charles_actor.py

import json
import ray
import time
import asyncio
import os
from clip_transform import CLIPTransform
from environment_state_actor import EnvironmentStateActor, EnvironmentState
from agent_state_actor import AgentStateActor
import asyncio

@ray.remote
class CharlesActor:
    def __init__(self):
        self._needs_init = True
        self._charles_actor_debug_output = ""
        self._environment_state:EnvironmentState = EnvironmentState(episode=0, step=0)  # Initialize as EnvironmentState
        self._state = "Initializing"
        self._clip_transform = CLIPTransform()
        
    def get_state(self):
        return self._state
    
    def get_charles_actor_debug_output(self):
        return self._charles_actor_debug_output
    
    def get_environment_state(self)->EnvironmentState:
        return self._environment_state
    
    async def _initalize_resources(self):
        # Initialize resources
        print("000 - create StreamlitAVQueue")
        from streamlit_av_queue import StreamlitAVQueue
        self._streamlit_av_queue = StreamlitAVQueue()
        self._out_audio_queue = self._streamlit_av_queue.get_out_audio_queue()

        print("001 - create RespondToPromptActor")
        from respond_to_prompt_actor import RespondToPromptActor
        self._environment_state_actor = EnvironmentStateActor.remote()
        self._agent_state_actor = AgentStateActor.remote()
        self._respond_to_prompt_actor = RespondToPromptActor.remote(self._environment_state_actor, self._out_audio_queue)

        print("002 - create SpeechToTextVoskActor")
        from speech_to_text_vosk_actor import SpeechToTextVoskActor
        self._speech_to_text_actor = SpeechToTextVoskActor.remote("small")
        # self._speech_to_text_actor = SpeechToTextVoskActor.remote("big")
        
        self._debug_queue = [
            # "hello, how are you today?",
            # "hmm, interesting, tell me more about that.",
        ]

        print("003 - create Prototypes")
        from prototypes import Prototypes
        self._prototypes = Prototypes()
        print("010")
        self._needs_init = True
        self._state = "Initialized"
        
    async def start(self):
        if self._needs_init:
            await self._initalize_resources()
            
        debug_output_history = []

        def render_debug_output(list_of_strings):
            table_content = "##### Chat history\n"
            for item in reversed(list_of_strings):
                # table_content += f"\n```markdown\n{item}\n```\n"
                table_content += f"\n{item}\n"
            self._charles_actor_debug_output = table_content

        def add_debug_output(output):
            debug_output_history.append(output)
            if len(debug_output_history) > 10:
                debug_output_history.pop(0)
            render_debug_output(debug_output_history)
        
        self._state = "Waiting for input"
        total_video_frames = 0
        skipped_video_frames = 0
        total_audio_frames = 0
        loops = 0
        start_time = time.time()
        vector_debug = "--n/a--"
        
        process_speech_to_text_future = []
        current_responses = []
        speech_chunks_per_response = []
        human_preview_text = ""
        robot_preview_text = ""


        while True:
            if len(self._debug_queue) > 0:
                prompt = self._debug_queue.pop(0)
                await self._respond_to_prompt_actor.enqueue_prompt.remote(prompt)
            
            env_state = await self._environment_state_actor.begin_next_step.remote()
            self._environment_state = env_state
            self._agent_state_actor.begin_step.remote()
            audio_frames = await self._streamlit_av_queue.get_in_audio_frames_async()    
            video_frames = await self._streamlit_av_queue.get_video_frames_async()

            if len(audio_frames) > 0:
                total_audio_frames += len(audio_frames)
                # Concatenate all audio frames into a single buffer
                audio_buffer = b"".join([buffer.tobytes() for buffer in audio_frames])
                future = self._speech_to_text_actor.process_speech.remote(audio_buffer)
                process_speech_to_text_future.append(future)
            # audio_frames_task = None

            if len(video_frames) > 0:
                vector_debug = f"found {len(video_frames)} video frames"
                total_video_frames += 1
                skipped_video_frames += (len(video_frames) -1)
                image_as_array = video_frames[-1]
                image_vector = self._clip_transform.image_to_embeddings(image_as_array)
                image_vector = image_vector[0]
                distances, closest_item_key, distance_debug_str = self._prototypes.get_distances(image_vector)
                vector_debug = f"{closest_item_key} {distance_debug_str}"

            if len(process_speech_to_text_future) > 0:
                ready, _ = ray.wait([process_speech_to_text_future[0]], timeout=0)
                if ready:
                    prompt, speaker_finished, raw_json = await process_speech_to_text_future[0]
                    del process_speech_to_text_future[0]

                    prompts_to_ignore = ["um", "uh", "ah", "huh", "hmm", "the", "but", "by", "just", "i'm"]

                    if speaker_finished and len(prompt) > 0 and prompt not in prompts_to_ignore:
                        print(f"Prompt: {prompt}")
                        line = ""
                        for i, response in enumerate(current_responses):
                            line += "🤖 " if len(line) == 0 else ""
                            # line += f"{response} [{speech_chunks_per_response[i]}]  \n"
                            line += f"[{speech_chunks_per_response[i]}] {response}  \n"
                        if len(line) > 0:
                            add_debug_output(line)
                        add_debug_output(f"👨 {prompt}")
                        current_responses = []
                        speech_chunks_per_response = []
                        env_state.llm_preview = ""
                        env_state.llm_responses = []
                        env_state.tts_raw_chunk_ids = []
                        human_preview_text = ""
                        robot_preview_text = ""
                        await self._respond_to_prompt_actor.enqueue_prompt.remote(prompt)
                    elif len(prompt) > 0 and prompt not in prompts_to_ignore:
                        human_preview_text = f"👨❓ {prompt}"

            for new_response in env_state.llm_responses:
                # add_debug_output(f"🤖 {new_response}")
                current_responses.append(new_response)
                speech_chunks_per_response.append(0)
                robot_preview_text = ""
            if len(env_state.llm_preview):
                robot_preview_text = f"🤖❓ {env_state.llm_preview}"

            for chunk in env_state.tts_raw_chunk_ids:
                chunk = json.loads(chunk)
                # prompt = chunk['prompt']
                response_id = chunk['llm_sentence_id']
                speech_chunks_per_response[response_id] += 1

            list_of_strings = debug_output_history.copy()
            line = ""
            for i, response in enumerate(current_responses):
                line += "🤖 " if len(line) == 0 else ""
                line += f"[{speech_chunks_per_response[i]}] {response}  \n"
                # line += f"{response} [{speech_chunks_per_response[i]}]  \n"
            if len(robot_preview_text) > 0:
                line += robot_preview_text+"  \n"
            list_of_strings.append(line)
            if len(human_preview_text) > 0:
                list_of_strings.append(human_preview_text)
            if len(list_of_strings) > 10:
                list_of_strings.pop(0)
            render_debug_output(list_of_strings)


            await asyncio.sleep(0.01)
            loops+=1
            self._state = f"Processed {total_video_frames} video frames and {total_audio_frames} audio frames, loops: {loops}. loops per second: {loops/(time.time()-start_time):.2f}. {vector_debug}"

async def main():
    if not ray.is_initialized():
        # Try to connect to a running Ray cluster
        ray_address = os.getenv('RAY_ADDRESS')
        import subprocess
        try:
            subprocess.check_output(["ray", "start", "--head"])
        except Exception as e:
            print (e)
        if ray_address:
            ray.init(ray_address, namespace="project_charles")
        else:
            ray.init(namespace="project_charles")

    charles_actor = CharlesActor.options(
        name="CharlesActor", 
        get_if_exists=True,
        ).remote() 
    future = charles_actor.start.remote()

    last_step = -1
    last_episode = -1
    try:
        while True:
            ready, _ = ray.wait([future], timeout=0)
            if ready:
                # The start method has terminated. You can fetch the result (if any) with ray.get().
                # If the method raised an exception, it will be re-raised here.
                try:
                    result = ray.get(future)
                    print(f"The start method has terminated with result: {result}")
                except Exception as e:
                    print(f"The start method raised an exception: {e}")
                break
            else:
                # The start method is still running. You can poll for debug information here.
                await asyncio.sleep(1)
                state = await charles_actor.get_state.remote()
                env_state = await charles_actor.get_environment_state.remote()
                if (env_state.episode != last_episode) or (env_state.step != last_step):
                    last_episode = env_state.episode
                    last_step = env_state.step
                    print(f"Charles is in state: {state}")
                    # if len(env_state.llm_preview):
                    #     print (f"llm_preview: {env_state.llm_preview}")
                    # if len(env_state.llm_responses):
                    #     print (f"llm_responses: {env_state.llm_responses}")
                    # if len(env_state.tts_raw_chunk_ids):
                    #     for chunk_json in env_state.tts_raw_chunk_ids:
                    #         chunk = json.loads(chunk_json)
                    #         prompt = chunk['prompt']
                    #         line = chunk['llm_sentence_id']
                    #         chunk_id = chunk['chunk_count']
                    #         print(f"Prompt: {prompt}, Line: {line}, Chunk: {chunk_id}")                            

    except KeyboardInterrupt as e:
        print("Script was manually terminated")
        raise(e)
    

if __name__ == "__main__":
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())