Tagmaster Python Client Documentation

# Get all projects for your account
projects = client.get_projects()

# Display project information
for project in projects:
    print(f"Project Name: {project['name']}")
    print(f"Project UUID: {project['uuid']}")
    print(f"Description: {project.get('description', 'No description')}")
    print(f"Created: {project['createdAt']}")
    print("---")

print(f"Total projects: {len(projects)}")

# Create a new project
project = client.create_project(
    name="Customer Support System",
    description="AI-powered customer support ticket classification system"
)

# Verify creation
if project.get('uuid'):
    print(f"✅ Project created successfully!")
    print(f"Name: {project['name']}")
    print(f"UUID: {project['uuid']}")
    print(f"Description: {project['description']}")
else:
    print(f"❌ Project creation failed: {project.get('error')}")

# Update project
updated_project = client.update_project(
    project_uuid="your-project-uuid",
    name="Updated Project Name",  # Optional
    description="Updated description"  # Optional
)

print(f"Updated project: {updated_project['name']}")

# Delete project
success = client.delete_project("your-project-uuid")

if success:
    print("Project deleted successfully")
else:
    print("Failed to delete project")

# Get categories for a project
categories = client.get_categories("your-project-uuid")

# Print category details
for category in categories:
    print(f"Name: {category['name']}")
    print(f"Description: {category.get('description', 'No description')}")
    print(f"UUID: {category['uuid']}")
    print("---")

# Create a new category
category = client.create_category(
    project_uuid="your-project-uuid",
    name="Login Issues",
    description="Problems with user authentication and login"
)

print(f"Created category: {category['name']}")
print(f"Category UUID: {category['uuid']}")

# Classify text content
text_to_classify = "Customer is having trouble logging into their account"

result = client.classify_text(text_to_classify)

# Process and analyze results
if result.get('success'):
    print(f"🎯 Classification Results for: '{text_to_classify}'")
    print("=" * 50)
    
    classifications = result.get('classifications', [])
    
    for i, classification in enumerate(classifications, 1):
        print(f"
{i}. Category: {classification['categoryName']}")
        print(f"   Match Percentage: {classification['matchPercentage']:.1f}%")
        print(f"   Confidence Score: {classification['confidence']:.3f}")
        print(f"   AI Reasoning: {classification['aiReasoning']}")
        print(f"   Category Description: {classification.get('description', 'N/A')}")
    
    print(f"
📊 Summary: {len(classifications)} categories matched")
    print(f"⚡ Response Time: {result.get('responseTime', 'N/A')}ms")
else:
    print(f"❌ Classification failed: {result.get('error')}")
    print(f"Error details: {result.get('details', 'No additional details')}")

# Classify image from URL
result = client.classify_image("https://example.com/image.jpg")

# Process results
if result.get('success'):
    classifications = result.get('classifications', [])
    
    for classification in classifications:
        print(f"Category: {classification['categoryName']}")
        print(f"Match: {classification['matchPercentage']}%")
        print(f"Confidence: {classification['confidence']}")
        print(f"Reasoning: {classification['aiReasoning']}")
        print("---")
else:
    print(f"Image classification failed: {result.get('error')}")

# Get classification history with filters
history = client.get_classification_history(
    limit=50,                    # Number of records (max 100)
    offset=0,                    # Number of records to skip
    classification_type='text',  # 'text' or 'image'
    success=True,                # Filter by success status
    start_date='2024-01-01',    # Start date filter
    end_date='2024-12-31'       # End date filter
)

# Process history
requests = history.get('requests', [])
print(f"Retrieved {len(requests)} classification requests")

for req in requests:
    print(f"Type: {req.get('type')}")
    print(f"Success: {req.get('success')}")
    print(f"Created: {req.get('createdAt')}")
    print(f"Response time: {req.get('responseTime')}ms")
    print("---")

# Get classification statistics
stats = client.get_classification_stats(
    start_date='2024-01-01',  # Optional start date
    end_date='2024-12-31'     # Optional end date
)

if stats.get('success'):
    statistics = stats.get('statistics', {})
    print("Classification Statistics:")
    print(f"  Total requests: {statistics.get('totalRequests')}")
    print(f"  Successful: {statistics.get('successfulRequests')}")
    print(f"  Failed: {statistics.get('failedRequests')}")
    print(f"  Average response time: {statistics.get('averageResponseTime')}ms")
    print(f"  Success rate: {statistics.get('successRate', 0):.1f}%")

from tagmaster import TagmasterClassificationClient
import json
from datetime import datetime

class CustomerSupportClassifier:
    def __init__(self, api_key):
        self.client = TagmasterClassificationClient(api_key=api_key)
        self.project_uuid = None
        self.categories = {}
    
    def setup_project(self):
        """Create project and categories for customer support"""
        # Create project
        project = self.client.create_project(
            name="Customer Support System",
            description="Automated classification of customer support tickets"
        )
        self.project_uuid = project['uuid']
        print(f"✅ Project created: {project['name']}")
        
        # Define categories
        category_definitions = [
            ("Login Issues", "Authentication and login problems"),
            ("Password Reset", "Password recovery and reset requests"),
            ("Billing", "Payment, billing, and subscription questions"),
            ("Technical", "Technical support and troubleshooting"),
            ("Feature Request", "New feature requests and suggestions"),
            ("General", "General inquiries and other questions")
        ]
        
        # Create categories
        for name, description in category_definitions:
            category = self.client.create_category(
                project_uuid=self.project_uuid,
                name=name,
                description=description
            )
            self.categories[name] = category['uuid']
            print(f"✅ Category created: {name}")
    
    def classify_ticket(self, ticket_text):
        """Classify a customer support ticket"""
        result = self.client.classify_text(ticket_text)
        
        if result.get('success'):
            top_match = result['classifications'][0]
            return {
                'category': top_match['categoryName'],
                'confidence': top_match['confidence'],
                'reasoning': top_match['aiReasoning'],
                'timestamp': datetime.now().isoformat()
            }
        else:
            return {'error': result.get('error')}
    
    def process_batch(self, tickets):
        """Process multiple tickets in batch"""
        results = []
        for i, ticket in enumerate(tickets, 1):
            print(f"Processing ticket {i}/{len(tickets)}...")
            result = self.classify_ticket(ticket)
            results.append({
                'ticket_id': i,
                'text': ticket,
                'classification': result
            })
        return results

# Usage example
if __name__ == "__main__":
    classifier = CustomerSupportClassifier(api_key="your-api-key")
    
    # Setup project and categories
    classifier.setup_project()
    
    # Sample tickets
    sample_tickets = [
        "User can't log in to account",
        "Need help resetting password",
        "Payment was charged twice",
        "App is crashing on startup",
        "Can you add dark mode feature?",
        "What are your business hours?"
    ]
    
    # Process tickets
    results = classifier.process_batch(sample_tickets)
    
    # Save results
    with open('classification_results.json', 'w') as f:
        json.dump(results, f, indent=2)
    
    print("
🎉 Classification complete! Results saved to classification_results.json")