#
# Copyright (c) 2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import os
import sys

import cv2
import numpy as np
from dotenv import load_dotenv
from loguru import logger

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame, InputImageRawFrame, OutputImageRawFrame
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.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
from pipecat.services.gemini_multimodal_live import GeminiMultimodalLiveLLMService
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.network.small_webrtc import SmallWebRTCTransport

load_dotenv(override=True)


class EdgeDetectionProcessor(FrameProcessor):
    def __init__(self, video_out_width, video_out_height: int):
        super().__init__()
        self._video_out_width = video_out_width
        self._video_out_height = video_out_height

    async def process_frame(self, frame: Frame, direction: FrameDirection):
        await super().process_frame(frame, direction)

        if isinstance(frame, InputImageRawFrame):
            # Convert bytes to NumPy array
            img = np.frombuffer(frame.image, dtype=np.uint8).reshape(
                (frame.size[1], frame.size[0], 3)
            )

            # perform edge detection
            img = cv2.cvtColor(cv2.Canny(img, 100, 200), cv2.COLOR_GRAY2BGR)

            # convert the size if needed
            desired_size = (self._video_out_width, self._video_out_height)
            if frame.size != desired_size:
                resized_image = cv2.resize(img, desired_size)
                frame = OutputImageRawFrame(resized_image.tobytes(), desired_size, frame.format)
                await self.push_frame(frame)
            else:
                await self.push_frame(
                    OutputImageRawFrame(image=img.tobytes(), size=frame.size, format=frame.format)
                )
        else:
            await self.push_frame(frame, direction)


SYSTEM_INSTRUCTION = f"""
"You are Gemini Chatbot, a friendly, helpful robot.

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. Keep your responses brief. One or two sentences at most.
"""


async def run_bot(webrtc_connection):
    transport_params = TransportParams(
        audio_in_enabled=True,
        audio_out_enabled=True,
        audio_out_10ms_chunks=2,
        video_in_enabled=True,
        video_out_enabled=True,
        video_out_is_live=True,
        vad_analyzer=SileroVADAnalyzer(),
    )

    pipecat_transport = SmallWebRTCTransport(
        webrtc_connection=webrtc_connection, params=transport_params
    )

    llm = GeminiMultimodalLiveLLMService(
        api_key=os.getenv("GOOGLE_API_KEY"),
        voice_id="Puck",  # Aoede, Charon, Fenrir, Kore, Puck
        transcribe_user_audio=True,
        system_instruction=SYSTEM_INSTRUCTION,
    )

    context = OpenAILLMContext(
        [
            {
                "role": "user",
                "content": "Start by greeting the user warmly and introducing yourself.",
            }
        ],
    )
    context_aggregator = llm.create_context_aggregator(context)

    # RTVI events for Pipecat client UI
    rtvi = RTVIProcessor(config=RTVIConfig(config=[]))

    pipeline = Pipeline(
        [
            pipecat_transport.input(),
            context_aggregator.user(),
            rtvi,
            llm,  # LLM
            EdgeDetectionProcessor(
                transport_params.video_out_width, transport_params.video_out_height
            ),  # Sending the video back to the user
            pipecat_transport.output(),
            context_aggregator.assistant(),
        ]
    )

    task = PipelineTask(
        pipeline,
        params=PipelineParams(
            enable_metrics=True,
            enable_usage_metrics=True,
        ),
        observers=[RTVIObserver(rtvi)],
    )

    @rtvi.event_handler("on_client_ready")
    async def on_client_ready(rtvi):
        logger.info("Pipecat client ready.")
        await rtvi.set_bot_ready()
        # Kick off the conversation.
        await task.queue_frames([context_aggregator.user().get_context_frame()])

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

    @pipecat_transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        logger.info("Pipecat Client disconnected")
        await task.cancel()

    runner = PipelineRunner(handle_sigint=False)

    await runner.run(task)