working with File, Customer1, Customer2 inputs

.vs/VSWorkspaceState.json

@@ -2,6 +2,6 @@
   "ExpandedNodes": [
     ""
   ],
-  "SelectedNode": "\\C:\\Python\\Programs\\Gradio\\HuggingSpace\\UBCFProductRecommendations",
+  "SelectedNode": "\\app.py",
   "PreviewInSolutionExplorer": false
 }

app.py

@@ -1,48 +1,62 @@
-
 import pandas as pd
 from sklearn.metrics.pairwise import cosine_similarity
-
-# Read data source Excel files.
-df1 = pd.read_excel('Online_Retail.xlsx')
-df1a = df1.dropna(subset=['CustomerID'])
-
-# Create CustomerID vs Item (Purchased Items, by StockCode) matrix by pivot table function.
-CustomerID_Item_matrix = df1a.pivot_table(
-    index='CustomerID', 
-    columns='StockCode', 
-    values='Quantity',
-    aggfunc='sum'
+import gradio as gr
+
+def recommend_items(file, customer_id_1, customer_id_2):
+    # Read data source Excel file.
+    df1 = pd.read_excel("UBCF_Online_Retail.xlsx")
+    df1a = df1.dropna(subset=['CustomerID'])
+
+    # Create CustomerID vs Item (Purchased Items, " StockCode) matrix by pivot table function.
+    CustomerID_Item_matrix = df1a.pivot_table(
+        index='CustomerID',
+        columns='StockCode',
+        values='Quantity',
+        aggfunc='sum'
+    )
+
+    # Update illustration of the matrix, 1 to represent customer have purchased item, 0 to represent customer haven't purchased.
+    CustomerID_Item_matrix = CustomerID_Item_matrix.applymap(lambda x: 1 if x > 0 else 0)
+
+    # Create User to User similarity matrix.
+    user_to_user_similarity_matrix = pd.DataFrame(
+        cosine_similarity(CustomerID_Item_matrix)
+    )
+
+    # Update index to corresponding CustomerID.
+    user_to_user_similarity_matrix.columns = CustomerID_Item_matrix.index
+    user_to_user_similarity_matrix['CustomerID'] = CustomerID_Item_matrix.index
+    user_to_user_similarity_matrix = user_to_user_similarity_matrix.set_index('CustomerID')
+
+    # Display CustomerID (customer_id_1) purchased items.
+    items_purchased_by_X = set(CustomerID_Item_matrix.loc[customer_id_1].iloc[
+        CustomerID_Item_matrix.loc[customer_id_1].to_numpy().nonzero()].index)
+
+    # Display CustomerID (customer_id_2) purchased items.
+    items_purchased_by_Y = set(CustomerID_Item_matrix.loc[customer_id_2].iloc[
+        CustomerID_Item_matrix.loc[customer_id_2].to_numpy().nonzero()].index)
+
+    # Find out items which purchased by X (customer_id_1) but not yet purchased by Y (customer_id_2).
+    items_to_recommend_to_Y = items_purchased_by_X - items_purchased_by_Y
+
+    # Return the list of items recommended for Y (customer_id_2) with item Description.
+    return df1a.loc[
+        df1a['StockCode'].isin(items_to_recommend_to_Y),
+        ['StockCode', 'Description']
+    ].drop_duplicates().set_index('StockCode')
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=recommend_items,
+    inputs=[
+        gr.inputs.File(label="Excel file (.xlsx)"),
+        gr.inputs.Number(label="Customer ID 1"),
+        gr.inputs.Number(label="Customer ID 2"),
+    ],
+    outputs="dataframe",
+    title="Item Recommendation System",
+    description="This system recommends items for a customer based on another customer's purchase history.",
+    allow_flagging=False
 )
 
-# Update illustration of the matrix, 1 to represent customer have purchased item, 0 to represent customer haven't purchased.
-CustomerID_Item_matrix = CustomerID_Item_matrix.applymap(lambda x: 1 if x > 0 else 0)
-
-# Create User to User similarity matrix. 
-user_to_user_similarity_matrix = pd.DataFrame(
-    cosine_similarity(CustomerID_Item_matrix)
-)
-
-# Update index to corresponding CustomerID.
-user_to_user_similarity_matrix.columns = CustomerID_Item_matrix.index
-user_to_user_similarity_matrix['CustomerID'] = CustomerID_Item_matrix.index
-user_to_user_similarity_matrix = user_to_user_similarity_matrix.set_index('CustomerID')
-
-# Display CustomerID (12702) purchased items.
-items_purchased_by_X = set(CustomerID_Item_matrix.loc[12702.0].iloc[
-    CustomerID_Item_matrix.loc[12702.0].to_numpy().nonzero()].index)
-
-# Display CustomerID (14608) purchased items.
-items_purchased_by_Y = set(CustomerID_Item_matrix.loc[14608.0].iloc[
-    CustomerID_Item_matrix.loc[14608.0].to_numpy().nonzero()].index)
-
-# Find out items which purchased by X (12702) but not yet purchased by Y (14608).
-items_to_recommend_to_Y = items_purchased_by_X - items_purchased_by_Y
-
-# Display the list of items recommended for Y (14608) with item Description.
-print(df1a.loc[
-    df1a['StockCode'].isin(items_to_recommend_to_Y), 
-    ['StockCode', 'Description']
-].drop_duplicates().set_index('StockCode'))
-
-
-
+iface.launch()