app.py

import asyncio
from collections import deque
import os
import threading
import time
import av
import numpy as np
import streamlit as st
from streamlit_webrtc import WebRtcMode, webrtc_streamer
import pydub
import torch
# import av
# import cv2
from sample_utils.turn import get_ice_servers
import json
from typing import List

from dotenv import load_dotenv
load_dotenv()

async def main():

    system_one = {
        "audio_bit_rate": 16000,
        # "audio_bit_rate": 32000,
        # "audio_bit_rate": 48000,

        # "vision_embeddings_fps": 5,
        "vision_embeddings_fps": 2,
    }


    system_one["video_detection_emotions"] = [
        "a happy person",
        "the person is happy",
        "the person's emotional state is happy",
        "a sad person",
        "a scared person",
        "a disgusted person",
        "an angry person",
        "a suprised person",
        "a bored person",
        "an interested person",
        "a guilty person",
        "an indiffert person",
        "a distracted person",
    ]


    # system_one["video_detection_emotions"] = [
    #     "Happiness",
    #     "Sadness",
    #     "Fear",
    #     "Disgust",
    #     "Anger",
    #     "Surprise",
    #     "Boredom",
    #     "Interest",
    #     "Excitement",
    #     "Guilt",
    #     "Shame",
    #     "Relief",
    #     "Love",
    #     "Embarrassment",
    #     "Pride",
    #     "Envy",
    #     "Jealousy",
    #     "Anxiety",
    #     "Hope",
    #     "Despair",
    #     "Frustration",
    #     "Confusion",
    #     "Curiosity",
    #     "Contentment",
    #     "Indifference",
    #     "Anticipation",
    #     "Gratitude",
    #     "Bitterness"
    # ]
    system_one["video_detection_engement"] = [
        "the person is engaged in the conversation",
        "the person is not engaged in the conversation",
        "the person is looking at me",
        "the person is not looking at me",
        "the person is talking to me",
        "the person is not talking to me",
        "the person is engaged",
        "the person is talking",
        "the person is listening",
    ]
    system_one["video_detection_present"] = [
        "the view from a webcam",
        "the view from a webcam we see a person",
        # "the view from a webcam. I see a person",
        # "the view from a webcam. The person is looking at the camera",
        # "i am a webcam",
        # "i am a webcam and i see a person",
        # "i am a webcam and i see a person. The person is looking at me",
    #     "a person",
    #     "a person on a Zoom call",
    #     "a person on a FaceTime call",
    #     "a person on a WebCam call",
    #     "no one",
    #     " ",
    #     "multiple people",
    #     "a group of people",
    ]

    system_one_audio_status = st.empty()


    playing = st.checkbox("Playing", value=True)

    def handle_audio_frame(frame):
        # if self.vosk.AcceptWaveform(data):
        pass

        # create frames to be returned.
        new_frames = []
        for frame in frames:
            input_array = frame.to_ndarray()
            new_frame = av.AudioFrame.from_ndarray(
                np.zeros(input_array.shape, dtype=input_array.dtype),
                layout=frame.layout.name,
            )
            new_frame.sample_rate = frame.sample_rate
            new_frames.append(new_frame)
        
        # TODO: replace with the audio we want to send to the other side.

        return new_frames

    system_one_audio_status.write("Initializing CLIP model")
    from clip_transform import CLIPTransform
    clip_transform = CLIPTransform()

    system_one_audio_status.write("Initializing chat pipeline")
    from chat_pipeline import ChatPipeline
    chat_pipeline = ChatPipeline()
    await chat_pipeline.start()

    system_one_audio_status.write("Initializing CLIP templates")

    embeddings = clip_transform.text_to_embeddings(system_one["video_detection_emotions"])
    system_one["video_detection_emotions_embeddings"] = embeddings 

    embeddings = clip_transform.text_to_embeddings(system_one["video_detection_engement"])
    system_one["video_detection_engement_embeddings"] = embeddings

    embeddings = clip_transform.text_to_embeddings(system_one["video_detection_present"])
    system_one["video_detection_present_embeddings"] = embeddings 

    system_one_audio_status.write("Initializing webrtc_streamer")
    webrtc_ctx = webrtc_streamer(
        key="charles",
        desired_playing_state=playing,
        # audio_receiver_size=4096,
        queued_audio_frames_callback=queued_audio_frames_callback,
        queued_video_frames_callback=queued_video_frames_callback,
        mode=WebRtcMode.SENDRECV,
        rtc_configuration={"iceServers": get_ice_servers()},
        async_processing=True,
    )


    if not webrtc_ctx.state.playing:
        exit

    system_one_audio_status.write("Initializing streaming")
    system_one_audio_output = st.empty()

    system_one_video_output = st.empty()

    system_one_audio_history = []
    system_one_audio_history_output = st.empty()


    sound_chunk = pydub.AudioSegment.empty()
    current_video_embedding = None
    current_video_embedding_timestamp = time.monotonic()


    def get_dot_similarities(video_embedding, embeddings, embeddings_labels):
        dot_product = torch.mm(embeddings, video_embedding.T)
        similarity_image_label = [(float("{:.4f}".format(dot_product[i][0])), embeddings_labels[i]) for i in range(len(embeddings_labels))]
        similarity_image_label.sort(reverse=True)
        return similarity_image_label

    def get_top_3_similarities_as_a_string(video_embedding, embeddings, embeddings_labels):
        similarities = get_dot_similarities(video_embedding, embeddings, embeddings_labels)
        top_3 = ""
        range_len = 3 if len(similarities) > 3 else len(similarities)
        for i in range(range_len):
            top_3 += f"{similarities[i][1]} ({similarities[i][0]}) "
        return top_3

    while True:
        try:
            if webrtc_ctx.state.playing:
                # handle video
                video_frames = []
                with video_frames_deque_lock:
                    while len(video_frames_deque) > 0:
                        frame = video_frames_deque.popleft()
                        video_frames.append(frame)
                get_embeddings = False
                get_embeddings |= current_video_embedding is None
                current_time = time.monotonic()
                elapsed_time = current_time - current_video_embedding_timestamp
                get_embeddings |= elapsed_time > 1. / system_one['vision_embeddings_fps']
                if get_embeddings and len(video_frames) > 0:
                    current_video_embedding_timestamp = current_time
                    current_video_embedding = clip_transform.image_to_embeddings(video_frames[-1].to_ndarray())

                    emotions_top_3 = get_top_3_similarities_as_a_string(current_video_embedding, system_one["video_detection_emotions_embeddings"], system_one["video_detection_emotions"])
                    engagement_top_3 = get_top_3_similarities_as_a_string(current_video_embedding, system_one["video_detection_engement_embeddings"], system_one["video_detection_engement"])
                    present_top_3 = get_top_3_similarities_as_a_string(current_video_embedding, system_one["video_detection_present_embeddings"], system_one["video_detection_present"])

                    # table_content = "**System 1 Video:**\n\n"
                    table_content = "| System 1 Video |    |\n| --- | --- |\n"
                    table_content += f"| Present | {present_top_3} |\n"
                    table_content += f"| Emotion | {emotions_top_3} |\n"
                    table_content += f"| Engagement | {engagement_top_3} |\n"
                    system_one_video_output.markdown(table_content)
                    # system_one_video_output.markdown(f"**System 1 Video:** \n [Emotion: {emotions_top_3}], \n [Engagement: {engagement_top_3}], \n [Present: {present_top_3}] ")
                    # for similarity, image_label in similarity_image_label:
                    #     print (f"{similarity} {image_label}")


                if len(audio_frames) == 0:
                    time.sleep(0.1)
                    system_one_audio_status.write("No frame arrived.")
                    continue

                system_one_audio_status.write("Running. Say something!")

                for audio_frame in audio_frames:
                    sound = pydub.AudioSegment(
                        data=audio_frame.to_ndarray().tobytes(),
                        sample_width=audio_frame.format.bytes,
                        frame_rate=audio_frame.sample_rate,
                        channels=len(audio_frame.layout.channels),
                    )
                    sound = sound.set_channels(1)
                    sound = sound.set_frame_rate(system_one['audio_bit_rate'])
                    sound_chunk += sound

                if len(sound_chunk) > 0:
                    buffer = np.array(sound_chunk.get_array_of_samples())
                    text, speaker_finished = do_work(buffer.tobytes())            
                    system_one_audio_output.markdown(f"**System 1 Audio:** {text}")
                    if speaker_finished and len(text) > 0:
                        system_one_audio_history.append(text)
                        if len(system_one_audio_history) > 10:
                            system_one_audio_history = system_one_audio_history[-10:]
                        table_content = "| System 1 Audio History |\n| --- |\n"
                        table_content += "\n".join([f"| {item} |" for item in reversed(system_one_audio_history)])
                        system_one_audio_history_output.markdown(table_content)
                        await chat_pipeline.enqueue(text)
                    sound_chunk = pydub.AudioSegment.empty()

            else:
                system_one_audio_status.write("Stopped.")
                break
        except KeyboardInterrupt:
            print("Pipeline interrupted by user")
        except Exception as e:
            print(f"An error occurred: {e}")


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