### Real-time Integration with Baserow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Provides an example of integrating contact forms in a web application with Baserow to automatically create records, using the baserow_integration library.

```python
# In your web application
from baserow_integration import get_integrator

def handle_contact_form_submission(form_data):
    integrator = get_integrator()
    result = integrator.sync_contact_form(form_data, DATABASE_ID)
    return result
```

--------------------------------

### Configure Baserow Integration

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Sets up the Baserow integration by prompting the user for their API token. This is a crucial step for authenticating with the Baserow API.

```bash
cd /opt/docker-stack/baserow_integration
python3 setup.py
```

--------------------------------

### Debug Mode

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Enables verbose output for debugging purposes when running the command-line interface.

```bash
python3 cli.py --verbose health
```

--------------------------------

### Batch Operations with Baserow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Illustrates how to perform batch operations, such as creating multiple property records simultaneously, using the Baserow client.

```python
# Create multiple properties at once
properties_data = [
    {'Property Address': '123 Main St', 'Status': 'Lead'},
    {'Property Address': '456 Oak Ave', 'Status': 'Under Review'}
]
client.batch_create_rows(table_id, properties_data)
```

--------------------------------

### API Monitoring with Baserow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Demonstrates how to perform a health check on the Baserow API to monitor its status and performance.

```python
client = get_client()
health = client.health_check()
# Monitor health['status'] for issues
```

--------------------------------

### Run Demo Workflow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Executes the complete demonstration workflow for the property analyzer. This includes testing API connectivity, creating a database, importing data, performing financial and property assessments, and generating analytics.

```bash
python3 example_workflow.py
```

--------------------------------

### Example Contact Data JSON Format

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

An example of the JSON structure required for importing contact form data. Each object represents a contact with fields like ID, name, email, phone, property address, desired price, and reason for selling.

```json
[
  {
    "id": "FORM_12345",
    "firstName": "John",
    "lastName": "Smith", 
    "email": "john.smith@email.com",
    "phone": "555-123-4567",
    "property_address": "123 Main St, Phoenix, AZ 85001",
    "desired_price": "250000",
    "reason_for_selling": "Job relocation - need quick sale"
  },
  {
    "id": "FORM_12346",
    "firstName": "Sarah",
    "lastName": "Johnson",
    "email": "sarah.j@email.com", 
    "phone": "813-987-6543",
    "property_address": "456 Oak Ave, Tampa, FL 33601",
    "desired_price": "180000",
    "reason_for_selling": "Inherited property, need cash"
  }
]
```

--------------------------------

### Baserow CLI Common Commands

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Provides a set of common commands for interacting with the Baserow Property Analysis system via its command-line interface (CLI).

```bash
# Health Check
python3 cli.py health

# List Resources
python3 cli.py list

# View Schema
python3 cli.py schema

# Import Data
python3 cli.py import --database-id 123 --data contacts.json

# Delete Database
python3 cli.py delete --database-id 123
```

--------------------------------

### Create Baserow Database

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Creates a new database in Baserow. It can either create an empty database with a specified name or create one with sample data for testing purposes.

```bash
# Create with sample data for testing
python3 cli.py create --sample

# Or create empty database
python3 cli.py create --name "My Property Analysis"
```

--------------------------------

### Log Analysis

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Configures basic logging to display debug messages for analyzing application behavior.

```python
import logging
logging.basicConfig(level=logging.DEBUG)
```

--------------------------------

### Install Property Analyzer

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Clones the repository, navigates to the directory, and installs project dependencies using pip.

```bash
git clone https://github.com/bigbrolgnd/propertyanalyzer.git
cd propertyanalyzer
pip install -r requirements.txt
```

--------------------------------

### Import Contact Data to Baserow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Imports contact form data into a specified Baserow database. The data should be in JSON format.

```bash
# Prepare your contact form data as JSON (see example below)
python3 cli.py import --database-id YOUR_DB_ID --data your_contacts.json
```

--------------------------------

### Custom Import Logic with Baserow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Shows how to implement custom import logic using the baserow_integration library to synchronize contact form data with a Baserow database.

```python
from baserow_integration import get_integrator

integrator = get_integrator()
result = integrator.sync_contact_form(form_data, database_id)
```

--------------------------------

### Add Custom Fields with Baserow

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/GETTING_STARTED.md

Demonstrates how to add custom text fields to a Baserow table using the baserow_integration library. Requires a valid table ID.

```python
from baserow_integration import get_client

client = get_client()
custom_field = client.create_text_field(
    table_id=YOUR_TABLE_ID,
    name="Custom Field"
)
```

--------------------------------

### Property Analyzer CLI Commands

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Lists available commands for the Property Analyzer CLI, including their descriptions and example usage for managing databases, importing data, and checking system status.

```APIDOC
CLI Commands:

- `health`: Check API connectivity.
  Example: `python cli.py health`

- `list`: List available databases.
  Example: `python cli.py list`

- `create`: Create a new database.
  Parameters:
    --name: Name of the new database (string, required)
    --description: Description for the database (string, optional)
  Example: `python cli.py create --name "Portfolio"`

- `import`: Import contact data from a file.
  Parameters:
    <file_path>: Path to the data file (e.g., JSON, CSV) (string, required)
    --table: Baserow table ID to import into (integer, required)
  Example: `python cli.py import leads.json --table 492`

- `schema`: Show schema information for a database.
  Parameters:
    --database: The ID of the database (integer, required)
  Example: `python cli.py schema --database 95`

- `export-schema`: Export the database schema as a JSON file.
  Parameters:
    --database: The ID of the database (integer, required)
  Example: `python cli.py export-schema --database 95`
```

--------------------------------

### Validate Property Data using Python

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Demonstrates how to use the `validate_property` function from the `property_validator` module to analyze property data, check for red flags, and get investment recommendations.

```python
from property_validator import validate_property

property_data = {
    'asking_price': '598888',
    'tax_assessment': '100014', 
    'property_address': '123 Main St, Fort Worth, TX',
    'reason_for_selling': 'Need quick sale'
}

results = validate_property(property_data)

# Check for red flags
print(f"🚩 Red Flags: {results['validation_summary']['total_red_flags']}")
print(f"📊 Risk Level: {results['validation_summary']['overall_risk']}")
print(f"💡 Recommendation: {results['recommendation']}")

# View corrected analysis
analysis = results['corrected_analysis']
print(f"💰 Realistic Value: ${results['realistic_value']:,.0f}")
print(f"🎯 Max Offer (70% rule): ${analysis['max_offer_70_rule']:,.0f}")
print(f"⭐ Deal Quality: {analysis['deal_quality']}")
```

--------------------------------

### Create Property Analysis Database via CLI

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Uses the command-line interface to create a new database in Baserow for property analysis, specifying its name and description.

```bash
python cli.py create --name "Property Analysis" --description "Investment analysis database"
```

--------------------------------

### Create Property Analysis Database Schema using Python

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Shows how to programmatically create a property analysis database schema using the `create_property_analysis_database` function, specifying the database name and description.

```python
from schema_generator import create_property_analysis_database

database_id = create_property_analysis_database(
    database_name="Real Estate Portfolio",
    description="Property investment tracking and analysis"
)
```

--------------------------------

### Configure Baserow Credentials

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Sets environment variables for Baserow URL, email, and password, which are required for connecting to the Baserow instance.

```bash
export BASEROW_URL="https://your-baserow-instance.com"
export BASEROW_EMAIL="your-email@domain.com"
export BASEROW_PASSWORD="your-password"
```

--------------------------------

### Validate Existing Leads via CLI

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Utilizes the CLI to validate existing leads by providing the Baserow table ID where the lead data is stored.

```bash
python validate_lead.py --table-id 492
```

--------------------------------

### Check System Health via CLI

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Executes a command-line check to verify the system's health and connectivity.

```bash
python cli.py health
```

--------------------------------

### Import Contact Data using Python

Source: https://github.com/bigbrolgnd/propertyanalyzer/blob/main/README.md

Illustrates how to import contact data from a JSON file into a specified Baserow table using the `import_contacts` function.

```python
from contact_integration import import_contacts

# Import from JSON file
imported = import_contacts('leads.json', table_id=492)
print(f"Imported {imported} contacts")
```

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