openclaw-workspace-2026/Projects/site-survey-ai/CODE_STRUCTURE_ANALYSIS.md

IT Site Survey AI - Code Structure Analysis

Overview

The application is a Flask-based web application that provides IT infrastructure site survey functionality with AI-powered analysis and recommendations.

Main Components

1. Core Application (app.py)

• Lines of Code: 792
• Framework: Flask
• Key Dependencies: requests, reportlab, flask-cors
• External Services: Ollama AI at http://192.168.19.25:11434

2. Data Model

• Template: concise_template.json (21 questions across 5 categories)
• In-Memory Storage:
  • surveys_db - Dictionary of survey definitions
  • survey_responses_db - Dictionary of survey responses
• Data Structure:
  • Surveys with metadata and questions
  • Responses with submission data and answers

3. Key Routes

Survey Management

• GET /api/surveys/template - Get survey template
• GET /api/surveys - List all surveys
• POST /api/surveys - Create new survey
• GET /api/surveys/<survey_id> - Get specific survey

Response Handling

• POST /api/surveys/<survey_id>/responses - Submit survey response
• GET /api/surveys/responses - Get all responses (recently added)

AI Analysis

• POST /api/surveys/<survey_id>/analyze - Generate AI recommendations
• POST /api/surveys/<survey_id>/generate-quote - Generate service quote

Export Functions

• POST /api/surveys/<survey_id>/export/pdf - Export to PDF
• POST /api/surveys/<survey_id>/export/text - Export to text
• POST /api/surveys/<survey_id>/export/email - Export to email format

System

• GET / - Main application page
• GET /<path:path> - Static file serving
• GET /api/health - Health check
• GET /api/ollama-status - Ollama connectivity check

4. Frontend Files

• index.html - Main survey interface (18,263 bytes)
• dashboard.html - Response dashboard (337 bytes)

5. Template Structure

21 Questions Across 5 Categories:

1. Client Information (2 questions)

   • Company Name (text)
   • Industry/Vertical (select)

2. Site Information (3 questions)

   • Site Size (select)
   • Building Type (select)
   • Square Footage (select)

3. Existing Infrastructure (4 questions)

   • Network Topology (select)
   • Network Size (select)
   • Internet Bandwidth (select)
   • ISP Connection Type (multiselect)

4. Wireless Infrastructure (2 questions)

   • Wireless Standard (select)
   • Wireless Coverage Quality (select)

5. Business Requirements (10 questions)

   • Critical Applications (multiselect)
   • Device Types (multiselect)
   • User Density (select)
   • Performance Requirements (multiselect)
   • Security Requirements (multiselect)
   • Compliance Requirements (select)
   • Budget Range (select)
   • Project Timeline (select)
   • Additional Notes (textarea)

Current Limitations

1. Data Persistence

• Issue: All data stored in memory (lost on restart)
• Impact: Critical for production use
• Solution Needed: Database persistence

2. Security

• Issue: No authentication on dashboard
• Impact: Survey data publicly accessible
• Solution Needed: User authentication

3. Scalability

• Issue: Single-process Flask application
• Impact: Limited concurrent users
• Solution Needed: Multi-process or async handling

Database Schema Requirements

For PostgreSQL implementation, the following tables would be needed:

surveys

• id (UUID, PK)
• name (VARCHAR)
• description (TEXT)
• client_name (VARCHAR)
• site_name (VARCHAR)
• created_at (TIMESTAMP)
• status (VARCHAR)

survey_questions

• id (UUID, PK)
• survey_id (UUID, FK)
• question_id (VARCHAR)
• category (VARCHAR)
• question_text (TEXT)
• question_type (VARCHAR)
• options (JSON)

survey_responses

• id (UUID, PK)
• survey_id (UUID, FK)
• submitted_by (VARCHAR)
• submitted_at (TIMESTAMP)

response_answers

• id (UUID, PK)
• response_id (UUID, FK)
• question_id (VARCHAR)
• answer_value (TEXT or JSON)

Implementation Considerations

1. Backward Compatibility

• All existing API endpoints must continue working
• No breaking changes to data structure
• Maintain same JSON response formats

2. Migration Strategy

• Seamless transition from memory to database
• No data loss during migration
• Fallback to memory if database unavailable

3. Configuration

• Database connection via environment variables
• Default to memory storage if no DB configured
• Clear setup instructions for PostgreSQL

4. Error Handling

• Graceful degradation if database unavailable
• Clear error messages for connectivity issues
• Logging for debugging database issues

Dependencies to Add

• psycopg2-binary - PostgreSQL driver
• sqlalchemy - ORM (optional but recommended)
• Database connection pooling

Estimated Implementation Effort

• Schema Design: 2 hours
• Database Integration: 6 hours
• Migration Logic: 3 hours
• Testing: 3 hours
• Documentation: 2 hours
• Total: 16 hours