import json
import requests
import pandas as pd

with open('cat_list.txt') as jf:
    cat_list = json.load(jf)

big_data = pd.read_csv('big_data.csv')

class FindingApi:
    '''Some docstring to get rid of linting errors'''
    def __init__(self, service, pageNumber):
        self.service = [
            'findItemsAdvanced', 'findCompletedItems',
            'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory',
            'findItemsByProduct'
            ][service]
        self.pageNumber = list(range(1, pageNumber)) # 64 pages is recommended
        # this will give equal weights to cats given call restraints

    # departments = ["3034","93427"] (womens and mens)
    def get_data(self):
        for category_id in cat_list:
            for i in self.pageNumber:
                params = {
                    "OPERATION-NAME":self.service,
                    "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
                    "SERVICE-VERSION":"1.13.0",
                    "RESPONSE-DATA-FORMAT":"JSON",
                    "categoryId":category_id,
                    "paginationInput.entriesPerPage":"100",
                    "paginationInput.PageNumber":i
                    }
                response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1",
                        params=params)
                data = response.json()
            return data

    def get_ids_from_cats(self):
        '''
        get_ids_from cats creates a 20-itemId list to use for the ShoppingApi
        call
        '''
        itemid_results_list = []
        for category_id in cat_list:
            for i in self.pageNumber:
                params = {
                    "OPERATION-NAME":self.service,
                    "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
                    "SERVICE-VERSION":"1.13.0",
                    "RESPONSE-DATA-FORMAT":"JSON",
                    "categoryId":category_id,
                    "paginationInput.entriesPerPage":"100",
                    "paginationInput.PageNumber":i
                    }
                response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1",
                        params=params)
                data = response.json()
                for item in data['findItemsByCategoryResponse'][0]['searchResult'][0]['item']:
                    if item not in big_data.values:
                        itemid_results_list.append(item['itemId'][0]) # itemId
                        # values are in lists for some reason
        item_id_results = [','.join(itemid_results_list[n:n+20]) for n in list(range(0,
            len(itemid_results_list), 20))]

        return item_id_results

class ShoppingApi(FindingApi):
    '''
    Creates objects from ShoppingApi service calls that can interact with
    pandas dataframes
    '''
    def get_item_from_findItemsByCategory(self, item_id_results):
        for twenty_id in item_id_results:
            params = {
                "callname":"GetMultipleItems",
                "appid":"scottbea-xlister-PRD-6796e0ff6-14862949",
                "version":"671",
                "responseencoding":"JSON",
                "ItemID":twenty_id,
                "IncludeSelector":"ItemSpecifics",
                }

            response = requests.get("https://open.api.ebay.com/shopping?", params=params)
            data = response.json()

            names = []
            values = []
            nvl = data['Item'][0]['ItemSpecifics']['NameValueList']

            for nvl_dict in nvl:
                names.append(nvl_dict['Name'])
                values.append(nvl_dict['Value'])

            nvl_dict = dict(zip(names, values))
            data.update(nvl_dict)
            df = pd.json_normalize(data)
            df.to_csv('big_data.csv')

# Limited to 5000 calls to shopping api per day, and getMultpileitems service maxes out at 20 items
# per call leaving you 100,000 items per day for you pandas dataframe initially. So you'll have
# to divide these up into the categories. This will leave you with about 6.25K results per cat.
# More than enough data for your dataset.

# Need to make sure dataframe gets important stuff outside of nvl. Also need to
# change init method in findingapi to have variable pages and possibly variable
# services.