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all_products_with_names.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2990e4a3d8e1326a0d6f211fe61bb58a05709490b6a20c8bad6ce2314ca2caf1
+size 476664

app.py

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+# import pickle
+# import streamlit as st
+# from html_information import html
+# from html_information2 import html2
+
+# import streamlit as st
+
+# st.set_page_config(page_title="My App", page_icon=":money_with_wings:", layout="wide", initial_sidebar_state="auto")
+# st.header("Frequently Bought Together Recommendations")
+
+# def read_pickle_files(pickle_file):
+#     with open(pickle_file, 'rb') as f:
+#         return pickle.load(f)
+
+# def get_mapped_values(uid_list, dict_map):
+#     res = []
+#     for val in uid_list:
+#         try:
+#             res.append(dict_map[val])
+#         except:
+#             continue
+#     return res
+
+# # Load sephora pickle files
+# filtered_products_list = read_pickle_files("filtered_products_list.pkl")
+# recommendation_products_list = read_pickle_files("recommendation_products_list.pkl")
+# transactions_list = read_pickle_files("transactions.pkl")
+# bought_together_dict = read_pickle_files("bought_together_dict_orders_data.pkl")
+# uid_name_map = read_pickle_files("uid_name_map.pkl")
+# uid_url_map = read_pickle_files("uid_url_map.pkl")
+
+# # Load digital pickle files
+# #transactions_list_digital = read_pickle_files("transaction_digital.pkl")
+# # bought_together_dict_digital = read_pickle_files("bought_together_dictionary_without_names.pkl")
+# corrected_fp_growth_results = read_pickle_files("corrected_fp_growth_results.pkl")
+# #list_of_products_to_display = read_pickle_files("list_of_products_to_display_pickle.pkl") # all products
+# all_products_with_names = read_pickle_files ("all_products_with_names.pkl")
+# dictionary_of_transactions = read_pickle_files("reliance_digital_transactions.pkl")
+
+# # Create product dictionary
+# product_dict = {item['product_id']: item for item in filtered_products_list}
+
+# # Dropdown for selecting the dataset
+# dataset_choice = st.selectbox(
+#     "Select A Dataset", 
+#     ["Sephora Order Complete Dataset", "Reliance Digital Order Complete Dataset"]
+# )
+
+# if dataset_choice == "Sephora Order Complete Dataset":
+#     # Dropdown for selecting a product
+#     uid_list = list(product_dict)
+#     uid_name_list = get_mapped_values(uid_list, uid_name_map)
+#     index = st.selectbox(
+#         "Select A Product From The Drop Down Menu", 
+#         range(len(uid_name_list)), 
+#         format_func=lambda x: uid_name_list[x]
+#     )
+#     query_id = uid_list[index]
+#     query_url = uid_url_map[query_id]
+#     url = "https://www.sephora.com/"
+#     st.write("Created using the clickstream order-completed and catalog data from [Sephora.com](%s)" % url)
+#     st.image(query_url, width=500)
+
+# if dataset_choice == "Reliance Digital Order Complete Dataset": 
+#     list_of_products_to_display = {}
+#     for itemid in (list(all_products_with_names.keys())): #for items in the total list of items in all transactions
+#         if itemid in (list(corrected_fp_growth_results.keys())): #if the item has a result from fp-growth
+#             list_of_products_to_display[itemid]= all_products_with_names[itemid] #show it in the drop down menu
+
+#     name_to_id = {name: product_id for product_id, name in list_of_products_to_display.items()}    # Reverse the dictionary to map product names to IDs
+#     selected_product_name = st.selectbox('Select A Product:', list(name_to_id.keys()))    # Create a dropdown menu with the product names
+
+#     # Get the corresponding product_id
+#     query_id = name_to_id[selected_product_name]
+#     url = "https://www.reliancedigital.in/"
+#     st.write("Created using the clickstream order-completed data from [reliancedigital.com](%s)" % url)
+
+# # Inject custom CSS for the tab headings
+# st.markdown(
+#     """
+#     <style>
+#     /* Style the tab container */
+#     div[data-testid="stTabs"] button {
+#         background-color: #e0e0e0; /* Light grey background */
+#         color: #333333; /* Dark grey text color */
+#         font-size: 18px; /* Increase font size */
+#         font-weight: bold; /* Make text bold */
+#         padding: 10px 20px; /* Add some padding to the tabs */
+#         border-radius: 10px; /* Rounded corners */
+#         border: none; /* Remove default border */
+#         margin: 5px; /* Add margin between tabs */
+#         transition: background-color 0.3s ease; /* Add hover effect */
+#     }
+    
+#     /* Hover effect for tabs */
+#     div[data-testid="stTabs"] button:hover {
+#         background-color: #b0b0b0; /* Darker grey on hover */
+#         color: #333333; /* Keep text color the same */
+#     }
+    
+#     /* Active tab styling */
+#     div[data-testid="stTabs"] button[aria-selected="true"] {
+#         background-color: #808080; /* Dark grey for active tab */
+#         color: white; /* White text for active tab */
+#         font-size: 20px; /* Larger font size for active tab */
+#     }
+#     </style>
+#     """,
+#     unsafe_allow_html=True
+# )
+
+# #tab1, tab2, tab3 = st.tabs(["Frequently Bought Together Demo", "Co-purchase Count And Confidence", "Historical Order Data"])
+# tab2, tab3 = st.tabs(["Frequently Bought Together Demo",  "Historical Order Data"])
+
+# with tab2:
+#     if dataset_choice == "Sephora Order Complete Dataset":
+
+#         st.subheader("Recommendations With Co-purchase Count and Confidence")
+#         url2 = "https://gofynd.quip.com/fOONA5yDkSr2/Frequently-Bought-Together-Recommendations"
+
+#         with st.expander("See explanation"):
+#             st.write('''
+#                 Co-purchase count refers to the number of times a recommended product has been purchased together with the selected item. 
+#                 Confidence is the number of times the two products were bought together divided by the number of times the selected product was purchased. 
+#                 This represents the likelihood that customers who purchased the selected item also purchased the suggested item. 
+#                 A higher confidence value indicates a stronger relationship between the items. To know more, click [here](%s)" 
+#             '''% url2)
+
+#         for rec in recommendation_products_list:
+#             if rec["product_id"] == query_id:
+#                 recommendations_found = True
+#                 text_rec_list = rec["recommendations"]
+#                 if text_rec_list:
+#                     text_rec_url = []
+#                     text_rec_name = []
+#                     for val in text_rec_list:
+#                         text_rec_url.append(uid_url_map.get(val["product_id"], ""))
+#                         text_rec_name.append(uid_name_map.get(val["product_id"], ""))
+
+#         if query_id in bought_together_dict:
+#             bought_together_items = bought_together_dict[query_id]
+
+#             # Sort the items based on the count in descending order
+#             sorted_items = sorted(bought_together_items.items(), key=lambda x: x[1], reverse=True)
+            
+#             # Separate the sorted items into IDs and counts
+#             item_ids = [item[0] for item in sorted_items]
+#             item_counts = [item[1] for item in sorted_items]
+#             confidence_list = []
+#             transactions_with_querry_item = len([transaction for transaction in transactions_list if query_id in transaction])
+#             copurchase_count = []
+
+#             for reccomended_item in item_ids:
+#                 transactions_with_item_and_query_item = []
+#                 transactions_with_item_and_query_item.extend([transaction for transaction in transactions_list if (reccomended_item in transaction) and (query_id in transaction)])
+#                 number_of_transactions_with_reccomended_item_and_query_item = len(transactions_with_item_and_query_item)
+#                 copurchase_count.append(number_of_transactions_with_reccomended_item_and_query_item)
+#                 confidence_list.append(number_of_transactions_with_reccomended_item_and_query_item/transactions_with_querry_item)
+
+#             # Calculate confidence scores
+
+#             # Retrieve URLs and names based on the sorted item IDs
+#             item_urls = get_mapped_values(item_ids, uid_url_map)
+#             item_names = get_mapped_values(item_ids, uid_name_map)
+            
+#             mid_section = ""
+#             for index, value in enumerate(item_urls):
+#                 count_info = f"Co-purchased {copurchase_count[index]}/{transactions_with_querry_item} times"
+#                 #count_info = f"CP = {copurchase_count[index]}"
+
+#                 item_counts_info = f"item-count {item_counts[index]} "
+#                 confidence_info = f"Confidence: {round(confidence_list[index], 3)}"
+                
+#                 # Use <br> to display each line separately
+#                 mid_section += f"""<div class="item">
+#                                     <div id="image-container"><img src='{str(value)}' /></div>
+#                                     <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(text_rec_name[index])}</p>
+#                                     <p>{count_info}<br>{confidence_info}<br></p>
+#                                 </div>"""
+                
+#             mid_html = html + mid_section + """</div></div></body>"""
+#             st.markdown(mid_html, unsafe_allow_html=True)
+#         else:
+#             st.write("No frequent purchases found for this item.")
+
+#     if dataset_choice == "Reliance Digital Order Complete Dataset":
+#         if query_id in corrected_fp_growth_results:
+
+#             # Separate the sorted items into IDs and counts
+#             item_ids = [item for item in corrected_fp_growth_results[query_id]]
+#             item_counts = [corrected_fp_growth_results[query_id][item] for item in corrected_fp_growth_results[query_id]]
+
+#             confidence_list = []
+#             transactions_list_digital = []
+
+#             for i in dictionary_of_transactions:
+#                 transactions_list_digital.append(i["transaction"])
+
+#             transactions_with_querry_item = len([transaction for transaction in transactions_list_digital if query_id in transaction])
+#             copurchase_count = []
+
+#             # Generate a list of product names to display
+#             product_names = []
+#             for each_item in corrected_fp_growth_results[query_id]:
+#                 product_names.append(all_products_with_names[each_item])
+
+#             for reccomended_item in item_ids:
+#                 transactions_with_item_and_query_item = []
+#                 transactions_with_item_and_query_item.extend([transaction for transaction in transactions_list_digital if (reccomended_item in transaction) and (query_id in transaction)])
+#                 number_of_transactions_with_reccomended_item_and_query_item = len(transactions_with_item_and_query_item)
+#                 copurchase_count.append(number_of_transactions_with_reccomended_item_and_query_item)
+#                 confidence_list.append(number_of_transactions_with_reccomended_item_and_query_item/transactions_with_querry_item)
+        
+#             mid_section = ""
+#             for index, value in enumerate(product_names):
+#                 count_info = f"Co-purchased {copurchase_count[index]}/{transactions_with_querry_item} times"
+#                 item_counts_info = f"item-count {item_counts[index]} "
+#                 confidence_info = f"Confidence: {round(confidence_list[index], 3)}"
+                
+#                 # Use <br> to display each line separately
+#                 mid_section += f"""<div class="item">
+#                                     <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(product_names[index])}</p>
+#                                     <p>{count_info}<br>{confidence_info}<br></p>
+#                                 </div>"""
+                
+
+#             mid_html = html2 + mid_section + """</div></div></body>"""
+#             st.markdown(mid_html, unsafe_allow_html=True)
+#         else:
+#             st.write("No frequent purchases found for this item.")
+
+# with tab3: #historical transactions tab
+#     if dataset_choice == "Sephora Order Complete Dataset":
+#         st.subheader("Historical Transactions With Chosen Product (shows maximum 20)")                    
+#         # Filter transactions that contain the selected product
+#         example_transactions = [transaction for transaction in transactions_list if query_id in transaction]
+        
+#         # Limit the number of displayed transactions to a maximum of 15
+#         if len(example_transactions) > 15:
+#             example_transactions = example_transactions[:15]
+
+#         if example_transactions:
+#             # Begin constructing the carousel for transactions
+#             for i, transaction in enumerate(example_transactions):
+#                 st.markdown(f"**Transaction {i+1}:**", unsafe_allow_html=True)
+                
+#                 # Retrieve the product names and images for each product in the transaction
+#                 transaction_names = get_mapped_values(transaction, uid_name_map)
+#                 transaction_images = get_mapped_values(transaction, uid_url_map)
+                
+#                 # Build the HTML for displaying the transaction in a carousel format
+#                 transaction_section = ""
+#                 for index, image_url in enumerate(transaction_images):
+#                     transaction_section += f"""<div class="item">
+#                                                     <div id="image-container"><img src='{str(image_url)}' /></div>
+#                                                     <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(transaction_names[index])}</p>
+#                                             </div>"""
+
+#                 # Complete the carousel HTML structure
+#                 transaction_html = html + transaction_section + """</div></div></body>"""
+
+#                 # Render the carousel for each transaction
+#                 st.markdown(transaction_html, unsafe_allow_html=True)
+
+#         else:
+#             st.write("No transactions found for this item.")
+
+#     if dataset_choice == "Reliance Digital Order Complete Dataset":
+#         st.subheader("Historical Transactions With Chosen Product (shows maximum 20)")
+
+#         example_transactions = []
+#         for transaction_dict in dictionary_of_transactions: 
+#             if query_id in transaction_dict["transaction"]:
+#                 example_transactions.append(transaction_dict)
+
+#         if example_transactions:
+#             # Begin constructing the carousel for transactions
+#             for i, transaction in enumerate(example_transactions):
+#                 st.markdown(f"**Transaction {i+1}:** Placed on {transaction['order_date']} by {transaction['customer_id']} from {transaction['delivery_city']}", unsafe_allow_html=True)
+
+#                 #Retrieve the product names and images for each product in the transaction
+#                 # transaction_names = []
+#                 # for i in transaction:
+#                 #     transaction_names.append(list_of_products_to_display[str(each_item[0])])
+
+#                 transaction_names = []
+#                 for i in transaction["transaction"]:
+#                     transaction_names.append(all_products_with_names[i])
+
+#                 # Build the HTML for displaying the transaction in a carousel format
+#                 transaction_section = ""
+#                 for index, image_url in enumerate(transaction_names):
+#                     transaction_section += f"""<div class="item">
+#                                                     <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(transaction_names[index])}</p>
+#                                             </div>"""
+
+#                 # Complete the carousel HTML structure
+#                 transaction_html = html2 + transaction_section + """</div></div></body>"""
+
+#                 # Render the carousel for each transaction
+#                 st.markdown(transaction_html, unsafe_allow_html=True)
+
+#         else:
+#             st.write("No transactions found for this item.")
+
+# # Inject custom CSS for the 'Know More' button
+# st.markdown(
+#     """
+#     <style>
+#     .know-more-button {
+#         background-color: #808080; /* Dark grey background */
+#         color: white !important; /* Force white text color */
+#         font-size: 18px; /* Font size */
+#         font-weight: bold; /* Bold text */
+#         padding: 10px 20px; /* Padding */
+#         border-radius: 10px; /* Rounded corners */
+#         border: none; /* Remove default border */
+#         cursor: pointer; /* Mouse pointer */
+#         text-align: center;
+#         text-decoration: none; /* Remove underline */
+#         display: inline-block; /* Make it inline */
+#         transition: background-color 0.3s ease;
+#     }
+#     .know-more-button:hover {
+#         background-color: #b0b0b0; /* Lighter grey background on hover */
+#         color: #333333 !important; /* Dark grey text on hover */
+#         text-decoration: none; /* Keep underline removed on hover */
+#     }
+#     </style>
+#     """,
+#     unsafe_allow_html=True
+# )
+
+# # Add the 'Know More' button with the link and no underline
+# st.markdown(
+#     '<a href="https://gofynd.quip.com/fOONA5yDkSr2/Frequently-Bought-Together-Recommendations" class="know-more-button">Know More</a>',
+#     unsafe_allow_html=True
+# )
+
+
+
+
+import pickle
+import streamlit as st
+from html_information import html
+from html_information2 import html2
+
+import streamlit as st
+
+st.set_page_config(page_title="My App", page_icon=":money_with_wings:", layout="wide", initial_sidebar_state="auto")
+st.header("Frequently Bought Together Recommendations")
+
+def read_pickle_files(pickle_file):
+    with open(pickle_file, 'rb') as f:
+        return pickle.load(f)
+
+def get_mapped_values(uid_list, dict_map):
+    res = []
+    for val in uid_list:
+        try:
+            res.append(dict_map[val])
+        except:
+            continue
+    return res
+
+# Load sephora pickle files
+# filtered_products_list = read_pickle_files("filtered_products_list.pkl")
+# recommendation_products_list = read_pickle_files("recommendation_products_list.pkl")
+# transactions_list = read_pickle_files("transactions.pkl")
+# bought_together_dict = read_pickle_files("bought_together_dict_orders_data.pkl")
+# uid_name_map = read_pickle_files("uid_name_map.pkl")
+# uid_url_map = read_pickle_files("uid_url_map.pkl")
+
+# Load sephora pickle files
+corrected_fp_growth_results_sephora = read_pickle_files("sephora_corrected_fp_growth_results_cleaned.pkl")
+all_products_with_names_sephora = read_pickle_files ("item_catalog.pkl")
+dictionary_of_transactions_sephora = read_pickle_files("transaction_metadata.pkl")
+
+# Load digital pickle files
+corrected_fp_growth_results = read_pickle_files("corrected_fp_growth_results.pkl")
+all_products_with_names = read_pickle_files ("all_products_with_names.pkl")
+dictionary_of_transactions = read_pickle_files("reliance_digital_transactions.pkl")
+
+# Create product dictionary
+#product_dict = {item['product_id']: item for item in filtered_products_list}
+
+# Dropdown for selecting the dataset
+dataset_choice = st.selectbox(
+    "Select A Dataset", 
+    ["Sephora Order Complete Dataset", "Reliance Digital Order Complete Dataset"]
+)
+
+if dataset_choice == "Sephora Order Complete Dataset":
+    list_of_products_to_display = {}
+    for itemid in (list(all_products_with_names_sephora.keys())): #for items in the total list of items in all transactions
+        if itemid in (list(corrected_fp_growth_results_sephora.keys())): #if the item has a result from fp-growth
+            list_of_products_to_display[itemid]= all_products_with_names_sephora[itemid] #show it in the drop down menu
+
+    name_to_id = {name: product_id for product_id, name in list_of_products_to_display.items()}    # Reverse the dictionary to map product names to IDs
+    selected_product_name = st.selectbox('Select A Product:', list(name_to_id.keys()))    # Create a dropdown menu with the product names
+
+    # Get the corresponding product_id
+    query_id = name_to_id[selected_product_name]
+    url = "https://www.sephora.com/"
+    st.write("Created using the clickstream order-completed and catalog data from [Sephora.com](%s)" % url)
+
+if dataset_choice == "Reliance Digital Order Complete Dataset": 
+    list_of_products_to_display = {}
+    for itemid in (list(all_products_with_names.keys())): #for items in the total list of items in all transactions
+        if itemid in (list(corrected_fp_growth_results.keys())): #if the item has a result from fp-growth
+            list_of_products_to_display[itemid]= all_products_with_names[itemid] #show it in the drop down menu
+
+    name_to_id = {name: product_id for product_id, name in list_of_products_to_display.items()}    # Reverse the dictionary to map product names to IDs
+    selected_product_name = st.selectbox('Select A Product:', list(name_to_id.keys()))    # Create a dropdown menu with the product names
+
+    # Get the corresponding product_id
+    query_id = name_to_id[selected_product_name]
+    url = "https://www.reliancedigital.in/"
+    st.write("Created using the clickstream order-completed data from [reliancedigital.com](%s)" % url)
+
+# Inject custom CSS for the tab headings
+st.markdown(
+    """
+    <style>
+    /* Style the tab container */
+    div[data-testid="stTabs"] button {
+        background-color: #e0e0e0; /* Light grey background */
+        color: #333333; /* Dark grey text color */
+        font-size: 18px; /* Increase font size */
+        font-weight: bold; /* Make text bold */
+        padding: 10px 20px; /* Add some padding to the tabs */
+        border-radius: 10px; /* Rounded corners */
+        border: none; /* Remove default border */
+        margin: 5px; /* Add margin between tabs */
+        transition: background-color 0.3s ease; /* Add hover effect */
+    }
+    
+    /* Hover effect for tabs */
+    div[data-testid="stTabs"] button:hover {
+        background-color: #b0b0b0; /* Darker grey on hover */
+        color: #333333; /* Keep text color the same */
+    }
+    
+    /* Active tab styling */
+    div[data-testid="stTabs"] button[aria-selected="true"] {
+        background-color: #808080; /* Dark grey for active tab */
+        color: white; /* White text for active tab */
+        font-size: 20px; /* Larger font size for active tab */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+tab2, tab3 = st.tabs(["Frequently Bought Together Demo",  "Historical Order Data"])
+
+with tab2:
+    if dataset_choice == "Sephora Order Complete Dataset":
+        if query_id in corrected_fp_growth_results_sephora:
+
+            # Separate the sorted items into IDs and counts
+            item_ids = [item for item in corrected_fp_growth_results_sephora[query_id]]
+            item_counts = [corrected_fp_growth_results_sephora[query_id][item] for item in corrected_fp_growth_results_sephora[query_id]]
+
+            confidence_list = []
+            transactions_list_sephora = []
+
+            for i in dictionary_of_transactions_sephora:
+                transactions_list_sephora.append(i["transaction"])
+
+            transactions_with_querry_item = len([transaction for transaction in transactions_list_sephora if int(query_id) in transaction])
+            copurchase_count = []
+
+            # Generate a list of product names to display
+            product_names = []
+            for each_item in corrected_fp_growth_results_sephora[query_id]:
+                product_names.append(all_products_with_names_sephora[each_item])
+
+            for reccomended_item in item_ids:
+                transactions_with_item_and_query_item = []
+                transactions_with_item_and_query_item.extend([transaction for transaction in transactions_list_sephora if (int(reccomended_item) in transaction) and (int(query_id) in transaction)])
+                number_of_transactions_with_reccomended_item_and_query_item = len(transactions_with_item_and_query_item)
+                copurchase_count.append(number_of_transactions_with_reccomended_item_and_query_item)
+                confidence_list.append(number_of_transactions_with_reccomended_item_and_query_item/transactions_with_querry_item)
+        
+            mid_section = ""
+            for index, value in enumerate(product_names):
+                count_info = f"Co-purchased {copurchase_count[index]}/{transactions_with_querry_item} times"
+                item_counts_info = f"item-count {item_counts[index]} "
+                confidence_info = f"Confidence: {round(confidence_list[index], 3)}"
+                
+                # Use <br> to display each line separately
+                mid_section += f"""<div class="item">
+                                    <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(product_names[index])}</p>
+                                    <p>{count_info}<br>{confidence_info}<br></p>
+                                </div>"""
+                
+
+            mid_html = html2 + mid_section + """</div></div></body>"""
+            st.markdown(mid_html, unsafe_allow_html=True)
+        else:
+            st.write("No frequent purchases found for this item.")
+
+    if dataset_choice == "Reliance Digital Order Complete Dataset":
+        if query_id in corrected_fp_growth_results:
+
+            # Separate the sorted items into IDs and counts
+            item_ids = [item for item in corrected_fp_growth_results[query_id]]
+            item_counts = [corrected_fp_growth_results[query_id][item] for item in corrected_fp_growth_results[query_id]]
+
+            confidence_list = []
+            transactions_list_digital = []
+
+            for i in dictionary_of_transactions:
+                transactions_list_digital.append(i["transaction"])
+
+            transactions_with_querry_item = len([transaction for transaction in transactions_list_digital if query_id in transaction])
+            copurchase_count = []
+
+            # Generate a list of product names to display
+            product_names = []
+            for each_item in corrected_fp_growth_results[query_id]:
+                product_names.append(all_products_with_names[each_item])
+
+            for reccomended_item in item_ids:
+                transactions_with_item_and_query_item = []
+                transactions_with_item_and_query_item.extend([transaction for transaction in transactions_list_digital if (reccomended_item in transaction) and (query_id in transaction)])
+                number_of_transactions_with_reccomended_item_and_query_item = len(transactions_with_item_and_query_item)
+                copurchase_count.append(number_of_transactions_with_reccomended_item_and_query_item)
+                confidence_list.append(number_of_transactions_with_reccomended_item_and_query_item/transactions_with_querry_item)
+        
+            mid_section = ""
+            for index, value in enumerate(product_names):
+                count_info = f"Co-purchased {copurchase_count[index]}/{transactions_with_querry_item} times"
+                item_counts_info = f"item-count {item_counts[index]} "
+                confidence_info = f"Confidence: {round(confidence_list[index], 3)}"
+                
+                # Use <br> to display each line separately
+                mid_section += f"""<div class="item">
+                                    <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(product_names[index])}</p>
+                                    <p>{count_info}<br>{confidence_info}<br></p>
+                                </div>"""
+                
+
+            mid_html = html2 + mid_section + """</div></div></body>"""
+            st.markdown(mid_html, unsafe_allow_html=True)
+        else:
+            st.write("No frequent purchases found for this item.")
+
+with tab3: #historical transactions tab
+    if dataset_choice == "Sephora Order Complete Dataset":
+        st.subheader("Historical Transactions With Chosen Product (shows maximum 20)")
+
+        example_transactions = []
+        for transaction_dict in dictionary_of_transactions_sephora: 
+            if int(query_id) in transaction_dict["transaction"]:
+                example_transactions.append(transaction_dict)
+        
+        if example_transactions:
+            # Begin constructing the carousel for transactions
+            for i, transaction in enumerate(example_transactions):
+                st.markdown(f"**Transaction {i+1}:** Placed on {transaction['event_timestamp']} by {transaction['user_id']}", unsafe_allow_html=True)
+
+                #Retrieve the product names and images for each product in the transaction
+                # transaction_names = []
+                # for i in transaction:
+                #     transaction_names.append(list_of_products_to_display[str(each_item[0])])
+
+                transaction_names = []
+                for i in transaction["transaction"]:
+                    transaction_names.append(all_products_with_names_sephora[str(i)])
+
+                # Build the HTML for displaying the transaction in a carousel format
+                transaction_section = ""
+                for index, image_url in enumerate(transaction_names):
+                    transaction_section += f"""<div class="item">
+                                                    <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(transaction_names[index])}</p>
+                                            </div>"""
+
+                # Complete the carousel HTML structure
+                transaction_html = html2 + transaction_section + """</div></div></body>"""
+
+                # Render the carousel for each transaction
+                st.markdown(transaction_html, unsafe_allow_html=True)
+
+        else:
+            st.write("No transactions found for this item.")
+
+
+    if dataset_choice == "Reliance Digital Order Complete Dataset":
+        st.subheader("Historical Transactions With Chosen Product (shows maximum 20)")
+
+        example_transactions = []
+        for transaction_dict in dictionary_of_transactions: 
+            if query_id in transaction_dict["transaction"]:
+                example_transactions.append(transaction_dict)
+
+        if example_transactions:
+            # Begin constructing the carousel for transactions
+            for i, transaction in enumerate(example_transactions):
+                st.markdown(f"**Transaction {i+1}:** Placed on {transaction['order_date']} by {transaction['customer_id']} from {transaction['delivery_city']}", unsafe_allow_html=True)
+
+                #Retrieve the product names and images for each product in the transaction
+                # transaction_names = []
+                # for i in transaction:
+                #     transaction_names.append(list_of_products_to_display[str(each_item[0])])
+
+                transaction_names = []
+                for i in transaction["transaction"]:
+                    transaction_names.append(all_products_with_names[i])
+
+                # Build the HTML for displaying the transaction in a carousel format
+                transaction_section = ""
+                for index, image_url in enumerate(transaction_names):
+                    transaction_section += f"""<div class="item">
+                                                    <p style="font-size: 16px; font-weight: bold; white-space: normal; word-wrap: break-word;">{str(transaction_names[index])}</p>
+                                            </div>"""
+
+                # Complete the carousel HTML structure
+                transaction_html = html2 + transaction_section + """</div></div></body>"""
+
+                # Render the carousel for each transaction
+                st.markdown(transaction_html, unsafe_allow_html=True)
+
+        else:
+            st.write("No transactions found for this item.")
+
+
+
+# Inject custom CSS for the 'Know More' button
+st.markdown(
+    """
+    <style>
+    .know-more-button {
+        background-color: #808080; /* Dark grey background */
+        color: white !important; /* Force white text color */
+        font-size: 18px; /* Font size */
+        font-weight: bold; /* Bold text */
+        padding: 10px 20px; /* Padding */
+        border-radius: 10px; /* Rounded corners */
+        border: none; /* Remove default border */
+        cursor: pointer; /* Mouse pointer */
+        text-align: center;
+        text-decoration: none; /* Remove underline */
+        display: inline-block; /* Make it inline */
+        transition: background-color 0.3s ease;
+    }
+    .know-more-button:hover {
+        background-color: #b0b0b0; /* Lighter grey background on hover */
+        color: #333333 !important; /* Dark grey text on hover */
+        text-decoration: none; /* Keep underline removed on hover */
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Add the 'Know More' button with the link and no underline
+st.markdown(
+    '<a href="https://gofynd.quip.com/fOONA5yDkSr2/Frequently-Bought-Together-Recommendations" class="know-more-button">Know More</a>',
+    unsafe_allow_html=True
+)

corrected_fp_growth_results.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d25f867e2a512c9e71f706e2f5f559476955153705a3f802f265297a64c284d4
+size 16505

html_information.py

@@ -0,0 +1,72 @@
+html = """
+<style>
+ body{
+  font-family: sans-serif;
+}
+h1{
+  font-weight: 100;
+}
+.container{
+  width: 100%;
+  display:block;
+  overflow:hidden;
+}
+.carousel{
+  display:block;
+  width: 100%;
+  height: 500px;
+  background: white;
+  padding: 10px;
+  margin: 0;
+  white-space: nowrap; 
+  border-top: 2px solid rgba(0, 0, 0, 0);
+  border-bottom: 2px solid rgba(0, 0, 0, 0);
+}
+.item {
+    display: inline-block;
+    overflow: hidden;
+    width: 250px;
+    margin: 0 10px;
+    height: calc(100%);
+    background: rgba(0, 0, 0, 0.05) no-repeat center center;
+    background-size: cover;
+    position: relative;
+    border-radius: 20px;
+    box-shadow: 0 0 10px #dfdfdf;
+}
+.item p {
+    padding: 10px; /* Reduced padding for less gap */
+    word-wrap: break-word; /* Ensures text breaks at word boundaries */
+    white-space: normal; /* Allows the text to wrap naturally */
+    overflow: hidden;
+    display: -webkit-box;
+    -webkit-line-clamp: 4;
+    -webkit-box-orient: vertical;
+    margin: 0;
+    text-align: center;
+}
+#image-container{
+  width: 100%;
+  height: 60%;
+  text-align:center;
+  font-size: 9em;
+  color: white;
+  overflow: hidden;
+  border-radius: 20px; /* Rounding the image corners */
+}
+#image-container img{
+    width: 90%;
+    height: 90%;
+    object-fit: contain;
+    border-radius: 20px; /* Ensures images are also rounded */
+    margin-bottom: 10px; /* Reduce margin for less space between image and text */
+}
+
+</style>
+</head>
+<body>
+<div class="container">
+  <div class="carousel">
+
+
+"""

html_information2.py

@@ -0,0 +1,72 @@
+html2 = """
+<style>
+ body{
+  font-family: sans-serif;
+}
+h1{
+  font-weight: 100;
+}
+.container{
+  width: 100%;
+  display:block;
+  overflow:hidden;
+}
+.carousel{
+  display:block;
+  width: 100%;
+  height: 275px;
+  background: white;
+  padding: 10px;
+  margin: 0;
+  white-space: nowrap; 
+  border-top: 2px solid rgba(0, 0, 0, 0);
+  border-bottom: 2px solid rgba(0, 0, 0, 0);
+}
+.item {
+    display: inline-block;
+    overflow: hidden;
+    width: 250px;
+    margin: 0 10px;
+    height: calc(100%);
+    background: rgba(0, 0, 0, 0.05) no-repeat center center;
+    background-size: cover;
+    position: relative;
+    border-radius: 20px;
+    box-shadow: 0 0 10px #dfdfdf;
+}
+.item p {
+    padding: 10px; /* Reduced padding for less gap */
+    word-wrap: break-word; /* Ensures text breaks at word boundaries */
+    white-space: normal; /* Allows the text to wrap naturally */
+    overflow: hidden;
+    display: -webkit-box;
+    -webkit-line-clamp: 6; 
+    -webkit-box-orient: vertical;
+    margin: 0;
+    text-align: center;
+}
+#image-container{
+  width: 100%;
+  height: 60%;
+  text-align:center;
+  font-size: 9em;
+  color: white;
+  overflow: hidden;
+  border-radius: 20px; /* Rounding the image corners */
+}
+#image-container img{
+    width: 90%;
+    height: 90%;
+    object-fit: contain;
+    border-radius: 20px; /* Ensures images are also rounded */
+    margin-bottom: 10px; /* Reduce margin for less space between image and text */
+}
+
+</style>
+</head>
+<body>
+<div class="container">
+  <div class="carousel">
+
+
+"""

item_catalog.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9239264b7b79762444bc610e837387ced0c58badeed884d79097d7218389a548
+size 299750

reliance_digital_transactions.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9527e5732b2f0364917e54a898e888253dcac64981c384e75ba3f7bb5f6aebbc
+size 1182171

sephora_corrected_fp_growth_results_cleaned.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:23263bc088274792f67a2a7e10e58da7fd5c232a09ec9ad48b2179cc0808ea20
+size 74882

transaction_metadata.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86bb8992712dedeb054fcf12b7d311d37dba3c182eaa7a85781c5d99ee83e157
+size 1912314