AI_companion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cloud project id.\n",
    "PROJECT_ID = \"merantix-genai23ber-9535\"  # @param {type:\"string\"}\n",
    "\n",
    "# The region you want to launch jobs in.\n",
    "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
    "\n",
    "# The Cloud Storage bucket for storing experiments output. Fill it without the 'gs://' prefix.\n",
    "GCS_BUCKET = \"blip2-example\"  # @param {type:\"string\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import aiplatform\n",
    "\n",
    "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=GCS_BUCKET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The pre-built serving docker image.\n",
    "# The model artifacts are embedded within the container, except for model weights which will be downloaded during deployment.\n",
    "SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-transformers-serve\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the TTS API and transform the example text 'hello world' into speech and play it \n",
    "# if the response need to be saved locally, name it as test_synthesis.wav \n",
    "from google.cloud import speech_v1p1beta1 as speech\n",
    "import io, os\n",
    "from pydub import AudioSegment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "import os\n",
    "from datetime import datetime\n",
    "from io import BytesIO\n",
    "\n",
    "import requests\n",
    "from google.cloud import aiplatform\n",
    "from PIL import Image\n",
    "\n",
    "\n",
    "def create_job_name(prefix):\n",
    "    user = os.environ.get(\"USER\")\n",
    "    now = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "    job_name = f\"{prefix}-{user}-{now}\"\n",
    "    return job_name\n",
    "\n",
    "\n",
    "def download_image(url):\n",
    "    response = requests.get(url)\n",
    "    return Image.open(BytesIO(response.content))\n",
    "\n",
    "\n",
    "def image_to_base64(image, format=\"JPEG\"):\n",
    "    buffer = BytesIO()\n",
    "    image.save(buffer, format=format)\n",
    "    image_str = base64.b64encode(buffer.getvalue()).decode(\"utf-8\")\n",
    "    return image_str\n",
    "\n",
    "\n",
    "def base64_to_image(image_str):\n",
    "    image = Image.open(BytesIO(base64.b64decode(image_str)))\n",
    "    return image\n",
    "\n",
    "\n",
    "def image_grid(imgs, rows=2, cols=2):\n",
    "    w, h = imgs[0].size\n",
    "    grid = Image.new(\"RGB\", size=(cols * w, rows * h))\n",
    "    for i, img in enumerate(imgs):\n",
    "        grid.paste(img, box=(i % cols * w, i // cols * h))\n",
    "    return grid\n",
    "\n",
    "\n",
    "def deploy_vqa_model(model_id, task):\n",
    "    model_name = \"blip2\"\n",
    "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
    "    serving_env = {\n",
    "        \"MODEL_ID\": model_id,\n",
    "        \"TASK\": task,\n",
    "    }\n",
    "    # If the model_id is a GCS path, use artifact_uri to pass it to serving docker.\n",
    "    artifact_uri = model_id if model_id.startswith(\"gs://\") else None\n",
    "    model = aiplatform.Model.upload(\n",
    "        display_name=model_name,\n",
    "        serving_container_image_uri=SERVE_DOCKER_URI,\n",
    "        serving_container_ports=[7080],\n",
    "        serving_container_predict_route=\"/predictions/transformers_serving\",\n",
    "        serving_container_health_route=\"/ping\",\n",
    "        serving_container_environment_variables=serving_env,\n",
    "        artifact_uri=artifact_uri,\n",
    "    )\n",
    "    model.deploy(\n",
    "        endpoint=endpoint,\n",
    "        machine_type=\"n1-standard-8\",\n",
    "        accelerator_type=\"NVIDIA_TESLA_T4\",\n",
    "        accelerator_count=1,\n",
    "        deploy_request_timeout=1800,\n",
    "    )\n",
    "    return model, endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ask_vqa(endpoint, user_prompt, image):\n",
    "    prompt =  f\"Question: {user_prompt}? Answer:\"\n",
    "    instances = [\n",
    "        {\"image\": image_to_base64(image), \"text\": prompt},\n",
    "    ]\n",
    "    preds = endpoint.predict(instances=instances).predictions\n",
    "    return preds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transcribe_file(speech_file):\n",
    "    \"\"\"Transcribe the given audio file.\"\"\"\n",
    "    client = speech.SpeechClient()\n",
    "    \n",
    "    # # Check if the file is mp3\n",
    "    # if speech_file.lower().endswith(\".mp3\"):\n",
    "    #     # Convert mp3 file to wav\n",
    "    #     audio = AudioSegment.from_mp3(speech_file)\n",
    "    #     # Export as wav\n",
    "    #     speech_file = \"temp_transcription_file.wav\"\n",
    "    #     audio.export(speech_file, format=\"wav\")\n",
    "    \n",
    "    # Determine the sample rate\n",
    "    audio = AudioSegment.from_wav(speech_file)\n",
    "    sample_rate = audio.frame_rate\n",
    "    \n",
    "    with io.open(speech_file, \"rb\") as audio_file:\n",
    "        content = audio_file.read()\n",
    "\n",
    "    audio = speech.RecognitionAudio(content=content)\n",
    "    config = speech.RecognitionConfig(\n",
    "        encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,\n",
    "        sample_rate_hertz=44100,  # Specify the correct sample rate\n",
    "        language_code=\"en-US\",\n",
    "    )\n",
    "\n",
    "    response = client.recognize(config=config, audio=audio)\n",
    "    \n",
    "    user_prompt = \"\"\n",
    "    for result in response.results:\n",
    "        print(\"Transcript: {}\".format(result.alternatives[0].transcript))\n",
    "        user_prompt += result.alternatives[0].transcript + \" \"\n",
    "    # If a temporary file was created, remove it\n",
    "    if speech_file == \"temp_transcription_file.wav\":\n",
    "        os.remove(speech_file)\n",
    "        \n",
    "    return user_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the TTS API and transform the example text 'hello world' into speech and play it \n",
    "# if the response need to be saved locally, name it as test_synthesis.wav \n",
    "from google.cloud import texttospeech\n",
    "from IPython.display import Audio, display\n",
    "\n",
    "# Instantiates a client\n",
    "tts_client = texttospeech.TextToSpeechClient()\n",
    "\n",
    "\n",
    "def speak(text):\n",
    "    # Set the text input to be synthesized\n",
    "    synthesis_input = texttospeech.SynthesisInput(text=text)\n",
    "\n",
    "    # Build the voice request, select the language code (\"en-US\") and the ssml\n",
    "    # voice gender (\"neutral\")\n",
    "    voice = texttospeech.VoiceSelectionParams(\n",
    "        language_code=\"en-US\", ssml_gender=texttospeech.SsmlVoiceGender.NEUTRAL\n",
    "    )\n",
    "\n",
    "    # Select the type of audio file you want returned\n",
    "    audio_config = texttospeech.AudioConfig(\n",
    "        audio_encoding=texttospeech.AudioEncoding.MP3\n",
    "    )\n",
    "\n",
    "    # Perform the text-to-speech request on the text input with the selected\n",
    "    # voice parameters and audio file type\n",
    "    response = tts_client.synthesize_speech(\n",
    "        input=synthesis_input, voice=voice, audio_config=audio_config\n",
    "    )\n",
    "\n",
    "    # The response's audio_content is binary.\n",
    "    with open(\"output.mp3\", \"wb\") as out:\n",
    "        # Write the response to the output file.\n",
    "        out.write(response.audio_content)\n",
    "        print('Audio content written to file \"output.mp3\"')\n",
    "\n",
    "    display(Audio(\"output.mp3\", autoplay=True))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "def ask_object_detector(object_detection_model, image, conf=0.25):\n",
    "    results = object_detection_model(image, conf=conf, save=True)[0]\n",
    "    w, h = image.width, image.height\n",
    "\n",
    "\n",
    "    xyxyn = results.boxes.xyxyn\n",
    "    cls = results.boxes.cls\n",
    "    \n",
    "    object_detector_output = \"\"\n",
    "    for i in range(len(results)):\n",
    "        if (xyxyn[i][0] + xyxyn[i][2]) / 2 < 0.33:\n",
    "            x_pos = \"left\"\n",
    "        elif (xyxyn[i][0] + xyxyn[i][2]) / 2 > 0.66:\n",
    "            x_pos = \"right\"\n",
    "        else:\n",
    "            x_pos = \"middle\"\n",
    "\n",
    "\n",
    "        object_detector_output += f\"{results.names[int(cls[i])]}, {x_pos} of the image; \"\n",
    "    \n",
    "    return object_detector_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vertexai\n",
    "from vertexai.preview.language_models import TextGenerationModel\n",
    "\n",
    "def predict_large_language_model_sample(\n",
    "    project_id: str,\n",
    "    model_name: str,\n",
    "    temperature: float,\n",
    "    max_decode_steps: int,\n",
    "    top_p: float,\n",
    "    top_k: int,\n",
    "    content: str,\n",
    "    location: str = \"us-central1\",\n",
    "    tuned_model_name: str = \"\",\n",
    "    ) :\n",
    "    \"\"\"Predict using a Large Language Model.\"\"\"\n",
    "    vertexai.init(project=project_id, location=location)\n",
    "    model = TextGenerationModel.from_pretrained(model_name)\n",
    "    if tuned_model_name:\n",
    "      model = model.get_tuned_model(tuned_model_name)\n",
    "    response = model.predict(\n",
    "        content,\n",
    "        temperature=temperature,\n",
    "        max_output_tokens=max_decode_steps,\n",
    "        top_k=top_k,\n",
    "        top_p=top_p,)\n",
    "    return response.text\n",
    "\n",
    "def ask_llm(user_prompt, vqa_output=None, object_detection_output=None):\n",
    "    prompt = f\"You are an AI assistant for vision impaired people. The user asked '{user_prompt}' and the VQA model outputs '{vqa_output}'. You also have the output from an object detector. Each element has the following format : class, position with respect to the image. Here are the detection results: '{object_detection_output}'. Rephrase for the text-to-speech model. Do not invent anything, only use what the VQA model and object detector output\"\"\"\n",
    "    return predict_large_language_model_sample(PROJECT_ID, \"text-bison@001\", 0.2, 256, 0.8, 40, prompt, \"us-central1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "object_detection_model = YOLO('yolov8x.pt')  # pretrained YOLOv8n model\n",
    "try:\n",
    "    vqa_vilt_endpoint = aiplatform.Endpoint('projects/71837958321/locations/us-central1/endpoints/1696047263379357696')\n",
    "except:\n",
    "    vqa_vilt_model, vqa_vilt_endpoint = deploy_model(\n",
    "        model_id=\"dandelin/vilt-b32-finetuned-vqa\", task=\"visual-question-answering\"\n",
    "    )\n",
    "    \n",
    "try:\n",
    "    vqa_endpoint = aiplatform.Endpoint('projects/71837958321/locations/us-central1/endpoints/6595963657958457344')\n",
    "except:\n",
    "    vqa_model, vqa_endpoint = deploy_vqa_model(\n",
    "        model_id=\"Salesforce/blip2-opt-2.7b\", task=\"visual-question-answering\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "import numpy as np\n",
    "\n",
    "def run(image_path, wav_filepath, voice_text, output_text):\n",
    "    image = Image.open(image_path).convert(\"RGB\")\n",
    "    user_prompt = transcribe_file(wav_filepath)\n",
    "    voice_text.config(text=user_prompt)\n",
    "    if isinstance(image, np.ndarray):\n",
    "        image = Image.fromarray(image)\n",
    "    vqa_output = ask_vqa(vqa_endpoint, user_prompt, image)\n",
    "    print(f\"vqa: {vqa_output}\")\n",
    "    vqa_vilt_output = ask_vqa(vqa_vilt_endpoint, user_prompt, image)\n",
    "    print(f\"vqa VILT: {vqa_vilt_output}\")\n",
    "    if vqa_vilt_output[\"score\"] > 0.5:\n",
    "        vqa_output = vqa_vilt_output[\"answer\"]\n",
    "    #vqa_output += \" \" + vqa_vilt_output[\"answer\"]\n",
    "    object_detection_output = ask_object_detector(object_detection_model, image, conf=0.45)\n",
    "    print(f\"object detection {object_detection_output}\")\n",
    "    llm_output = ask_llm(user_prompt, vqa_output, object_detection_output)\n",
    "    output_text.config(text=llm_output)\n",
    "    print(f\"output: {llm_output}\")\n",
    "    speak(llm_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARN:0@2045.323] global cap_v4l.cpp:982 open VIDEOIO(V4L2:/dev/video0): can't open camera by index\n",
      "[ERROR:0@2045.323] global obsensor_uvc_stream_channel.cpp:156 getStreamChannelGroup Camera index out of range\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import tkinter as tk\n",
    "import sounddevice as sd\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "from PIL import Image, ImageTk\n",
    "from pydub import AudioSegment\n",
    "import playsound\n",
    "\n",
    "\n",
    "class WebcamAudioRecorderApp:\n",
    "    def __init__(self, root):\n",
    "        self.root = root\n",
    "        self.root.title(\"Webcam Audio Recorder\")\n",
    "        \n",
    "        self.cap = cv2.VideoCapture(0)\n",
    "        self.audio_stream = None\n",
    "        self.audio_frames = []\n",
    "        self.recording = False\n",
    "\n",
    "        self.video_label = tk.Label(root)\n",
    "        self.video_label.pack()\n",
    "        \n",
    "        self.start_button = tk.Button(root, text=\"Start Recording\", command=self.start_recording)\n",
    "        self.start_button.pack()\n",
    "        \n",
    "        self.stop_button = tk.Button(root, text=\"Stop Recording\", command=self.stop_recording)\n",
    "        self.stop_button.pack()\n",
    "        self.stop_button[\"state\"] = \"disabled\"\n",
    "\n",
    "        self.voice_text = tk.Label(root, text=\"Input: \")\n",
    "        self.voice_text.pack()\n",
    "        self.vqa_option1 = tk.Label(root, text=\"VQA 1: \")\n",
    "        self.vqa_option1.pack()\n",
    "        self.vqa_option2 = tk.Label(root, text=\"VQA 2: \")\n",
    "        self.vqa_option2.pack()\n",
    "        self.object_detection = tk.Label(root, text=\"Object Detection: \")\n",
    "        self.object_detection.pack()\n",
    "        self.output_text = tk.Label(root, text=\"Output: \")\n",
    "        self.output_text.pack()\n",
    "\n",
    "        self.running = False\n",
    "\n",
    "        self.update()\n",
    "        \n",
    "    def start_recording(self):\n",
    "        self.audio_stream = sd.InputStream(callback=self.audio_callback)\n",
    "        self.audio_stream.start()\n",
    "        self.audio_frames = []\n",
    "        self.recording = True\n",
    "        self.start_button[\"state\"] = \"disabled\"\n",
    "        self.stop_button[\"state\"] = \"active\"\n",
    "        \n",
    "    def stop_recording(self):\n",
    "        if self.recording:\n",
    "            self.audio_stream.stop()\n",
    "            self.recording = False\n",
    "            self.start_button[\"state\"] = \"active\"\n",
    "            self.stop_button[\"state\"] = \"disabled\"\n",
    "            self.capture_image()\n",
    "            self.save_audio(\"audio.wav\")\n",
    "            # run(\"snapshot.png\", \"audio.wav\", self.voice_text, self.output_text)\n",
    "            self.run_app(\"snapshot.png\", \"audio.wav\")\n",
    "            playsound.playsound('output.mp3', True)\n",
    "\n",
    "            \n",
    "    def audio_callback(self, indata, frames, time, status):\n",
    "        if status:\n",
    "            print(\"Audio recording error:\", status, flush=True)\n",
    "        if self.recording:\n",
    "            self.audio_frames.append(indata.copy())\n",
    "            \n",
    "    def save_audio(self, audio_path):\n",
    "        audio_data = np.concatenate(self.audio_frames, axis=0).mean(-1)\n",
    "        sf.write(audio_path, audio_data, 44100)\n",
    "\n",
    "\n",
    "\n",
    "    def capture_image(self):\n",
    "        ret, frame = self.cap.read()\n",
    "        if ret:\n",
    "            cv2.imwrite(\"snapshot.png\", frame)\n",
    "            self.display_image(\"snapshot.png\")\n",
    "\n",
    "    def display_image(self, image_path):\n",
    "        img = cv2.imread(image_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        img = Image.fromarray(img)\n",
    "        img = ImageTk.PhotoImage(image=img)\n",
    "        self.video_label.config(image=img)\n",
    "        self.video_label.image = img\n",
    "\n",
    "    def update(self):\n",
    "        ret, frame = self.cap.read()\n",
    "        if ret and not self.running:\n",
    "            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            img = Image.fromarray(frame)\n",
    "            img = ImageTk.PhotoImage(image=img)\n",
    "            self.video_label.config(image=img)\n",
    "            self.video_label.image = img\n",
    "        self.root.after(10, self.update)\n",
    "        \n",
    "    def run(self):\n",
    "        self.root.mainloop()\n",
    "        self.cap.release()\n",
    "\n",
    "    def run_app(self, image_path, wav_filepath):\n",
    "        self.running = True\n",
    "        image = Image.open(image_path).convert(\"RGB\")\n",
    "        user_prompt = transcribe_file(wav_filepath)\n",
    "        self.voice_text.config(text=\"Input: \" + user_prompt)\n",
    "        self.root.update()\n",
    "        if isinstance(image, np.ndarray):\n",
    "            image = Image.fromarray(image)\n",
    "        vqa_output = ask_vqa(vqa_endpoint, user_prompt, image)\n",
    "        self.vqa_option1.config(text=\"VQA 1: \" + vqa_output)\n",
    "        self.root.update()\n",
    "        vqa_vilt_output = ask_vqa(vqa_vilt_endpoint, user_prompt, image)\n",
    "        self.vqa_option2.config(text=\"VQA 2: \" + vqa_vilt_output[\"answer\"])\n",
    "        self.root.update()\n",
    "        if vqa_vilt_output[\"score\"] > 0.5:\n",
    "            vqa_output = vqa_vilt_output[\"answer\"]\n",
    "        object_detection_output = ask_object_detector(object_detection_model, image, conf=0.45)\n",
    "        self.object_detection.config(text=\"Object Detection: \" + object_detection_output)\n",
    "        self.root.update()\n",
    "        llm_output = ask_llm(user_prompt, vqa_output, object_detection_output)\n",
    "        self.output_text.config(text=\"Output: \" + llm_output)\n",
    "        self.root.update()\n",
    "        speak(llm_output)\n",
    "        self.running=False\n",
    "\n",
    "\n",
    "root = tk.Tk()\n",
    "app = WebcamAudioRecorderApp(root)\n",
    "app.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in Tkinter callback\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/tkinter/__init__.py\", line 1892, in __call__\n",
      "    return self.func(*args)\n",
      "TypeError: stop_recording() missing 1 required positional argument: 'event'\n",
      "\n",
      "0: 480x640 1 cell phone, 36.3ms\n",
      "Speed: 13.2ms preprocess, 36.3ms inference, 4.7ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict10\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio content written to file \"output.mp3\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in Tkinter callback\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/tkinter/__init__.py\", line 1892, in __call__\n",
      "    return self.func(*args)\n",
      "  File \"/tmp/ipykernel_2877/4259255808.py\", line 67, in stop_recording\n",
      "    playsound.playsound('output.mp3', True)\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/site-packages/playsound.py\", line 254, in <lambda>\n",
      "    playsound = lambda sound, block = True: _playsoundAnotherPython('/usr/bin/python3', sound, block, macOS = False)\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/site-packages/playsound.py\", line 229, in _playsoundAnotherPython\n",
      "    t.join()\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/site-packages/playsound.py\", line 216, in join\n",
      "    super().join(timeout)\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/threading.py\", line 1011, in join\n",
      "    self._wait_for_tstate_lock()\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/threading.py\", line 1027, in _wait_for_tstate_lock\n",
      "    elif lock.acquire(block, timeout):\n",
      "KeyboardInterrupt\n",
      "\n",
      "0: 480x640 1 cell phone, 34.7ms\n",
      "Speed: 1.1ms preprocess, 34.7ms inference, 2.1ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict10\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio content written to file \"output.mp3\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 480x640 1 cell phone, 1 toothbrush, 34.3ms\n",
      "Speed: 0.9ms preprocess, 34.3ms inference, 2.4ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict10\u001b[0m\n",
      "Exception ignored from cffi callback <function _StreamBase.__init__.<locals>.callback_ptr at 0x7fb7b4416a60>:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/site-packages/sounddevice.py\", line 846, in callback_ptr\n",
      "    return _wrap_callback(callback, data, frames, time, status)\n",
      "  File \"/home/tgieruc/miniconda3/lib/python3.8/site-packages/sounddevice.py\", line 2687, in _wrap_callback\n",
      "    callback(*args)\n",
      "  File \"/tmp/ipykernel_2877/3221092376.py\", line 72, in audio_callback\n",
      "AttributeError: 'WebcamAudioRecorderApp' object has no attribute 'recording'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio content written to file \"output.mp3\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
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\" type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 480x640 1 toothbrush, 34.3ms\n",
      "Speed: 1.2ms preprocess, 34.3ms inference, 1.3ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict10\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio content written to file \"output.mp3\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0: 480x640 1 cell phone, 1 toothbrush, 34.4ms\n",
      "Speed: 1.3ms preprocess, 34.4ms inference, 2.2ms postprocess per image at shape (1, 3, 480, 640)\n",
      "Results saved to \u001b[1mruns/detect/predict10\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audio content written to file \"output.mp3\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" autoplay=\"autoplay\">\n",
       "                    <source 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\" type=\"audio/mpeg\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import tkinter as tk\n",
    "import sounddevice as sd\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "from PIL import Image, ImageTk\n",
    "from pydub import AudioSegment\n",
    "import playsound\n",
    "import os\n",
    "\n",
    "class WebcamAudioRecorderApp:\n",
    "    def __init__(self, root):\n",
    "        self.root = root\n",
    "        self.root.title(\"Webcam Audio Recorder\")\n",
    "        \n",
    "        self.cap = cv2.VideoCapture(0)\n",
    "        self.audio_stream = None\n",
    "        self.audio_frames = []\n",
    "        self.recording = False\n",
    "        self.running = False\n",
    "\n",
    "        self.video_label = tk.Label(root)\n",
    "        self.video_label.pack()\n",
    "        \n",
    "        self.start_button = tk.Button(root, text=\"Start Recording\", command=self.start_recording)\n",
    "        self.start_button.pack()\n",
    "        \n",
    "        self.stop_button = tk.Button(root, text=\"Stop Recording\", command=self.stop_recording)\n",
    "        self.stop_button.pack()\n",
    "        self.stop_button[\"state\"] = \"disabled\"\n",
    "\n",
    "        self.voice_text = tk.Label(root, text=\"Input: \")\n",
    "        self.voice_text.pack()\n",
    "        self.vqa_option1 = tk.Label(root, text=\"VQA 1: \")\n",
    "        self.vqa_option1.pack()\n",
    "        self.vqa_option2 = tk.Label(root, text=\"VQA 2: \")\n",
    "        self.vqa_option2.pack()\n",
    "        self.object_detection = tk.Label(root, text=\"Object Detection: \")\n",
    "        self.object_detection.pack()\n",
    "        self.output_text = tk.Label(root, text=\"Output: \")\n",
    "        self.output_text.pack()\n",
    "\n",
    "        # Bind the space bar press and release events\n",
    "        root.bind(\"<KeyPress-space>\", self.start_recording)\n",
    "        root.bind(\"<KeyRelease-space>\", self.stop_recording)\n",
    "\n",
    "        self.update()\n",
    "        \n",
    "    def start_recording(self, event):\n",
    "        if not self.recording:\n",
    "            self.recording = True\n",
    "            self.audio_stream = sd.InputStream(callback=self.audio_callback)\n",
    "            self.audio_stream.start()\n",
    "            self.audio_frames = []\n",
    "            self.start_button[\"state\"] = \"disabled\"\n",
    "            self.stop_button[\"state\"] = \"active\"\n",
    "        \n",
    "    def stop_recording(self, event):\n",
    "        if self.recording:\n",
    "            self.audio_stream.stop()\n",
    "            self.recording = False\n",
    "            self.start_button[\"state\"] = \"active\"\n",
    "            self.stop_button[\"state\"] = \"disabled\"\n",
    "            self.capture_image()\n",
    "            self.save_audio(\"audio.wav\")\n",
    "            self.run_app(\"snapshot.png\", \"audio.wav\")\n",
    "            playsound.playsound('output.mp3', True)\n",
    "\n",
    "    def audio_callback(self, indata, frames, time, status):\n",
    "        if status:\n",
    "            print(\"Audio recording error:\", status, flush=True)\n",
    "        if self.recording:\n",
    "            self.audio_frames.append(indata.copy())\n",
    "            \n",
    "    def save_audio(self, audio_path):\n",
    "        audio_data = np.concatenate(self.audio_frames, axis=0).mean(-1)\n",
    "        sf.write(audio_path, audio_data, 44100)\n",
    "\n",
    "    def capture_image(self):\n",
    "        ret, frame = self.cap.read()\n",
    "        if ret:\n",
    "            cv2.imwrite(\"snapshot.png\", frame)\n",
    "            self.display_image(\"snapshot.png\")\n",
    "\n",
    "    def display_image(self, image_path):\n",
    "        img = cv2.imread(image_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        img = Image.fromarray(img)\n",
    "        img = ImageTk.PhotoImage(image=img)\n",
    "        self.video_label.config(image=img)\n",
    "        self.video_label.image = img\n",
    "\n",
    "    def update(self):\n",
    "        ret, frame = self.cap.read()\n",
    "        if ret and not self.running:\n",
    "            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            img = Image.fromarray(frame)\n",
    "            img = ImageTk.PhotoImage(image=img)\n",
    "            self.video_label.config(image=img)\n",
    "            self.video_label.image = img\n",
    "        self.root.after(10, self.update)\n",
    "        \n",
    "    def run(self):\n",
    "        self.root.mainloop()\n",
    "        self.cap.release()\n",
    "\n",
    "    def run_app(self, image_path, wav_filepath):\n",
    "        self.running = True\n",
    "        image = Image.open(image_path).convert(\"RGB\")\n",
    "        user_prompt = transcribe_file(wav_filepath)\n",
    "        self.voice_text.config(text=\"Input: \" + user_prompt)\n",
    "        self.root.update()\n",
    "        if isinstance(image, np.ndarray):\n",
    "            image = Image.fromarray(image)\n",
    "        vqa_output = ask_vqa(vqa_endpoint, user_prompt, image)\n",
    "        self.vqa_option1.config(text=\"VQA 1: \" + vqa_output)\n",
    "        self.root.update()\n",
    "        vqa_vilt_output = ask_vqa(vqa_vilt_endpoint, user_prompt, image)\n",
    "        self.vqa_option2.config(text=\"VQA 2: \" + vqa_vilt_output[\"answer\"])\n",
    "        self.root.update()\n",
    "        if vqa_vilt_output[\"score\"] > 0.5:\n",
    "            vqa_output = vqa_vilt_output[\"answer\"]\n",
    "        object_detection_output = ask_object_detector(object_detection_model, image, conf=0.45)\n",
    "        self.object_detection.config(text=\"Object Detection: \" + object_detection_output)\n",
    "        self.root.update()\n",
    "        llm_output = ask_llm(user_prompt, vqa_output, object_detection_output)\n",
    "        self.output_text.config(text=\"Output: \" + llm_output)\n",
    "        self.root.update()\n",
    "        speak(llm_output)\n",
    "        self.running=False\n",
    "\n",
    "\n",
    "root = tk.Tk()\n",
    "app = WebcamAudioRecorderApp(root)\n",
    "app.run()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import tkinter as tk\n",
    "import sounddevice as sd\n",
    "import soundfile as sf\n",
    "import numpy as np\n",
    "from PIL import Image, ImageTk\n",
    "from pydub import AudioSegment\n",
    "import playsound\n",
    "\n",
    "\n",
    "class WebcamAudioRecorderApp:\n",
    "    def __init__(self, root):\n",
    "        self.root = root\n",
    "        self.root.title(\"Webcam Audio Recorder\")\n",
    "        \n",
    "        self.cap = cv2.VideoCapture(0)\n",
    "        self.audio_stream = None\n",
    "        self.audio_frames = []\n",
    "        self.recording = False\n",
    "\n",
    "        self.video_label = tk.Label(root)\n",
    "        self.video_label.pack()\n",
    "        \n",
    "        self.start_button = tk.Button(root, text=\"Start Recording\", command=self.start_recording)\n",
    "        self.start_button.pack()\n",
    "        \n",
    "        self.stop_button = tk.Button(root, text=\"Stop Recording\", command=self.stop_recording)\n",
    "        self.stop_button.pack()\n",
    "        self.stop_button[\"state\"] = \"disabled\"\n",
    "\n",
    "        self.voice_text = tk.Label(root, text=\"Input: \")\n",
    "        self.voice_text.pack()\n",
    "        self.vqa_option1 = tk.Label(root, text=\"VQA 1: \")\n",
    "        self.vqa_option1.pack()\n",
    "        self.vqa_option2 = tk.Label(root, text=\"VQA 2: \")\n",
    "        self.vqa_option2.pack()\n",
    "        self.object_detection = tk.Label(root, text=\"Object Detection: \")\n",
    "        self.object_detection.pack()\n",
    "        self.output_text = tk.Label(root, text=\"Output: \")\n",
    "        self.output_text.pack()\n",
    "\n",
    "        self.running = False\n",
    "        root.bind(\"<KeyPress-space>\", self.toggle_recording)\n",
    "\n",
    "        self.update()\n",
    "\n",
    "    def toggle_recording(self, event):\n",
    "        if self.recording:\n",
    "            self.stop_recording()\n",
    "        else:\n",
    "            self.start_recording()\n",
    "        \n",
    "    def start_recording(self):\n",
    "        self.audio_stream = sd.InputStream(callback=self.audio_callback)\n",
    "        self.audio_stream.start()\n",
    "        self.audio_frames = []\n",
    "        self.recording = True\n",
    "        self.start_button[\"state\"] = \"disabled\"\n",
    "        self.stop_button[\"state\"] = \"active\"\n",
    "        \n",
    "    def stop_recording(self):\n",
    "        if self.recording:\n",
    "            self.audio_stream.stop()\n",
    "            self.recording = False\n",
    "            self.start_button[\"state\"] = \"active\"\n",
    "            self.stop_button[\"state\"] = \"disabled\"\n",
    "            self.capture_image()\n",
    "            self.save_audio(\"audio.wav\")\n",
    "            # run(\"snapshot.png\", \"audio.wav\", self.voice_text, self.output_text)\n",
    "            self.run_app(\"snapshot.png\", \"audio.wav\")\n",
    "            playsound.playsound('output.mp3', True)\n",
    "\n",
    "            \n",
    "    def audio_callback(self, indata, frames, time, status):\n",
    "        if status:\n",
    "            print(\"Audio recording error:\", status, flush=True)\n",
    "        if self.recording:\n",
    "            self.audio_frames.append(indata.copy())\n",
    "            \n",
    "    def save_audio(self, audio_path):\n",
    "        audio_data = np.concatenate(self.audio_frames, axis=0).mean(-1)\n",
    "        sf.write(audio_path, audio_data, 44100)\n",
    "\n",
    "\n",
    "\n",
    "    def capture_image(self):\n",
    "        ret, frame = self.cap.read()\n",
    "        if ret:\n",
    "            cv2.imwrite(\"snapshot.png\", frame)\n",
    "            self.display_image(\"snapshot.png\")\n",
    "\n",
    "    def display_image(self, image_path):\n",
    "        img = cv2.imread(image_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        img = Image.fromarray(img)\n",
    "        img = ImageTk.PhotoImage(image=img)\n",
    "        self.video_label.config(image=img)\n",
    "        self.video_label.image = img\n",
    "\n",
    "    def update(self):\n",
    "        ret, frame = self.cap.read()\n",
    "        if ret and not self.running:\n",
    "            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "            img = Image.fromarray(frame)\n",
    "            img = ImageTk.PhotoImage(image=img)\n",
    "            self.video_label.config(image=img)\n",
    "            self.video_label.image = img\n",
    "        self.root.after(10, self.update)\n",
    "        \n",
    "    def run(self):\n",
    "        self.root.mainloop()\n",
    "        self.cap.release()\n",
    "\n",
    "    def run_app(self, image_path, wav_filepath):\n",
    "        self.running = True\n",
    "        image = Image.open(image_path).convert(\"RGB\")\n",
    "        user_prompt = transcribe_file(wav_filepath)\n",
    "        self.voice_text.config(text=\"Input: \" + user_prompt)\n",
    "        self.root.update()\n",
    "        if isinstance(image, np.ndarray):\n",
    "            image = Image.fromarray(image)\n",
    "        vqa_output = ask_vqa(vqa_endpoint, user_prompt, image)\n",
    "        self.vqa_option1.config(text=\"VQA 1: \" + vqa_output)\n",
    "        self.root.update()\n",
    "        vqa_vilt_output = ask_vqa(vqa_vilt_endpoint, user_prompt, image)\n",
    "        self.vqa_option2.config(text=\"VQA 2: \" + vqa_vilt_output[\"answer\"])\n",
    "        self.root.update()\n",
    "        if vqa_vilt_output[\"score\"] > 0.5:\n",
    "            vqa_output = vqa_vilt_output[\"answer\"]\n",
    "        object_detection_output = ask_object_detector(object_detection_model, image, conf=0.45)\n",
    "        self.object_detection.config(text=\"Object Detection: \" + object_detection_output)\n",
    "        self.root.update()\n",
    "        llm_output = ask_llm(user_prompt, vqa_output, object_detection_output)\n",
    "        self.output_text.config(text=\"Output: \" + llm_output)\n",
    "        self.root.update()\n",
    "        speak(llm_output)\n",
    "        self.running=False\n",
    "\n",
    "\n",
    "root = tk.Tk()\n",
    "app = WebcamAudioRecorderApp(root)\n",
    "app.run()\n"
   ]
  }
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