Upload app.py

app.py

@@ -1,72 +1,15 @@
 import pandas as pd
 from sklearn.metrics.pairwise import cosine_similarity
 import gradio as gr
-
-def recommend_items(customer_id_1, customer_id_2):
-    # Read data source Excel file.
-    try:
-        df1 = pd.read_excel("UBCF_Online_Retail.xlsx")
-    except FileNotFoundError:
-        return "Error: Excel file not found."
-    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.
-    try:
-        items_purchased_by_X = set(CustomerID_Item_matrix.loc[customer_id_1].iloc[
-            CustomerID_Item_matrix.loc[customer_id_1].to_numpy().nonzero()].index)
-    except KeyError:
-        return pd.DataFrame({"Error": ["Customer ID 1 is invalid. Please enter a valid Customer ID"]})
-
-
-
-    # Display CustomerID (customer_id_2) purchased items.
-    try:
-        items_purchased_by_Y = set(CustomerID_Item_matrix.loc[customer_id_2].iloc[
-            CustomerID_Item_matrix.loc[customer_id_2].to_numpy().nonzero()].index)
-    except KeyError:
-        return pd.DataFrame({"Error": ["Customer ID 2 is invalid. Please enter a valid Customer ID"]})
-
-
-    # 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.Number(label="Customer ID 1"),
-        gr.inputs.Number(label="Customer ID 2"),
-    ],
-    outputs=gr.outputs.Dataframe(label="Recommended Items for Customer 2",type="pandas"),
-    theme=gr.themes.Default(primary_hue="slate"),
-    allow_flagging=False
-)
-iface.launch()
-
+def recommend_items(customer_id_1,customer_id_2):
+        H='Error';G=customer_id_2;F=customer_id_1;D='StockCode';C='CustomerID'
+        try:I=pd.read_excel('UBCF_Online_Retail.xlsx')
+        except FileNotFoundError:return'Error: Excel file not found.'
+        E=I.dropna(subset=[C]);A=E.pivot_table(index=C,columns=D,values='Quantity',aggfunc='sum');A=A.applymap(lambda x:1 if x>0 else 0);B=pd.DataFrame(cosine_similarity(A));B.columns=A.index;B[C]=A.index;B=B.set_index(C)
+        try:J=set(A.loc[F].iloc[A.loc[F].to_numpy().nonzero()].index)
+        except KeyError:return pd.DataFrame({H:['Customer ID 1 is invalid. Please enter a valid Customer ID']})
+        try:K=set(A.loc[G].iloc[A.loc[G].to_numpy().nonzero()].index)
+        except KeyError:return pd.DataFrame({H:['Customer ID 2 is invalid. Please enter a valid Customer ID']})
+        L=J-K;return E.loc[E[D].isin(L),[D,'Description']].drop_duplicates().set_index(D)
+iface=gr.Interface(fn=recommend_items,inputs=[gr.inputs.Number(label='Customer ID 1'),gr.inputs.Number(label='Customer ID 2')],outputs=gr.outputs.Dataframe(label='Recommended Items for Customer 2',type='pandas'),theme=gr.themes.Default(primary_hue='slate'),allow_flagging=False)
+iface.launch()