app.py

# App v3.0
import pandas as pd
import pickle
import gradio as gr

with open(r'vect_index.pickle', 'rb') as file:
    vect_index = pickle.load(file)
with open(r'vect_values.pickle', 'rb') as file:
    vect_values = pickle.load(file)
    
df = pd.read_excel('perfume_database_cleaned.xlsx')
brand_options = list(df['brand'].unique())
perfume_options = list(df['perfume'].unique())

def perfume_change(brand):
    names = list(df.query(f"brand=='{brand}'")['perfume'])
    return gr.update(choices=names, value=None)

def filter_by_perfume(selected_brand, selected_perfume):
    perfume_index = df.query(f"brand=='{selected_brand}' & perfume=='{selected_perfume}'").index[0]
    df_filter = df.iloc[vect_index[perfume_index], :].reset_index(drop=True)
    df_filter['similarity'] = vect_values[perfume_index]
    df_filter['similarity'] = df_filter['similarity'].map("{:.2%}".format)
    df_filter['notes'] = df_filter['notes'].str.split(',').apply(sorted, 1).str.join(',').str.strip(',')
    df_filter = df_filter[['brand', 'perfume', 'similarity', 'notes']]
    return df_filter

with gr.Blocks(theme='freddyaboulton/dracula_revamped') as demo:
    gr.Markdown(
    """
    # Perfume Recommendation
    Select brand and perfume below to find top 5 most similar perfumes based on notes
    """)
    
    with gr.Row():
        brand_dropdown = gr.Dropdown(choices=brand_options, value='Hugo Boss', label="Brand")
        perfume_dropdown = gr.Dropdown(choices=perfume_options, value='Boss Soul', label="Perfume Name")
        brand_dropdown.change(fn=perfume_change, inputs=[brand_dropdown], outputs=[perfume_dropdown])
        btn = gr.Button(value="Search", scale=1)
        
    with gr.Row():
        output_df = gr.Dataframe()
        btn.click(filter_by_perfume, inputs=[brand_dropdown, perfume_dropdown], outputs=[output_df])
        
demo.launch()