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

import asyncio
import aiohttp
import os
import sys

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.services.cartesia import CartesiaTTSService
from pipecat.services.anthropic import AnthropicLLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport

from runner import configure

from loguru import logger

from dotenv import load_dotenv

load_dotenv(override=True)

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

video_participant_id = None


async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback):
    location = arguments["location"]
    await result_callback(f"The weather in {location} is currently 72 degrees and sunny.")


async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback):
    question = arguments["question"]
    await llm.request_image_frame(user_id=video_participant_id, text_content=question)


async def main():
    global llm

    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(),
            ),
        )

        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",
            model="claude-3-5-sonnet-latest",
            enable_prompt_caching_beta=True,
        )
        llm.register_function("get_weather", get_weather)
        llm.register_function("get_image", get_image)

        tools = [
            {
                "name": "get_weather",
                "description": "Get the current weather in a given location",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        }
                    },
                    "required": ["location"],
                },
            },
            {
                "name": "get_image",
                "description": "Get an image from the video stream.",
                "input_schema": {
                    "type": "object",
                    "properties": {
                        "question": {
                            "type": "string",
                            "description": "The question that the user is asking about the image.",
                        }
                    },
                    "required": ["question"],
                },
            },
        ]

        # todo: test with very short initial user message

        system_prompt = """\
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.

Your response will be turned into speech so use only simple words and punctuation.

You have access to two tools: get_weather and get_image.

You can respond to questions about the weather using the get_weather 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?

If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
        """

        messages = [
            {
                "role": "system",
                "content": [
                    {
                        "type": "text",
                        "text": system_prompt,
                    }
                ],
            },
            {"role": "user", "content": "Start the conversation by introducing yourself."},
        ]

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

        pipeline = Pipeline(
            [
                transport.input(),  # Transport user input
                context_aggregator.user(),  # User speech to text
                llm,  # LLM
                tts,  # TTS
                transport.output(),  # Transport bot output
                context_aggregator.assistant(),  # Assistant spoken responses and tool context
            ]
        )

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

        @transport.event_handler("on_first_participant_joined")
        async def on_first_participant_joined(transport, participant):
            global video_participant_id
            video_participant_id = participant["id"]
            await transport.capture_participant_transcription(video_participant_id)
            await transport.capture_participant_video(video_participant_id, framerate=0)
            # 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())