alabrashJr

from sklearn.model_selection import train_test_split
train, test = train_test_split(data, random_state=42, test_size=0.30, shuffle=True)
train_text = train['النص'].values.astype('U')
test_text = test['النص'].values.astype('U')

alabrashJr

labels=["الجودة","السلوك","مراجعة","الاستخدام","السعر"]
y_train=train[labels]
y_test=test[labels]

alabrashJr

def predict(text):
    print("text -> ",text)
    X=vectorizer.transform([text_s])
    X_pred=lp_classifier.predict(X).toarray()
    return [data.columns[i] for i,x in enumerate(X_pred[0]) if x==1]

text="كان العمال في الورشة لطفاء وكان عملهن جيد جداً"

predict(test_txt)

alabrashJr

import pysolr
from xml.etree import ElementTree
import argparse

skip = set(["shippingDetails", "priceDetail", "dailyOfferCategory", "stockInfo_availability", "abroad",
                "imageDetail", "bigImageUrl", "specialOffer", "editorsChoice", "shippingDate", "shippingTime", "types",
                "stockInfo",
                "hasVariant", "member", "images", "shippingFee", "image", "globalTradeItemNumber", "quantity",
                "soldQuantity", "variants", "subTitle"
                   , 'windowOptionFlag', 'productLine', 'affiliate'])

alabrashJr

import argparse
import pandas as pd 
import pymongo
from datetime import datetime 

def args():
    parser = argparse.ArgumentParser()
    parser.add_argument("path", help="path to ebay index html")
    parser.add_argument("mongodb",help="ip to mongodb",default="mongodb://localhost:27017/")
    parser.add_argument("mongodb_prname",help="monogdb project name",default="shelock")