CP_Assessment_engine/docs/DEPLOYMENT_GUIDE.md

Deployment Guide - Standalone Production

✅ Project Status: 100% Standalone

This project is completely self-contained - all files and dependencies are within the Simulated_Assessment_Engine directory. No external file dependencies.

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Quick Deployment

Step 1: Copy Project

Copy the entire Simulated_Assessment_Engine folder to your target location:

# Example: Copy to production server
cp -r Simulated_Assessment_Engine /path/to/production/
# Or on Windows:
xcopy Simulated_Assessment_Engine C:\production\Simulated_Assessment_Engine /E /I

Step 2: Set Up Python Environment

Using Virtual Environment (Recommended):

cd Simulated_Assessment_Engine

# Create virtual environment
python -m venv venv

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

# Install dependencies
pip install pandas anthropic openpyxl python-dotenv

Step 3: Configure API Key

Create .env file in project root:

# Windows (PowerShell)
echo "ANTHROPIC_API_KEY=sk-ant-api03-..." > .env

# macOS/Linux
echo "ANTHROPIC_API_KEY=sk-ant-api03-..." > .env

Or manually create .env file with:

ANTHROPIC_API_KEY=sk-ant-api03-...

Step 4: Verify Standalone Status

Run production verification:

python scripts/final_production_verification.py

Expected Output: ✅ PRODUCTION READY - ALL CHECKS PASSED

Step 5: Prepare Data (First Time Only)

Ensure support files are in support/ folder:

• support/3000-students.xlsx
• support/3000_students_output.xlsx
• support/fixed_3k_personas.xlsx

Then run:

python scripts/prepare_data.py

This creates data/merged_personas.xlsx (79 columns, 3000 rows).

Step 6: Run Pipeline

Option A: Complete Pipeline (All 3 Steps):

python run_complete_pipeline.py --all

Option B: Individual Steps:

# Step 1: Prepare personas (if needed)
python scripts/prepare_data.py

# Step 2: Run simulation
python main.py --full

# Step 3: Post-process
python scripts/comprehensive_post_processor.py

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File Structure Verification

After deployment, verify this structure exists:

Simulated_Assessment_Engine/
├── .env                          # API key (create this)
├── data/
│   ├── AllQuestions.xlsx         # ✅ Required
│   └── merged_personas.xlsx     # ✅ Generated by Step 1
├── support/
│   ├── 3000-students.xlsx       # ✅ Required for Step 1
│   ├── 3000_students_output.xlsx # ✅ Required for Step 1
│   └── fixed_3k_personas.xlsx   # ✅ Required for Step 1
├── scripts/
│   ├── prepare_data.py          # ✅ Step 1
│   ├── comprehensive_post_processor.py  # ✅ Step 3
│   └── final_production_verification.py # ✅ Verification
├── services/
│   ├── data_loader.py           # ✅ Core service
│   ├── simulator.py             # ✅ Core service
│   └── cognition_simulator.py   # ✅ Core service
├── main.py                       # ✅ Step 2
├── config.py                     # ✅ Configuration
└── run_complete_pipeline.py      # ✅ Orchestrator

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Verification Checklist

Before running production:

• Project folder copied to target location
• Python 3.8+ installed
• Virtual environment created and activated (recommended)
• Dependencies installed (pip install pandas anthropic openpyxl python-dotenv)
• .env file created with ANTHROPIC_API_KEY
• Support files present in support/ folder
• Verification script passes: python scripts/final_production_verification.py
• data/merged_personas.xlsx generated (79 columns, 3000 rows)
• API connection verified: python check_api.py

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Troubleshooting

Issue: "ModuleNotFoundError: No module named 'pandas'"

Solution: Activate virtual environment or install dependencies:

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

# Then install
pip install pandas anthropic openpyxl python-dotenv

Issue: "FileNotFoundError: 3000-students.xlsx not found"

Solution: Ensure files are in support/ folder:

• support/3000-students.xlsx
• support/3000_students_output.xlsx
• support/fixed_3k_personas.xlsx

Issue: "ANTHROPIC_API_KEY not found"

Solution: Create .env file in project root with:

ANTHROPIC_API_KEY=sk-ant-api03-...

Issue: Verification fails

Solution: Run verification script to see specific issues:

python scripts/final_production_verification.py

Check the output for specific file path or dependency issues.

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Cross-Platform Compatibility

Windows

• ✅ Tested on Windows 10/11
• ✅ Path handling: Uses pathlib.Path (cross-platform)
• ✅ Encoding: UTF-8 with Windows console fix

macOS/Linux

• ✅ Compatible (uses relative paths)
• ✅ Virtual environment: source venv/bin/activate
• ✅ Path separators: Handled by pathlib

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Production Deployment Checklist

• All file paths use relative resolution
• No hardcoded external paths
• All dependencies are Python packages (no external files)
• Virtual environment instructions included
• Verification script available
• Documentation complete
• Code evidence verified

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Support

For deployment issues:

1. Run python scripts/final_production_verification.py to identify issues
2. Check production_verification_report.json for detailed report
3. Verify all files in support/ folder exist
4. Ensure .env file is in project root

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Status: ✅ 100% Standalone - Ready for Production Deployment