docs/INTEGRATION_SUMMARY.md

Rule-Based Analysis Integration Summary

✅ COMPLETED CHANGES

1. Report Generator (core/utils/report_generator.py)

• Line 298: Added "ruleBased_result": rule_result to final JSON output
• Location: Right after vitResult field
• Contains: Complete output from core/engines/rules.py

2. Flask Server (apps/flask_server.py)

• Lines 147-158: Added error handling for AI Agent analysis with fallback to rule-based results
• Lines 209-233: Added error handling for recommendations generation with fallback
• Lines 235-255: Added error handling for final report generation with fallback

3. AI Agent Diagnosis (core/agents/diagnosis.py)

• Lines 483-507: Added comprehensive error handling for Agent 2 (file upload, empty response, generation failures)
• Lines 527-530: Added error handling in merge function to gracefully handle Agent 2 failures

4. Recommendations Agent (core/agents/recommendation.py)

• Lines 363-376: Added empty response detection and handling

📋 OUTPUT STRUCTURE

The final JSON now includes:

{
  "_id": "...",
  "aiVerdict": { ... },
  "cbhi": { ... },
  "kpis": [ ... ],
  "phaseWiseAnalysis": [ ... ],
  
  "vitResult": {
    "class": "Main Contact Wear",
    "confidence": 0.92,
    "details": "..."
  },
  
  "ruleBased_result": {
    "Fault_Detection": [
      {
        "defect_name": "Main Contact Wear",
        "Confidence": "88.00 %",
        "Severity": "High",
        "description": "CRITICAL wear: Resistance 311.3 µΩ..."
      }
    ],
    "overall_health_assessment": {
      "Contacts (moving & arcing)": "High Risk",
      "SF6 Gas Chamber": "Normal",
      "Operating Mechanism": "Normal",
      "Coil": "Normal"
    },
    "classifications": [
      {"Class": "Healthy", "Confidence": 0.0},
      {"Class": "Main Contact Wear", "Confidence": 0.88},
      {"Class": "Arcing Contact Wear", "Confidence": 0.0},
      // ... all 12 classes
    ]
  },
  
  "status": "completed",
  "waveform": [ ... ]
}

🔧 ERROR HANDLING IMPROVEMENTS

API Quota/Permission Errors

• Problem: Google Gemini API quota exceeded or file upload permission denied
• Solution:
  • AI Agent failures → fallback to rule-based results
  • Recommendations failures → fallback to basic recommendations from rule faults
  • Report generation failures → minimal valid report with available data

Empty Response Handling

• Problem: API returns empty response (finish_reason = 2 = safety/quota)
• Solution: Detect empty responses and retry or skip gracefully

Parallel Processing Safety

• Problem: 3 phases running in parallel may hit API rate limits
• Solution: Each phase has independent error handling and fallback mechanisms

🚀 HOW IT WORKS

Data Flow for Each Phase:

1. KPI Calculation → Always succeeds
2. Phase Segmentation (LLM-based) → May fail, uses programmatic fallback
3. Rule Engine → Always succeeds (physics-based, no API calls)
4. AI Agent → May fail → Fallback to Rule Engine results
5. ViT Model → May fail → vitResult: null
6. CBHI Calculation → Always succeeds
7. Recommendations → May fail → Fallback to basic recommendations
8. Final Report → May fail → Fallback to minimal valid JSON

Key Integration Point:

# In flask_server.py - Line 147
rule_engine_result = analyze_dcrm_advanced(row_values, raj_kpis)  # Always runs

# In report_generator.py - Line 298
"ruleBased_result": rule_result  # Always included in output

✅ VERIFICATION

The integration ensures:

1. ✅ Rule-based analysis always runs (no API dependency)
2. ✅ Rule-based result always appears in final JSON
3. ✅ If AI fails, rule-based serves as reliable fallback
4. ✅ All 3 phases return valid JSON even if some components fail
5. ✅ ruleBased_result contains all 12 defect class probabilities

🎯 ADVANTAGES

1. Reliability: Physics-based analysis never fails
2. Transparency: All 12 defect probabilities visible
3. Redundancy: AI agent can use rule results as backup
4. Consistency: Same rule engine for all phases
5. Speed: No API call delays for rule-based analysis

📝 SOURCE CODE LOCATIONS

• Rule Engine: core/engines/rules.py (function: analyze_dcrm_advanced)
• Integration Point 1: apps/flask_server.py (line 147)
• Integration Point 2: core/utils/report_generator.py (line 298)
• API Key Fallback: apps/flask_server.py (lines 147-158)