stock_prediction_api.ipynb

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      "source": [
        "!pip install prophet"
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      "metadata": {
        "id": "aQhTFyEYdUDW"
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      "source": [
        "from datetime import datetime, timedelta\n",
        "import yfinance as yf\n",
        "from prophet import Prophet\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Prophet(daily_seasonality = True)"
      ],
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        "id": "WSBKaT4UuBCA"
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      "execution_count": 29,
      "outputs": []
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      "source": [
        "def train_model(ticker):\n",
        "    end_date = datetime.now()\n",
        "    start_date = end_date - timedelta(days=1825)\n",
        "    df = yf.download(ticker, start=start_date, end=end_date)\n",
        "    print(df)\n",
        "    df_prophet = df.reset_index()[[\"Date\", \"Close\"]]\n",
        "    df_prophet.columns = [\"ds\", \"y\"]\n",
        "    print(df_prophet)\n",
        "    model.fit(df_prophet)"
      ],
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      "execution_count": 30,
      "outputs": []
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        "train_model('META')"
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        "outputId": "7e9e3e72-cf68-4ff3-df5c-a5e719295b5e"
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      "execution_count": 31,
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            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpskuwr502/po4oej67.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpskuwr502/3011d93c.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=18453', 'data', 'file=/tmp/tmpskuwr502/po4oej67.json', 'init=/tmp/tmpskuwr502/3011d93c.json', 'output', 'file=/tmp/tmpskuwr502/prophet_modelump1mrfo/prophet_model-20240825130055.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "13:00:55 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n"
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          "text": [
            "                  Open        High         Low       Close   Adj Close  \\\n",
            "Date                                                                     \n",
            "2019-08-27  181.929993  184.039993  181.009995  181.300003  180.928207   \n",
            "2019-08-28  180.529999  181.949997  178.919998  181.759995  181.387253   \n",
            "2019-08-29  183.770004  186.080002  183.470001  185.570007  185.189453   \n",
            "2019-08-30  186.779999  186.800003  183.460007  185.669998  185.289246   \n",
            "2019-09-03  184.000000  185.669998  182.110001  182.389999  182.015976   \n",
            "...                ...         ...         ...         ...         ...   \n",
            "2024-08-19  526.869995  531.659973  522.760010  529.280029  529.280029   \n",
            "2024-08-20  528.349976  531.090027  525.880005  526.729980  526.729980   \n",
            "2024-08-21  527.150024  539.400024  526.929993  535.159973  535.159973   \n",
            "2024-08-22  537.000000  544.229980  528.590027  531.929993  531.929993   \n",
            "2024-08-23  536.919983  539.869995  525.059998  528.000000  528.000000   \n",
            "\n",
            "              Volume  \n",
            "Date                  \n",
            "2019-08-27  14399600  \n",
            "2019-08-28   9386100  \n",
            "2019-08-29  10128700  \n",
            "2019-08-30  10774500  \n",
            "2019-09-03   9779400  \n",
            "...              ...  \n",
            "2024-08-19   9879700  \n",
            "2024-08-20   7944400  \n",
            "2024-08-21  13423300  \n",
            "2024-08-22  15708300  \n",
            "2024-08-23  11308600  \n",
            "\n",
            "[1257 rows x 6 columns]\n",
            "             ds           y\n",
            "0    2019-08-27  181.300003\n",
            "1    2019-08-28  181.759995\n",
            "2    2019-08-29  185.570007\n",
            "3    2019-08-30  185.669998\n",
            "4    2019-09-03  182.389999\n",
            "...         ...         ...\n",
            "1252 2024-08-19  529.280029\n",
            "1253 2024-08-20  526.729980\n",
            "1254 2024-08-21  535.159973\n",
            "1255 2024-08-22  531.929993\n",
            "1256 2024-08-23  528.000000\n",
            "\n",
            "[1257 rows x 2 columns]\n"
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            "13:00:55 - cmdstanpy - INFO - Chain [1] done processing\n",
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      "source": [
        "def predict(days):\n",
        "  future = model.make_future_dataframe(periods=days)\n",
        "  forecast = model.predict(future)\n",
        "  current_date = pd.Timestamp(datetime.now().date())\n",
        "  future_forecast = forecast[forecast['ds'] > current_date]\n",
        "  results = future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].to_dict('records')\n",
        "  ##for result in results:\n",
        "  ##      result['ds'] = result['ds'].strftime('%Y-%m-%d')\n",
        "  ##return results\n",
        "  results_df = future_forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].reset_index(drop=True)\n",
        "  results_df['ds'] = results_df['ds'].dt.strftime('%Y-%m-%d')\n",
        "  #results_df['ds'] = pd.to_datetime(results_df['ds'])\n",
        "  return results_df"
      ],
      "metadata": {
        "id": "AgboGq8cr42k"
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      "execution_count": 48,
      "outputs": []
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      "source": [
        "forecast_df = predict(365)\n",
        "print(forecast_df)\n"
      ],
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      "execution_count": 49,
      "outputs": [
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          "text": [
            "             ds        yhat  yhat_lower  yhat_upper\n",
            "0    2024-08-26  557.983007  530.060951  584.918139\n",
            "1    2024-08-27  558.543753  528.582952  588.202732\n",
            "2    2024-08-28  559.587756  530.956215  588.448617\n",
            "3    2024-08-29  560.370704  532.738378  589.971960\n",
            "4    2024-08-30  561.274217  534.157748  590.621860\n",
            "..          ...         ...         ...         ...\n",
            "358  2025-08-19  788.049760  691.278616  878.063829\n",
            "359  2025-08-20  789.441300  696.488487  877.801612\n",
            "360  2025-08-21  790.654471  698.573406  884.305378\n",
            "361  2025-08-22  792.070197  691.444034  882.785036\n",
            "362  2025-08-23  792.023299  694.883094  881.133181\n",
            "\n",
            "[363 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_df.head()"
      ],
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          "height": 206
        },
        "id": "OnublLJdkYNv",
        "outputId": "ff51159e-69e5-489d-e3e1-02651f31b4f3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
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              "          ds        yhat  yhat_lower  yhat_upper\n",
              "0 2024-08-26  557.983007  529.776076  585.333860\n",
              "1 2024-08-27  558.543753  528.390301  584.074250\n",
              "2 2024-08-28  559.587756  531.856013  586.851734\n",
              "3 2024-08-29  560.370704  532.516433  589.417948\n",
              "4 2024-08-30  561.274217  531.960782  590.993504"
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bad2aa1-d313-4184-9757-c2075ac676bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "forecast_df",
              "summary": "{\n  \"name\": \"forecast_df\",\n  \"rows\": 363,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-08-26 00:00:00\",\n        \"max\": \"2025-08-23 00:00:00\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2025-02-05 00:00:00\",\n          \"2024-09-28 00:00:00\",\n          \"2024-09-10 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72.67179167016528,\n        \"min\": 554.7037079292405,\n        \"max\": 792.0701970735881,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          653.6269308023989,\n          555.2441755036815,\n          561.825995441112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.95783466263328,\n        \"min\": 524.0595681287748,\n        \"max\": 693.5217343249219,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          611.7089221203753,\n          524.231294511006,\n          532.8263823899675\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92.58268226899627,\n        \"min\": 582.857271320339,\n        \"max\": 891.8601799111061,\n        \"num_unique_values\": 363,\n        \"samples\": [\n          695.9211528560928,\n          585.8294264193302,\n          590.8709633290491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "DG5XePpjkcbp",
        "outputId": "2c846f7e-d4f5-4bcf-cd02-f1d360d27aef"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        ds        yhat  yhat_lower  yhat_upper\n",
              "count                  363  363.000000  363.000000  363.000000\n",
              "mean   2025-02-23 00:00:00  660.586024  609.084973  710.327635\n",
              "min    2024-08-26 00:00:00  554.703708  524.059568  582.857271\n",
              "25%    2024-11-24 12:00:00  594.786114  562.630523  625.939552\n",
              "50%    2025-02-23 00:00:00  656.241082  612.487925  697.904609\n",
              "75%    2025-05-24 12:00:00  726.463293  657.442402  793.011460\n",
              "max    2025-08-23 00:00:00  792.070197  693.521734  891.860180\n",
              "std                    NaN   72.671792   51.957835   92.582682"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9fdcbb26-8939-408a-9171-99f66b419207\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>363</td>\n",
              "      <td>363.000000</td>\n",
              "      <td>363.000000</td>\n",
              "      <td>363.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2025-02-23 00:00:00</td>\n",
              "      <td>660.586024</td>\n",
              "      <td>609.084973</td>\n",
              "      <td>710.327635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2024-08-26 00:00:00</td>\n",
              "      <td>554.703708</td>\n",
              "      <td>524.059568</td>\n",
              "      <td>582.857271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2024-11-24 12:00:00</td>\n",
              "      <td>594.786114</td>\n",
              "      <td>562.630523</td>\n",
              "      <td>625.939552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2025-02-23 00:00:00</td>\n",
              "      <td>656.241082</td>\n",
              "      <td>612.487925</td>\n",
              "      <td>697.904609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2025-05-24 12:00:00</td>\n",
              "      <td>726.463293</td>\n",
              "      <td>657.442402</td>\n",
              "      <td>793.011460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2025-08-23 00:00:00</td>\n",
              "      <td>792.070197</td>\n",
              "      <td>693.521734</td>\n",
              "      <td>891.860180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>72.671792</td>\n",
              "      <td>51.957835</td>\n",
              "      <td>92.582682</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fdcbb26-8939-408a-9171-99f66b419207')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9fdcbb26-8939-408a-9171-99f66b419207 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9fdcbb26-8939-408a-9171-99f66b419207');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1205fd5c-2673-4d06-b912-93a131b387c5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"forecast_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000363\",\n        \"max\": \"2025-08-23 00:00:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"363\",\n          \"2025-02-23 00:00:00\",\n          \"2025-08-23 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 232.3913585401758,\n        \"min\": 72.67179167016528,\n        \"max\": 792.0701970735881,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          660.5860240319225,\n          726.4632934192254,\n          363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_lower\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 210.48112815944148,\n        \"min\": 51.95783466263328,\n        \"max\": 693.5217343249219,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          609.0849728302181,\n          657.442401628718,\n          363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yhat_upper\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 255.95072004837783,\n        \"min\": 92.58268226899627,\n        \"max\": 891.8601799111061,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          710.3276345584843,\n          793.0114597820552,\n          363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(forecast_df['ds'], forecast_df['yhat'])\n",
        "plt.fill_between(forecast_df['ds'], forecast_df['yhat_lower'], forecast_df['yhat_upper'], alpha=0.3)\n",
        "plt.title('Stock Price Forecast')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "8hXzfuDQmPK9",
        "outputId": "c75e9f8e-9160-4205-e57e-c534b8044c0e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install fastapi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2uXr7hW6wZd",
        "outputId": "f4030301-2254-4cac-8142-0ff1ae4dc218"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting fastapi\n",
            "  Downloading fastapi-0.112.2-py3-none-any.whl.metadata (27 kB)\n",
            "Collecting starlette<0.39.0,>=0.37.2 (from fastapi)\n",
            "  Downloading starlette-0.38.2-py3-none-any.whl.metadata (5.9 kB)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from fastapi) (2.8.2)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from fastapi) (4.12.2)\n",
            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi) (0.7.0)\n",
            "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4->fastapi) (2.20.1)\n",
            "Requirement already satisfied: anyio<5,>=3.4.0 in /usr/local/lib/python3.10/dist-packages (from starlette<0.39.0,>=0.37.2->fastapi) (3.7.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.4.0->starlette<0.39.0,>=0.37.2->fastapi) (3.7)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.4.0->starlette<0.39.0,>=0.37.2->fastapi) (1.3.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.4.0->starlette<0.39.0,>=0.37.2->fastapi) (1.2.2)\n",
            "Downloading fastapi-0.112.2-py3-none-any.whl (93 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.5/93.5 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading starlette-0.38.2-py3-none-any.whl (72 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: starlette, fastapi\n",
            "Successfully installed fastapi-0.112.2 starlette-0.38.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from fastapi import FastAPI\n",
        "from pydantic import BaseModel\n",
        "\n",
        "app = FastAPI()\n",
        "\n",
        "class InputData(BaseModel):\n",
        "  days: int\n",
        "\n",
        "class PredictionOutput(BaseModel):\n",
        "  ds: str\n",
        "  yhat: float\n",
        "  yhat_lower: float\n",
        "  yhat_upper: float\n",
        "\n",
        "@app.post('/predict', response_model=list[PredictionOutput])\n",
        "async def get_prediction(data: InputData):\n",
        "    results = predict(data.days)\n",
        "    return results.to_dict('records')\n"
      ],
      "metadata": {
        "id": "v9_ugJpTqCzF"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install uvicorn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9H5ZuAz8szj",
        "outputId": "d816dc78-a4e8-4f4b-95eb-e815f4a041e8"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting uvicorn\n",
            "  Downloading uvicorn-0.30.6-py3-none-any.whl.metadata (6.6 kB)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn) (8.1.7)\n",
            "Collecting h11>=0.8 (from uvicorn)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
            "Requirement already satisfied: typing-extensions>=4.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn) (4.12.2)\n",
            "Downloading uvicorn-0.30.6-py3-none-any.whl (62 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.8/62.8 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: h11, uvicorn\n",
            "Successfully installed h11-0.14.0 uvicorn-0.30.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import threading\n",
        "import uvicorn\n",
        "\n",
        "def run_fastapi():\n",
        "    uvicorn.run(app, host=\"127.0.0.1\", port=8000)\n",
        "\n",
        "# Run the FastAPI app in a separate thread\n",
        "threading.Thread(target=run_fastapi, daemon=True).start()\n",
        "\n",
        "print(\"FastAPI application is running...\")\n",
        "!npx localtunnel --port 8000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2Vvz1Un8We0",
        "outputId": "dc102a6b-2fc1-4b42-92a7-537e8d3626c8"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FastAPI application is running...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:     Started server process [223]\n",
            "INFO:     Waiting for application startup.\n",
            "INFO:     Application startup complete.\n",
            "ERROR:    [Errno 98] error while attempting to bind on address ('127.0.0.1', 8000): address already in use\n",
            "INFO:     Waiting for application shutdown.\n",
            "INFO:     Application shutdown complete.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "your url is: https://sixty-experts-repeat.loca.lt\n",
            "INFO:     34.150.252.92:0 - \"POST /predict HTTP/1.1\" 200 OK\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!npx localtunnel --port 8000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9vv10GJ8504",
        "outputId": "13b3ab3f-98a8-4394-e52e-df2200e1a922"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1G\u001b[0JNeed to install the following packages:\r\n",
            "  localtunnel@2.0.2\n",
            "Ok to proceed? (y) \u001b[20Gy\n",
            "\u001b[K\u001b[?25hyour url is: https://every-moons-visit.loca.lt\n",
            "^C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "jHRxJLum9FZy",
        "outputId": "88a9bcdd-b6a6-4ba4-a658-afabf742394b"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-45-11a5a360d318>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Make a prediction request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"days\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    113\u001b[0m     \"\"\"\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    587\u001b[0m         }\n\u001b[1;32m    588\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    665\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 667\u001b[0;31m             resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    668\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    789\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m             \u001b[0;31m# Make the request on the HTTPConnection object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m             response = self._make_request(\n\u001b[0m\u001b[1;32m    792\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    793\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;31m# Receive the response from the server\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    536\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 537\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    538\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/urllib3/connection.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m         \u001b[0;31m# Get the response from http.client.HTTPConnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m         \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1373\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1375\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1376\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1377\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    706\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1301\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1303\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1304\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1305\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}