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

import argparse
import asyncio
import os

from dotenv import load_dotenv
from loguru import logger

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
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.processors.filters.stt_mute_filter import STTMuteConfig, STTMuteFilter, STTMuteStrategy
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.deepgram.tts import DeepgramTTSService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.network.fastapi_websocket import FastAPIWebsocketParams
from pipecat.transports.services.daily import DailyParams

load_dotenv(override=True)


async def fetch_weather_from_api(params: FunctionCallParams):
    # Add a delay to test interruption during function calls
    logger.info("Weather API call starting...")
    await asyncio.sleep(5)  # 5-second delay
    logger.info("Weather API call completed")
    await params.result_callback({"conditions": "nice", "temperature": "75"})


# We store functions so objects (e.g. SileroVADAnalyzer) don't get
# instantiated. The function will be called when the desired transport gets
# selected.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "twilio": lambda: FastAPIWebsocketParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        vad_analyzer=SileroVADAnalyzer(),
    ),
}


async def run_example(transport: BaseTransport, _: argparse.Namespace, handle_sigint: bool):
    logger.info(f"Starting bot")

    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))

    # Configure the mute processor with both strategies
    stt_mute_processor = STTMuteFilter(
        config=STTMuteConfig(
            strategies={
                STTMuteStrategy.MUTE_UNTIL_FIRST_BOT_COMPLETE,
                STTMuteStrategy.FUNCTION_CALL,
            }
        ),
    )

    tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")

    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
    llm.register_function("get_current_weather", fetch_weather_from_api)

    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"],
    )
    tools = ToolsSchema(standard_tools=[weather_function])

    messages = [
        {
            "role": "system",
            "content": "You are a helpful assistant who can check the weather. Always check the weather when a location is mentioned. Respond concisely and naturally. Your output will be converted to audio so use only simple words and punctuation.",
        },
    ]

    context = OpenAILLMContext(messages, tools)
    context_aggregator = llm.create_context_aggregator(context)

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt_mute_processor,  # Add the mute processor before STT
            stt,  # STT
            context_aggregator.user(),  # User responses
            llm,  # LLM
            tts,  # TTS
            transport.output(),  # Transport bot output
            context_aggregator.assistant(),  # Assistant spoken responses
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            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 with a weather-related prompt
        messages.append(
            {
                "role": "system",
                "content": "Ask the user what city they'd like to know the weather for.",
            }
        )
        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")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=handle_sigint)

    await runner.run(task)


if __name__ == "__main__":
    from pipecat.examples.run import main

    main(run_example, transport_params=transport_params)