NewsAnalysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sohammhatre10/Predicto/blob/main/NewsAnalysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "!pip install transformers\n",
        "!pip install sentencepiece"
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      },
      "execution_count": 1,
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          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m37.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m87.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m68.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n"
          ]
        }
      ]
    },
    {
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        "from transformers import PegasusTokenizer, PegasusForConditionalGeneration\n",
        "\n",
        "model_name = \"human-centered-summarization/financial-summarization-pegasus\"\n",
        "tokenizer = PegasusTokenizer.from_pretrained(model_name)\n",
        "model = PegasusForConditionalGeneration.from_pretrained(model_name)"
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            "53dfdc23ca0a43f89117fece3722c47e",
            "995dc843fa6e4b8ebd2bb14f287b75c8",
            "bd37f9f8c72c4949a8937f229d56392d",
            "b641b0f1afb64429976121b2758bdc4d",
            "5a98324113bb47f691d427d280ae7c20",
            "60e6d9d43db946a0ac4bc30e636a9f57",
            "07a6f7bcc48743e19c961a83d6a409c6",
            "7d46cd7657f04c868bad77585ca04a8b",
            "868e80dbb52449658c9145c9160f3fb3",
            "be47bc488e4a4c08a3829ec3f66aece7",
            "9e86cfe8f7c94398963bff0ed198fcb8",
            "c10d81c8736e45d7b2b465af877bf6fa",
            "acab83fb03774b25816cd923feb894d3",
            "0ff0ad58d6ab4fcdbe95fe7dd99316f9",
            "69ab20a80d674085860bc38c5d858096",
            "c391882e13704d0085326266a7170c9c",
            "ece41515595b431a90ca3f6a3fd31cc8",
            "e6903b8d88434c2f81bcd8af665c24a4"
          ]
        },
        "id": "VZ6pWcfjGRYR",
        "outputId": "429ec691-7561-4674-b471-d844d2b29686"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spiece.model:   0%|          | 0.00/1.91M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73332513293c4d208e056ad72497aea9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/1.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0244b0e9e2b444bdaee96a4074916275"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.44k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9667151acb74e138e0cf979cc799403"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e02104df61a94597af20242d102bbcd7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/2.28G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d46cd7657f04c868bad77585ca04a8b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of PegasusForConditionalGeneration were not initialized from the model checkpoint at human-centered-summarization/financial-summarization-pegasus and are newly initialized: ['model.decoder.embed_positions.weight', 'model.encoder.embed_positions.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from transformers import PegasusTokenizer, PegasusForConditionalGeneration\n",
        "from bs4 import BeautifulSoup\n",
        "import requests"
      ],
      "metadata": {
        "id": "fz26_EDtWwv4"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "monitored_ticker = ['Bitcoin', 'Dogecoin', 'Litecoin', 'Ethereum', 'XRP']"
      ],
      "metadata": {
        "id": "mRGNtK8XC8pd"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def search_for_crypto_news(ticker):\n",
        "    search_url = \"https://www.google.com/search?q=yahoo+finance+{}&sca_esv=554867176&rlz=1C1ONGR_enIN1063IN1063&tbs=sbd:1,qdr:d&tbm=nws&sxsrf=AB5stBjN5-syjYtVHcPU9P30_SJyTV9fUg:1691523802642&source=lnt&sa=X&ved=2ahUKEwi7s4qb6c2AAxUmpVYBHfCNBSoQpwV6BAgBEAg&biw=1280&bih=595&dpr=1.5\".format(ticker)\n",
        "    r = requests.get(search_url)\n",
        "    soup = BeautifulSoup(r.text, 'html.parser')\n",
        "    atags = soup.find_all('a')\n",
        "    hrefs = [link['href'] for link in atags]\n",
        "    return hrefs"
      ],
      "metadata": {
        "id": "aQ9hcUaeDA5m"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "raw_urls = {}\n",
        "for ticker in monitored_ticker:\n",
        "  raw_urls[ticker] = search_for_crypto_news(ticker)"
      ],
      "metadata": {
        "id": "GDIbEn0JDmXL"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re"
      ],
      "metadata": {
        "id": "uUfC9E_RDik1"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "exclude_list = ['maps', 'policies','preferences', 'accounts', 'support']"
      ],
      "metadata": {
        "id": "oWBApYy4EBma"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def strip_unwanted_urls(urls, exclude_list):\n",
        "    val = []\n",
        "    for url in urls:\n",
        "        if 'https://' in url and not any(exclude_word in url for exclude_word in exclude_list):\n",
        "            res = re.findall(r'(https?://\\S+)', url)[0].split('&')[0]\n",
        "            val.append(res)\n",
        "    return list(set(val))"
      ],
      "metadata": {
        "id": "EQd0SO3_EWvq"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleaned_urls = {ticker:strip_unwanted_urls(raw_urls[ticker], exclude_list) for ticker in monitored_ticker}\n",
        "cleaned_urls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULv5rn33FJ2F",
        "outputId": "54c571aa-3578-421b-d189-4b932642101d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Bitcoin': ['https://finance.yahoo.com/news/opportunity-palantir-technologies-inc-nyse-110015683.html',\n",
              "  'https://cryptosaurus.tech/the-next-cryptocurrencies-to-explode-on-sunday-august-13th-arwave-algorand-solana/',\n",
              "  'https://finance.yahoo.com/news/more-bitcoin-etf-decisions-already-140000373.html',\n",
              "  'https://finance.yahoo.com/news/free-press-withers-el-salvador-040745716.html',\n",
              "  'https://www.google.com/search?q%3Dyahoo%2Bfinance%2BBitcoin%26sca_esv%3D554867176%26rlz%3D1C1ONGR_enIN1063IN1063%26tbs%3Dsbd:1,qdr:d%26tbm%3Dnws%26sxsrf%3DAB5stBjN5-syjYtVHcPU9P30_SJyTV9fUg:1691523802642%26source%3Dlnt%26sa%3DX%26ved%3D2ahUKEwi7s4qb6c2AAxUmpVYBHfCNBSoQpwV6BAgBEAg%26biw%3D1280%26bih%3D595%26dpr%3D1.5%26pccc%3D1',\n",
              "  'https://finance.yahoo.com/news/ensuring-peace-mind-cybernetics-safeguards-193922656.html',\n",
              "  'https://finance.yahoo.com/news/chatgpt-given-20k-invest-stocks-155928839.html',\n",
              "  'https://theglobalherald.com/business/finance/what-is-the-lk-99-superconductor-plus-amsc-ceo-breaks-down-the-impact-on-markets-and-industry/',\n",
              "  'https://cryptosaurus.tech/binance-removes-perpetual-contracts-for-cardano-ada-and-polygon-matic-amid-regulatory-changes/',\n",
              "  'https://finance.yahoo.com/news/onschain-revolutionary-stride-toward-wider-211900992.html'],\n",
              " 'Dogecoin': ['https://www.google.com/search?q%3Dyahoo%2Bfinance%2BDogecoin%26sca_esv%3D554867176%26rlz%3D1C1ONGR_enIN1063IN1063%26tbs%3Dsbd:1,qdr:d%26tbm%3Dnws%26sxsrf%3DAB5stBjN5-syjYtVHcPU9P30_SJyTV9fUg:1691523802642%26source%3Dlnt%26sa%3DX%26ved%3D2ahUKEwi7s4qb6c2AAxUmpVYBHfCNBSoQpwV6BAgBEAg%26biw%3D1280%26bih%3D595%26dpr%3D1.5%26pccc%3D1'],\n",
              " 'Litecoin': ['https://www.google.com/search?q%3Dyahoo%2Bfinance%2BLitecoin%26sca_esv%3D554867176%26rlz%3D1C1ONGR_enIN1063IN1063%26tbs%3Dsbd:1,qdr:d%26tbm%3Dnws%26sxsrf%3DAB5stBjN5-syjYtVHcPU9P30_SJyTV9fUg:1691523802642%26source%3Dlnt%26sa%3DX%26ved%3D2ahUKEwi7s4qb6c2AAxUmpVYBHfCNBSoQpwV6BAgBEAg%26biw%3D1280%26bih%3D595%26dpr%3D1.5%26pccc%3D1'],\n",
              " 'Ethereum': ['https://www.google.com/search?q%3Dyahoo%2Bfinance%2BEthereum%26sca_esv%3D554867176%26rlz%3D1C1ONGR_enIN1063IN1063%26tbs%3Dsbd:1,qdr:d%26tbm%3Dnws%26sxsrf%3DAB5stBjN5-syjYtVHcPU9P30_SJyTV9fUg:1691523802642%26source%3Dlnt%26sa%3DX%26ved%3D2ahUKEwi7s4qb6c2AAxUmpVYBHfCNBSoQpwV6BAgBEAg%26biw%3D1280%26bih%3D595%26dpr%3D1.5%26pccc%3D1',\n",
              "  'https://finance.yahoo.com/news/flash-news-okx-wallet-extension-071700408.html',\n",
              "  'https://finance.yahoo.com/news/chatgpt-given-20k-invest-stocks-155928839.html'],\n",
              " 'XRP': ['https://www.google.com/search?q%3Dyahoo%2Bfinance%2BXRP%26sca_esv%3D554867176%26rlz%3D1C1ONGR_enIN1063IN1063%26tbs%3Dsbd:1,qdr:d%26tbm%3Dnws%26sxsrf%3DAB5stBjN5-syjYtVHcPU9P30_SJyTV9fUg:1691523802642%26source%3Dlnt%26sa%3DX%26ved%3D2ahUKEwi7s4qb6c2AAxUmpVYBHfCNBSoQpwV6BAgBEAg%26biw%3D1280%26bih%3D595%26dpr%3D1.5%26pccc%3D1']}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def scrape_and_process(URLs):\n",
        "    ARTICLES = []\n",
        "    for url in URLs:\n",
        "        r = requests.get(url)\n",
        "        soup = BeautifulSoup(r.text, 'html.parser')\n",
        "        paragraphs = soup.find_all('p')\n",
        "        text = [paragraph.text for paragraph in paragraphs]\n",
        "        words = ' '.join(text).split(' ')[:350]\n",
        "        ARTICLE = ' '.join(words)\n",
        "        ARTICLES.append(ARTICLE)\n",
        "    return ARTICLES"
      ],
      "metadata": {
        "id": "0rxzDn3EFjFd"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "articles = {ticker:scrape_and_process(cleaned_urls[ticker]) for ticker in monitored_ticker}\n",
        "articles"
      ],
      "metadata": {
        "id": "ZOgZP64FYIod",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72f2ab0a-4ad4-4553-e932-6ec38f479cc3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Bitcoin': [\"Using the 2 Stage Free Cash Flow to Equity, Palantir Technologies fair value estimate is US$26.70 Current share price of US$15.41 suggests Palantir Technologies is potentially 42% undervalued  The US$13.83 analyst price target for PLTR is 48% less than our estimate of fair value Today we'll do a simple run through of a valuation method used to estimate the attractiveness of Palantir Technologies Inc. (NYSE:PLTR) as an investment opportunity by estimating the company's future cash flows and discounting them to their present value. The Discounted Cash Flow (DCF) model is the tool we will apply to do this. Before you think you won't be able to understand it, just read on! It's actually much less complex than you'd imagine. Remember though, that there are many ways to estimate a company's value, and a DCF is just one method. For those who are keen learners of equity analysis, the Simply Wall St analysis model here may be something of interest to you.  See our latest analysis for Palantir Technologies  We are going to use a two-stage DCF model, which, as the name states, takes into account two stages of growth. The first stage is generally a higher growth period which levels off heading towards the terminal value, captured in the second 'steady growth' period. To begin with, we have to get estimates of the next ten years of cash flows. Where possible we use analyst estimates, but when these aren't available we extrapolate the previous free cash flow (FCF) from the last estimate or reported value. We assume companies with shrinking free cash flow will slow their rate of shrinkage, and that companies with growing free cash flow will see their growth rate slow, over this period. We do this to reflect that growth tends to slow more in the early years than it does in later years. Generally we assume that a dollar today is more valuable than a dollar in the future, so we need to discount the sum of these future cash flows to arrive at a present value estimate: 2024 2025 2026 2027 2028\",\n",
              "  '',\n",
              "  '(Bloomberg) -- Bitcoin ETF candidates got another dose of disappointment when US regulators on Friday punted on making a decision on such a product. But the next time they hear from them might be just a few weeks away. Most Read from Bloomberg America’s Fastest Growing City Is Embracing ‘Yellowstone’ Mania David Solomon Scrutiny Spreads From Goldman to Alma Mater Look to the Options Market for Signs of Stock Market Trouble Bond Market Sees No End to Tumult as Fed Casts a Hawkish Shadow Hackers Trick AI With ‘Bad Math’ to Expose Flaws and Biases The US Securities and Exchange Commission needs to come to a conclusion on the Bitwise Bitcoin ETP Trust by Sept. 1, according to a Bloomberg Intelligence tally. Regulators can reject, approve or delay. Decisions for applications from BlackRock, VanEck, WisdomTree and Invesco are due just a day later, with others following closely behind. Crypto adherents are keeping track of the dates because expectations are running high that this time around — unlike past attempts — an exchange-traded fund that invests directly in Bitcoin could receive the regulatory greenlight. In particular, many have glommed onto the fact that BlackRock, the asset-managing powerhouse, has thrown its name into the race — it has a near-pristine record of getting ETFs launched. Still, the road to a potential spot fund hasn’t been easy nor quick. “We fully expect a delay in those decisions as well,” said James Seyffart, an analyst at Bloomberg Intelligence. “The only thing that could possibly change that view is if we get a decision in Grayscale’s lawsuit against the SEC, and even then, it’s likely we’d see a delay by those deadlines as well.” Grayscale is suing the SEC as it attempts to convert its Bitcoin trust into an ETF. Many in the crypto community — and fans outside of it — have been longing for a spot-Bitcoin ETF for years. They argue that it would not only make investing in Bitcoin more accessible to everyday investors, but that it would also help bring the digital-assets space closer into traditional financial markets. On the other',\n",
              "  \"SAN SALVADOR, El Salvador (AP) — Douglas Guzmán's TikTok feed was dotted with workout routines and videos showcasing his favorite parts of his country. That changed about a year ago, as rights groups, civil society and even some officials criticized El Salvador's President Nayib Bukele for violating human rights in his crackdown on criminal gangs, and said that his unconstitutional bid for re-election would corrode the country’s democracy. Within days of Bukele announcing his bid for a second five-year term, Guzmán’s feed was plastered with videos describing Bukele as the “future liberator of Latin America” and slick montages of the leader’s “mega-prison” for accused gangsters. Views on the social media influencer’s videos skyrocketed. The 39-year-old member of Bukele's party said he found a new mission: counteracting negative press from independent media about his populist president. “(Journalists) don’t know anything. All they do is sit at their desks and watch as President Bukele … makes a massive effort to save thousands of lives. But they don’t see that because they’ve never cared about the lives of Salvadorans,” Guzmán said. “That’s why we’re here. To show the true reality.” Guzmán is part of an expanding network of social media personalities acting as a megaphone for the millennial leader. At the same time Bukele has cracked down on the press, his government has embraced those influencers. As the president seeks to hold onto power, he has harnessed that flood of pro-Bukele content slowly turning his Central American nation into an informational echo chamber. “A news organization doing an investigation can’t compare to the sounding board that these influencers have because they flood your social media with the government’s narrative,” said Roberto Dubon, a communications strategist and congressional candidate for Bukele’s former party, FMLN. “What you have is an apparatus to spread their propaganda.” Bukele, a 42-year-old leader often donning a backwards baseball cap, worked years in political advertising before social media became a key to his rise to power five years ago. Since, his approval ratings have soared to 90%, according to a June CID Gallup poll. Bukele's modern political messaging, charisma and\",\n",
              "  '© 2023 - Privacy - Terms',\n",
              "  'STOCKPORT, United Kingdom - (NewMediaWire) - August 12, 2023 - (King NewsWire) - Cybernetics is proud to announce a groundbreaking advancement that ensures the utmost peace of mind for users during the process of crypto restoration. With the increasing reliance on digital assets and the growing threats to data integrity, Cybernetics has harnessed cutting-edge cybernetic technology to provide users a new level of protection and assurance during crypto recovery services on how to get their stolen crypto back. Crypto restoration has historically posed challenges when it comes to safeguarding user data, often leaving individuals and organizations vulnerable to security breaches and data loss. Recognizing the critical importance of securing valuable digital assets, Cybernetics has engineered a revolutionary solution that leverages advanced principles to ensure the seamless and secure restoration of cryptocurrency holdings and hence helping those impacted by crypto trading platform crimes. Key features of this groundbreaking innovation include: Enhanced Data Encryption: Cybernetics has implemented state-of-the-art encryption protocols that provide an extra layer of security during the restoration process. This safeguards sensitive information from potential threats and unauthorized access. Real-time Monitoring: Their cutting-edge system enables real-time monitoring of the restoration procedure, allowing for immediate identification and mitigation of any anomalies or potential breaches. Adaptive Learning: The system employs adaptive learning algorithms that continuously evolve and adjust to emerging cyber threats, ensuring that each restoration process remains robust against evolving attack vectors. User Empowerment: Cybernetics believes in putting the user in control. Our interface provides intuitive tools that allow users to oversee and validate the restoration process, fostering transparency and trust. Chief Information Officer at Cybernetics,Jessica Walker, expressed excitement about this transformative achievement, stating, \"In an era where digital assets and data privacy are paramount, we are proud to introduce a solution that redefines the security landscape during crypto restoration. Our team\\'s tireless dedication to innovation has culminated in a cybernetic safeguard that empowers users and strengthens their confidence in the crypto recovery services to help get stolen bitcoin back.\" As cyber threats continue to evolve, Cybernetics remains committed to staying at the forefront of technological advancements that protect users\\' digital',\n",
              "  'The surging popularity of artificial intelligence (or large language models), notably ChatGPT, has been helping people be more efficient at work, among other functions. But what about investing? NewsBTC recently conducted an experiment, giving the AI tool $20,000 to invest in stocks and crypto. The results were surprising. See: 10 Best ChatGPT Prompts To Use for Making MoneyHow To Buy ChatGPT Stock: Your 2023 Guide to AI Investing In the end, $1,946 profit was made after following ChatGPT’s advice for a month, with crypto investments making 130% more than stocks and Rivian and Solana having the highest return on investment (ROI). Crypto investments earned $1,359 and stocks returned $587, according to the experiment. NewsBTC said that after a little bit of training, ChatGPT suggested exactly how much of the budget to invest in each stock and coin. Jonathan Millet, business consultant at NewsBTC, said that while he was fully aware of Solana’s strong community backing and the widespread trust in its stability, he didn’t anticipate that its increased profits would be more than double the ROI of Bitcoin. In terms of stocks, he said it was interesting that Rivian saw the greatest ROI overall — despite ChatGPT recommending investing the least in this stock. What he was surprised most by, though, was a particular stock missing from ChatGPT’s investment suggestions: Meta, which recently released Twitter’s competitor, Threads. “It was released at the perfect time, after news that Twitter was limiting the number of Tweets its users could see. This isn’t one of the stocks ChatGPT suggested investing in, despite Threads’ rapid user growth and positive impact on Meta’s stock price. Had ChatGPT recommended investing $1,000 in Meta, this value would have risen to $1,100 by the study’s close — a greater ROI than Apple or Amazon,” Millet said. Another “hiccup” was one of the stocks ChatGPT suggested — Waymo. Waymo is not publicly traded, something some experts think proves ChatGPT is still in its infancy… and not entirely trustworthy when it comes to investing advice. “ChatGPT isn’t going to make you rich by picking stocks. ChatGPT might be useful for',\n",
              "  'Home » Business Yahoo Finance published this video item, entitled “What is the LK-99 superconductor? plus, AMSC CEO breaks down the impact on markets and industry” – below is their description. Yahoo Finance’s Julie Hyman spoke to American Superconductor (AMSC) CEO Daniel McGahn about LK-99, the superconductor industry, and the impact on his company’s shares. McGahn said, “If there are technological advances in our space, it’s only going to benefit us … we need to be able to develop the future but deliver the present.” Video highlights:  00:00:55 – LK-99 and technological advancements in superconductor industry  00:03:50 – Commercialization of superconductors  00:06:30 – Impact on AMSC #LK99 #youtube #yahoofinance  Subscribe to Yahoo Finance: https://yhoo.it/2fGu5Bb About Yahoo Finance:  At Yahoo Finance, you get free stock quotes, up-to-date news, portfolio management resources, international market data, social interaction and mortgage rates that help you manage your financial life. Yahoo Finance Plus: With a subscription to Yahoo Finance Plus get the tools you need to invest with confidence. Discover new opportunities with expert research and investment ideas backed by technical and fundamental analysis. Optimize your trades with advanced portfolio insights, fundamental analysis, enhanced charting, and more. To learn more about Yahoo Finance Plus please visit: https://yhoo.it/33jXYBp Connect with Yahoo Finance: Get the latest news: https://yhoo.it/2fGu5Bb Find Yahoo Finance on Facebook: http://bit.ly/2A9u5Zq Follow Yahoo Finance on Twitter: http://bit.ly/2LMgloP Follow Yahoo Finance on Instagram: http://bit.ly/2LOpNYz Follow Yahoo Finance Premium on Twitter: https://bit.ly/3hhcnmV Got a comment? Leave your thoughts in the comments section, below. Please note comments are moderated before publication. \\n\\nLeave a Comment\\n\\n\\nSubscribe in Google News\\n\\n  We don\\'t require your email address, or your name, for anyone to leave a comment. If you do add an email address, you may be notified if there are replies to your comment - we won\\'t use it for any other purpose.\\r\\n\\r\\nPlease make respectful comments, which add value, and avoid personal attacks on others. \\r\\n\\r\\nLinks are not allowed in comments - 99% of spam comments, attempt to post links. Please describe where people may find additional information - for example \"visit the UN website\"',\n",
              "  '',\n",
              "  'Onschain, the world’s first platform that has been a result of the integration of blockchain and AI, the two pioneering technologies, marks a milestone in the space. New York, NY , Aug. 12, 2023 (GLOBE NEWSWIRE) -- In the fast-moving world that embraces revolutionary strides, the adoption of the blockchain as well as Artificial Intelligence (AI) has been a stupendous advance towards wider technological acquisitions. Onschain, the world’s first platform that has been a result of the integration of blockchain and AI, the two pioneering technologies, marks a milestone in the space. Onschain’s groundbreaking invention of amalgamating blockchain technology with Artificial Intelligence, focuses mainly on a decentralized, transparent, and safe ecosystem. While Onschain’s decentralized system is not only built by AI but also monitored by it, the term “decentralized” becomes the most reliable and promising as there is no human interference. While merging the most secure and unclouded blockchain technology and the capable AI, Onschain aims at building an ideal platform that facilitates real-time data-based decisions, guaranteeing efficiency and accuracy. The novel concept of the integration of these creative technologies sets a precursor for the future that excludes any human intervention, especially in tech-related areas. By incorporating AI technology into the blockchain ecosystem, Onschain looks forward to introducing a more appreciable digital asset management system. The reports and analysis that surround Onschain also appreciate the company’s novel standards and growing opportunities, utilizing AI and blockchain technologies. In addition, the leading crypto exchange Binance has also shared its interest in Onschain, by opening a blank chart page for the Onschain project. The level of automation that Onschain targets and implements was impossible in the previous centuries when everything was under human control. However, with the clutch on AI, Onschain’s blockchain ecosystem has made it a reality. The advancements make transactions easier and more secure, improving user experience. In addition, the possibility of threats such as hacks or breaches is minimal under the monitoring of AI. About Us Onschain is a platform that shapes the future of blockchain technology by integrating it with effective and capable information technology. The novel project intends'],\n",
              " 'Dogecoin': ['© 2023 - Privacy - Terms'],\n",
              " 'Litecoin': ['© 2023 - Privacy - Terms'],\n",
              " 'Ethereum': ['© 2023 - Privacy - Terms',\n",
              "  \"SAN FRANCISCO, Aug. 13, 2023 (GLOBE NEWSWIRE) -- OKX, a leading Web3 technology company, has issued updates for August 13, 2023. OKX Wallet Web Extension is now Fully Integrated into DackieSwap  OKX Wallet users can now easily experience DackieSwap following the OKX Wallet's integration with the DackieSwap DEX. DackieSwap is an Ethereum-based decentralized exchange (DEX) that aims to offer users a smooth and effective trading platform for ERC-20 tokens. DEXs enable direct cryptocurrency trading from wallets, eliminating the necessity for intermediaries. OKX Wallet's DEX is a multi-chain, cross-chain decentralized exchange (DEX) that empowers you with the best token swap rates within a transparent, private, and secure trading environment. The OKX DEX supports 10+ chains including Ethereum, BSC, Polygon, Solana, Tron, and more. Find out more about OKX Wallet here. For further information, please contact:Media@okx.com About OKX A leading global technology company driving the future of Web3, OKX provides a comprehensive suite of products to meet the needs of beginners and experts alike, including the OKX Wallet, NFT Marketplace and DEX. OKX partners with a number of the world's top brands and athletes, including: English Premier League champions Manchester City F.C., McLaren Formula 1, The Tribeca Festival, Olympian Scotty James, and F1 driver Daniel Ricciardo. As a leader building innovative technology products, OKX believes in challenging the status quo. The company recently launched a global brand campaign entitled, The System Needs a Rewrite, which advocates for a new paradigm led by Web3 self-managed technology to replace existing centralized systems. To learn more about OKX, download our app or visit: okx.com Disclaimer The information displayed is strictly for education and informational purposes only. It does not constitute and shall not be considered as an offer, solicitation or recommendation, to deal in any products (including any NFT or otherwise), or as financial or investment advice. Both OKX Web3 Wallet and OKX NFT Marketplace are subject to separate terms of service at www.okx.com. \",\n",
              "  'The surging popularity of artificial intelligence (or large language models), notably ChatGPT, has been helping people be more efficient at work, among other functions. But what about investing? NewsBTC recently conducted an experiment, giving the AI tool $20,000 to invest in stocks and crypto. The results were surprising. See: 10 Best ChatGPT Prompts To Use for Making MoneyHow To Buy ChatGPT Stock: Your 2023 Guide to AI Investing In the end, $1,946 profit was made after following ChatGPT’s advice for a month, with crypto investments making 130% more than stocks and Rivian and Solana having the highest return on investment (ROI). Crypto investments earned $1,359 and stocks returned $587, according to the experiment. NewsBTC said that after a little bit of training, ChatGPT suggested exactly how much of the budget to invest in each stock and coin. Jonathan Millet, business consultant at NewsBTC, said that while he was fully aware of Solana’s strong community backing and the widespread trust in its stability, he didn’t anticipate that its increased profits would be more than double the ROI of Bitcoin. In terms of stocks, he said it was interesting that Rivian saw the greatest ROI overall — despite ChatGPT recommending investing the least in this stock. What he was surprised most by, though, was a particular stock missing from ChatGPT’s investment suggestions: Meta, which recently released Twitter’s competitor, Threads. “It was released at the perfect time, after news that Twitter was limiting the number of Tweets its users could see. This isn’t one of the stocks ChatGPT suggested investing in, despite Threads’ rapid user growth and positive impact on Meta’s stock price. Had ChatGPT recommended investing $1,000 in Meta, this value would have risen to $1,100 by the study’s close — a greater ROI than Apple or Amazon,” Millet said. Another “hiccup” was one of the stocks ChatGPT suggested — Waymo. Waymo is not publicly traded, something some experts think proves ChatGPT is still in its infancy… and not entirely trustworthy when it comes to investing advice. “ChatGPT isn’t going to make you rich by picking stocks. ChatGPT might be useful for'],\n",
              " 'XRP': ['© 2023 - Privacy - Terms']}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "btc = articles[\"Bitcoin\"]\n",
        "eth = articles[\"Ethereum\"]\n",
        "lte = articles[\"Litecoin\"]\n",
        "xrp = articles[\"XRP\"]\n",
        "doge = articles[\"Dogecoin\"]"
      ],
      "metadata": {
        "id": "WYRGYYRRXhc7"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def summarize(articles):\n",
        "    summaries = []\n",
        "    for ticker, article_list in articles.items():\n",
        "        article_text = '\\n\\n'.join(article_list)  # Join the list of articles into a single string\n",
        "        ticker_summaries = []\n",
        "        for chunk in article_text.split('\\n\\n'):  # Split the article into paragraphs (you can adjust this based on your article structure)\n",
        "            input_ids = tokenizer.encode(chunk, return_tensors='pt', max_length=512, truncation=True)\n",
        "            output = model.generate(input_ids, max_length=55, num_beams=5, early_stopping=True)\n",
        "            summary = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "            ticker_summaries.append(summary)\n",
        "        summaries.append({ticker: ticker_summaries})\n",
        "    return summaries\n",
        "\n",
        "# Assuming you have a dictionary with lists of articles for each ticker (e.g., btc, eth, doge, xrp, lte)\n",
        "articles = {\n",
        "    'BTC': btc,\n",
        "    'ETH': eth,\n",
        "    'DOGE': doge,\n",
        "    'XRP': xrp,\n",
        "    'LTC': lte\n",
        "}\n",
        "\n",
        "# Call the summarize function with the articles dictionary\n",
        "summaries = summarize(articles)\n",
        "print(summaries)"
      ],
      "metadata": {
        "id": "t4kz0fNVF_76",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "95e32e7a-bd65-414e-a3d9-be076df81cf5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'BTC': ['View our latest analysis for Palantir Technologies.', 'All photographs subject to copyright.', 'Bitwise’s application for an ETP rejected by SEC. Decisions for applications from BlackRock, VanEck, WisdomTree and Invesco are due', 'Social media influencers are a key part of President Nayib Bukele’s campaign.', 'All images are copyrighted.', 'DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch DropCatch has launched a revolutionary solution to restore stolen crypto.', 'NewsBTC conducted an experiment with ChatGPT to invest $20,000. Meta, Rivian and Solana had the highest returns', 'American Superconductor is commercialization of LK-99. McGahn says, ‘We need to be able to develop the future but deliver the present’', 'Follow us on Instagram, Snapchat and Twitter for the latest news.', '.', 'All comments are moderated.', 'All photographs subject to copyright.', 'Onschain’s platform aims at building an ideal platform that facilitates real-time data-based decisions.']}, {'ETH': ['All images are copyrighted.', 'OKX Wallet now fully integrated into DackieSwap. DackieSwap is an Ethereum-based exchange that aims to offer users a smooth and effective trading platform', 'NewsBTC conducted an experiment with ChatGPT to invest $20,000. Meta, Rivian and Solana had the highest returns']}, {'DOGE': ['All images are copyrighted.']}, {'XRP': ['All images are copyrighted.']}, {'LTC': ['All images are copyrighted.']}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "# Initialize the sentiment analysis pipeline\n",
        "model_name = \"distilbert-base-uncased-finetuned-sst-2-english\"\n",
        "model_revision = \"af0f99b\"\n",
        "sentiment_classifier = pipeline('sentiment-analysis', model=model_name, revision=model_revision)\n",
        "\n",
        "def analyze_sentiment(summaries):\n",
        "    sentiment_results = sentiment_classifier(summaries)  # Use the pipeline directly on the list of summaries\n",
        "    return sentiment_results\n",
        "\n",
        "# Assuming you want to analyze the sentiment for the 'BTC' ticker's summaries\n",
        "btc_summaries = [summary for ticker_summaries in summaries for summary in ticker_summaries.get('BTC', [])]\n",
        "eth_summaries = [summary for ticker_summaries in summaries for summary in ticker_summaries.get('ETH', [])]  # List of summaries for ETH\n",
        "doge_summaries = [summary for ticker_summaries in summaries for summary in ticker_summaries.get('DOGE', [])]  # List of summaries for DOGE\n",
        "xrp_summaries = [summary for ticker_summaries in summaries for summary in ticker_summaries.get('XRP', [])]  # List of summaries for XRP\n",
        "lte_summaries = [summary for ticker_summaries in summaries for summary in ticker_summaries.get('LTC', [])]  # List of summaries for LTE\n",
        "\n",
        "sentiment_results_btc = analyze_sentiment(btc_summaries)\n",
        "sentiment_results_eth = analyze_sentiment(eth_summaries)\n",
        "sentiment_results_doge = analyze_sentiment(doge_summaries)\n",
        "sentiment_results_xrp = analyze_sentiment(xrp_summaries)\n",
        "sentiment_results_lte = analyze_sentiment(lte_summaries)\n",
        "\n",
        "\n",
        "data = {\n",
        "    'Ticker': ['BTC'] * len(sentiment_results_btc) + ['ETH'] * len(sentiment_results_eth) +\n",
        "              ['DOGE'] * len(sentiment_results_doge) + ['XRP'] * len(sentiment_results_xrp) +\n",
        "              ['LTC'] * len(sentiment_results_lte),\n",
        "    'Result_state': [result['label'] for result in sentiment_results_btc] +\n",
        "                       [result['label'] for result in sentiment_results_eth] +\n",
        "                       [result['label'] for result in sentiment_results_doge] +\n",
        "                       [result['label'] for result in sentiment_results_xrp] +\n",
        "                       [result['label'] for result in sentiment_results_lte],\n",
        "    'Score': [result['score'] for result in sentiment_results_btc] +\n",
        "                       [result['score'] for result in sentiment_results_eth] +\n",
        "                       [result['score'] for result in sentiment_results_doge] +\n",
        "                       [result['score'] for result in sentiment_results_xrp] +\n",
        "                       [result['score'] for result in sentiment_results_lte],\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Save the DataFrame to a CSV file\n",
        "df.to_csv('results.csv', index=False)"
      ],
      "metadata": {
        "id": "jO6dBTzmL_lf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 155,
          "referenced_widgets": [
            "a3b9390eae9949a9a29bd363ccef94cf",
            "dfcd7f3bd57048c2bd51d552591d92b6",
            "edfc119601114288a8371d7cae0fee75",
            "c45e1a0c6c154a9ebae5c9ea1c87e960",
            "51b6fb6a3f6b4da886d436a8c7a49cb4",
            "3ece32d59d7d4752afdfaddd6b1cd6f2",
            "70227e8680db4e6d86b8044b432cc2da",
            "f0cab45ee276418b8458be5923c518b2",
            "8dd3b70d6e4c43b295e6ed524b53584e",
            "cd2aba36565a4a288ad876b2067dd8ed",
            "93f527d705a54dfa884f0e299aab8104",
            "1fbce8d570ff4e409e0ec02cecda173f",
            "d1ed95f8fea24d2ebb8e4776c6eb04d4",
            "851665c89b594659bf586ba661261047",
            "793c951f051b4f7ea84b54afcc568601",
            "563ca2ab1fe04b9998f3fdcdbfe181e3",
            "62bc17cae8824bbab2a115415c284ba5",
            "75a8ba8670a64de18117efe8f83530b1",
            "ca54799190b54203980dabcf568056ca",
            "ed0bc052f22f47ed8da1afd2e3fbd843",
            "d70c3f8049eb4bdf9ea571989231e996",
            "833872bfbeaf42f9a27139823c7bd148"
          ]
        },
        "outputId": "eb60c707-a169-423e-a719-ac64783e969f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/af0f99b/config.json:   0%|          | 0.00/629 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3b9390eae9949a9a29bd363ccef94cf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1fbce8d570ff4e409e0ec02cecda173f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n",
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import math\n",
        "from datetime import datetime, timedelta\n",
        "coin_results = {}\n",
        "coins = ['BTC', 'ETH', 'XRP', 'LTC', 'DOGE']\n",
        "csv_file_path = \"modified_data.csv\"\n",
        "coin_data = pd.read_csv(csv_file_path)\n",
        "today = datetime.now()\n",
        "\n",
        "tomorrow = today + timedelta(days=1)\n",
        "for coin in coins:\n",
        "    positive_sum = df[(df['Ticker'] == coin) & (df['Result_state'] == 'POSITIVE')]['Score'].sum()\n",
        "    negative_sum = df[(df['Ticker'] == coin) & (df['Result_state'] == 'NEGATIVE')]['Score'].sum()\n",
        "    if (len(df[(df['Ticker'] == coin)])) < 6:\n",
        "         coin_results[coin] =  1-(((positive_sum - negative_sum)/(pow(len(df[(df['Ticker'] == coin)]),(1234/100)))))\n",
        "    else:\n",
        "        coin_results[coin] = 1-(((positive_sum - negative_sum)/(pow(len(df[(df['Ticker'] == coin)]),(13589029/10000000)))))\n",
        "new_data = {\n",
        "    \"Date\": [tomorrow.strftime('%Y-%m-%d')],\n",
        "    \"BTC\": coin_results[\"BTC\"],\n",
        "    \"ETH\": coin_results[\"ETH\"],\n",
        "    \"DOGE\": coin_results[\"DOGE\"],\n",
        "    \"LTC\": coin_results[\"LTC\"],\n",
        "    \"XRP\": coin_results[\"XRP\"],\n",
        "}\n",
        "new_data_df = pd.DataFrame(new_data)\n",
        "\n",
        "coin_data = coin_data.append(new_data_df, ignore_index=True)\n",
        "coin_data.to_csv(csv_file_path, index=False)\n",
        "\n",
        "files.download('modified_data.csv')\n"
      ],
      "metadata": {
        "id": "5hcF38LgkYqx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "f93809ed-3f7a-4661-fcf4-ad1b38fdd393"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-aa83ec8e3623>:28: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  coin_data = coin_data.append(new_data_df, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_659e25b4-1542-4e5b-94f2-ac2b3f7f3d89\", \"modified_data.csv\", 344)"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}