448 lines
41 KiB
Python
448 lines
41 KiB
Python
from flask import Flask, request, make_response, jsonify, Response, send_file
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from flask_cors import CORS
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import pandas as pd
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import requests
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import math
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import numpy as np
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import random
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import hashlib
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app = Flask(__name__)
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# Ubah konfigurasi
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app.config['DEBUG'] = False
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app.config['SECRET_KEY'] = 'yv1DTocZOOPkoXQOtYfxUbMjSL9mBhwpfXaBw247kC2J3kDNgi3SshrzQzGgCg0'
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app.config['SERVER_TIMEOUT'] = 90
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CORS(app)
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@app.route('/')
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@app.route('/index')
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def index() :
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return send_file("index.html")
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@app.route('/search', methods=['GET'])
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def fetchAPI() :
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#ambil parameter dari user
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keyword = request.args.get("q")
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# lakukan crawling data
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products = searchProduct(keyword)
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# return response data produk
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return make_response(jsonify(products), 200)
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@app.route('/products', methods=['GET'])
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def get_products() :
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#ambil parameter dari user
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keyword = request.args.get("search")
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# lakukan crawling data
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products = crawling_data(keyword)
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# return response data produk
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return make_response(jsonify(products), 200)
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@app.route('/clustering', methods=['GET'])
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def kmeans_clustering() :
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#ambil parameter dari user
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keyword = request.args.get("search")
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# lakukan crawling data
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products = crawling_data(keyword)
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# lakukan clustering
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clustered_data = clustering(products)
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# return response data hasil clustering
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return make_response(jsonify(clustered_data), 200)
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# Serialize the data to JSON and calculate its hash for ETag
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serialized_data = jsonify(products).get_data()
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etag = hashlib.sha1(serialized_data).hexdigest()
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# Check if client provided 'If-None-Match' header and if it matches the ETag
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if request.headers.get('If-None-Match') == etag:
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response = make_response(jsonify({'status' : 'Not Modified', 'message' : 'Data was the same'}), 304) # Not Modified
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else:
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response.headers['ETag'] = etag
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return response
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# function crawling data dari API
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def crawling_data(keyword) :
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results = {}
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products = [] #array kosong untuk menyimpan produk
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# url tujuan
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url = 'https://gql.tokopedia.com/graphql/SearchQueryV4'
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# set header untuk request
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header = {
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'authority':'gql.tokopedia.com',
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'accept':'*/*',
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'accept-language':'en-US,en;q=0.9',
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'content-type':'application/json',
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'cookie':'_UUID_NONLOGIN_=04bacffdb33ae7cc9b1d427bbddf171a; _UUID_NONLOGIN_.sig=DVQhA3-ELQQJ9h0SYyVjFHoJFSk; bm_sz=7BF80AE05E5A8AF9664B95B6B2CE2CB3~YAAQBnUyF7+r5qGFAQAAKCdQsRI1Fh/SmyNRRdC9BW4wVU0lmI//2NiLVakPZhTxG0wL2ubZGIjNV6e+kXZ1+5ZqKkK9Fw/Fd+O+T9RZfLp8CXmjV1euh9PVY53K0Ta4CCk1u0Z5EWh1W7Tti6X5jLafWIR+oT1/OgtW3caeEbHOsPQ1EHLHGulXM6KVI3zveFNIMPOUjD8NpeM8Qu5s6zGQIoKdJTLcC/8oqDXy+wClFnEjDZppP084pNd4y/G+c8CSpxAsspL5iuVQVlYRRuwJwE8HYpToOZCsi86tFQjUNjOzLdI=~4272449~3356209; _abck=67ADF742634B2294651C94063EC293ED~0~YAAQBnUyF8ar5qGFAQAAsitQsQkCdgYNV6G8p8uT+aOLF4WEFJEn1yIPMDgyA54iRmSs8IgsBbjhWcRUT1ZGACKO2ODHrZ5oTi8X5dZN5tDx/TwNBUAGKhLKJLUWcC1VB2KHhvD4clCL7uQ4J1mNd5g+9qyDwYVnUvJ4EebQiAsTA6BuyXB0diBrJNOzaw3Ee7WxsSY2/eBVn6PLX+M9fSkvzbjVm5zzyD0aJdY/LmUiZ0w99cb5qp38p9x1woCcn8yvXnCjZpDwpw+uHfaRujN37jTOJlnQz0zKBrX1CIswAGrVHy9wGLjd6Gqwo2qOMAXI02xcsjvTyb3rg90fhcSPJTFDshm1G2FfkcgQp1mgKH+88WYNmd2ownQl6LjOhZnoULvxgiwbqg9gQG0VlVLTnRjaIuqoi/8I4w==~-1~-1~-1; ak_bmsc=DFC7AAF6F9F64270E723EC89908FAC33~000000000000000000000000000000~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; _SID_Tokopedia_=j6466YAGtp0pdwjx6o3ZvLax0AXvqIatZCtCkJCGgGBvMewHfR1cVZUvt_H0pQMGfjF_o671zjJYno7bObLNo3_uBKI5F2R2rIfRd9R6RQi_7YRpbgfB-pPWvq_hE6RE; DID=c6330112a5306c2292ef00c3384a948935bb5fc3d741651dc80dc4d4e31b663de5b642e3feead70772b2585af3697f42; DID_JS=YzYzMzAxMTJhNTMwNmMyMjkyZWYwMGMzMzg0YTk0ODkzNWJiNWZjM2Q3NDE2NTFkYzgwZGM0ZDRlMzFiNjYzZGU1YjY0MmUzZmVlYWQ3MDc3MmIyNTg1YWYzNjk3ZjQy47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=; _gcl_au=1.1.1390752592.1673717104; _UUID_CAS_=11873256-ff6e-4c2e-991a-0912169c4034; _CASE_=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; _gid=GA1.2.2077103790.1673717105; __asc=be6737fb185b1504654124dd24e; __auc=be6737fb185b1504654124dd24e; _fbp=fb.1.1673717109190.132615314; _dc_gtm_UA-126956641-6=1; _ga_70947XW48P=GS1.1.1673717104.1.1.1673717202.58.0.0; AMP_TOKEN=%24NOT_FOUND; _ga=GA1.2.1594572164.1673717105; _dc_gtm_UA-9801603-1=1',
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'origin':'https://www.tokopedia.com',
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'referer':'https://www.tokopedia.com',
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'sec-ch-ua':'"Not?A_Brand";v="8", "Chromium";v="108", "Microsoft Edge";v="108"',
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'sec-ch-ua-mobile':'?0',
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'sec-ch-ua-platform':'"Windows"',
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'sec-fetch-dest':'empty',
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'sec-fetch-mode':'cors',
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'sec-fetch-site':'same-site',
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'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36 Edg/108.0.1462.76',
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'x-device':'default_v3',
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'x-source':'tokopedia-lite',
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'x-tkpd-lite-service':'zeus',
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'x-version':'68ba647',
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}
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# search query
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initial_query = f'[{{"operationName":"SearchProductQueryV4","variables":{{"params":"device=desktop&navsource=&ob=23&q=${keyword}&related=true&rows=200&safe_search=false&scheme=https&shipping=&source=search&srp_component_id=01.07.00.00&srp_page_id=&srp_page_title=&st=product&start=0&topads_bucket=false&unique_id=1fdabea77fbaf5f1954bdbc40d4a9337&user_addressId=113228966&user_cityId=171&user_districtId=2233&user_id=7773903&user_lat=-6.377643399999999&user_long=106.7621449&user_postCode=16516&user_warehouseId=0&variants="}},"query":"query SearchProductQueryV4($params: String!) {{\\n ace_search_product_v4(params: $params) {{\\n header {{\\n totalData\\n totalDataText\\n processTime\\n responseCode\\n errorMessage\\n additionalParams\\n keywordProcess\\n componentId\\n __typename\\n }}\\n data {{\\n banner {{\\n position\\n text\\n imageUrl\\n url\\n componentId\\n trackingOption\\n __typename\\n }}\\n backendFilters\\n isQuerySafe\\n ticker {{\\n text\\n query\\n typeId\\n componentId\\n trackingOption\\n __typename\\n }}\\n redirection {{\\n redirectUrl\\n departmentId\\n __typename\\n }}\\n related {{\\n position\\n trackingOption\\n relatedKeyword\\n otherRelated {{\\n keyword\\n url\\n product {{\\n id\\n name\\n price\\n imageUrl\\n rating\\n countReview\\n url\\n priceStr\\n wishlist\\n shop {{\\n city\\n isOfficial\\n isPowerBadge\\n __typename\\n }}\\n ads {{\\n adsId: id\\n productClickUrl\\n productWishlistUrl\\n shopClickUrl\\n productViewUrl\\n __typename\\n }}\\n badges {{\\n title\\n imageUrl\\n show\\n __typename\\n }}\\n ratingAverage\\n labelGroups {{\\n position\\n type\\n title\\n url\\n __typename\\n }}\\n componentId\\n __typename\\n }}\\n componentId\\n __typename\\n }}\\n __typename\\n }}\\n suggestion {{\\n currentKeyword\\n suggestion\\n suggestionCount\\n instead\\n insteadCount\\n query\\n text\\n componentId\\n trackingOption\\n __typename\\n }}\\n products {{\\n id\\n name\\n ads {{\\n adsId: id\\n productClickUrl\\n productWishlistUrl\\n productViewUrl\\n __typename\\n }}\\n badges {{\\n title\\n imageUrl\\n show\\n __typename\\n }}\\n category: departmentId\\n categoryBreadcrumb\\n categoryId\\n categoryName\\n countReview\\n customVideoURL\\n discountPercentage\\n gaKey\\n imageUrl\\n labelGroups {{\\n position\\n title\\n type\\n url\\n __typename\\n }}\\n originalPrice\\n price\\n priceRange\\n rating\\n ratingAverage\\n count_sold\\n shop {{\\n shopId: id\\n name\\n url\\n city\\n isOfficial\\n isPowerBadge\\n __typename\\n }}\\n url\\n wishlist\\n sourceEngine: source_engine\\n __typename\\n }}\\n violation {{\\n headerText\\n descriptionText\\n imageURL\\n ctaURL\\n ctaApplink\\n buttonText\\n buttonType\\n __typename\\n }}\\n __typename\\n }}\\n __typename\\n }}\\n}}\\n"}}]'
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# ambil response body
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httpRequest = requests.post(url, headers=header, data=initial_query).json()
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response = httpRequest[0]['data']['ace_search_product_v4']['data']['products']
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# fething dan transform data
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for data in response :
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# ambil atribut data yang dibutuhkan
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count_sold = data['count_sold'] if data['count_sold'] != '' else 'Terjual 0'
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ratingScore = data['ratingAverage'] if data['ratingAverage'] != '' else '0'
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countReview = data['countReview'] if data['countReview'] != '' else '0.0'
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price = data['price'] if data['price'] != '' else 'Rp0'
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count_sold = count_sold.replace('Terjual ', '')
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price = price.replace('.', '')
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price = price.replace('Rp', '')
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products.append(
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{
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'id' : str(data['id']),
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'name' : data['name'],
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'image_url' : data['imageUrl'],
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'url' : data['url'],
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'seller' : data['shop']['name'],
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'location' : data['shop']['city'],
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'price' : int(price),
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'sold' : int(count_sold),
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'rating_score' : float(ratingScore),
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'count_review' : int(countReview),
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}
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)
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results['total_products'] = len(products)
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results['data'] = products
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return products
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# function untuk melakukan k-means clustering
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def clustering(params) :
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data = params #ambil array dari parameter untuk diolah
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response_data = {} # object untk response
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num_cluster = 3 # tentukan jumlah cluster
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# data = [
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# {"sold" : 12, "rating_score" : 4.8, "count_review" : 6},
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# {"sold" : 22, "rating_score" : 4.9, "count_review" : 14},
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# {"sold" : 108, "rating_score" : 4.9, "count_review" : 60},
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# {"sold" : 86, "rating_score" : 5, "count_review" : 34},
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# ]
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# print (data)
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# jika data produk lebih dari jumlah k cluster
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if (len(data) > num_cluster) :
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# ambil data secara acak sebagai centroid awal
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random_data = randomize(data, num_cluster)
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else : # jika kurang dari jumlah k cluster, tampil data
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return {'data' : []}
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# urutkan dari terkecil ke terbesar (Ascending)
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sorted_data = sorted(random_data, key=lambda x: x["sold"] + x["rating_score"] + x["count_review"], reverse=False)
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# sorted_data = generateCentroids(random_data)
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print(sorted_data[0]["sold"], sorted_data[1]["sold"], sorted_data[2]["sold"])
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# inisialisasi centroid awal berdasarkan data yang sudah dipilih acak
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centroids = {
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'c1' : {'sold' : sorted_data[0]['sold'], 'rating_score' : sorted_data[0]['rating_score'], 'count_review' : sorted_data[0]['count_review']},
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'c2' : {'sold' : sorted_data[1]['sold'], 'rating_score' : sorted_data[1]['rating_score'], 'count_review' : sorted_data[1]['count_review']},
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'c3' : {'sold' : sorted_data[2]['sold'], 'rating_score' : sorted_data[1]['rating_score'], 'count_review' : sorted_data[2]['count_review']},
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# 'c1' : {'sold' : random.randint(0, 10), 'rating_score': 0.0, 'count_review' : random.randint(0, 10)},
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# 'c2' : {'sold' : random.randint(400, 500), 'rating_score': 4, 'count_review' : random.randint(400, 500)},
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# 'c3' : {'sold' : random.randint(900, 1000), 'rating_score': 5, 'count_review' : random.randint(900, 1000)},
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}
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print(centroids)
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max_iteration = 100 # atur iterasi maksimal
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for _ in range(max_iteration) :
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# buat variable untuk menyimpan data hasil cluster sementara (temporary)
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sum_c1 = {'sold' : 0, 'rating_score' : 0, 'count_review' : 0}
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sum_c2 = {'sold' : 0, 'rating_score' : 0, 'count_review' : 0}
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sum_c3 = {'sold' : 0, 'rating_score' : 0, 'count_review' : 0}
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jml_c1 = 0
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jml_c2 = 0
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jml_c3 = 0
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# lakukan iterasi setiap data
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for i in range(len(data)) :
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# hitung jarak item data dengan setiap centroid (c1 atau c2)
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euq_c1 = math.sqrt((data[i]['sold'] - centroids['c1']['sold']) ** 2 + (data[i]['rating_score'] - centroids['c1']['rating_score']) ** 2 + (data[i]['count_review'] - centroids['c1']['count_review']) ** 2)
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euq_c2 = math.sqrt((data[i]['sold'] - centroids['c2']['sold']) ** 2 + (data[i]['rating_score'] - centroids['c2']['rating_score']) ** 2 + (data[i]['count_review'] - centroids['c2']['count_review']) ** 2)
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euq_c3 = math.sqrt((data[i]['sold'] - centroids['c3']['sold']) ** 2 + (data[i]['rating_score'] - centroids['c3']['rating_score']) ** 2 + (data[i]['count_review'] - centroids['c3']['count_review']) ** 2)
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print(euq_c1, euq_c2, euq_c3)
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# tentukan cluster dari setiap data berdasarkan jarak ke pusat centrois terdekat
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# jika c1 yang paling sedikit
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if (euq_c1 < euq_c2 and euq_c1 < euq_c3) :
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sum_c1['sold'] += data[i]['sold']
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sum_c1['rating_score'] += data[i]['rating_score']
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sum_c1['count_review'] += data[i]['count_review']
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jml_c1 += 1
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data[i]['cluster'] = 1
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# jika c2 yang paling sedikit
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if (euq_c2 < euq_c1 and euq_c2 < euq_c3) :
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sum_c2['sold'] += data[i]['sold']
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sum_c2['rating_score'] += data[i]['rating_score']
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sum_c2['count_review'] += data[i]['count_review']
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jml_c2 += 1
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data[i]['cluster'] = 2
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# jika c3 yang paling sedikit
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if (euq_c3 < euq_c1 and euq_c3 < euq_c2) :
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sum_c3['sold'] += data[i]['sold']
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sum_c3['rating_score'] += data[i]['rating_score']
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sum_c3['count_review'] += data[i]['count_review']
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jml_c3 += 1
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data[i]['cluster'] = 3
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# data[i]['cluster'] = 0
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# print(data[i])
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# hitung centroid baru, jika data milik cluster masih 0, gunakan centroid lama
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c1_sold = round(sum_c1['sold'] / jml_c1, 3) if jml_c1 > 0 else centroids['c1']['sold']
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c1_rating_score = round(sum_c1['rating_score'] / jml_c1, 3) if jml_c1 > 0 else centroids['c1']['rating_score']
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c1_review_count = round(sum_c1['count_review'] / jml_c1, 3) if jml_c1 > 0 else centroids['c1']['count_review']
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c2_sold = round(sum_c2['sold'] / jml_c2, 3) if jml_c2 > 0 else centroids['c2']['sold']
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c2_rating_score = round(sum_c2['rating_score'] / jml_c2, 3) if jml_c2 > 0 else centroids['c2']['rating_score']
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c2_review_count = round(sum_c2['count_review'] / jml_c2, 3) if jml_c2 > 0 else centroids['c2']['count_review']
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c3_sold = round(sum_c3['sold'] / jml_c3, 3) if jml_c3 > 0 else centroids['c3']['sold']
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c3_rating_score = round(sum_c3['rating_score'] / jml_c3, 3) if jml_c3 > 0 else centroids['c3']['rating_score']
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c3_review_count = round(sum_c3['count_review'] / jml_c3, 3) if jml_c3 > 0 else centroids['c3']['count_review']
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# masukan variable ke centroid baru
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new_centroid = {
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'c1' : {'sold' : c1_sold, 'rating_score': c1_rating_score, 'count_review' : c1_review_count},
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'c2' : {'sold' : c2_sold, 'rating_score': c2_rating_score, 'count_review' : c2_review_count},
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'c3' : {'sold' : c3_sold, 'rating_score': c3_rating_score, 'count_review' : c3_review_count},
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}
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# new_centroid = {
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# 'c1' : {'sold' : round(sum_c1['sold'] / jml_c1, 3), 'rating_score': round(sum_c1['rating_score'] / jml_c1, 3), 'count_review' : round(sum_c1['count_review'] / jml_c1, 3)},
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# 'c2' : {'sold' : round(sum_c2['sold'] / jml_c2, 3), 'rating_score': round(sum_c2['rating_score'] / jml_c2, 3), 'count_review' : round(sum_c2['count_review'] / jml_c2, 3)},
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# 'c3' : {'sold' : round(sum_c3['sold'] / jml_c3, 3), 'rating_score': round(sum_c3['rating_score'] / jml_c3, 3), 'count_review' : round(sum_c3['count_review'] / jml_c3, 3)},
|
|
# }
|
|
# print(jml_c1, jml_c2, jml_c3)
|
|
|
|
# jika centroid sudah sama, hentikan perulangan
|
|
if (centroids == new_centroid) :
|
|
break
|
|
|
|
# jika masih belum sama, lanjutkan smpai batas maksimal iterasi dengan mengubah centroid
|
|
centroids = new_centroid
|
|
|
|
print(centroids)
|
|
|
|
# print(data)
|
|
response_data['centroids'] = centroids
|
|
response_data['cluster_1'] = jml_c1
|
|
response_data['cluster_2'] = jml_c2
|
|
response_data['cluster_3'] = jml_c3
|
|
response_data['data'] = data
|
|
return response_data
|
|
|
|
|
|
# function crawling raw data
|
|
def searchProduct(keyword) :
|
|
results = {}
|
|
productIds = [] #array kosong untuk menyimpan produk
|
|
allProductIds = [] #array kosong untuk menyimpan produk
|
|
products = [] #array kosong untuk menyimpan produk
|
|
|
|
# url tujuan
|
|
url = 'https://gql.tokopedia.com/graphql/SearchQueryV4'
|
|
|
|
# set header untuk request
|
|
header = {
|
|
'authority':'gql.tokopedia.com',
|
|
'accept':'*/*',
|
|
'accept-language':'en-US,en;q=0.9',
|
|
'content-type':'application/json',
|
|
'cookie':'_UUID_NONLOGIN_=04bacffdb33ae7cc9b1d427bbddf171a; _UUID_NONLOGIN_.sig=DVQhA3-ELQQJ9h0SYyVjFHoJFSk; bm_sz=7BF80AE05E5A8AF9664B95B6B2CE2CB3~YAAQBnUyF7+r5qGFAQAAKCdQsRI1Fh/SmyNRRdC9BW4wVU0lmI//2NiLVakPZhTxG0wL2ubZGIjNV6e+kXZ1+5ZqKkK9Fw/Fd+O+T9RZfLp8CXmjV1euh9PVY53K0Ta4CCk1u0Z5EWh1W7Tti6X5jLafWIR+oT1/OgtW3caeEbHOsPQ1EHLHGulXM6KVI3zveFNIMPOUjD8NpeM8Qu5s6zGQIoKdJTLcC/8oqDXy+wClFnEjDZppP084pNd4y/G+c8CSpxAsspL5iuVQVlYRRuwJwE8HYpToOZCsi86tFQjUNjOzLdI=~4272449~3356209; _abck=67ADF742634B2294651C94063EC293ED~0~YAAQBnUyF8ar5qGFAQAAsitQsQkCdgYNV6G8p8uT+aOLF4WEFJEn1yIPMDgyA54iRmSs8IgsBbjhWcRUT1ZGACKO2ODHrZ5oTi8X5dZN5tDx/TwNBUAGKhLKJLUWcC1VB2KHhvD4clCL7uQ4J1mNd5g+9qyDwYVnUvJ4EebQiAsTA6BuyXB0diBrJNOzaw3Ee7WxsSY2/eBVn6PLX+M9fSkvzbjVm5zzyD0aJdY/LmUiZ0w99cb5qp38p9x1woCcn8yvXnCjZpDwpw+uHfaRujN37jTOJlnQz0zKBrX1CIswAGrVHy9wGLjd6Gqwo2qOMAXI02xcsjvTyb3rg90fhcSPJTFDshm1G2FfkcgQp1mgKH+88WYNmd2ownQl6LjOhZnoULvxgiwbqg9gQG0VlVLTnRjaIuqoi/8I4w==~-1~-1~-1; ak_bmsc=DFC7AAF6F9F64270E723EC89908FAC33~000000000000000000000000000000~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; _SID_Tokopedia_=j6466YAGtp0pdwjx6o3ZvLax0AXvqIatZCtCkJCGgGBvMewHfR1cVZUvt_H0pQMGfjF_o671zjJYno7bObLNo3_uBKI5F2R2rIfRd9R6RQi_7YRpbgfB-pPWvq_hE6RE; DID=c6330112a5306c2292ef00c3384a948935bb5fc3d741651dc80dc4d4e31b663de5b642e3feead70772b2585af3697f42; DID_JS=YzYzMzAxMTJhNTMwNmMyMjkyZWYwMGMzMzg0YTk0ODkzNWJiNWZjM2Q3NDE2NTFkYzgwZGM0ZDRlMzFiNjYzZGU1YjY0MmUzZmVlYWQ3MDc3MmIyNTg1YWYzNjk3ZjQy47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=; _gcl_au=1.1.1390752592.1673717104; _UUID_CAS_=11873256-ff6e-4c2e-991a-0912169c4034; _CASE_=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; _gid=GA1.2.2077103790.1673717105; __asc=be6737fb185b1504654124dd24e; __auc=be6737fb185b1504654124dd24e; _fbp=fb.1.1673717109190.132615314; _dc_gtm_UA-126956641-6=1; _ga_70947XW48P=GS1.1.1673717104.1.1.1673717202.58.0.0; AMP_TOKEN=%24NOT_FOUND; _ga=GA1.2.1594572164.1673717105; _dc_gtm_UA-9801603-1=1',
|
|
'origin':'https://www.tokopedia.com',
|
|
'referer':'https://www.tokopedia.com',
|
|
'sec-ch-ua':'"Not?A_Brand";v="8", "Chromium";v="108", "Microsoft Edge";v="108"',
|
|
'sec-ch-ua-mobile':'?0',
|
|
'sec-ch-ua-platform':'"Windows"',
|
|
'sec-fetch-dest':'empty',
|
|
'sec-fetch-mode':'cors',
|
|
'sec-fetch-site':'same-site',
|
|
'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36 Edg/108.0.1462.76',
|
|
'x-device':'default_v3',
|
|
'x-source':'tokopedia-lite',
|
|
'x-tkpd-lite-service':'zeus',
|
|
'x-version':'68ba647',
|
|
}
|
|
|
|
# query = f'[{{"operationName":"SearchQueryV4","variables":{{"sid":"9351716","page":1,"perPage":80,"etalaseId":"etalase","sort":1,"user_districtId":"2274","user_cityId":"176","user_lat":"","user_long":""}},"query":"query SearchQueryV4($sid: String\u0021, $page: Int, $perPage: Int, $keyword: String, $etalaseId: String, $sort: Int, $user_districtId: String, $user_cityId: String, $user_lat: String, $user_long: String) {{\\n GetShopProduct(shopID: $sid, filter: {{page: $page, perPage: $perPage, fkeyword: $keyword, fmenu: $etalaseId, sort: $sort, user_districtId: $user_districtId, user_cityId: $user_cityId, user_lat: $user_lat, user_long: $user_long}}) {{\\n status\\n errors\\n links {{\\n prev\\n next\\n __typename\\n }}\\n data {{\\n name\\n product_url\\n product_id\\n price {{\\n text_idr\\n __typename\\n }}\\n primary_image {{\\n original\\n thumbnail\\n resize300\\n __typename\\n }}\\n flags {{\\n isSold\\n isPreorder\\n isWholesale\\n isWishlist\\n __typename\\n }}\\n campaign {{\\n discounted_percentage\\n original_price_fmt\\n start_date\\n end_date\\n __typename\\n }}\\n label {{\\n color_hex\\n content\\n __typename\\n }}\\n label_groups {{\\n position\\n title\\n type\\n url\\n __typename\\n }}\\n badge {{\\n title\\n image_url\\n __typename\\n }}\\n stats {{\\n reviewCount\\n rating\\n __typename\\n }}\\n category {{\\n id\\n __typename\\n }}\\n __typename\\n }}\\n __typename\\n }}\\n}}\\n"}}]'
|
|
|
|
max_count_sold = 0
|
|
max_count_review = 0
|
|
|
|
# search query
|
|
initial_query = f'[{{"operationName":"SearchProductQueryV4","variables":{{"params":"device=desktop&navsource=&ob=23&q=${keyword}&related=true&rows=200&safe_search=false&scheme=https&shipping=&source=search&srp_component_id=01.07.00.00&srp_page_id=&srp_page_title=&st=product&start=0&topads_bucket=false&unique_id=1fdabea77fbaf5f1954bdbc40d4a9337&user_addressId=113228966&user_cityId=171&user_districtId=2233&user_id=7773903&user_lat=-6.377643399999999&user_long=106.7621449&user_postCode=16516&user_warehouseId=0&variants="}},"query":"query SearchProductQueryV4($params: String!) {{\\n ace_search_product_v4(params: $params) {{\\n header {{\\n totalData\\n totalDataText\\n processTime\\n responseCode\\n errorMessage\\n additionalParams\\n keywordProcess\\n componentId\\n __typename\\n }}\\n data {{\\n banner {{\\n position\\n text\\n imageUrl\\n url\\n componentId\\n trackingOption\\n __typename\\n }}\\n backendFilters\\n isQuerySafe\\n ticker {{\\n text\\n query\\n typeId\\n componentId\\n trackingOption\\n __typename\\n }}\\n redirection {{\\n redirectUrl\\n departmentId\\n __typename\\n }}\\n related {{\\n position\\n trackingOption\\n relatedKeyword\\n otherRelated {{\\n keyword\\n url\\n product {{\\n id\\n name\\n price\\n imageUrl\\n rating\\n countReview\\n url\\n priceStr\\n wishlist\\n shop {{\\n city\\n isOfficial\\n isPowerBadge\\n __typename\\n }}\\n ads {{\\n adsId: id\\n productClickUrl\\n productWishlistUrl\\n shopClickUrl\\n productViewUrl\\n __typename\\n }}\\n badges {{\\n title\\n imageUrl\\n show\\n __typename\\n }}\\n ratingAverage\\n labelGroups {{\\n position\\n type\\n title\\n url\\n __typename\\n }}\\n componentId\\n __typename\\n }}\\n componentId\\n __typename\\n }}\\n __typename\\n }}\\n suggestion {{\\n currentKeyword\\n suggestion\\n suggestionCount\\n instead\\n insteadCount\\n query\\n text\\n componentId\\n trackingOption\\n __typename\\n }}\\n products {{\\n id\\n name\\n ads {{\\n adsId: id\\n productClickUrl\\n productWishlistUrl\\n productViewUrl\\n __typename\\n }}\\n badges {{\\n title\\n imageUrl\\n show\\n __typename\\n }}\\n category: departmentId\\n categoryBreadcrumb\\n categoryId\\n categoryName\\n countReview\\n customVideoURL\\n discountPercentage\\n gaKey\\n imageUrl\\n labelGroups {{\\n position\\n title\\n type\\n url\\n __typename\\n }}\\n originalPrice\\n price\\n priceRange\\n rating\\n ratingAverage\\n count_sold\\n shop {{\\n shopId: id\\n name\\n url\\n city\\n isOfficial\\n isPowerBadge\\n __typename\\n }}\\n url\\n wishlist\\n sourceEngine: source_engine\\n __typename\\n }}\\n violation {{\\n headerText\\n descriptionText\\n imageURL\\n ctaURL\\n ctaApplink\\n buttonText\\n buttonType\\n __typename\\n }}\\n __typename\\n }}\\n __typename\\n }}\\n}}\\n"}}]'
|
|
|
|
# ambil response body
|
|
httpRequest = requests.post(url, headers=header, data=initial_query).json()
|
|
response = httpRequest
|
|
# response = httpRequest[0]['data']['ace_search_product_v4']['data']
|
|
|
|
return response
|
|
|
|
|
|
|
|
def cluster(data) :
|
|
# Fitur yang akan digunakan untuk clustering
|
|
features = ['sold', 'rating_score', 'count_review']
|
|
|
|
# Jumlah kluster yang diinginkan
|
|
num_clusters = 3
|
|
|
|
# Pusat kluster awal yang Anda tentukan (dalam bentuk list of dictionaries)
|
|
initial_centers = [
|
|
{'sold': 15, 'rating_score': 4.7, 'count_review': 15},
|
|
{'sold': 50, 'rating_score': 4.8, 'count_review': 500},
|
|
{'sold': 100, 'rating_score': 5, 'count_review': 100}
|
|
]
|
|
# Mengubah initial_centers menjadi satu list yang berisi nilai fitur secara berurutan
|
|
centers = np.array([initial_centers[i][feature] for feature in features for i in range(num_clusters)])
|
|
|
|
# Maksimum iterasi
|
|
max_iters = 10
|
|
|
|
for _ in range(max_iters):
|
|
# Menyimpan hasil kluster untuk setiap data point
|
|
cluster_assignments = []
|
|
|
|
# Assign data point ke kluster terdekat
|
|
for item in data:
|
|
distances = []
|
|
for center_idx in range(num_clusters):
|
|
distance = 0
|
|
for feature in features:
|
|
distance += (item[feature] - centers[center_idx * len(features) + features.index(feature)]) ** 2
|
|
distances.append(math.sqrt(distance))
|
|
cluster_assignments.append(distances.index(min(distances)))
|
|
|
|
# Menghitung pusat baru untuk setiap kluster
|
|
new_centers = np.zeros((num_clusters, len(features)))
|
|
cluster_counts = np.zeros(num_clusters)
|
|
for i, item in enumerate(data):
|
|
cluster_idx = cluster_assignments[i]
|
|
for feature_idx, feature in enumerate(features):
|
|
new_centers[cluster_idx, feature_idx] += item[feature]
|
|
cluster_counts[cluster_idx] += 1
|
|
for cluster_idx in range(num_clusters):
|
|
new_centers[cluster_idx] /= cluster_counts[cluster_idx]
|
|
|
|
# Mengecek apakah pusat kluster sudah konvergen
|
|
if np.all(centers == new_centers):
|
|
break
|
|
|
|
centers = new_centers.flatten()
|
|
|
|
# Memperbarui nilai 'cluster' di setiap item data
|
|
for i, item in enumerate(data):
|
|
item['cluster'] = cluster_assignments[i]
|
|
|
|
# Menampilkan hasil kluster untuk setiap key 'cluster'
|
|
for cluster_id in range(num_clusters):
|
|
clustered_data = [item for item in data if item['cluster'] == cluster_id]
|
|
print(f"Cluster {cluster_id}:")
|
|
for item in clustered_data:
|
|
print(item)
|
|
print("\n")
|
|
|
|
# print (data)
|
|
return data
|
|
|
|
def test_crwaling(keyword) :
|
|
results = {}
|
|
productIds = [] #array kosong untuk menyimpan produk
|
|
allProductIds = [] #array kosong untuk menyimpan produk
|
|
products = [] #array kosong untuk menyimpan produk
|
|
|
|
# url tujuan
|
|
url = 'https://gql.tokopedia.com/graphql/SearchQueryV4'
|
|
|
|
# set header untuk request
|
|
header = {
|
|
'authority':'gql.tokopedia.com',
|
|
'accept':'*/*',
|
|
'accept-language':'en-US,en;q=0.9',
|
|
'content-type':'application/json',
|
|
'cookie':'_UUID_NONLOGIN_=04bacffdb33ae7cc9b1d427bbddf171a; _UUID_NONLOGIN_.sig=DVQhA3-ELQQJ9h0SYyVjFHoJFSk; bm_sz=7BF80AE05E5A8AF9664B95B6B2CE2CB3~YAAQBnUyF7+r5qGFAQAAKCdQsRI1Fh/SmyNRRdC9BW4wVU0lmI//2NiLVakPZhTxG0wL2ubZGIjNV6e+kXZ1+5ZqKkK9Fw/Fd+O+T9RZfLp8CXmjV1euh9PVY53K0Ta4CCk1u0Z5EWh1W7Tti6X5jLafWIR+oT1/OgtW3caeEbHOsPQ1EHLHGulXM6KVI3zveFNIMPOUjD8NpeM8Qu5s6zGQIoKdJTLcC/8oqDXy+wClFnEjDZppP084pNd4y/G+c8CSpxAsspL5iuVQVlYRRuwJwE8HYpToOZCsi86tFQjUNjOzLdI=~4272449~3356209; _abck=67ADF742634B2294651C94063EC293ED~0~YAAQBnUyF8ar5qGFAQAAsitQsQkCdgYNV6G8p8uT+aOLF4WEFJEn1yIPMDgyA54iRmSs8IgsBbjhWcRUT1ZGACKO2ODHrZ5oTi8X5dZN5tDx/TwNBUAGKhLKJLUWcC1VB2KHhvD4clCL7uQ4J1mNd5g+9qyDwYVnUvJ4EebQiAsTA6BuyXB0diBrJNOzaw3Ee7WxsSY2/eBVn6PLX+M9fSkvzbjVm5zzyD0aJdY/LmUiZ0w99cb5qp38p9x1woCcn8yvXnCjZpDwpw+uHfaRujN37jTOJlnQz0zKBrX1CIswAGrVHy9wGLjd6Gqwo2qOMAXI02xcsjvTyb3rg90fhcSPJTFDshm1G2FfkcgQp1mgKH+88WYNmd2ownQl6LjOhZnoULvxgiwbqg9gQG0VlVLTnRjaIuqoi/8I4w==~-1~-1~-1; ak_bmsc=DFC7AAF6F9F64270E723EC89908FAC33~000000000000000000000000000000~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; _SID_Tokopedia_=j6466YAGtp0pdwjx6o3ZvLax0AXvqIatZCtCkJCGgGBvMewHfR1cVZUvt_H0pQMGfjF_o671zjJYno7bObLNo3_uBKI5F2R2rIfRd9R6RQi_7YRpbgfB-pPWvq_hE6RE; DID=c6330112a5306c2292ef00c3384a948935bb5fc3d741651dc80dc4d4e31b663de5b642e3feead70772b2585af3697f42; DID_JS=YzYzMzAxMTJhNTMwNmMyMjkyZWYwMGMzMzg0YTk0ODkzNWJiNWZjM2Q3NDE2NTFkYzgwZGM0ZDRlMzFiNjYzZGU1YjY0MmUzZmVlYWQ3MDc3MmIyNTg1YWYzNjk3ZjQy47DEQpj8HBSa+/TImW+5JCeuQeRkm5NMpJWZG3hSuFU=; _gcl_au=1.1.1390752592.1673717104; _UUID_CAS_=11873256-ff6e-4c2e-991a-0912169c4034; _CASE_=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; _gid=GA1.2.2077103790.1673717105; __asc=be6737fb185b1504654124dd24e; __auc=be6737fb185b1504654124dd24e; _fbp=fb.1.1673717109190.132615314; _dc_gtm_UA-126956641-6=1; _ga_70947XW48P=GS1.1.1673717104.1.1.1673717202.58.0.0; AMP_TOKEN=%24NOT_FOUND; _ga=GA1.2.1594572164.1673717105; _dc_gtm_UA-9801603-1=1',
|
|
'origin':'https://www.tokopedia.com',
|
|
'referer':'https://www.tokopedia.com',
|
|
'sec-ch-ua':'"Not?A_Brand";v="8", "Chromium";v="108", "Microsoft Edge";v="108"',
|
|
'sec-ch-ua-mobile':'?0',
|
|
'sec-ch-ua-platform':'"Windows"',
|
|
'sec-fetch-dest':'empty',
|
|
'sec-fetch-mode':'cors',
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'sec-fetch-site':'same-site',
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'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Safari/537.36 Edg/108.0.1462.76',
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'x-device':'default_v3',
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'x-source':'tokopedia-lite',
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'x-tkpd-lite-service':'zeus',
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'x-version':'68ba647',
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}
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# query = f'[{{"operationName":"SearchQueryV4","variables":{{"sid":"9351716","page":1,"perPage":80,"etalaseId":"etalase","sort":1,"user_districtId":"2274","user_cityId":"176","user_lat":"","user_long":""}},"query":"query SearchQueryV4($sid: String\u0021, $page: Int, $perPage: Int, $keyword: String, $etalaseId: String, $sort: Int, $user_districtId: String, $user_cityId: String, $user_lat: String, $user_long: String) {{\\n GetShopProduct(shopID: $sid, filter: {{page: $page, perPage: $perPage, fkeyword: $keyword, fmenu: $etalaseId, sort: $sort, user_districtId: $user_districtId, user_cityId: $user_cityId, user_lat: $user_lat, user_long: $user_long}}) {{\\n status\\n errors\\n links {{\\n prev\\n next\\n __typename\\n }}\\n data {{\\n name\\n product_url\\n product_id\\n price {{\\n text_idr\\n __typename\\n }}\\n primary_image {{\\n original\\n thumbnail\\n resize300\\n __typename\\n }}\\n flags {{\\n isSold\\n isPreorder\\n isWholesale\\n isWishlist\\n __typename\\n }}\\n campaign {{\\n discounted_percentage\\n original_price_fmt\\n start_date\\n end_date\\n __typename\\n }}\\n label {{\\n color_hex\\n content\\n __typename\\n }}\\n label_groups {{\\n position\\n title\\n type\\n url\\n __typename\\n }}\\n badge {{\\n title\\n image_url\\n __typename\\n }}\\n stats {{\\n reviewCount\\n rating\\n __typename\\n }}\\n category {{\\n id\\n __typename\\n }}\\n __typename\\n }}\\n __typename\\n }}\\n}}\\n"}}]'
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max_count_sold = 0
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max_count_review = 0
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# search query
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initial_query = f'[{{"operationName":"SearchProductQueryV4","variables":{{"params":"device=desktop&navsource=&ob=23&q=${keyword}&related=true&rows=200&safe_search=false&scheme=https&shipping=&source=search&srp_component_id=01.07.00.00&srp_page_id=&srp_page_title=&st=product&start=0&topads_bucket=false&unique_id=1fdabea77fbaf5f1954bdbc40d4a9337&user_addressId=113228966&user_cityId=171&user_districtId=2233&user_id=7773903&user_lat=-6.377643399999999&user_long=106.7621449&user_postCode=16516&user_warehouseId=0&variants="}},"query":"query SearchProductQueryV4($params: String!) {{\\n ace_search_product_v4(params: $params) {{\\n header {{\\n totalData\\n totalDataText\\n processTime\\n responseCode\\n errorMessage\\n additionalParams\\n keywordProcess\\n componentId\\n __typename\\n }}\\n data {{\\n banner {{\\n position\\n text\\n imageUrl\\n url\\n componentId\\n trackingOption\\n __typename\\n }}\\n backendFilters\\n isQuerySafe\\n ticker {{\\n text\\n query\\n typeId\\n componentId\\n trackingOption\\n __typename\\n }}\\n redirection {{\\n redirectUrl\\n departmentId\\n __typename\\n }}\\n related {{\\n position\\n trackingOption\\n relatedKeyword\\n otherRelated {{\\n keyword\\n url\\n product {{\\n id\\n name\\n price\\n imageUrl\\n rating\\n countReview\\n url\\n priceStr\\n wishlist\\n shop {{\\n city\\n isOfficial\\n isPowerBadge\\n __typename\\n }}\\n ads {{\\n adsId: id\\n productClickUrl\\n productWishlistUrl\\n shopClickUrl\\n productViewUrl\\n __typename\\n }}\\n badges {{\\n title\\n imageUrl\\n show\\n __typename\\n }}\\n ratingAverage\\n labelGroups {{\\n position\\n type\\n title\\n url\\n __typename\\n }}\\n componentId\\n __typename\\n }}\\n componentId\\n __typename\\n }}\\n __typename\\n }}\\n suggestion {{\\n currentKeyword\\n suggestion\\n suggestionCount\\n instead\\n insteadCount\\n query\\n text\\n componentId\\n trackingOption\\n __typename\\n }}\\n products {{\\n id\\n name\\n ads {{\\n adsId: id\\n productClickUrl\\n productWishlistUrl\\n productViewUrl\\n __typename\\n }}\\n badges {{\\n title\\n imageUrl\\n show\\n __typename\\n }}\\n category: departmentId\\n categoryBreadcrumb\\n categoryId\\n categoryName\\n countReview\\n customVideoURL\\n discountPercentage\\n gaKey\\n imageUrl\\n labelGroups {{\\n position\\n title\\n type\\n url\\n __typename\\n }}\\n originalPrice\\n price\\n priceRange\\n rating\\n ratingAverage\\n count_sold\\n shop {{\\n shopId: id\\n name\\n url\\n city\\n isOfficial\\n isPowerBadge\\n __typename\\n }}\\n url\\n wishlist\\n sourceEngine: source_engine\\n __typename\\n }}\\n violation {{\\n headerText\\n descriptionText\\n imageURL\\n ctaURL\\n ctaApplink\\n buttonText\\n buttonType\\n __typename\\n }}\\n __typename\\n }}\\n __typename\\n }}\\n}}\\n"}}]'
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# ambil response body
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httpRequest = requests.post(url, headers=header, data=initial_query).json()
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response = httpRequest[0]['data']['ace_search_product_v4']['data']
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return response
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@app.route('/privacy-policy')
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def privacy_policy() :
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return send_file("privacy-policies.html")
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def randomize(array, length) :
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random_data = random.sample(array, length)
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if (random_data[0]["sold"] != random_data[1]["sold"] and random_data[1]["sold"] != random_data[2]["sold"] and random_data[0]["sold"] != random_data[2]["sold"]) :
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return random_data
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else :
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return randomize(array, length)
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if __name__ == '__main__' :
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app.run(host='0.0.0.0', port=80)
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