from omnidimension import Client

client = Client("your_api_key_here")

restaurant_agent = client.agent.create(
    name="Arnav - Restaurant Assistant",
    welcome_message="Namaste! This is Arnav from Spice Garden Restaurant. I'd be happy to take your order for authentic North Indian cuisine. How may I help you today?",
    context_breakdown=[
        {
            "title": "Restaurant Menu",
            "body": """APPETIZERS: Vegetable Samosas ₹120 | Paneer Tikka ₹220 | Chicken Tikka ₹250 | Aloo Chaat ₹150
            
MAIN COURSES: Butter Chicken ₹320 | Palak Paneer ₹280 | Dal Makhani ₹240 | Chicken Curry ₹300 | Lamb Biryani ₹380
            
BREADS & RICE: Plain Naan ₹50 | Garlic Naan ₹70 | Butter Naan ₹60 | Basmati Rice ₹80 | Vegetable Biryani ₹220
            
BEVERAGES: Lassi ₹80 | Masala Chai ₹40 | Fresh Lime Soda ₹60"""
        },
        {
            "title": "Order Process",
            "body": "1. Greet customer warmly 2. Take complete order with quantities 3. Confirm items and total 4. Collect delivery address 5. Ask for payment preference 6. Provide order confirmation and estimated delivery time"
        },
        {
            "title": "Restaurant Policies",
            "body": "Delivery available within 5km radius. Minimum order ₹200. Free delivery above ₹500. Payment methods: Cash on delivery, UPI, Card. Standard delivery time: 30-45 minutes."
        }
    ],
    call_type="Incoming",
    voice={
        "provider": "eleven_labs",
        "voice_id": "JBFqnCBsd6RMkjVDRZzb"
    },
    model={
        "provider": "anthropic",
        "model": "gpt-4o-mini", 
        "temperature": 0.7
    }
)

agent_id = restaurant_agent["json"]["id"]
print(f"Restaurant agent created with ID: {agent_id}")

import base64

def upload_restaurant_menu(agent_id, menu_pdf_path):
    """Upload menu PDF to knowledge base and attach to agent"""
    
    # Read and encode the PDF file
    with open(menu_pdf_path, "rb") as file:
        file_data = base64.b64encode(file.read()).decode('utf-8')
    
    # Upload to knowledge base
    kb_response = client.knowledge_base.create(file_data, "Restaurant_Menu.pdf")
    file_id = kb_response["json"]["file"]["id"]
    
    # Attach to agent
    attach_response = client.knowledge_base.attach([file_id], agent_id)
    
    print(f"Menu uploaded successfully. File ID: {file_id}")
    return file_id

# Upload your restaurant menu
menu_file_id = upload_restaurant_menu(agent_id, "restaurant_menu.pdf")

def launch_marketing_campaign(agent_id, customer_list, offer_message):
    """Launch outbound marketing calls to customer list"""
    
    # Update agent for marketing calls
    marketing_message = f"Hello! This is Arnav from Spice Garden Restaurant. {offer_message} Would you like to place an order today?"
    
    client.agent.update(
        agent_id,
        welcome_message=marketing_message,
        call_type="Outgoing"
    )
    
    # Dispatch calls to customers
    call_results = []
    for customer_phone in customer_list:
        try:
            call_response = client.call.dispatch_call(
                agent_id=agent_id,
                to_number=customer_phone,
                call_context="Marketing campaign - Special dinner offer"
            )
            call_results.append({
                "phone": customer_phone,
                "status": "dispatched",
                "call_id": call_response.get("call_id")
            })
        except Exception as e:
            call_results.append({
                "phone": customer_phone, 
                "status": "failed",
                "error": str(e)
            })
    
    return call_results

# Launch dinner special campaign
dinner_customers = [
    "+919876543210",
    "+919876543211", 
    "+919876543212"
]

offer_text = "We have a special 20% discount on our dinner combo meals today!"
campaign_results = launch_marketing_campaign(agent_id, dinner_customers, offer_text)

print("Marketing campaign launched:")
for result in campaign_results:
    print(f"{result['phone']}: {result['status']}")

def get_restaurant_analytics(agent_id, days=7):
    """Get comprehensive analytics for restaurant agent"""
    
    # Get recent call logs
    call_logs = client.call.get_call_logs(agent_id=agent_id, page_size=100)
    calls_data = call_logs.get('data', [])
    
    # Calculate metrics
    total_calls = len(calls_data)
    successful_calls = sum(1 for call in calls_data if call.get('status') == 'completed')
    average_duration = sum(call.get('duration', 0) for call in calls_data) / total_calls if total_calls > 0 else 0
    
    # Generate report
    analytics_report = {
        "period_days": days,
        "total_calls": total_calls,
        "successful_calls": successful_calls,
        "completion_rate": f"{(successful_calls/total_calls*100):.1f}%" if total_calls > 0 else "0%",
        "average_call_duration": f"{average_duration:.1f} seconds",
        "recent_calls": calls_data[:5]  # Last 5 calls
    }
    
    return analytics_report

# Get weekly performance report
weekly_report = get_restaurant_analytics(agent_id, days=7)

print("Restaurant Agent Performance Report")
print(f"Total Calls: {weekly_report['total_calls']}")
print(f"Successful Calls: {weekly_report['successful_calls']}")
print(f"Completion Rate: {weekly_report['completion_rate']}")
print(f"Average Call Duration: {weekly_report['average_call_duration']}")

def update_agent_details(agent_id, **kwargs):
    """Update agent configuration"""
    response = client.agent.update(agent_id, **kwargs)
    print(f"Agent updated successfully")
    return response

# Update welcome message for dinner hours
update_agent_details(
    agent_id,
    welcome_message="Good evening! Welcome to Spice Garden Restaurant. Our dinner specials are ready! How can I help you?"
)

# Switch back to incoming calls after marketing campaign
update_agent_details(
    agent_id,
    call_type="Incoming"
)

from omnidimension import Client
import base64

class RestaurantVoiceAgent:
    def __init__(self, api_key):
        self.client = Client(api_key)
        self.agent_id = None
        
    def setup_complete_restaurant_agent(self):
        """Set up a complete restaurant voice agent with all features"""
        
        # Create the main agent
        agent = self.client.agent.create(
            name="Spice Garden - Voice Assistant",
            welcome_message="Namaste! Welcome to Spice Garden Restaurant. I'm here to help you with orders, reservations, and any questions. How can I assist you today?",
            context_breakdown=[
                {
                    "title": "Full Menu & Pricing",
                    "body": """
                    APPETIZERS: Veg Samosas ₹120 | Paneer Tikka ₹220 | Chicken Tikka ₹250 | Fish Tikka ₹280 | Aloo Chaat ₹150
                    
                    MAIN COURSES: Butter Chicken ₹320 | Palak Paneer ₹280 | Dal Makhani ₹240 | Chicken Curry ₹300 | Lamb Biryani ₹380 | Fish Curry ₹300
                    
                    BREADS: Plain Naan ₹50 | Garlic Naan ₹70 | Butter Naan ₹60 | Roti ₹30 | Kulcha ₹60
                    
                    RICE: Basmati Rice ₹80 | Veg Biryani ₹220 | Jeera Rice ₹100
                    
                    BEVERAGES: Sweet Lassi ₹80 | Salted Lassi ₹80 | Masala Chai ₹40 | Fresh Lime ₹60 | Cold Coffee ₹90
                    
                    DESSERTS: Gulab Jamun ₹100 | Rasmalai ₹120 | Kulfi ₹80
                    """
                },
                {
                    "title": "Service Guidelines",
                    "body": "Always be warm and helpful. Suggest popular combinations. Inform about cooking time for special requests. Confirm orders clearly before processing. Handle complaints with empathy."
                },
                {
                    "title": "Operational Details",
                    "body": "Operating hours: 11 AM - 11 PM daily. Delivery radius: 5km. Minimum order: ₹200. Free delivery above ₹500. Estimated delivery: 30-45 minutes. Payment: Cash, UPI, Cards accepted."
                }
            ],
            call_type="Incoming",
            voice={
                "provider": "eleven_labs",
                "voice_id": "JBFqnCBsd6RMkjVDRZzb"
            },
            model={
                "provider": "anthropic",
                "model": "gpt-4o-mini",
                "temperature": 0.7
            }
        )
        
        self.agent_id = agent["json"]["id"]
        print(f"Restaurant agent created: {self.agent_id}")
        
        return self.agent_id
    
    def get_performance_summary(self):
        """Get a quick performance summary"""
        if not self.agent_id:
            return "No agent configured"
            
        calls = self.client.call.get_call_logs(agent_id=self.agent_id, page_size=50)
        total_calls = len(calls.get('data', []))
        
        return {
            "agent_id": self.agent_id,
            "total_calls_handled": total_calls,
            "status": "Active and ready for orders!"
        }
    
    def upload_menu_pdf(self, menu_pdf_path):
        """Upload menu PDF to knowledge base"""
        if not self.agent_id:
            print("Please create agent first")
            return None
            
        with open(menu_pdf_path, "rb") as file:
            file_data = base64.b64encode(file.read()).decode('utf-8')
        
        kb_response = self.client.knowledge_base.create(file_data, "Restaurant_Menu.pdf")
        file_id = kb_response["json"]["file"]["id"]
        
        self.client.knowledge_base.attach([file_id], self.agent_id)
        print(f"Menu PDF uploaded: {file_id}")
        return file_id

# Initialize and deploy restaurant agent
restaurant = RestaurantVoiceAgent("your_api_key_here")
agent_id = restaurant.setup_complete_restaurant_agent()

# Upload menu if you have a PDF
# restaurant.upload_menu_pdf("restaurant_menu.pdf")

print("
Your restaurant voice agent is now live!")
print(f"Agent ID: {agent_id}")
print("Features enabled:")
print("Order taking with menu knowledge")
print("Customer service and inquiries")  
print("Call analytics and monitoring")
print("Outbound marketing campaigns")
print("Agent configuration updates")

# Check status
summary = restaurant.get_performance_summary()
print(f"
Current Status: {summary['status']}")