MT_1076.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Errimerranrish/Errimerranrish.github.io/blob/main/MT_1076.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPUI30-YeOCo"
      },
      "source": [
        "# Т.З.\n",
        "1. По API забираем данные из Shoppilot https://help.aplaut.com/article/14680\n",
        "и формируем файл по ТЗ\n",
        "\n",
        "2.Файл, который выкладываете на сервер необходимо формировать в формате csv. Предпочтительно использовать 65001 (UTF-8). Необходимо, чтобы наименование файла содержило префикс Productrating.\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OM4xsA7Yfq5t"
      },
      "source": [
        "https://astrakhan.petrovich.ru/product/985686/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2leo6nz7AfI"
      },
      "source": [
        "# Рейтинг товаров и количество отзывов"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4a-PZNLAaDfe"
      },
      "source": [
        "## Функции (обязательно везде)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rchW9MyZ2qfk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8407348-77f5-45bc-c78f-dd1bd845e6ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "env: HOSTNAME=ftp.mindbox.ru\n",
            "env: USERNAME=Petrovich\n",
            "env: PASSWORD=F4xqYkhNuMsR\n",
            "env: KEY_API=3GT2Cj92JQYHHERyF_xg\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "%env HOSTNAME = ftp.mindbox.ru\n",
        "%env USERNAME = Petrovich\n",
        "%env PASSWORD = F4xqYkhNuMsR\n",
        "%env KEY_API = 3GT2Cj92JQYHHERyF_xg\n",
        "\n",
        "# os.setenv[\"HOSTNAME\"] = \"ftp.mindbox.ru\"\n",
        "# os.setenv[\"USERNAME\"]  = \"Petrovich\"\n",
        "# os.setenv[\"PASSWORD\"] = \"F4xqYkhNuMsR\"\n",
        "# os.setenv[\"KEY_API\"] = '3GT2Cj92JQYHHERyF_xg'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PB6HL8UVed68"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import datetime\n",
        "import csv\n",
        "import pandas as pd\n",
        "import time\n",
        "from ftplib import FTP\n",
        "import os\n",
        "\n",
        "KEY_API = os.getenv(\"KEY_API\") #ключ\n",
        "HOSTNAME = os.getenv(\"HOSTNAME\")\n",
        "USERNAME = os.getenv(\"USERNAME\")\n",
        "PASSWORD = os.getenv(\"PASSWORD\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HOSTNAME"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "N7WPRdm7XMFf",
        "outputId": "c632ece9-fdb4-43b4-f66b-652c75cbe521"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'ftp.mindbox.ru'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "XMsBDrlP--Ga",
        "outputId": "3a8ce66a-b95f-447b-8ce6-0af0c5801ca5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Успешный запрос на экспорт\n",
            "Данные успешно загружены\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-7d41936394ff>:279: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  new_rating = pd.read_csv(\"old_productrating_data.csv\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'221 Goodbye.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Загрузка всех файлов папки, чтобы достать последний\n",
        "ftp = FTP(HOSTNAME)\n",
        "ftp.login(user=USERNAME, passwd=PASSWORD)\n",
        "\n",
        "files = ftp.nlst('Processed')\n",
        "\n",
        "ftp.quit()\n",
        "# в какую дату была последняя загрузка\n",
        "last_loaded = files[-1]\n",
        "last_loaded = last_loaded.replace('Productrating_all_', '')\n",
        "last_loaded = last_loaded.replace('.csv', '')\n",
        "request_ids = []\n",
        "\n",
        "# запрос на экспорт за 1 день\n",
        "def export_request(KEY_API, date):\n",
        "  response = requests.post(url=\"https://api.aplaut.io/v4/export_tasks\", headers= {'Authorization': f\"Bearer {KEY_API}\", 'Accept': 'application/vnd.api+json', 'Content-Type':'application/vnd.api+json'},\n",
        "                        json = {\n",
        "                            'data' : {\n",
        "                              \"type\": \"export_tasks\",\n",
        "                              \"attributes\":\n",
        "                               {\n",
        "                                  \"records_type\": \"products\", # тип записей - продукты\n",
        "                                  \"search_options\": {\n",
        "                                      \"sort\" : {\n",
        "                                          \"updated_at\":\"asc\" # сортировка от меньшего id к большему\n",
        "                                          },\n",
        "                                      \"filter\": {\n",
        "                                          \"updated_at\":{ # за какую дату от 00:00 до 23:59\n",
        "                                              \"gt\": f\"{last_loaded}T00:00:00\",\n",
        "                                              \"lt\": f\"{date}T23:59:59\"\n",
        "                                              }\n",
        "                                          }\n",
        "                                                    },\n",
        "                                  \"format\": \"csv\",\n",
        "                                  \"export_format\": \"[.updated_at?, .external_id?, .rating?, .published_group_reviews_count?]\" # external_id - id продукта на сайте (есть в карточке)\n",
        "                                }\n",
        "                              }\n",
        "                            }\n",
        "                        )\n",
        "\n",
        "  try:\n",
        "    # проверка на отказ выгрузки\n",
        "    if response.json()['data']['attributes']['refuse_reason']:\n",
        "      refuse = {'server_refuse': response.json()['data']['attributes']['refuse_reason']}\n",
        "      return refuse\n",
        "    else: # если всё хорошо то возвращает Id\n",
        "      id = {'id' : response.json()['data']['id']}\n",
        "      print('Успешный запрос на экспорт')\n",
        "      return id\n",
        "  except: # сбор ошибок\n",
        "    try:\n",
        "      errors = {'server_error' : response.json()['errors']}\n",
        "      return errors\n",
        "    except Exception as e:\n",
        "      return e\n",
        "# запрос результата экспорта\n",
        "def status_check(id, KEY_API):\n",
        "  url = f\"https://api.aplaut.io/v4/export_tasks/{id}\"\n",
        "  response = requests.get(url=url, headers= {'Authorization': f\"Bearer {KEY_API}\", 'Accept': 'application/vnd.api+json', 'Content-Type':'application/vnd.api+json'})\n",
        "\n",
        "  status = response.json()['data']['attributes']['state']\n",
        "\n",
        "  # проверка статуса экспорта\n",
        "  try:\n",
        "    if status == 'completed':\n",
        "        data = pd.read_csv(response.json()['data']['attributes']['archive_url'], compression='gzip')\n",
        "        print(\"Данные успешно загружены\")\n",
        "        return {'data': data}\n",
        "    elif status == 'rejected':\n",
        "        error = {\"Export error\": response.json()['data']['attributes']}\n",
        "        return error\n",
        "    else:\n",
        "        time.sleep(30)\n",
        "        return status_check(id, KEY_API)\n",
        "  except Exception as e:\n",
        "      return e\n",
        "# загрузка отчёта за 1 день\n",
        "def report(KEY_API, date, last_id):\n",
        "\n",
        "  try:\n",
        "    export_id = export_request(KEY_API, date) # запрос на получение id экспорта\n",
        "\n",
        "    start = time.time()\n",
        "\n",
        "    try:\n",
        "      request_ids.append(export_id['id'])\n",
        "\n",
        "    except:\n",
        "      request_ids.append(export_id)\n",
        "\n",
        "    try: # если получен id экспорта - запрос на получение данных, если нет - отчёт об ошибках\n",
        "\n",
        "      productrating_report = status_check(export_id['id'], KEY_API)\n",
        "\n",
        "    except:\n",
        "      return export_id, request_ids\n",
        "\n",
        "    try: #если данные получены - извлечение датафрейма из словаря, если нет - отчёт об ошибках\n",
        "      productrating_report_data = productrating_report['data']\n",
        "\n",
        "    except:\n",
        "      return productrating_report, request_ids\n",
        "\n",
        "    if len(productrating_report_data) < 500000:    # проверка на лимит\n",
        "\n",
        "      productrating_report_data = productrating_report_data.rename(columns={'EXTERNAL ID':'Petrovichru', 'RATING':'CustomFieldAttributerating',\n",
        "                                                'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "      productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "      return productrating_report_data, request_ids\n",
        "\n",
        "    else: # если лимит достигнут, то повторяется запрос с фильтром дата > последней полученной и собирается в один фрейм\n",
        "\n",
        "      update_date = datetime.datetime.strptime(productrating_report_data['UPDATED AT'].iloc[-1], '%Y-%m-%dT%H:%M:%S.%f%z').date()\n",
        "\n",
        "      end = time.time()\n",
        "\n",
        "      if (end - start) < 60:\n",
        "        time.sleep(61 - (end - start))\n",
        "\n",
        "      additional_report = report(KEY_API, update_date, request_ids)\n",
        "\n",
        "      try:\n",
        "        concantenated_report = pd.concat([productrating_report_data, additional_report])\n",
        "\n",
        "        concantenated_report = concantenated_report.rename(columns={'EXTERNAL ID':'Petrovichru',    'RATING':'CustomFieldAttributerating',\n",
        "                                                  'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "        concantenated_report.drop_duplicates(inplace=True)\n",
        "\n",
        "        return concantenated_report, request_ids\n",
        "\n",
        "      except:\n",
        "        print(f\"Дозагрузка не удалась,  update_date = {update_date},  last_id = {last_id}\")\n",
        "        productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "        return productrating_report_data, request_ids\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    try:\n",
        "      return productrating_report_data, request_ids\n",
        "    except:\n",
        "      productrating_report_data = 'None'\n",
        "      return productrating_report_data, request_ids\n",
        "\n",
        "# запрос на экспорт ВСЕХ имеющихся продуктов\n",
        "def total_export_request(KEY_API, date):\n",
        "\n",
        "  response = requests.post(url=\"https://api.aplaut.io/v4/export_tasks\", headers= {'Authorization': f\"Bearer {KEY_API}\", 'Accept': 'application/vnd.api+json', 'Content-Type':'application/vnd.api+json'},\n",
        "                        json = {\n",
        "                            'data' : {\n",
        "                              \"type\": \"export_tasks\",\n",
        "                              \"attributes\":\n",
        "                               {\n",
        "                                  \"records_type\": \"products\", # тип записей - продукты\n",
        "                                  \"search_options\": {\n",
        "                                      \"sort\" : {\n",
        "                                          \"updated_at\":\"asc\" # сортировка по дате последнего обновления\n",
        "                                          },\n",
        "                                      \"filter\": {\n",
        "                                          \"updated_at\":{ # обновлено позже чем date, загрузит всё вплоть до момента постановки задачи\n",
        "                                              \"gt\": f\"{date}T00:00:00\"\n",
        "                                              }\n",
        "                                          }\n",
        "                                                    },\n",
        "                                  \"format\": \"csv\",\n",
        "                                  \"export_format\": \"[.updated_at?, .external_id?, .rating?, .published_group_reviews_count?]\" # external_id - id продукта на сайте (есть в карточке)\n",
        "                                }\n",
        "                              }\n",
        "                            }\n",
        "                        )\n",
        "  time.sleep(3)\n",
        "  try:\n",
        "    # проверка на отказ выгрузки\n",
        "    if response.json()['data']['attributes']['refuse_reason']:\n",
        "      refuse = {'server_refuse': response.json()['data']['attributes']['refuse_reason']}\n",
        "      return refuse\n",
        "    else: # если всё хорошо то возвращает Id\n",
        "      id = {'id' : response.json()['data']['id']}\n",
        "      print('Успешный запрос на экспорт')\n",
        "      return id\n",
        "  except: # сбор ошибок\n",
        "    try:\n",
        "      errors = {'server_error' : response.json()['errors']}\n",
        "      return errors\n",
        "    except Exception as e:\n",
        "      return e\n",
        "\n",
        "# загрузка отчёта по всем продуктам\n",
        "def total_report(KEY_API, date, request_ids):\n",
        "\n",
        "  try:\n",
        "    export_id = total_export_request(KEY_API, date) # запрос на получение id экспорта\n",
        "\n",
        "    start = time.time()\n",
        "\n",
        "    try:\n",
        "      request_ids.append(export_id['id'])\n",
        "\n",
        "    except:\n",
        "      request_ids.append(export_id)\n",
        "\n",
        "    try: # если получен id экспорта - запрос на получение данных, если нет - отчёт об ошибках\n",
        "\n",
        "      productrating_report = status_check(export_id['id'], KEY_API)\n",
        "\n",
        "    except:\n",
        "      return export_id, request_ids\n",
        "\n",
        "    try: #если данные получены - извлечение датафрейма из словаря, если нет - отчёт об ошибках\n",
        "      productrating_report_data = productrating_report['data']\n",
        "\n",
        "    except:\n",
        "      return productrating_report, request_ids\n",
        "\n",
        "    if len(productrating_report_data) < 500000:    # проверка на лимит\n",
        "\n",
        "      productrating_report_data = productrating_report_data.rename(columns={'EXTERNAL ID':'Petrovichru', 'RATING':'CustomFieldAttributerating',\n",
        "                                                'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "      productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "      return productrating_report_data, request_ids\n",
        "\n",
        "    else: # если лимит достигнут, то повторяется запрос с фильтром дата > последней полученной и собирается в один фрейм\n",
        "\n",
        "      update_date = datetime.datetime.strptime(productrating_report_data['UPDATED AT'].iloc[-1], '%Y-%m-%dT%H:%M:%S.%f%z').date()\n",
        "\n",
        "      end = time.time()\n",
        "\n",
        "      if (end - start) < 60:\n",
        "        time.sleep(61 - (end - start))\n",
        "\n",
        "      additional_report = total_report(KEY_API, update_date, request_ids)\n",
        "\n",
        "      try:\n",
        "        concantenated_report = pd.concat([productrating_report_data, additional_report])\n",
        "\n",
        "        concantenated_report = concantenated_report.rename(columns={'EXTERNAL ID':'Petrovichru',    'RATING':'CustomFieldAttributerating',\n",
        "                                                  'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "        concantenated_report.drop_duplicates(inplace=True)\n",
        "\n",
        "        return concantenated_report, request_ids\n",
        "\n",
        "      except:\n",
        "        print(f\"Дозагрузка не удалась,  update_date = {update_date},  last_id = {last_id}\")\n",
        "        productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "        return productrating_report_data, request_ids\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    try:\n",
        "      return productrating_report_data, request_ids\n",
        "    except:\n",
        "      productrating_report_data = 'None'\n",
        "      return productrating_report_data, request_ids\n",
        "\n",
        "\n",
        "## Загрузка отчёта за вчерашний день\n",
        "past_date = datetime.date.today() - datetime.timedelta(days=1) # ищем прошлую дату\n",
        "productrating, request_id_list = report(KEY_API, past_date, request_ids)\n",
        "productrating.drop(columns=['UPDATED AT'], inplace=True)\n",
        "productrating = productrating.astype({\"Petrovichru\":'str',\n",
        "                                      \"CustomFieldAttributerating\":'int8', \"CustomFieldAttributereview\":'int32'})\n",
        "\n",
        "productrating\n",
        "productrating.memory_usage() #7577\n",
        "request_id_list\n",
        "\n",
        "ftp = FTP(HOSTNAME)\n",
        "ftp.login(user=USERNAME,passwd = PASSWORD)\n",
        "file_path = f'/Processed/Productrating_all_{last_loaded}.csv'\n",
        "with open('old_productrating_data.csv', 'wb') as file:\n",
        "  ftp.retrbinary(f\"RETR {file_path}\", file.write)\n",
        "ftp.quit()\n",
        "\n",
        "new_rating = pd.read_csv(\"old_productrating_data.csv\")\n",
        "new_rating = new_rating.astype({\"Petrovichru\":'str', \"CustomFieldAttributerating\":'int8', \"CustomFieldAttributereview\":'int32'})\n",
        "\n",
        "petr_ids = list(new_rating['Petrovichru'])\n",
        "for index in list(productrating['Petrovichru']):\n",
        "  if index not in petr_ids:\n",
        "    new_rating = pd.concat([new_rating,productrating.iloc[list(productrating[productrating['Petrovichru']==index].index)]])\n",
        "\n",
        "new_rating.reset_index(inplace=True, drop=True)\n",
        "productrating = productrating.set_index('Petrovichru')\n",
        "new_rating = new_rating.set_index('Petrovichru')\n",
        "new_rating.update(productrating)\n",
        "\n",
        "filename = f\"Productrating_all_{past_date}.csv\"\n",
        "new_rating.to_csv(filename)\n",
        "\n",
        "# загрузка файла на сервер\n",
        "ftp = FTP(HOSTNAME)\n",
        "ftp.login(user=USERNAME,passwd = PASSWORD)\n",
        "ftp.encoding = \"utf-8\"\n",
        "with open(filename, \"rb\") as file:\n",
        "    ftp.storbinary(f\"STOR /Processed/{filename}\", file)\n",
        "ftp.quit()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kthU6b8gbYQ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Чтобы проверить наличие файла в папке ⬇"
      ],
      "metadata": {
        "id": "NxAoiAS-OCI7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i08DBkuuhK80",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "cdc0c776-8c6b-40d9-a3eb-815b28565ef9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Productrating_all_2024-05-22.csv\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'221 Goodbye.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "ftp = FTP(HOSTNAME)\n",
        "ftp.login(user=USERNAME, passwd=PASSWORD)\n",
        "files = ftp.nlst('Processed')\n",
        "print(files[-1])\n",
        "ftp.quit()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bvKkkfKyP-Kk"
      },
      "source": [
        "## Выкачать все продукты"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cpurqV9h-aHh"
      },
      "outputs": [],
      "source": [
        "# загрузка отчёта по всем продуктам\n",
        "def total_report(KEY_API , date , request_ids ):\n",
        "\n",
        "  try:\n",
        "    export_id = total_export_request(KEY_API, date) # запрос на получение id экспорта\n",
        "\n",
        "    start = time.time()\n",
        "\n",
        "    try:\n",
        "      request_ids.append(export_id['id'])\n",
        "\n",
        "    except:\n",
        "      request_ids.append(export_id)\n",
        "\n",
        "    try: # если получен id экспорта - запрос на получение данных, если нет - отчёт об ошибках\n",
        "\n",
        "      productrating_report = status_check(export_id['id'], KEY_API)\n",
        "\n",
        "    except:\n",
        "      return export_id, request_ids\n",
        "\n",
        "    try: #если данные получены - извлечение датафрейма из словаря, если нет - отчёт об ошибках\n",
        "      productrating_report_data = productrating_report['data']\n",
        "\n",
        "    except:\n",
        "      return productrating_report, request_ids\n",
        "\n",
        "    if len(productrating_report_data) < 500000:    # проверка на лимит\n",
        "\n",
        "      productrating_report_data = productrating_report_data.rename(columns={'EXTERNAL ID':'Petrovichru', 'RATING':'CustomFieldAttributerating',\n",
        "                                                'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "      productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "      return productrating_report_data, request_ids\n",
        "\n",
        "    else: # если лимит достигнут, то повторяется запрос с фильтром дата > последней полученной и собирается в один фрейм\n",
        "\n",
        "      update_date = datetime.datetime.strptime(productrating_report_data['UPDATED AT'].iloc[-1], '%Y-%m-%dT%H:%M:%S.%f%z').date()\n",
        "\n",
        "      end = time.time()\n",
        "\n",
        "      if (end - start) < 60:\n",
        "        time.sleep(61 - (end - start))\n",
        "\n",
        "      additional_report = total_report(KEY_API, update_date, request_ids)\n",
        "\n",
        "      try:\n",
        "        concantenated_report = pd.concat([productrating_report_data, additional_report])\n",
        "\n",
        "        concantenated_report = concantenated_report.rename(columns={'EXTERNAL ID':'Petrovichru',\t'RATING':'CustomFieldAttributerating',\n",
        "                                                  'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "        concantenated_report.drop_duplicates(inplace=True)\n",
        "\n",
        "        return concantenated_report, request_ids\n",
        "\n",
        "      except:\n",
        "        print(f\"Дозагрузка не удалась,  update_date = {update_date},  last_id = {last_id}\")\n",
        "        productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "        return productrating_report_data, request_ids\n",
        "\n",
        "  except KeyboardInterrupt:\n",
        "    try:\n",
        "      return productrating_report_data, request_ids\n",
        "    except:\n",
        "      productrating_report_data = 'None'\n",
        "      return productrating_report_data, request_ids"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MBgU-NKs9Po7"
      },
      "outputs": [],
      "source": [
        "productrating, request_id_list = total_report(KEY_API, datetime.date(2018, 4, 19), request_ids)\n",
        "#productrating, request_id_list = total_report(KEY_API, datetime.date(2004, 1, 1), request_ids)\n",
        "#productrating, request_id_list = total_report(KEY_API, datetime.date(2024, 2, 20), request_ids)   -  использовалось для проверки"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PlGaeWEmz-a",
        "outputId": "576e35f2-d675-430d-de43-e1c5e93e571e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['65f1ab8aa78708001650a8cc']"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "request_id_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eS2Bnubg9WZi"
      },
      "outputs": [],
      "source": [
        "productrating.drop(columns=['UPDATED AT'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ORc9mfXdAsPB"
      },
      "outputs": [],
      "source": [
        "productrating.to_csv(f'Productrating_all_{datetime.date.today()}.csv', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yq2SZ72IF8Oh"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzA4PoGkQ8j-"
      },
      "source": [
        "## Таблица статусов экспорта"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhmLBbMRRYi7"
      },
      "source": [
        "Чтобы проверить все ли экспорты были успешны или сохранить список готовых для перезапуска на следующий день\n",
        "\n",
        "Есть ограничение в 60 минут - максимальное количество времени, которое платформа в сумме потратит на все экспорты. При достижении лимита запросы будут отложены до завтра (запуск серверов не раньше 8:30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r2Gwd4kODm2h"
      },
      "outputs": [],
      "source": [
        "request_sucsess_df = pd.DataFrame(columns=['request_id', 'status'])\n",
        "request_sucsess_df['request_id'] = request_ids\n",
        "\n",
        "for i in range(len(request_sucsess_df)):\n",
        "  i_id = request_sucsess_df['request_id'].iloc[i]\n",
        "\n",
        "  if isinstance(i_id, dict):\n",
        "    request_sucsess_df['status'].iloc[i] = 'error'\n",
        "\n",
        "  else:\n",
        "    url = f\"https://api.aplaut.io/v4/export_tasks/{i_id}\"\n",
        "    response = requests.get(url=url, headers= {'Authorization': f\"Bearer {KEY_API}\", 'Accept': 'application/vnd.api+json', 'Content-Type':'application/vnd.api+json'})\n",
        "    status = response.json()['data']['attributes']['state']\n",
        "    request_sucsess_df['status'].iloc[i] = status\n",
        "\n",
        "request_sucsess_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "36IUJacIRDza"
      },
      "outputs": [],
      "source": [
        "request_sucsess_df.to_csv(f'Export_id_table_{datetime.date.today()}.csv', index = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usx4x9cgJMpg"
      },
      "source": [
        "# Для работы с гугл таблицами"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ygngK462JRNs"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import json\n",
        "\n",
        "from typing import List\n",
        "from apiclient.discovery import build\n",
        "from oauth2client.service_account import ServiceAccountCredentials\n",
        "\n",
        "id_table =\n",
        "name_sheet =\n",
        "\n",
        "\n",
        "dict_google_sheets = \"/content/google_sheets.json\"\n",
        "\n",
        "\n",
        "class Google_sheets_api():\n",
        "\n",
        "    def __init__(\n",
        "        self, path_to_dict: str, api_name: str = \"sheets\", api_version: str = \"v4\",\n",
        "        scopes: List[str] = ['https://www.googleapis.com/auth/spreadsheets','https://www.googleapis.com/auth/drive']\n",
        "        ) -> None:\n",
        "\n",
        "        if type(path_to_dict) == dict:\n",
        "            credentials = ServiceAccountCredentials.from_json_keyfile_dict(path_to_dict, scopes=scopes)\n",
        "        else:\n",
        "            credentials = ServiceAccountCredentials.from_json_keyfile_name(path_to_dict, scopes=scopes)\n",
        "\n",
        "\n",
        "        # Build the service object.\n",
        "        self._service = build(api_name, api_version, credentials=credentials)\n",
        "\n",
        "    def get_value_from_google_sheets(self, spreadsheet_id: str, range_to_sheet: str)-> dict:\n",
        "        return self._service.spreadsheets().values().get(spreadsheetId=spreadsheet_id, range=range_to_sheet).execute()\n",
        "\n",
        "    def append_to_google_sheets(self, spreadsheet_id: str, values: list, range_to_sheet: str ,format=\"ROWS\")-> dict:\n",
        "        return self._service.spreadsheets().values().append(spreadsheetId = spreadsheet_id,\n",
        "        range = range_to_sheet,\n",
        "        valueInputOption = \"USER_ENTERED\",\n",
        "        body = {\n",
        "            \"majorDimension\": format,     # сначала заполнять ряды, затем столбцы (т.е. самые внутренние списки в values - это ряды)\n",
        "            \"values\": values}\n",
        "        ).execute()\n",
        "\n",
        "    def update_to_google_sheets(self, spreadsheet_id: str, values: list, range_to_sheet: str ,format=\"ROWS\")-> dict:\n",
        "        return self._service.spreadsheets().values().update(spreadsheetId = spreadsheet_id,\n",
        "        range = range_to_sheet,\n",
        "        valueInputOption = \"USER_ENTERED\",\n",
        "        body = {\n",
        "            \"majorDimension\": format,     # сначала заполнять ряды, затем столбцы (т.е. самые внутренние списки в values - это ряды)\n",
        "            \"values\": values}\n",
        "        ).execute()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "dict_google_sheets = \"/content/google_sheets.json\"\n",
        "\n",
        "sheets = Google_sheets_api(dict_google_sheets)\n",
        "response_sheet = sheets.get_value_from_google_sheets(id_table, name_sheet)\n",
        "\n",
        "for index, row in enumerate(response_sheet['values'][1:]):\n",
        "    print(row)\n",
        "    try:\n",
        "        row[3]\n",
        "    except:\n",
        "        body = { \"message\" : \"33\",\n",
        "                \"phone\" : row[1]}\n",
        "\n",
        "        response = requests.post(url_salebot, data = body)\n",
        "\n",
        "        sheets.update_to_google_sheets(spreadsheet_id = id_table,\n",
        "                                  values = [[True]],\n",
        "                                  range_to_sheet = f\"{name_sheet}!R{index+2}C{4}\" ,\n",
        "                                  format = \"ROWS\")\n",
        "\n",
        "\n",
        "# response"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7o-JyW8laRDq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g_SAVKGiaQ7_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import datetime\n",
        "import pandas as pd\n",
        "from ftplib import FTP\n",
        "import os\n",
        "import time\n",
        "\n",
        "\n",
        "\n",
        "def upload_from_shoppilot_to_FTPserver():\n",
        "\n",
        "  # подготовительный этап\n",
        "  def last_loaded_date(host, login, password):\n",
        "    ftp = FTP(host)\n",
        "    ftp.login(user=login, passwd=password)\n",
        "    files = ftp.nlst('Processed')\n",
        "    ftp.quit()\n",
        "\n",
        "    last_loaded = files[-1]\n",
        "    last_loaded = last_loaded.replace('Productrating_all_', '')\n",
        "    last_loaded = last_loaded.replace('.csv', '')\n",
        "    return  last_loaded\n",
        "\n",
        "\n",
        "  # запрос на экспорт\n",
        "  def export_request(KEY_API, date, last_loaded):\n",
        "    response = requests.post(url=\"https://api.aplaut.io/v4/export_tasks\",\n",
        "                          headers= {'Authorization': f\"Bearer {KEY_API}\",\n",
        "                                    'Accept': 'application/vnd.api+json',\n",
        "                                    'Content-Type':'application/vnd.api+json'},\n",
        "                          json = {\n",
        "                              'data' : {\n",
        "                                \"type\": \"export_tasks\",\n",
        "                                \"attributes\":\n",
        "                                {\n",
        "                                    \"records_type\": \"products\", # тип записей - продукты\n",
        "                                    \"search_options\": {\n",
        "                                        \"sort\" : {\n",
        "                                            \"updated_at\":\"asc\" # сортировка от меньшего id к большему\n",
        "                                            },\n",
        "                                        \"filter\": {\n",
        "                                            \"updated_at\":{ # за какую дату от 00:00 до 23:59\n",
        "                                                \"gt\": f\"{last_loaded}T00:00:00\",\n",
        "                                                \"lt\": f\"{date}T23:59:59\"\n",
        "                                                }\n",
        "                                            }\n",
        "                                                      },\n",
        "                                    \"format\": \"csv\",\n",
        "                                    \"export_format\": \"[.updated_at?, .external_id?, .rating?, .published_group_reviews_count?]\" # external_id - id продукта на сайте (есть в карточке)\n",
        "                                  }\n",
        "                                }\n",
        "                              }\n",
        "                          )\n",
        "\n",
        "    try:\n",
        "      # проверка на отказ выгрузки\n",
        "      if response.json()['data']['attributes']['refuse_reason']:\n",
        "        refuse = {'server_refuse': response.json()['data']['attributes']['refuse_reason']}\n",
        "        return refuse\n",
        "      else: # если всё хорошо то возвращает Id\n",
        "        id = {'id' : response.json()['data']['id']}\n",
        "        print('Успешный запрос на экспорт')\n",
        "        return id\n",
        "\n",
        "    except: # сбор ошибок\n",
        "      try:\n",
        "        errors = {'server_error' : response.json()['errors']}\n",
        "        return errors\n",
        "      except Exception as e:\n",
        "        return e\n",
        "\n",
        "\n",
        "  # запрос результата экспорта\n",
        "  def status_check(id, KEY_API):\n",
        "    url = f\"https://api.aplaut.io/v4/export_tasks/{id}\"\n",
        "    response = requests.get(url=url, headers= {'Authorization': f\"Bearer {KEY_API}\", 'Accept': 'application/vnd.api+json', 'Content-Type':'application/vnd.api+json'})\n",
        "\n",
        "    status = response.json()['data']['attributes']['state']\n",
        "\n",
        "    # проверка статуса экспорта\n",
        "    try:\n",
        "      if status == 'completed':\n",
        "          data = pd.read_csv(response.json()['data']['attributes']['archive_url'], compression='gzip')\n",
        "          print(\"Данные успешно загружены\")\n",
        "          return {'data': data}\n",
        "      elif status == 'rejected':\n",
        "          error = {\"Export error\": response.json()['data']['attributes']}\n",
        "          return error\n",
        "      else:\n",
        "          time.sleep(30)\n",
        "          return status_check(id, KEY_API)\n",
        "    except Exception as e:\n",
        "        return e\n",
        "\n",
        "\n",
        "  # загрузка отчёта\n",
        "  def report(KEY_API, date, last_id):\n",
        "\n",
        "    last_loaded = last_loaded_date(HOSTNAME, USERNAME, PASSWORD)\n",
        "\n",
        "    try:\n",
        "      export_id = export_request(KEY_API, date, last_loaded) # запрос на получение id экспорта\n",
        "\n",
        "      start = time.time()\n",
        "\n",
        "      try:\n",
        "        request_ids.append(export_id['id'])\n",
        "\n",
        "      except:\n",
        "        request_ids.append(export_id)\n",
        "\n",
        "      try: # если получен id экспорта - запрос на получение данных, если нет - отчёт об ошибках\n",
        "\n",
        "        productrating_report = status_check(export_id['id'], KEY_API)\n",
        "\n",
        "      except:\n",
        "        return export_id, request_ids\n",
        "\n",
        "      try: #если данные получены - извлечение датафрейма из словаря, если нет - отчёт об ошибках\n",
        "        productrating_report_data = productrating_report['data']\n",
        "\n",
        "      except:\n",
        "        return productrating_report, request_ids\n",
        "\n",
        "      if len(productrating_report_data) < 500000:    # проверка на лимит\n",
        "\n",
        "        productrating_report_data = productrating_report_data.rename(columns={'EXTERNAL ID':'Petrovichru', 'RATING':'CustomFieldAttributerating',\n",
        "                                                  'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "        productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "        return productrating_report_data, request_ids\n",
        "\n",
        "      else: # если лимит достигнут, то повторяется запрос с фильтром дата > последней полученной и собирается в один фрейм\n",
        "\n",
        "        update_date = datetime.datetime.strptime(productrating_report_data['UPDATED AT'].iloc[-1], '%Y-%m-%dT%H:%M:%S.%f%z').date()\n",
        "\n",
        "        end = time.time()\n",
        "\n",
        "        if (end - start) < 60:\n",
        "          time.sleep(61 - (end - start))\n",
        "\n",
        "        additional_report = report(KEY_API, update_date, request_ids)\n",
        "\n",
        "        try:\n",
        "          concantenated_report = pd.concat([productrating_report_data, additional_report])\n",
        "\n",
        "          concantenated_report = concantenated_report.rename(columns={'EXTERNAL ID':'Petrovichru',\t'RATING':'CustomFieldAttributerating',\n",
        "                                                    'PUBLISHED GROUP REVIEWS COUNT':'CustomFieldAttributereview'})\n",
        "\n",
        "          concantenated_report.drop_duplicates(inplace=True)\n",
        "\n",
        "          return concantenated_report, request_ids\n",
        "\n",
        "        except:\n",
        "          print(f\"Дозагрузка не удалась,  update_date = {update_date},  last_id = {last_id}\")\n",
        "          productrating_report_data.drop_duplicates(inplace=True)\n",
        "\n",
        "          return productrating_report_data, request_ids\n",
        "\n",
        "    except KeyboardInterrupt:\n",
        "      try:\n",
        "        return productrating_report_data, request_ids\n",
        "      except:\n",
        "        productrating_report_data = 'None'\n",
        "        return productrating_report_data, request_ids\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  # начало\n",
        "\n",
        "  KEY_API = '3GT2Cj92JQYHHERyF_xg' # ключ от API Shoppilot\n",
        "\n",
        "  # данные для авторизации на сервере\n",
        "  HOSTNAME = \"ftp.mindbox.ru\"\n",
        "  USERNAME = \"Petrovich\"\n",
        "  PASSWORD = 'F4xqYkhNuMsR'\n",
        "\n",
        "  # в какую дату была последняя загрузка\n",
        "  last_loaded = last_loaded_date(HOSTNAME, USERNAME, PASSWORD)\n",
        "\n",
        "\n",
        "  past_date = datetime.date.today() - datetime.timedelta(days=1) # ищем вчерашнюю дату\n",
        "  request_ids = []\n",
        "\n",
        "  productrating, request_id_list = report(KEY_API, past_date, request_ids)\n",
        "  productrating.drop(columns=['UPDATED AT'], inplace=True)\n",
        "  productrating = productrating.astype({\"Petrovichru\":'str', \"CustomFieldAttributerating\":'int8', \"CustomFieldAttributereview\":'int32'})\n",
        "\n",
        "  productrating_test = productrating.head()\n",
        "  t = type(productrating)\n",
        "  return {'statusCode':200,\n",
        "  'body': productrating}\n"
      ],
      "metadata": {
        "id": "YUIIZupwX5aT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "a = upload_from_shoppilot_to_FTPserver()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKZN992-ZpEE",
        "outputId": "aac9613b-83b6-48f4-e7d0-647ec71b2b70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Успешный запрос на экспорт\n",
            "Данные успешно загружены\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYr8g6ufaFTz",
        "outputId": "a5890ad8-4f46-4900-f4e8-d179290b6de4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'statusCode': 200,\n",
              " 'body':     Petrovichru  CustomFieldAttributerating  CustomFieldAttributereview\n",
              " 0        623839                           4                           2\n",
              " 1        633075                           1                           1\n",
              " 2        633078                           0                           0\n",
              " 3        658309                           0                           0\n",
              " 4        655917                           0                           0\n",
              " ..          ...                         ...                         ...\n",
              " 568      801941                           3                           8\n",
              " 569     1000188                           1                           1\n",
              " 570     1011920                           1                           1\n",
              " 571     1018667                           4                          15\n",
              " 572     1026049                           0                           0\n",
              " \n",
              " [573 rows x 3 columns]}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8xssPQSvcVYr"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "TTsuZWIU_A85"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}