tab (/video-qc)

   • Landing page with BETA badge
   • Upload interface loads
   • Feature list displayed
   • BETA notice present

4. ✅ Video Master (BETA) Tab (/video-master)

   • Landing page with BETA badge
   • 4-tier system description
   • Upload and Masters links work
   • BETA notice present

Test 4: Authentication Flow

1. Navigate to any protected route
2. Redirected to Azure AD login
3. After login, session persists across tabs
4. Logout clears session from all tabs

Test 5: Tab Switching

1. Switch between tabs
2. Verify state preservation (using sessionStorage)
3. Verify active tab highlighting
4. Verify BETA badges displayed correctly

Module-Specific Testing

HM QC Module Testing

Test Workflow:

# 1. Upload PDF/image
# 2. Select profile (standard_pdf)
# 3. Configure checks (enable/disable)
# 4. Execute QC
# 5. View progress (SSE or polling)
# 6. View results with score (0-100)
# 7. Download HTML report

Expected Output:

• Overall score: 0-100
• Status: passed (90+), warning (70-89), failed (<70)
• Check breakdown with individual scores
• Recommendations for improvements

Video QC Module Testing

Current Status: BETA structure only

• Landing page functional
• Upload interface ready
• Full execution workflow: Coming soon

Video Master Module Testing

Test Matching Engine (Python):

from modules.video_master.matching import VideoMatcher

# Initialize
matcher = VideoMatcher(
    data_dir="data",
    enable_ai_vision=True,
    use_akaze=True,
    use_metadata_filter=True
)

# Add masters
matcher.add_master("/path/to/master.mp4", "master_1")

# Match adaptation
matches = matcher.match_adaptation("/path/to/adaptation.mp4")

# Check results
for match in matches:
    print(f"Master: {match['master_id']}")
    print(f"Confidence: {match['confidence_score']}/100")
    print(f"Method: {match['matching_method']}")

Expected Output:

• Stage 0: Metadata filtering reduces candidates
• Tier 1: Perceptual hash matching
• Tier 2: AKAZE verification (if enabled)
• Tier 3: AI Vision (only if needed)
• Match results with 0-100 confidence scores

Reporting Module Testing

Test Consolidation:

1. Run HM QC on a PDF with job number "12345"
2. Navigate to Reporting tab
3. Search for "12345"
4. Verify both Box.com and HM QC reports appear
5. Verify source badges ("HM QC", "Box")
6. Test export functionality

Performance Benchmarks

HM QC Performance

• Simple filename check: <1 second
• AI-powered quality check: 2-5 seconds (depends on LLM)
• Full profile execution: 5-15 seconds (2 checks)

Video Master Performance

• Stage 0 (metadata): <1 second
• Tier 1 (perceptual hash): 5-10 seconds for 50 masters
• Tier 2 (AKAZE): 5-10 seconds per candidate (top 5)
• Tier 3 (AI Vision): 3-5 seconds per comparison

Expected Cost (AI Vision)

• GPT-4o: ~$0.006 per video comparison
• Smart triggering: Only used when needed (saves ~97%)

Security Checklist

✅ NO hardcoded API keys - All from environment variables ✅ httpOnly cookies - Session tokens not accessible via JavaScript ✅ Azure AD JWT validation - Proper signature verification ✅ Input validation - File upload size limits, type checking ✅ CORS configuration - Appropriate for production ✅ Secret key - Random, not committed to git

Troubleshooting

Issue: Database locked

Solution: Check that no other process is using the database

lsof database/qc_platform.db

Issue: ffmpeg not found

Solution: Install ffmpeg system-wide

brew install ffmpeg  # macOS

Issue: LLM API errors

Solution: Verify API keys in .env

echo $OPENAI_API_KEY  # Should show your key

Issue: Box.com authentication fails

Solution: Verify config/box_config.json exists and is valid

Issue: Import errors

Solution: Verify virtual environment is activated and dependencies installed

which python  # Should show venv path
pip list | grep Flask  # Should show Flask installed

Production Deployment

Option 1: Gunicorn (Recommended)

gunicorn -c gunicorn_config.py wsgi:app

Option 2: Docker (Future)

# Dockerfile to be created
docker build -t hm-qc-platform .
docker run -p 7183:7183 hm-qc-platform

Backup & Recovery

Database Backup

cp database/qc_platform.db database/qc_platform_backup_$(date +%Y%m%d).db

Fingerprint Backup

tar -czf data_backup_$(date +%Y%m%d).tar.gz data/

Monitoring

Health Check Endpoint

curl http://localhost:7183/health

Expected response:

{
  "status": "healthy",
  "timestamp": "2026-02-02T...",
  "box_connected": true
}

Log Monitoring

tail -f logs/app.log  # If logging to file configured

Success Criteria

✅ All 4 tabs accessible ✅ BETA labels displayed on Video QC and Video Master ✅ Authentication works across tabs ✅ HM QC complete workflow functional ✅ Reporting consolidation works (Box + HM QC) ✅ Video Master matching engine operational ✅ No hardcoded API keys ✅ Scoring system returns 0-100 scores ✅ Progress tracking works ✅ Tab state preservation works

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Platform Status: ✅ READY FOR DEPLOYMENT

• All modules integrated
• AI_QC improvements applied
• Security hardened
• Documentation complete