Resolved Merge Conflicts

logger.txt

@@ -293,6 +293,7 @@ amazon query:Iphone
 amazon query:Iphone
 amazon query:Iphone
 amazon query:Iphone
+<<<<<<< HEAD
 amazon query:iPad
 amazon query:iPad
 amazon query:iPad
@@ -316,3 +317,10 @@ amazon query:iPad
 amazon query:iPad
 amazon query:iPad
 amazon query:iPad
+=======
+amazon query:iphone
+amazon query:iphone
+amazon query:iphone
+amazon query:iphone
+amazon query:iphone
+>>>>>>> 193d9b395c989284203bd4d60446536812830a5e

slash_user_interface.py

@@ -9,8 +9,9 @@
 import sys
 sys.path.append('../')
 import streamlit as st
-from src.main_streamlit import search_items_API
+from src.main_streamlit import search_items_API, rakuten
 from src.url_shortener import shorten_url
+import src.configs as conf
 import pandas as pd
 import re
 import streamlit
@@ -69,7 +70,7 @@ def split_description(description):
 title = """
 <div class="header">
     <div class="t">SHOPSYNC</div>
-    <p>Get the Best Deals At Ease</p>
+    <p>Discover unparalleled savings effortlessly.</p>
 </div>
 <style>
         .header {
@@ -146,7 +147,7 @@ def highlight_row(dataframe):
 # Display Image
 # st.image("assets/ShopSync_p.png")
 
-st.write("ShopSync is an android application, website and a command line tool that scrapes the most popular e-commerce websites to get the best deals on the searched items across these websites.")
+st.write("ShopSync is a versatile platform comprising an Android application, a user-friendly website, and a command line tool. It adeptly scours the leading e-commerce websites, extracting optimal deals for the searched items across this diverse range of platforms.")
 product = st.text_input('Enter the product item name')
 website = st.selectbox('Select the website',('All','Walmart', 'Amazon', 'Ebay', 'BestBuy', 'Target', 'Costco', 'All'))
 
@@ -161,12 +162,15 @@ def highlight_row(dataframe):
         }
 # Pass product and website to method
 if st.button('Search') and product and website:
+    rakuten_discount = rakuten()
+    company_list = conf.getCompanies()
     results = search_items_API(website_dict[website], product)
     # Use st.columns based on return values
     description = []
     url = []
     price = []
     site = []
+    rakuten = []
 
     if results is not None and isinstance(results, list):
          for result in results:
@@ -182,10 +186,14 @@ def highlight_row(dataframe):
             else:
                 print("Unable to extract a valid price from the string")
             site.append(result['website'])
+
+    for i in range(len(site)):
+        k = company_list.index(site[i])
+        rakuten.append(str(rakuten_discount[k]) + "%")
 
     if len(price):
 
-        dataframe = pd.DataFrame({'Description': description,'Price':price,'Link':url,'Website':site})
+        dataframe = pd.DataFrame({'Description': description,'Price':price,'Link':url,'Website':site, 'Rakuten':rakuten})
         dataframe['Description'] = dataframe['Description'].apply(split_description)
         dataframe['Product'] = dataframe['Description'].str.split().str[:3].str.join(' ')
         dataframe['Product'] = dataframe['Product'].str.replace('[,"]', '', regex=True)
@@ -264,9 +272,9 @@ def add_http_if_not_present(url):
 }
 </style>
 <div class="footer">
-<p>Developed with ❤ by <a style='display: block; text-align: center;' href="https://github.com/Kashika08/ShopSync.git" target="_blank">ShopSync</a></p>
+<p>Developed with ❤ by <a style='display: block; text-align: center;' href="https://github.com/Neel317/ShopSync" target="_blank">ShopSync</a></p>
 <p><a style='display: block; text-align: center;' href="https://github.com/Kashika08/CSC510_ShopSync_Group40/blob/main/LICENSE" target="_blank">MIT License Copyright (c) 2023</a></p>
-<p>Contributors: Kashika, Riya, Sinchana, Sweta</p>
+<p>Contributors: Neel, Shubh, Tanay, Tanishq</p>
 </div>
 """
 st.markdown(footer,unsafe_allow_html=True)

src/configs.py

@@ -153,3 +153,13 @@ def scrape_ebay(query):
 
 
 CONFIGS = [WALMART, AMAZON, COSTCO, BESTBUY]
+
+def getRakutenList():
+    cashback = [10, 0, 1, 4, 1, 0]
+    return cashback
+
+def getCompanies():
+    companies = ["walmart", "amazon", "ebay", "bestbuy", "target", "costco"]
+    return companies
+
+

src/configs_mt.py

@@ -53,11 +53,11 @@
 BESTBUY = {
     'site': 'bestbuy',
     'url': 'https://www.bestbuy.com/site/searchpage.jsp?st=',
-    'item_component': 'li',
+    'item_component': 'div',
     'item_indicator': {
-        'class': 'sku-item'
+        'class': 'embedded-sku'
     },
-    'title_indicator': 'h4.sku-header a',
+    'title_indicator': 'h4.sku-title a',
     'price_indicator': 'div.priceView-customer-price span',
     'link_indicator': 'a.image-link',
 }

src/main.py

@@ -16,6 +16,9 @@
 from pydantic import BaseModel
 import csv
 import nest_asyncio
+import sys
+import os
+sys.path.append(os.path.abspath("Slash"))
 
 # local imports
 import scraper_mt as scr

src/main_streamlit.py

@@ -7,9 +7,15 @@
 
 from typing import Optional
 from pydantic import BaseModel
+from bs4 import BeautifulSoup
+import requests
 
 # local imports
 import src.scraper_mt as scr
+from src.configs import getRakutenList, getCompanies
+
+cashback = getRakutenList()
+companies = getCompanies()
 
 # response type define
 class jsonScraps(BaseModel):
@@ -76,3 +82,20 @@ def search_items_API(
     else:
         # No results
         return None
+
+def rakuten():
+    for i in range(len(companies)):
+        url = "https://www.rakuten.com/search?term=" + companies[i]
+        response = requests.get(url)
+        # Check if the request was successful (status code 200)
+        if response.status_code == 200:
+            # Parse the HTML content of the page
+            soup = BeautifulSoup(response.text, 'html.parser')
+            # Find the element containing the cashback information
+            cashback_element = soup.find('div', {'class': 'css-1i7dpco'})  # Adjust the class based on the actual HTML structure
+            if cashback_element:
+                # Extract the cashback value
+                cashback_value = cashback_element.text.strip()
+                if (cashback_value):
+                    cashback[i] = cashback_value
+    return cashback

src/scraper_mt.py

@@ -11,6 +11,10 @@
 from datetime import datetime
 from threading import Thread
 
+import sys
+import os
+sys.path.append(os.path.abspath("Slash"))
+
 # local imports
 import formattr as form
 from configs_mt import AMAZON, WALMART, COSTCO, BESTBUY, scrape_ebay, scrape_target