#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

import glob
import json
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 ToolsSchema
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
    LLMContextAggregatorPair,
    LLMUserAggregatorParams,
)
from pipecat.processors.frame_processor import FrameDirection
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import (
    create_transport,
    get_transport_client_id,
    maybe_capture_participant_camera,
)
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.google.llm import GoogleLLMService
from pipecat.services.llm_service import FunctionCallParams
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams

load_dotenv(override=True)


BASE_FILENAME = "/tmp/pipecat_conversation_"


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"),
        }
    )


async def get_image(params: FunctionCallParams):
    user_id = params.arguments["user_id"]
    question = params.arguments["question"]
    logger.debug(f"Requesting image with user_id={user_id}, question={question}")

    # Request a user image frame and indicate that it should be added to the
    # context. Also associate it to the function call. Pass the result_callback
    # so it can be invoked when the image is actually retrieved.
    await params.llm.push_frame(
        UserImageRequestFrame(
            user_id=user_id,
            text=question,
            append_to_context=True,
            function_name=params.function_name,
            tool_call_id=params.tool_call_id,
            result_callback=params.result_callback,
        ),
        FrameDirection.UPSTREAM,
    )


async def get_saved_conversation_filenames(params: FunctionCallParams):
    # Construct the full pattern including the BASE_FILENAME
    full_pattern = f"{BASE_FILENAME}*.json"

    # Use glob to find all matching files
    matching_files = glob.glob(full_pattern)
    logger.debug(f"matching files: {matching_files}")

    await params.result_callback({"filenames": matching_files})


async def save_conversation(params: FunctionCallParams):
    timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
    filename = f"{BASE_FILENAME}{timestamp}.json"
    logger.debug(
        f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
    )
    try:
        with open(filename, "w") as file:
            messages = params.context.get_messages()
            # remove the last message (the instruction to save the context)
            messages.pop()
            json.dump(messages, file, indent=2)
        await params.result_callback({"success": True})
    except Exception as e:
        logger.debug(f"error saving conversation: {e}")
        await params.result_callback({"success": False, "error": str(e)})


async def load_conversation(params: FunctionCallParams):
    filename = params.arguments["filename"]
    logger.debug(f"loading conversation from {filename}")
    try:
        with open(filename, "r") as file:
            params.context.set_messages(json.load(file))
        await params.result_callback(
            {
                "success": True,
                "message": "The most recent conversation has been loaded. Awaiting further instructions.",
            }
        )
    except Exception as e:
        await params.result_callback({"success": False, "error": str(e)})


system_instruction = """You are a helpful assistant in a voice conversation. Your goal is to demonstrate your
capabilities in a succinct way. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Keep responses concise. Respond to what the user said in a creative
can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative
and helpful way.

You have several tools you can use to help you.

You can respond to questions about the weather using the get_weather tool.

You can save the current conversation using the save_conversation tool. This tool allows you to save
the current conversation to external storage. If the user asks you to save the conversation, use this
save_conversation too.

You can load a saved conversation using the load_conversation tool. This tool allows you to load a
conversation from external storage. You can get a list of conversations that have been saved using the
get_saved_conversation_filenames tool.

You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
indicate you should use the get_image tool are:
  - What do you see?
  - What's in the video?
  - Can you describe the video?
  - Tell me about what you see.
  - Tell me something interesting about what you see.
  - What's happening in the video?
"""

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 users location.",
        },
    },
    required=["location", "format"],
)

save_conversation_function = FunctionSchema(
    name="save_conversation",
    description="Save the current conversation. Use this function to persist the current conversation to external storage.",
    properties={
        "user_request_text": {
            "type": "string",
            "description": "The text of the user's request to save the conversation.",
        }
    },
    required=["user_request_text"],
)

get_filenames_function = FunctionSchema(
    name="get_saved_conversation_filenames",
    description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
    properties={},
    required=[],
)

load_conversation_function = FunctionSchema(
    name="load_conversation",
    description="Load a conversation history. Use this function to load a conversation history into the current session.",
    properties={
        "filename": {
            "type": "string",
            "description": "The filename of the conversation history to load.",
        }
    },
    required=["filename"],
)

get_image_function = FunctionSchema(
    name="get_image",
    description="Called when the user requests a description of their camera feed",
    properties={
        "user_id": {
            "type": "string",
            "description": "The ID of the user to grab the image from",
        },
        "question": {
            "type": "string",
            "description": "The question that the user is asking about the image",
        },
    },
    required=["user_id", "question"],
)

tools = ToolsSchema(
    standard_tools=[
        weather_function,
        save_conversation_function,
        get_filenames_function,
        load_conversation_function,
        get_image_function,
    ]
)


# We use lambdas to defer transport parameter creation until the transport
# type is selected at runtime.
transport_params = {
    "daily": lambda: DailyParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_in_enabled=True,
    ),
    "webrtc": lambda: TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        video_in_enabled=True,
    ),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    logger.info(f"Starting bot")

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

    tts = CartesiaTTSService(
        api_key=os.getenv("CARTESIA_API_KEY"),
        settings=CartesiaTTSService.Settings(
            voice="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
        ),
    )

    llm = GoogleLLMService(
        api_key=os.getenv("GOOGLE_API_KEY"),
        system_instruction=system_instruction,
    )

    # you can either register a single function for all function calls, or specific functions
    # llm.register_function(None, fetch_weather_from_api)
    llm.register_function("get_current_weather", fetch_weather_from_api)
    llm.register_function("save_conversation", save_conversation)
    llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
    llm.register_function("load_conversation", load_conversation)
    llm.register_function("get_image", get_image)

    context = LLMContext(tools=tools)
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
        context,
        user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
    )

    pipeline = Pipeline(
        [
            transport.input(),  # Transport user input
            stt,  # STT
            user_aggregator,
            llm,  # LLM
            tts,
            transport.output(),  # Transport bot output
            assistant_aggregator,
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
    )

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

        await maybe_capture_participant_camera(transport, client)

        client_id = get_transport_client_id(transport, client)

        # Kick off the conversation.
        context.add_message(
            {
                "role": "user",
                "content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
            }
        )
        await task.queue_frames([LLMRunFrame()])

    @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=runner_args.handle_sigint)

    await runner.run(task)


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)


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

    main()
