Хакатон_мтс_тета.ipynb

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)"]},{"cell_type":"code","metadata":{"id":"N4SyXt6tpAP-"},"source":["import math\n","import numpy as np\n","import os \n","import pandas as pd\n","from datetime import datetime, timedelta\n","import matplotlib\n","import re\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dCE7ZfgH7hKV"},"source":["#!pip install -U https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n","#!pip install -U pandas-profiling\n","import pandas_profiling as pp\n","from pandas_profiling import ProfileReport"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"5WNhfGOno7Mv"},"source":["### Загрузка и первичная обработка *набора* хостов"]},{"cell_type":"code","metadata":{"id":"Sx-OiB0zpQyY","colab":{"base_uri":"https://localhost:8080/","height":374},"executionInfo":{"status":"error","timestamp":1629030590259,"user_tz":-180,"elapsed":231,"user":{"displayName":"Павел Тихомиров","photoUrl":"","userId":"02574252321867529351"}},"outputId":"ec578750-ba90-46b2-fd2a-29c69dae1916"},"source":["data = pd.read_csv(\"host.csv\",  names=[\"Request\"], squeeze=True)"],"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-8-0b4b82b9d1ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"host.csv\"\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Request\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m=\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[0m","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    686\u001b[0m     )\n\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 688\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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    689\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 454\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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    455\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\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/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    946\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\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/usr/local/lib/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\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[1;32m   1179\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\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   1181\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   1182\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\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/python3.7/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   2008\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2009\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2010\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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   2011\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'host.csv'"]}]},{"cell_type":"code","metadata":{"id":"b-jz-7KSJuzG"},"source":["data.drop_duplicates(inplace=True)\n","data.reset_index(drop = True, inplace = True)\n","data = data.astype(\"string\")\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_cb59ngPo2zn"},"source":["Загрузка набора хостов с разметкой (разметка проставлена в другом файле)"]},{"cell_type":"code","metadata":{"id":"w8InOZBuXtG5"},"source":["subset0_200 = pd.read_csv(\"subset0-200.csv\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mSRrPJXDph_x"},"source":["### Создание датасета с *признаками*"]},{"cell_type":"code","metadata":{"id":"dYiNzGlmyb03"},"source":["df = pd.DataFrame(data)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KPp6qHjo02cM"},"source":["reg_exp = '[.]local([^\\w]|$)|sba[.]|[.]onion([^\\w]|$)|[.]oz([^\\w]|$)|[.]zs([^\\w]|$)|[.]i2p([^\\w]|$)|m[0-9]|api[0-9]|node[0-9]'\n","#data.str.contains(reg_exp).value_counts()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"de0OKwoIyjGf"},"source":["df['len'] = data.str.len() #длина хоста\n","df['point_ammount'] = data.str.count(\"\\.\") #количество точек\n","df['has_special']=data.str.contains(reg_exp).astype(int) #наличие спец. слов\n","df['digit_end'] = data.str.rsplit(\".\", expand=True, n=1)[1].str.contains('\\d').astype(int) #наличие цифр в домене верхнего уровня\n","df['has_www.'] = data.str.contains('www.').astype(int) #наличие www. в запросе"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MzlGseyyY0lI"},"source":["df['is_Tech'] = subset0_200['request_status']  #вставка колонки с разметкой"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Bn5jBFwJdWSJ"},"source":["df.head()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Tj1UF9BvAqZc"},"source":["### Pandas Profiling Report"]},{"cell_type":"code","metadata":{"id":"Kd-UL2GG76SB"},"source":["#profile = ProfileReport(df, title='Pandas Profiling Report')\n","#profile.to_file(output_file=\"data_profiling_report.html\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AU-kaf39oqMi"},"source":["### ScLearn. Поиск оптимальной модели (была выбрана линейная регрессия)"]},{"cell_type":"code","metadata":{"id":"9lR6LgQLw_b1"},"source":["df['is_Tech']=(df['is_Tech']+1)%2 #манипуляции для установки нетехнического запроса на 1, технического на 0\n","df.rename(columns={\"is_Tech\": \"is_useful\",}, inplace = True)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"67KZJ_CfrqA5"},"source":["digit_features = ['len',\t'point_ammount',\t'has_special',\t'digit_end',\t'has_www.']\n","target = ['is_useful']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"X67rdp0EvLYF"},"source":["from sklearn.linear_model import LogisticRegression  \n","from sklearn.discriminant_analysis import LinearDiscriminantAnalysis  \n","from sklearn.neighbors import KNeighborsClassifier  \n","from sklearn.naive_bayes import GaussianNB  \n","from sklearn.tree import DecisionTreeClassifier  \n","from sklearn.svm import SVC "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"232nf-mBw119"},"source":["from sklearn.model_selection import train_test_split"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"YjZQIo-gsJaZ"},"source":["Валидация"]},{"cell_type":"code","metadata":{"id":"4FNoCr7BwCgi"},"source":["X_train, X_val, y_train, y_val = train_test_split(df[digit_features], df[target], stratify=df[target], test_size=0.25, random_state = 42)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ijl-Ft9CxyTX"},"source":["from sklearn.model_selection import StratifiedKFold, permutation_test_score\n","from sklearn.model_selection import cross_val_score"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SgKlBpWOrKte"},"source":["from sklearn import linear_model\n","reg = linear_model.LinearRegression()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"WpnoUbBirQpR"},"source":["reg.fit(X_train,y_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kf_BvJawt6C7"},"source":["a = np.int8( reg.predict(X_val) > 0.25 ) #установка порога для целевого класса"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pk4EKgRuszuB"},"source":["Подсчёт метрик"]},{"cell_type":"code","metadata":{"id":"pheCNrIZxtXY"},"source":["from sklearn.metrics import f1_score, precision_score, recall_score"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JEmEqhPCxu9e","outputId":"bc85702f-5318-4be2-e72c-650dfe1cdafd"},"source":["f1_score(y_val,a),  recall_score(y_val,a)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(0.45794642857142853, 0.6112501489691335)"]},"metadata":{"tags":[]},"execution_count":167}]},{"cell_type":"markdown","metadata":{"id":"3_-nI7lys4uA"},"source":["### Скачивание модели для чат-бота"]},{"cell_type":"code","metadata":{"id":"V59UIIjYzSsa"},"source":["import joblib"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fdrnOV5nzbA-","outputId":"6ebbebe6-1a70-4acc-fc0d-d1a54d2eaac3"},"source":["joblib.dump(reg, \"linear_regression.pkl\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/linear_regression.pkl']"]},"metadata":{"tags":[]},"execution_count":169}]},{"cell_type":"markdown","metadata":{"id":"zRpNu-g2vAOf"},"source":["# Работающий чат-бот"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_bFK_3UIMrnR","outputId":"547a6c88-0bc7-4b92-823a-322ee80eb14a"},"source":["#!pip uninstall telebot\n","!pip install PyTelegramBotAPI\n","#!pip install PyTelegramBotAPI==2.2.3"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Collecting PyTelegramBotAPI\n","  Downloading pyTelegramBotAPI-3.8.2.tar.gz (102 kB)\n","\u001b[?25l\r\u001b[K     |███▏                            | 10 kB 22.2 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 20 kB 27.3 MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 30 kB 32.0 MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 40 kB 30.9 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 51 kB 33.2 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 61 kB 35.5 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 71 kB 32.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▌      | 81 kB 31.3 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 92 kB 32.5 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▉| 102 kB 34.5 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 102 kB 34.5 MB/s \n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from PyTelegramBotAPI) (2.23.0)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->PyTelegramBotAPI) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->PyTelegramBotAPI) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->PyTelegramBotAPI) (2021.5.30)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->PyTelegramBotAPI) (1.24.3)\n","Building wheels for collected packages: PyTelegramBotAPI\n","  Building wheel for PyTelegramBotAPI (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for PyTelegramBotAPI: filename=pyTelegramBotAPI-3.8.2-py3-none-any.whl size=78015 sha256=9d5024055fda86ccdef58fa562ba809cfd6838f5f5c98193f646897a8fedc1af\n","  Stored in directory: /root/.cache/pip/wheels/05/20/79/0ec340bba3293984eac13cbd480839b754e4edffe1e517f606\n","Successfully built PyTelegramBotAPI\n","Installing collected packages: PyTelegramBotAPI\n","Successfully installed PyTelegramBotAPI-3.8.2\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tmdBT_PTztTG"},"source":["clf = joblib.load(\"linear_regression.pkl\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xqPcco-D4bMN"},"source":["class host_predictor(object):\n","    \n","    def __init__(self):\n","      pass\n","\n","    def predict(self, host):\n","      length = len(host)\n","      point_ammount = host.count(\".\")\n","      reg_exp = '[.]local([^\\w]|$)|sba[.]|[.]onion([^\\w]|$)|[.]oz([^\\w]|$)|[.]zs([^\\w]|$)|[.]i2p([^\\w]|$)|m[0-9]|api[0-9]|node[0-9]'\n","      has_special = int(re.search(reg_exp,host)!=None)\n","      digit_end = int(re.search('\\d', host.rsplit(\".\")[-1])!=None)\n","      has_www = int('www.' in host)      \n","\n","      host = np.array([[length, point_ammount, has_special, digit_end, has_www]])\n","      status = clf.predict(host)>0.25\n","      answer = \"Запрос \" + (\"не технический\" if status else \"технический\")\n","      return answer\n","      "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"s-9U4-Z86_k-"},"source":["predictor = host_predictor()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"fkX7PSTk7D_P","outputId":"57d4413b-57b3-490e-8809-a33271780a3d"},"source":["predictor.predict('google.com')\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'Запрос не технический'"]},"metadata":{"tags":[]},"execution_count":174}]},{"cell_type":"code","metadata":{"id":"zLm6kHHmMfvC"},"source":["import telebot\n","\n","bot = telebot.TeleBot(\"1993262311:AAHrAmvbFRcoUxwbzacuA7FDVms0Iz5Girk\")\n","@bot.message_handler(commands=['text'])\n","def send_welcome(message):\n","  bot.send_message(message.chat.id, message.text)\n","@bot.message_handler(func=lambda message: True)\n","def echo_all(message):\n","  bot.send_message(message.chat.id, predictor.predict(message.text))\n","bot.polling(none_stop = True)"],"execution_count":null,"outputs":[]}]}