# 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