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import streamlit as st |
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import time |
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import pandas as pd |
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import numpy as np |
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from surprise import Dataset, Reader |
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from surprise import KNNBaseline |
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df_wine_model = pd.read_pickle('./data/df_wine_us_rate.pkl') |
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df_wine_combi = pd.read_pickle('./data/df_wine_combi.pkl') |
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all_traits = ['almond', 'anise', 'apple', 'apricot', 'baked', 'baking_spices', 'berry', 'black_cherry', 'black_currant', 'black_pepper', 'black_tea', 'blackberry', 'blueberry', |
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'boysenberry', 'bramble', 'bright', 'butter', 'candy', 'caramel', 'cardamom', 'cassis', 'cedar', 'chalk', 'cherry', 'chocolate', 'cinnamon', 'citrus', 'clean', 'closed', |
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'clove', 'cocoa', 'coffee', 'cola', 'complex', 'concentrated', 'cranberry', 'cream', 'crisp', 'dark', 'dark_chocolate', 'dense', 'depth', 'dried_herb', 'dry', 'dust', |
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'earth', 'edgy', 'elderberry', 'elegant', 'fennel', 'firm', 'flower', 'forest_floor', 'french_oak', 'fresh', 'fruit', 'full_bodied', 'game', 'grapefruit', 'graphite', |
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'green', 'gripping', 'grippy', 'hearty', 'herb', 'honey', 'honeysuckle', 'jam', 'juicy', 'lavender', 'leafy', 'lean', 'leather', 'lemon', 'lemon_peel', 'length', 'licorice', |
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'light_bodied', 'lime', 'lush', 'meaty', 'medium_bodied', 'melon', 'milk_chocolate', 'minerality', 'mint', 'nutmeg', 'oak', 'olive', 'orange', 'orange_peel', 'peach', |
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'pear', 'pencil_lead', 'pepper', 'pine', 'pineapple', 'plum', 'plush', 'polished', 'pomegranate', 'powerful', 'purple', 'purple_flower', 'raspberry', 'refreshing', |
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'restrained', 'rich', 'ripe', 'robust', 'rose', 'round', 'sage', 'salt', 'savory', 'sharp', 'silky', 'smoke', 'smoked_meat', 'smooth', 'soft', 'sparkling', 'spice', |
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'steel', 'stone', 'strawberry', 'succulent', 'supple', 'sweet', 'tangy', 'tannin', 'tar', 'tart', 'tea', 'thick', 'thyme', 'tight', 'toast', 'tobacco', 'tropical_fruit', |
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'vanilla', 'velvety', 'vibrant', 'violet', 'warm', 'weight', 'wet_rocks', 'white', 'white_pepper', 'wood'] |
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def recommend_scores(): |
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reader = Reader(rating_scale=(88, 100)) |
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data = Dataset.load_from_df(df_wine_model, reader) |
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sim_options={'name':'cosine'} |
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model = KNNBaseline(k=35, min_k=1, sim_options=sim_options, verbose=False) |
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train=data.build_full_trainset() |
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model.fit(train) |
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recommend_list = [] |
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user_wines = df_wine_model[df_wine_model.taster_name == 'mockuser']['title'].unique() |
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not_user_wines = [] |
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for wine in df_wine_model['title'].unique(): |
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if wine not in user_wines: |
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not_user_wines.append(wine) |
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for wine in not_user_wines: |
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wine_compatibility = [] |
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prediction = model.predict(uid='mockuser', iid=wine) |
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wine_compatibility.append(prediction.iid) |
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wine_compatibility.append(prediction.est) |
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recommend_list.append(wine_compatibility) |
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result_df = pd.DataFrame(recommend_list, columns = ['title', 'est_match_pts']) |
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return result_df |
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def add_bg_from_url(): |
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st.markdown( |
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f""" |
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<style> |
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[data-testid="stAppViewContainer"] {{ |
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background-image: url("https://www.ackerwines.com/wp-content/uploads/2021/09/Montrose4.jpg"); |
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background-attachment: fixed; |
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background-size: cover |
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}} |
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[data-testid="stVerticalBlock"] {{ |
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background-color: rgba(255,255,255,0.75) |
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}} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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st.title("Which wine should I get?") |
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st.text("") |
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st.write("You can type the wine traits that you want in the dropdown list below") |
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add_bg_from_url() |
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select_temptrait = st.multiselect('Choose the traits that you want in your wine', options = all_traits) |
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if st.button('Show me the wines!'): |
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with st.spinner('Should you have some wine now?'): |
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time.sleep(2) |
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if len(select_temptrait) == 0: |
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selected_traits = all_traits |
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else: |
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selected_traits = select_temptrait |
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recommend_df = recommend_scores() |
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trait_filter = ['title'] |
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trait_filter.extend(selected_traits) |
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df_temp_traits = df_wine_combi.drop(columns=['taster_name', 'points', 'variety', 'designation', 'winery', 'country', 'province', 'region_1', 'region_2', 'price', 'description', |
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'desc_wd_count', 'traits']) |
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df_temp_traits = df_temp_traits[trait_filter] |
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df_temp_traits['sum'] = df_temp_traits.sum(axis=1, numeric_only=True) |
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df_temp_traits = df_temp_traits[df_temp_traits['sum'] != 0] |
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df_selectrec_temp = df_temp_traits.merge(recommend_df, on='title', how='left') |
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df_selectrec_detail = df_selectrec_temp.merge(df_wine_combi, on='title', how='left') |
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df_selectrec_detail.drop_duplicates(inplace=True) |
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df_rec_raw = df_selectrec_detail.sort_values('est_match_pts', ascending=False).head(10) |
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df_rec_final = df_rec_raw[['title', 'country', 'province', 'variety', 'winery', 'points', 'price', 'traits', 'description']].reset_index(drop=True) |
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df_rec_final.index = df_rec_final.index + 1 |
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df_rec_final['traits']=df_rec_final['traits'].str.replace(" ", " | ") |
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df_rec_final.rename(columns={'title':'Name', |
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'country':'Country', |
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'province':'State/Province', |
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'variety':'Type', |
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'winery':'Winery', |
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'points':'Rating', |
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'price':'Price', |
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'description':'Review', |
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'traits':'Key Traits'}, inplace=True) |
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st.balloons() |
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st.dataframe(df_rec_final) |
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