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import gradio as gr
import pandas as pd
import joblib
data = pd.read_csv(r"data_final.csv")
model = joblib.load("KNN_Model.joblib")
def product_recommender(customer_id):
list_predicted = []
for id in data['product_id'].unique():
preds = list(model.predict(customer_id, id))
product_id = preds[1]
product_score = preds[3]
list_predicted.append((product_id, product_score))
top_5_products_raw = sorted(list_predicted, key=lambda x:x[1], reverse=True)[:5]
top_5_products = [product[0] for product in top_5_products_raw]
product_1_category = data[data['product_id']==top_5_products[0]]['category'].values[0]
product_2_category = data[data['product_id']==top_5_products[1]]['category'].values[0]
product_3_category = data[data['product_id']==top_5_products[2]]['category'].values[0]
product_4_category = data[data['product_id']==top_5_products[3]]['category'].values[0]
product_5_category = data[data['product_id']==top_5_products[4]]['category'].values[0]
result_1 = f"Recommendation Product ID {top_5_products[0]} with Category {product_1_category}"
result_2 = f"Recommendation Product ID {top_5_products[1]} with Category {product_2_category}"
result_3 = f"Recommendation Product ID {top_5_products[2]} with Category {product_3_category}"
result_4 = f"Recommendation Product ID {top_5_products[3]} with Category {product_4_category}"
result_5 = f"Recommendation Product ID {top_5_products[4]} with Category {product_5_category}"
return result_1, result_2, result_3, result_4, result_5
demo = gr.Interface(
title="Product Recommendation System",
description="""This User Interface is Powered by Machine Learning to
Predict the Top 5 of Product that customer likely to buy in the next purchase.
All you need is to Input Customer ID and then the Recommendation will be appear.""",
fn=product_recommender,
inputs=[
gr.Number(label="Input Customer ID")
],
outputs=[
gr.Textbox(label="Recommendation Product 1"),
gr.Textbox(label="Recommendation Product 2"),
gr.Textbox(label="Recommendation Product 3"),
gr.Textbox(label="Recommendation Product 4"),
gr.Textbox(label="Recommendation Product 5")
]
)
if __name__ == "__main__":
demo.launch()
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