#agent.py
import logging
import os
from dotenv import load_dotenv
from mem0 import Memory
from mem0.configs.base import MemoryConfig
from mem0.vector_stores.configs import VectorStoreConfig

from livekit.agents import (
    Agent,
    AgentServer,
    AgentSession,
    JobContext,
    RunContext,
    function_tool,
    cli,
    WorkerOptions,
)
from livekit.plugins import openai, silero
from livekit.plugins import noise_cancellation

load_dotenv()
logger = logging.getLogger("voice-agent")


memory = Memory(config=MemoryConfig(vector_store=VectorStoreConfig(provider='chroma', config={'path': 'data/mem0'})))   # uses local storage by default; swap for mem0 cloud later

def get_memory(user_id: str) -> str:
    search_response = memory.search(query="user preferences and history", user_id=user_id, limit=10)
    results = search_response.get("results", []) if isinstance(search_response, dict) else search_response
    if not results:
        return ""
    return "\n".join([f"- {r['memory']}" for r in results if isinstance(r, dict) and "memory" in r])

def save_memory(user_id: str, content: str):
    memory.add(content, user_id=user_id)
#./.venv311/bin/python agent.py console --input-device 0 --output-device 1
#python agent.py console --input-device 0 --output-device 1
@function_tool
async def remember_this(context: RunContext, fact: str):
    """Save an important fact or user preference to long-term memory."""
    user_id = context.userdata.get("user_id", "default_user")
    save_memory(user_id, fact)
    return {"status": "saved", "fact": fact}

@function_tool
async def recall_memory(context: RunContext, query: str):
    """Search long-term memory for something the user mentioned before."""
    user_id = context.userdata.get("user_id", "default_user")
    search_response = memory.search(query=query, user_id=user_id, limit=5)
    results = search_response.get("results", []) if isinstance(search_response, dict) else search_response
    if not results:
        return {"found": False, "results": []}
    return {"found": True, "results": [r["memory"] for r in results if isinstance(r, dict) and "memory" in r]}
    
    

class VoiceAssistant(Agent):
    def __init__(self, user_id: str, past_memory: str) -> None:
        memory_block = (
            f"\n\nWhat you already know about this user:\n{past_memory}"
            if past_memory else ""
        )
        super().__init__(
            instructions=f"""
                You are a friendly, intelligent voice assistant.
                Keep responses short and conversational — 1 to 3 sentences max.
                Never use markdown, bullet points, emojis, or asterisks.
                Speak naturally as if on a phone call.
                your name is benita.
                If the user shares something personal or useful to remember, call remember_this.
                If the user references something from before, call recall_memory.
                
                {memory_block}
            """,
            tools=[remember_this, recall_memory],
        )
        self.user_id = user_id

    async def on_enter(self):
        await self.session.say("Hey there! am benita  How can I help you today?")

    async def on_exit(self):
       
        messages = self.session.history.messages
        if messages:
            summary = " | ".join([
                f"{m.role}: {m.content[:80]}"
                for m in messages[-6:]  # last 6 turns
                if hasattr(m, "content") and m.content
            ])
            save_memory(self.user_id, f"Recent conversation: {summary}")
            logger.info(f"Saved session memory for user {self.user_id}")



server = AgentServer()

@server.rtc_session(agent_name="benita")
async def entrypoint(ctx: JobContext):
    
    await ctx.connect()
    if ctx.is_fake_job():
        user_id = "console"
        logger.info("Console fake job detected; skipping wait_for_participant")
    else:
        participant = await ctx.wait_for_participant()
        user_id = participant.identity or "default_user"

    logger.info(f"Session started | room={ctx.room.name} | user={user_id}")

   
    past_memory = get_memory(user_id)

    session = AgentSession(
        userdata={"user_id": user_id},

        # VAD  detects when user starts/stops speaking
        vad=silero.VAD.load(),

        # STT 
        stt=openai.STT(model="whisper-1", language='en'),

        # LLM 
        llm=openai.LLM(model="gpt-4o-mini"),

        # TTS 
        tts=openai.TTS(model="tts-1", voice="nova"),

        
        turn_detection=None,

        # Interruption settings 
        allow_interruptions=True,
        min_endpointing_delay=0.4,
    )

   
    @session.on("user_speech_committed")
    def on_user_spoke(msg):
        logger.info(f"[{user_id}] User: {msg.content}")

    @session.on("agent_speech_committed")
    def on_agent_spoke(msg):
        logger.info(f"[{user_id}] Agent: {msg.content}")

    await session.start(
        agent=VoiceAssistant(user_id=user_id, past_memory=past_memory),
        room=ctx.room,
    )


if __name__ == "__main__":
    cli.run_app(server)