Gopala Krishna
commited on
Commit
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ff26c0e
1
Parent(s):
8cb45d3
Working Clean
Browse files
.vs/UBCFProductRecommendations/FileContentIndex/52856ff2-fe43-4178-af57-cb76a26a0459.vsidx
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Binary file (11.7 kB). View file
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.vs/UBCFProductRecommendations/FileContentIndex/7ff40909-b5d8-4c88-8906-d6cc681c52b1.vsidx
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Binary file (11.7 kB)
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.vs/UBCFProductRecommendations/v17/.wsuo
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Binary files a/.vs/UBCFProductRecommendations/v17/.wsuo and b/.vs/UBCFProductRecommendations/v17/.wsuo differ
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app.py
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@@ -2,7 +2,7 @@ import pandas as pd
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from sklearn.metrics.pairwise import cosine_similarity
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import gradio as gr
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def recommend_items(
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# Read data source Excel file.
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df1 = pd.read_excel("UBCF_Online_Retail.xlsx")
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df1a = df1.dropna(subset=['CustomerID'])
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@@ -49,13 +49,10 @@ def recommend_items(file, customer_id_1, customer_id_2):
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iface = gr.Interface(
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fn=recommend_items,
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inputs=[
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gr.inputs.
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gr.inputs.Number(label="Customer ID
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gr.inputs.Number(label="Customer ID 2"),
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],
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outputs="dataframe",
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title="Item Recommendation System",
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description="This system recommends items for a customer based on another customer's purchase history.",
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allow_flagging=False
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)
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from sklearn.metrics.pairwise import cosine_similarity
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import gradio as gr
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def recommend_items(customer_id_1, customer_id_2):
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# Read data source Excel file.
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df1 = pd.read_excel("UBCF_Online_Retail.xlsx")
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df1a = df1.dropna(subset=['CustomerID'])
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iface = gr.Interface(
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fn=recommend_items,
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inputs=[
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gr.inputs.Number(label="Customer ID 1",default=12702),
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gr.inputs.Number(label="Customer ID 2",default=14608),
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],
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outputs="dataframe",
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allow_flagging=False
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)
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