#
# Copyright (c) 2024–2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import argparse
import os
from datetime import datetime

from dotenv import load_dotenv
from loguru import logger

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection

load_dotenv(override=True)


async def fetch_weather_from_api(params: FunctionCallParams):
    temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
    await params.result_callback(
        {
            "conditions": "nice",
            "temperature": temperature,
            "format": params.arguments["format"],
            "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
        }
    )


system_instruction = """
You are a helpful assistant who can answer questions and use tools.

You have a tool called "get_current_weather" that can be used to get the current weather. If the user asks
for the weather, call this function.
"""


async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
    logger.info(f"Starting bot")

    # Initialize the SmallWebRTCTransport with the connection
    transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection,
        params=TransportParams(
            audio_in_enabled=True,
            audio_out_enabled=True,
            # set stop_secs to something roughly similar to the internal setting
            # of the Multimodal Live api, just to align events. This doesn't really
            # matter because we can only use the Multimodal Live API's phrase
            # endpointing, for now.
            vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
        ),
    )

    weather_function = FunctionSchema(
        name="get_current_weather",
        description="Get the current weather",
        properties={
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA",
            },
            "format": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "The temperature unit to use. Infer this from the user's location.",
            },
        },
        required=["location", "format"],
    )
    search_tool = {"google_search": {}}
    tools = ToolsSchema(
        standard_tools=[weather_function], custom_tools={AdapterType.GEMINI: [search_tool]}
    )

    llm = GeminiMultimodalLiveLLMService(
        api_key=os.getenv("GOOGLE_API_KEY"),
        system_instruction=system_instruction,
        transcribe_user_audio=True,
        tools=tools,
    )

    llm.register_function("get_current_weather", fetch_weather_from_api)

    context = OpenAILLMContext(
        [{"role": "user", "content": "Say hello."}],
    )
    context_aggregator = llm.create_context_aggregator(context)

    pipeline = Pipeline(
        [
            transport.input(),
            context_aggregator.user(),
            llm,
            transport.output(),
            context_aggregator.assistant(),
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            allow_interruptions=True,
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
    )

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        logger.info(f"Client connected")
        # Kick off the conversation.
        await task.queue_frames([context_aggregator.user().get_context_frame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info(f"Client disconnected")

    @transport.event_handler("on_client_closed")
    async def on_client_closed(transport, client):
        logger.info(f"Client closed connection")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=False)

    await runner.run(task)


if __name__ == "__main__":
    from run import main

    main()