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

import asyncio
import glob
import json
import os
import sys
from datetime import datetime

import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure

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.cartesia import CartesiaTTSService
from pipecat.services.anthropic import AnthropicLLMService

from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.vad.silero import SileroVADAnalyzer
from pipecat.vad.vad_analyzer import VADParams

load_dotenv(override=True)

logger.remove(0)
logger.add(sys.stderr, level="DEBUG")

BASE_FILENAME = "/tmp/pipecat_conversation_"
tts = None


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


async def get_saved_conversation_filenames(
    function_name, tool_call_id, args, llm, context, result_callback
):
    # 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 result_callback({"filenames": matching_files})


async def save_conversation(function_name, tool_call_id, args, llm, context, result_callback):
    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(context.messages, indent=4)}")
    try:
        with open(filename, "w") as file:
            # todo: extract 'system' into the first message in the list
            messages = context.get_messages_for_persistent_storage()
            # remove the last message, which is the instruction we just gave to save the conversation
            messages.pop()
            json.dump(messages, file, indent=2)
        await result_callback({"success": True})
    except Exception as e:
        await result_callback({"success": False, "error": str(e)})


async def load_conversation(function_name, tool_call_id, args, llm, context, result_callback):
    global tts
    filename = args["filename"]
    logger.debug(f"loading conversation from {filename}")
    try:
        with open(filename, "r") as file:
            context.set_messages(json.load(file))
            logger.debug(
                f"loaded conversation from {filename}\n{json.dumps(context.messages, indent=4)}"
            )
        await tts.say("Ok, I've loaded that conversation.")
    except Exception as e:
        await result_callback({"success": False, "error": str(e)})


# Test message munging ...
messages = [
    {
        "role": "system",
        "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
    },
    {"role": "user", "content": ""},
    {"role": "assistant", "content": []},
    {"role": "user", "content": "Tell me"},
    {"role": "user", "content": "a joke"},
]
tools = [
    {
        "name": "get_current_weather",
        "description": "Get the current weather",
        "input_schema": {
            "type": "object",
            "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"],
        },
    },
    {
        "name": "save_conversation",
        "description": "Save the current conversation. Use this function to persist the current conversation to external storage.",
        "input_schema": {
            "type": "object",
            "properties": {},
            "required": [],
        },
    },
    {
        "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.",
        "input_schema": {
            "type": "object",
            "properties": {},
            "required": [],
        },
    },
    {
        "name": "load_conversation",
        "description": "Load a conversation history. Use this function to load a conversation history into the current session.",
        "input_schema": {
            "type": "object",
            "properties": {
                "filename": {
                    "type": "string",
                    "description": "The filename of the conversation history to load.",
                }
            },
            "required": ["filename"],
        },
    },
]


async def main():
    global tts
    async with aiohttp.ClientSession() as session:
        (room_url, token) = await configure(session)

        transport = DailyTransport(
            room_url,
            token,
            "Respond bot",
            DailyParams(
                audio_out_enabled=True,
                transcription_enabled=True,
                vad_enabled=True,
                vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),
            ),
        )

        tts = CartesiaTTSService(
            api_key=os.getenv("CARTESIA_API_KEY"),
            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
        )

        llm = AnthropicLLMService(
            api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
        )

        # 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)

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

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                context_aggregator.user(),
                llm,  # LLM
                tts,
                context_aggregator.assistant(),
                transport.output(),  # Transport bot output
            ]
        )

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

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            transport.capture_participant_transcription(participant["id"])
            # Kick off the conversation.
            await task.queue_frames([context_aggregator.user().get_context_frame()])

        runner = PipelineRunner()

        await runner.run(task)


if __name__ == "__main__":
    asyncio.run(main())