import requests
import json
from bs4 import BeautifulSoup as b
import pandas as p

# keywords = input('keyword search: ')

with open('cat_list.txt') as jf:
    cat_list = json.load(jf)
finding_service = ['findItemsAdvanced', 'findCompletedItems', 'findItemsByKeywords', 'findItemsIneBayStores', 'findItemsByCategory', 'findItemsByProduct']

pageNumber = list(range(1, 63))

# departments = ["3034","93427"]

def get_ids():
    itemid_results_list = []
    for categoryID in cat_list[0:2]:
        params = {
            "OPERATION-NAME":finding_service[4],
            "SECURITY-APPNAME":"scottbea-xlister-PRD-6796e0ff6-14862949",
            "SERVICE-VERSION":"1.13.0",
            "RESPONSE-DATA-FORMAT":"JSON",
            "categoryId":categoryID ,
            "paginationInput.entriesPerPage":"100",
            "paginationInput.PageNumber":pageNumber[0]
            }
        # extract item id here for piping into shopping_test.py

        response = requests.get("https://svcs.ebay.com/services/search/FindingService/v1", params=params)
        data = response.json()
        pretty_data = json.dumps(data, indent=2)
        return data
# can use pandas.json_normalize(custom dict cobbled from respons.json())


# Additional problem you will run into when getting labeled data is shoe types and features not in features, accents, styles, categories or subcategories.

# also limited to 5000 calls per day. This leaves you with 500k listings

# If you want to split up each cat equally with their respective maxes then use 62 pages with 100
# Entries per page. At this amount you'll have the max number of calls you can make on the 
# shopping api.