Complete Implementation

from dotenv import load_dotenv
from livekit import agents
from livekit.agents import AgentSession, Agent, RoomInputOptions
from livekit.plugins import (
    openai,
    deepgram,
    noise_cancellation,
    silero,
    elevenlabs,
)
from whispey import LivekitObserve
import os

# Load environment variables
load_dotenv()

# Initialize Whispey with your Agent ID from the dashboard
whispey = LivekitObserve(
    agent_id="your-agent-id-from-dashboard",  # Get this from dashboard
    apikey=os.getenv("WHISPEY_API_KEY")
)

class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant.")

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
        
    # Configure your AI agent session
    session = AgentSession(
        stt=deepgram.STT(model="nova-3", language="multi"),
        llm=openai.LLM(model="gpt-4o-mini"),
        tts=elevenlabs.TTS(
            voice_id="H8bdWZHK2OgZwTN7ponr",
            model="eleven_flash_v2_5",
            language="en",
            voice_settings=elevenlabs.VoiceSettings(
                similarity_boost=1,
                stability=0.7,
                style=0.7,
                use_speaker_boost=False,
                speed=1.1
            )
        ),
        vad=silero.VAD.load(),
    )
    
    # Start Whispey Voice Analytics
    session_id = whispey.start_session(
        session,
        phone_number="+1234567890",     # Optional: Customer phone number
        customer_name="John Doe",       # Optional: Customer name
        conversation_type="voice_call"  # Optional: Type of conversation
    )
    
    print(f"Whispey Analytics started for session: {session_id}")

    # Export analytics data when session ends
    async def whispey_shutdown():
        try:
            result = await whispey.export(session_id)
            
            if result.get("success"):
                print("Successfully exported to Whispey Voice Analytics!")
                print(f"Log ID: {result.get('data', {}).get('log_id')}")
            else:
                print(f"Export failed: {result.get('error')}")
                
        except Exception as e:
            print(f"Export error: {e}")

    # Register cleanup callback
    ctx.add_shutdown_callback(whispey_shutdown)

    # Start the agent session
    await session.start(
        room=ctx.room,
        agent=Assistant(),
        room_input_options=RoomInputOptions(
            noise_cancellation=noise_cancellation.BVC(), 
        ),
    )

    # Generate initial greeting
    await session.generate_reply(
        instructions="Greet the user and offer your assistance."
    )

if __name__ == "__main__":
    agents.cli.run_app(agents.WorkerOptions(entrypoint_fnc=entrypoint))