first deploy

app.py

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+# Import python lib
+import streamlit as st
+import time
+import pandas as pd
+import numpy as np
+from surprise import Dataset, Reader
+from surprise import KNNBaseline
+
+# Import wine dataframes
+df_wine_model = pd.read_pickle('./data/df_wine_us_rate.pkl')
+df_wine_combi = pd.read_pickle('./data/df_wine_combi.pkl')
+
+# Instantiate the list of wine traits
+all_traits = ['almond', 'anise', 'apple', 'apricot', 'baked', 'baking_spices', 'berry', 'black_cherry', 'black_currant', 'black_pepper', 'black_tea', 'blackberry', 'blueberry', 
+              'boysenberry', 'bramble', 'bright', 'butter', 'candy', 'caramel', 'cardamom', 'cassis', 'cedar', 'chalk', 'cherry', 'chocolate', 'cinnamon', 'citrus', 'clean', 'closed',
+              'clove', 'cocoa', 'coffee', 'cola', 'complex', 'concentrated', 'cranberry', 'cream', 'crisp', 'dark', 'dark_chocolate', 'dense', 'depth', 'dried_herb', 'dry', 'dust',
+              'earth', 'edgy', 'elderberry', 'elegant', 'fennel', 'firm', 'flower', 'forest_floor', 'french_oak', 'fresh', 'fruit', 'full_bodied', 'game', 'grapefruit', 'graphite',
+              'green', 'gripping', 'grippy', 'hearty', 'herb', 'honey', 'honeysuckle', 'jam', 'juicy', 'lavender', 'leafy', 'lean', 'leather', 'lemon', 'lemon_peel', 'length', 'licorice',
+              'light_bodied', 'lime', 'lush', 'meaty', 'medium_bodied', 'melon', 'milk_chocolate', 'minerality', 'mint', 'nutmeg', 'oak', 'olive', 'orange', 'orange_peel', 'peach',
+              'pear', 'pencil_lead', 'pepper', 'pine', 'pineapple', 'plum', 'plush', 'polished', 'pomegranate', 'powerful', 'purple', 'purple_flower', 'raspberry', 'refreshing',
+              'restrained', 'rich', 'ripe', 'robust', 'rose', 'round', 'sage', 'salt', 'savory', 'sharp', 'silky', 'smoke', 'smoked_meat', 'smooth', 'soft', 'sparkling', 'spice',
+              'steel', 'stone', 'strawberry', 'succulent', 'supple', 'sweet', 'tangy', 'tannin', 'tar', 'tart', 'tea', 'thick', 'thyme', 'tight', 'toast', 'tobacco', 'tropical_fruit',
+              'vanilla', 'velvety', 'vibrant', 'violet', 'warm', 'weight', 'wet_rocks', 'white', 'white_pepper', 'wood']
+
+#---------------------------------------------------------------------------------------------------------
+
+# Function to instantiate the model & return the est recsys scores
+def recommend_scores():
+    
+    # Instantiate reader & data for surprise
+    reader = Reader(rating_scale=(88, 100))
+    data = Dataset.load_from_df(df_wine_model, reader)
+    
+    # Instantiate recsys model
+    sim_options={'name':'cosine'}
+    model = KNNBaseline(k=35, min_k=1, sim_options=sim_options, verbose=False)
+
+    # Train & fit the data into model
+    train=data.build_full_trainset()
+    model.fit(train)
+
+    # Start the model to compute the best estimate match score on wine list
+    recommend_list = []
+    user_wines = df_wine_model[df_wine_model.taster_name == 'mockuser']['title'].unique()
+    not_user_wines = []
+    
+    for wine in df_wine_model['title'].unique():
+        if wine not in user_wines:
+            not_user_wines.append(wine)
+
+    for wine in not_user_wines:
+        wine_compatibility = []
+        prediction = model.predict(uid='mockuser', iid=wine)
+        wine_compatibility.append(prediction.iid)
+        wine_compatibility.append(prediction.est)
+        recommend_list.append(wine_compatibility)
+        
+    result_df = pd.DataFrame(recommend_list, columns = ['title', 'est_match_pts'])
+    
+    return result_df
+
+def add_bg_from_url():
+    st.markdown(
+        f"""
+        <style>
+        
+        [data-testid="stAppViewContainer"] {{
+        background-image: url("https://images.pexels.com/photos/391213/pexels-photo-391213.jpeg");
+        background-attachment: fixed;
+        background-size: cover      
+        }}
+        
+        [data-testid="stVerticalBlock"] {{
+        background-color: rgba(255,255,255,0.75)
+        }}
+        
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+#----------------------------------------------------------------------------------------------------------
+
+st.title("Which wine should I get?")
+st.write("By Lee Wan Xian ([GitHub](https://github.com/leewanxian/wine_recommender))")
+st.text("")
+st.write("You can type the wine traits that you want in the dropdown list below")
+add_bg_from_url()
+
+select_temptrait = st.multiselect('Choose the traits that you want in your wine', options = all_traits)
+
+if st.button('Show me the wines!'):
+    with st.spinner('Should you have some wine now?'):
+        
+        time.sleep(2)
+        # Instantiate selected wine traits
+        if len(select_temptrait) == 0:
+            selected_traits = all_traits
+        else:
+            selected_traits = select_temptrait
+
+        # Run recommender model
+        recommend_df = recommend_scores()
+    
+        # Instantiate traits filter
+        trait_filter = ['title']
+
+        # Add on any traits selected by user
+        trait_filter.extend(selected_traits)
+
+        # Create dataframe for wine name and traits
+        df_temp_traits = df_wine_combi.drop(columns=['taster_name', 'points', 'variety', 'designation', 'winery', 'country', 'province', 'region_1', 'region_2', 'price', 'description',
+                                                     'desc_wd_count', 'traits'])
+
+        # Code to start filtering out wines with either one of the selected traits
+        df_temp_traits = df_temp_traits[trait_filter]
+        df_temp_traits['sum'] = df_temp_traits.sum(axis=1, numeric_only=True)
+        df_temp_traits = df_temp_traits[df_temp_traits['sum'] != 0]
+
+        # Merge the selected wines traits with recommend scores
+        df_selectrec_temp = df_temp_traits.merge(recommend_df, on='title', how='left')
+
+        # Merge the selected wines with recommendations with df on details
+        df_selectrec_detail = df_selectrec_temp.merge(df_wine_combi, on='title', how='left')
+        df_selectrec_detail.drop_duplicates(inplace=True)
+
+        # Pull out the top 10 recommendations (raw)
+        df_rec_raw = df_selectrec_detail.sort_values('est_match_pts', ascending=False).head(10)
+        
+        # Prepare the display for the top 10 recommendations
+        df_rec_final = df_rec_raw[['title', 'country', 'province', 'variety', 'winery', 'points', 'price', 'traits', 'description']].reset_index(drop=True)
+        df_rec_final.index = df_rec_final.index + 1
+        df_rec_final['traits']=df_rec_final['traits'].str.replace(" ", " | ")
+        df_rec_final.rename(columns={'title':'Name',
+                                     'country':'Country',
+                                     'province':'State/Province',
+                                     'variety':'Type',
+                                     'winery':'Winery',
+                                     'points':'Rating',
+                                     'price':'Price',
+                                     'description':'Review',
+                                     'traits':'Key Traits'}, inplace=True)
+        st.balloons()
+        st.dataframe(df_rec_final)

data/df_wine_combi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5324ee33b8c5daeefd5529101d1ac3095ebcc115b759c1f6f0b96daa493f9f87
+size 18288367

data/df_wine_us_rate.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:76893c95a7f28fedb325f5a89576ac3344e7117cdc470d3894f5d6614bfaf5dd
+size 1219978

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-surprise