notebooks/deploy_whisper_and_sentence_transformer_to_huggingface.ipynb

{
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    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/atilatech/atlas-service/blob/master/notebooks/deploy_whisper_and_sentence_transformer_to_huggingface.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fiKqeBJ9zf0w"
      },
      "source": [
        "# Deploy Whisper and Sentence Transformer Model to HuggingFace"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CKA6Yhfz3Hn"
      },
      "source": [
        "### Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pojSykoTUB6I"
      },
      "outputs": [],
      "source": [
        "!pip install transformers pytube sentence-transformers\n",
        "\n",
        "# optional install pytorch so you can use a gpu for faster transcription\n",
        "# command below is for Linux. See instructions for mac and windows: https://pytorch.org/get-started/locally/\n",
        "!pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu\n",
        "\n",
        "!pip install git+https://github.com/openai/whisper.git -q\n",
        "!apt install ffmpeg # https://stackoverflow.com/questions/51856340/how-to-install-package-ffmpeg-in-google-colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW7gBEJpgsDE"
      },
      "source": [
        "## Create a Custom inference handler\n",
        "\n",
        "See:\n",
        "\n",
        "1. https://huggingface.co/docs/inference-endpoints/guides/custom_handler\n",
        "2. https://www.philschmid.de/custom-inference-handler\n",
        "\n",
        "Another alternative is using a [Serverless Deployment of the sentence-transformer model](https://aseifert.com/p/serverless-sentence-transformer/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qeMVfKrMaM5z"
      },
      "outputs": [],
      "source": [
        "!pip install transformers pytube sentence-transformers\n",
        "\n",
        "# optional install pytorch so you can use a gpu for faster transcription\n",
        "# command below is for Linux. See instructions for mac and windows: https://pytorch.org/get-started/locally/\n",
        "!pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu\n",
        "\n",
        "!pip install git+https://github.com/openai/whisper.git -q\n",
        "!apt install ffmpeg # https://stackoverflow.com/questions/51856340/how-to-install-package-ffmpeg-in-google-colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "JAc8Yxdggumo"
      },
      "outputs": [],
      "source": [
        "from typing import Dict\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "from tqdm import tqdm\n",
        "import whisper\n",
        "import torch\n",
        "import pytube\n",
        "import time\n",
        "\n",
        "\n",
        "class EndpointHandler():\n",
        "    def __init__(self, path=\"\"):\n",
        "        # load the model\n",
        "        WHISPER_MODEL_NAME = \"tiny.en\"\n",
        "        SENTENCE_TRANSFORMER_MODEL_NAME = \"multi-qa-mpnet-base-dot-v1\"\n",
        "\n",
        "        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "        print(f'whisper will use: {device}')\n",
        "\n",
        "        t0 = time.time()\n",
        "        self.whisper_model = whisper.load_model(WHISPER_MODEL_NAME).to(device)\n",
        "        t1 = time.time()\n",
        "\n",
        "        total = t1 - t0\n",
        "        print(f'Finished loading whisper_model in {total} seconds')\n",
        "\n",
        "        t0 = time.time()\n",
        "        self.sentence_transformer_model = SentenceTransformer(SENTENCE_TRANSFORMER_MODEL_NAME)\n",
        "        t1 = time.time()\n",
        "\n",
        "        total = t1 - t0\n",
        "        print(f'Finished loading sentence_transformer_model in {total} seconds')\n",
        "\n",
        "    def __call__(self, data: Dict[str, str]) -> Dict:\n",
        "        \"\"\"\n",
        "        Args:\n",
        "            data (:obj:):\n",
        "                includes the URL to video for transcription\n",
        "        Return:\n",
        "            A :obj:`dict`:. transcribed dict\n",
        "        \"\"\"\n",
        "        # process input\n",
        "        print('data', data)\n",
        "\n",
        "        if \"inputs\" not in data:\n",
        "            raise Exception(f\"data is missing 'inputs' key which  EndpointHandler expects. Received: {data}\"\n",
        "                            f\" See: https://huggingface.co/docs/inference-endpoints/guides/custom_handler#2-create-endpointhandler-cp\")\n",
        "        video_url = data.pop(\"video_url\", None)\n",
        "        query = data.pop(\"query\", None)\n",
        "        encoded_segments = {}\n",
        "        if video_url:\n",
        "            video_with_transcript = self.transcribe_video(video_url)\n",
        "            encode_transcript = data.pop(\"encode_transcript\", True)\n",
        "            if encode_transcript:\n",
        "                encoded_segments = self.combine_transcripts(video_with_transcript)\n",
        "                encoded_segments = {\n",
        "                    \"encoded_segments\": self.encode_sentences(encoded_segments)\n",
        "                }\n",
        "            return {\n",
        "                **video_with_transcript,\n",
        "                **encoded_segments\n",
        "            }\n",
        "        elif query:\n",
        "            query = [{\"text\": query, \"id\": \"\"}]\n",
        "            encoded_segments = self.encode_sentences(query)\n",
        "\n",
        "            return {\n",
        "                \"encoded_segments\": encoded_segments\n",
        "            }\n",
        "\n",
        "    def transcribe_video(self, video_url):\n",
        "        decode_options = {\n",
        "            # Set language to None to support multilingual,\n",
        "            # but it will take longer to process while it detects the language.\n",
        "            # Realized this by running in verbose mode and seeing how much time\n",
        "            # was spent on the decoding language step\n",
        "            \"language\": \"en\",\n",
        "            \"verbose\": True\n",
        "        }\n",
        "        yt = pytube.YouTube(video_url)\n",
        "        video_info = {\n",
        "            'id': yt.video_id,\n",
        "            'thumbnail': yt.thumbnail_url,\n",
        "            'title': yt.title,\n",
        "            'views': yt.views,\n",
        "            'length': yt.length,\n",
        "            # Althhough, this might seem redundant since we already have id\n",
        "            # but it allows the link to the video be accessed in 1-click in the API response\n",
        "            'url': f\"https://www.youtube.com/watch?v={yt.video_id}\"\n",
        "        }\n",
        "        stream = yt.streams.filter(only_audio=True)[0]\n",
        "        path_to_audio = f\"{yt.video_id}.mp3\"\n",
        "        stream.download(filename=path_to_audio)\n",
        "        t0 = time.time()\n",
        "        transcript = self.whisper_model.transcribe(path_to_audio, **decode_options)\n",
        "        t1 = time.time()\n",
        "        for segment in transcript['segments']:\n",
        "            # Remove the tokens array, it makes the response too verbose\n",
        "            segment.pop('tokens', None)\n",
        "\n",
        "        total = t1 - t0\n",
        "        print(f'Finished transcription in {total} seconds')\n",
        "\n",
        "        # postprocess the prediction\n",
        "        return {\"transcript\": transcript, 'video': video_info}\n",
        "\n",
        "    def encode_sentences(self, transcripts, batch_size=64):\n",
        "        \"\"\"\n",
        "        Encoding all of our segments at once or storing them locally would require too much compute or memory.\n",
        "        So we do it in batches of 64\n",
        "        :param transcripts:\n",
        "        :param batch_size:\n",
        "        :return:\n",
        "        \"\"\"\n",
        "        # loop through in batches of 64\n",
        "        all_batches = []\n",
        "        for i in tqdm(range(0, len(transcripts), batch_size)):\n",
        "            # find end position of batch (for when we hit end of data)\n",
        "            i_end = min(len(transcripts), i + batch_size)\n",
        "            # extract the metadata like text, start/end positions, etc\n",
        "            batch_meta = [{\n",
        "                **row\n",
        "            } for row in transcripts[i:i_end]]\n",
        "            # extract only text to be encoded by embedding model\n",
        "            batch_text = [\n",
        "                row['text'] for row in batch_meta\n",
        "            ]\n",
        "            # create the embedding vectors\n",
        "            batch_vectors = self.sentence_transformer_model.encode(batch_text).tolist()\n",
        "\n",
        "            batch_details = [\n",
        "                {\n",
        "                    **batch_meta[x],\n",
        "                    'vectors':batch_vectors[x]\n",
        "                } for x in range(0, len(batch_meta))\n",
        "            ]\n",
        "            all_batches.extend(batch_details)\n",
        "\n",
        "        return all_batches\n",
        "\n",
        "    @staticmethod\n",
        "    def combine_transcripts(video, window=6, stride=3):\n",
        "        \"\"\"\n",
        "\n",
        "        :param video:\n",
        "        :param window: number of sentences to combine\n",
        "        :param stride: number of sentences to 'stride' over, used to create overlap\n",
        "        :return:\n",
        "        \"\"\"\n",
        "        new_transcript_segments = []\n",
        "\n",
        "        video_info = video['video']\n",
        "        transcript_segments = video['transcript']['segments']\n",
        "        for i in tqdm(range(0, len(transcript_segments), stride)):\n",
        "            i_end = min(len(transcript_segments), i + window)\n",
        "            text = ' '.join(transcript['text']\n",
        "                            for transcript in\n",
        "                            transcript_segments[i:i_end])\n",
        "            # TODO: Should int (float to seconds) conversion happen at the API level?\n",
        "            start = int(transcript_segments[i]['start'])\n",
        "            end = int(transcript_segments[i]['end'])\n",
        "            new_transcript_segments.append({\n",
        "                **video_info,\n",
        "                **{\n",
        "                    'start': start,\n",
        "                    'end': end,\n",
        "                    'title': video_info['title'],\n",
        "                    'text': text,\n",
        "                    'id': f\"{video_info['id']}-t{start}\",\n",
        "                    'url': f\"https://youtu.be/{video_info['id']}?t={start}\",\n",
        "                    'video_id': video_info['id'],\n",
        "                }\n",
        "            })\n",
        "        return new_transcript_segments\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FlPfk9CDg6UP"
      },
      "outputs": [],
      "source": [
        "# Initialize the Handler\n",
        "\n",
        "my_handler = EndpointHandler(path=\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mQlIqL13hVx6"
      },
      "outputs": [],
      "source": [
        "# prepare sample payload\n",
        "# payload = {\"video_url\": \"https://www.youtube.com/watch?v=aNxigRg1yEQ\"}\n",
        "payload = {\"video_url\": \"https://www.youtube.com/watch?v=bGk8qcHc1A0\"} # Joe Rogan & Lex Fridman: Lionel Messi Is The GOAT Over Cristiano Ronaldo\n",
        "\n",
        "\n",
        "# # test the handler\n",
        "payload_pred=my_handler(payload)\n",
        "payload_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9sMbMHcwjOkQ"
      },
      "outputs": [],
      "source": [
        "my_handler = EndpointHandler(path=\"\")\n",
        "payload = {\"query\": \"mighty mouse\", \"inputs\": \"\"} # Joe Rogan & Lex Fridman: Lionel Messi Is The GOAT Over Cristiano Ronaldo\n",
        "\n",
        "\n",
        "# # test the handler\n",
        "payload_pred=my_handler(payload)\n",
        "payload_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h911-KwZkcff"
      },
      "outputs": [],
      "source": [
        "my_handler = EndpointHandler(path=\"\")\n",
        "payload = {\"video_url\": \"https://www.youtube.com/watch?v=ciKdF97JWpU\", \"encode_transcript\": True} # Jimmy Butler Reveals What Made Him Leave the Philadelphia 76ers | The JJ Redick Podcast | The Ringer\n",
        "\n",
        "\n",
        "# # test the handler\n",
        "payload_pred=my_handler(payload)\n",
        "payload_pred\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qD1xESiMoOad"
      },
      "source": [
        "# Transcribe and Search Video\n",
        "\n",
        "1. Call the Model Inference we created in the last step\n",
        "    1. Using the Inference endpoint class locally\n",
        "    1. Using the deployed model on Huggingface\n",
        "\n",
        "1. Combine 6 segments together to create more meaningful sentences\n",
        "\n",
        "1. Embed sentences into vectors using transformers\n",
        "\n",
        "1. Save vectors into a vector database\n",
        "\n",
        "1. Query phrases using vector database\n",
        "\n",
        "1. [Fixing YouTube Search with OpenAI's Whisper](https://www.pinecone.io/learn/openai-whisper/)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ft_jUAO9l12g"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGn0vh4mNnvz"
      },
      "source": [
        "## Transcribe Video"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eFDHTYCGoN5k"
      },
      "outputs": [],
      "source": [
        "!pip install requests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FguK7Efesc-V",
        "outputId": "b5e9a1fb-c186-41d6-9baf-9385db9b4ca2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter HUGGING_FACE_ENDPOINT_URL··········\n",
            "Enter HUGGING_FACE_API_KEY··········\n"
          ]
        }
      ],
      "source": [
        "from getpass import getpass\n",
        "\n",
        "HUGGING_FACE_ENDPOINT_URL = getpass('Enter HUGGING_FACE_ENDPOINT_URL')\n",
        "HUGGING_FACE_API_KEY = getpass('Enter HUGGING_FACE_API_KEY')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GhdTGCYir7Yl"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "from typing import List\n",
        "import requests\n",
        "import base64\n",
        "import mimetypes\n",
        "\n",
        "def send_transcription_request(url:str=None):\n",
        "    payload = json.dumps({\n",
        "      \"inputs\": video_url\n",
        "    })\n",
        "    headers = {\n",
        "      'Authorization': f'Bearer {HUGGING_FACE_API_KEY}',\n",
        "      'Content-Type': 'application/json'\n",
        "    }\n",
        "\n",
        "    response = requests.request(\"POST\", HUGGING_FACE_ENDPOINT_URL, headers=headers, data=payload)\n",
        "    return response.json()\n",
        "\n",
        "video_url=\"https://www.youtube.com/watch?v=bGk8qcHc1A0\" # Joe Rogan & Lex Fridman: Lionel Messi Is The GOAT Over Cristiano Ronaldo\n",
        "video_data = send_transcription_request(video_url)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nxPiFCwh0hJO"
      },
      "outputs": [],
      "source": [
        "# verify that it worked\n",
        "video_data['transcript']['segments'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaE_LCeWtsef"
      },
      "source": [
        "## Combine Transcript Segments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fhT9RrLbt2_x",
        "outputId": "c2ca4e72-3342-44be-c5e3-30fc9f2db87b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (4.64.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yWMtgKIVt57o"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "new_transcript_segments = []\n",
        "\n",
        "def combine_transcripts(video):\n",
        "  window = 6  # number of sentences to combine\n",
        "  stride = 3  # number of sentences to 'stride' over, used to create overlap\n",
        "\n",
        "  video_info=video['video']\n",
        "  transcript_segments=video['transcript']['segments']\n",
        "  for i in tqdm(range(0, len(transcript_segments), stride)):\n",
        "      i_end = min(len(transcript_segments)-1, i+window)\n",
        "      text = ' '.join(transcript['text'] \n",
        "                    for transcript in\n",
        "                    transcript_segments[i:i_end])\n",
        "      # TODO: Should int (float to seconds) conversion happen at the API level?\n",
        "      start=int(transcript_segments[i]['start'])\n",
        "      end=int(transcript_segments[i]['end'])\n",
        "      new_transcript_segments.append({\n",
        "          **video_info,\n",
        "          **{\n",
        "          'start': start,\n",
        "          'end': end,\n",
        "          'title': video_info['title'],\n",
        "          'text': text,\n",
        "          'id': f\"{video_info['id']}-t{start}\",\n",
        "          'url': f\"https://youtu.be/{video_info['id']}?t={start}\",\n",
        "          'video_id': video_info['id'],\n",
        "          }\n",
        "      })\n",
        "  return new_transcript_segments\n",
        "combined_transcripts = combine_transcripts(video_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hrGxQwu5xT2W"
      },
      "outputs": [],
      "source": [
        "combined_transcripts[3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IqWn4pwH2LTY"
      },
      "source": [
        "## Convert Transcripts to Vectors\n",
        "\n",
        "1. Use Sentence Tranformers\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J7MdtKx92Xzq"
      },
      "outputs": [],
      "source": [
        "!pip install -U sentence-transformers pinecone-client"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EmLw4-e32lh",
        "outputId": "446df1e7-4957-4875-8989-b0f48cd4dc68"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter PINECONE_API_KEY··········\n"
          ]
        }
      ],
      "source": [
        "from getpass import getpass\n",
        "\n",
        "PINECONE_API_KEY = getpass('Enter PINECONE_API_KEY')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        {
          "data": {
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          "metadata": {},
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        {
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          "metadata": {},
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      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model_id = \"multi-qa-mpnet-base-dot-v1\"\n",
        "\n",
        "sentence_transformer_model = SentenceTransformer(model_id)\n",
        "sentence_transformer_model\n",
        "\n",
        "dimensions = sentence_transformer_model.get_sentence_embedding_dimension()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xQI4C1C36fN",
        "outputId": "f533b480-c621-48e1-f608-aa20e0744fdc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'dimension': 768,\n",
              " 'index_fullness': 0.0,\n",
              " 'namespaces': {'': {'vector_count': 30}},\n",
              " 'total_vector_count': 30}"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import pinecone  # !pip install pinecone-client\n",
        "\n",
        "index_id = \"youtube-search\"\n",
        "\n",
        "pinecone.init(\n",
        "    api_key=PINECONE_API_KEY,  # app.pinecone.io\n",
        "    environment=\"us-west1-gcp\"\n",
        ")\n",
        "\n",
        "if index_id not in pinecone.list_indexes():\n",
        "    pinecone.create_index(\n",
        "        index_id,\n",
        "        dimensions,\n",
        "        metric=\"dotproduct\"\n",
        "    )\n",
        "\n",
        "pinecone_index = pinecone.Index(index_id)\n",
        "pinecone_index.describe_index_stats()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_277qCm5MiI"
      },
      "source": [
        "## Upload to Vector Database"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uB5Xmdgu5PMS"
      },
      "outputs": [],
      "source": [
        "# we encode and insert in batches of 64\n",
        "batch_size = 64\n",
        "\n",
        "def upload_transcripts_to_vector_db(transcripts_for_upload):\n",
        "  # loop through in batches of 64\n",
        "  for i in tqdm(range(0, len(transcripts_for_upload), batch_size)):\n",
        "      # find end position of batch (for when we hit end of data)\n",
        "      i_end = min(len(transcripts_for_upload)-1, i+batch_size)\n",
        "      # extract the metadata like text, start/end positions, etc\n",
        "      batch_meta = [{\n",
        "          **transcripts_for_upload[x]\n",
        "      } for x in range(i, i_end)]\n",
        "      # extract only text to be encoded by embedding model\n",
        "      batch_text = [\n",
        "          row['text'] for row in transcripts_for_upload[i:i_end]\n",
        "      ]\n",
        "      # create the embedding vectors\n",
        "      batch_embeds = sentence_transformer_model.encode(batch_text).tolist()\n",
        "      # extract IDs to be attached to each embedding and metadata\n",
        "      batch_ids = [\n",
        "          row['id'] for row in transcripts_for_upload[i:i_end]\n",
        "      ]\n",
        "      # 'upsert' (insert) IDs, embeddings, and metadata to index\n",
        "      to_upsert = list(zip(\n",
        "          batch_ids, batch_embeds, batch_meta\n",
        "      ))\n",
        "      pinecone_index.upsert(to_upsert)\n",
        "      print(f'Uploaded Batches: {i} to {i_end}')\n",
        "\n",
        "upload_transcripts_to_vector_db(combined_transcripts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KN2dkwvn5h5T"
      },
      "source": [
        "## Search Transcript"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PVIgtSKw5lCl"
      },
      "outputs": [],
      "source": [
        "def query_model(query, video_id=\"\"):\n",
        "  encoded_query = sentence_transformer_model.encode(query).tolist()\n",
        "  metadata_filter = { \"video_id\": {\"$eq\": video_id}} if video_id else None\n",
        "  return pinecone_index.query(encoded_query, top_k=5,\n",
        "                              include_metadata=True,\n",
        "                              filter=metadata_filter)\n",
        "\n",
        "query_phrase = \"basketball\"\n",
        "results = query_model(query_phrase, \"bGk8qcHc1A0\")\n",
        "\n",
        "results['matches'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taljiYb2j0Uv"
      },
      "source": [
        "## Add some utility functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZRWig3lBjzzW"
      },
      "outputs": [],
      "source": [
        "from datetime import timedelta\n",
        "import urllib\n",
        "\n",
        "def convert_seconds_to_string(seconds):\n",
        "    days, seconds = divmod(seconds, 86400)\n",
        "    return str(timedelta(days=days, seconds=seconds)).split(',')[-1].strip()\n",
        "\n",
        "\n",
        "def parse_video_id(url):\n",
        "    # Parse the URL\n",
        "    parsed_url = urllib.parse.urlparse(url)\n",
        "    \n",
        "    # Check if the URL is a YouTube URL\n",
        "    if parsed_url.netloc in ['www.youtube.com', 'youtu.be']:\n",
        "        # Extract the video ID from the path or query parameters\n",
        "        if parsed_url.netloc == 'www.youtube.com':\n",
        "            video_id = urllib.parse.parse_qs(parsed_url.query)['v'][0]\n",
        "        else:\n",
        "            video_id = parsed_url.path.split('/')[-1]\n",
        "        return video_id\n",
        "    else:\n",
        "        return None\n",
        "\n",
        "def does_video_exist(video_url):\n",
        "  # create a placeholder vector of zeros to see if any vectors with the \n",
        "  # given video_id match.\n",
        "  video_id = parse_video_id(video_url)\n",
        "  query_response = pinecone_index.query(\n",
        "      top_k=1,\n",
        "      vector=[0] * dimensions,\n",
        "      filter={\n",
        "          \"video_id\": {\"$eq\": video_id}\n",
        "      }\n",
        "  )\n",
        "  return len(query_response['matches']) > 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWGGE6q6Nyw8"
      },
      "source": [
        "# Putting it all Together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0-n3WzovN3Sz"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "video_url=\"https://www.youtube.com/watch?v=lKXv19eRLZg\" # Making Friends with Machine Learning\n",
        "query_phrase = \"three degrees\"\n",
        "\n",
        "\n",
        "def transcribe_and_search_video(url, query, verbose=True):\n",
        "  t0 = time.time()\n",
        "  if not does_video_exist(url):\n",
        "    video_with_transcript = send_transcription_request(url)\n",
        "    video_with_transcript_combined = combine_transcripts(video_with_transcript)\n",
        "\n",
        "    upload_transcripts_to_vector_db(video_with_transcript_combined)\n",
        "  else:\n",
        "    print(f'Skipping transcribing and embedding.'\\\n",
        "    ' Video already exists:{url}')\n",
        "  results = query_model(query)\n",
        "  t1 = time.time()\n",
        "  total = t1-t0\n",
        "  if verbose:\n",
        "    video_length = f\"{convert_seconds_to_string(results['matches'][0]['metadata']['length'])} \"\\\n",
        "                      \"long video\" \\\n",
        "      if len(results['matches']) > 0 else 'no video found'\n",
        "    print(f'Transcribed and searched {video_length} in {total} seconds')\n",
        "  return results\n",
        "\n",
        "search_results = transcribe_and_search_video(video_url, query_phrase)\n",
        "search_results['matches'][0:3]"
      ]
    },
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        "## Examples"
      ]
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        "video_url=\"https://www.youtube.com/watch?v=s5yguqapy6s\" # How Jimmy Butler and Mark Wahlberg Became Close Friends\n",
        "query_phrase = \"filming transformers\"\n",
        "\n",
        "results = transcribe_and_search_video(video_url, query_phrase)\n",
        "results['matches'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "video_url=\"https://www.youtube.com/watch?v=Gqev5NrWnvM\" # Rio Ferdinand on Messi | Most Embarrassing Night of my Life\n",
        "query_phrase = \"we would have won\"\n",
        "\n",
        "results = transcribe_and_search_video(video_url, query_phrase)\n",
        "results['matches'][0]\n"
      ]
    }
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