data, train/predict script

.gitignore

@@ -0,0 +1,2 @@
+.vscode/launch.json
+amazon-books.csv

amazon-books.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:13ccd03c37364738fe39d04e02cf1a07dc5b907f4534846feb9044a87ed8fb57
+size 9962722

app.py

@@ -1,4 +1,60 @@
+from surprise import SVDpp
+from surprise import Dataset
+from surprise import Reader
 import streamlit as st
+import pandas as pd
+import pickle
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+
+def predict():
+    with open('./model.pkl', 'rb') as f:
+        model = pickle.load(f)
+
+    titles = pd.read_csv('./amazon-books-titles.csv')
+    predictions = []
+    for row in titles.iterrows():
+        predictions.append({'title': row[1]['title'], 'rating': model.predict(x, row[1]['asin']).est})
+    sorted_predictions = sorted(predictions, key=lambda p: -p['rating'])
+    st.write(pd.DataFrame(sorted_predictions))
+
+
+def train_model():
+    books = pd.read_csv('./amazon-books.zip')
+    titles = pd.read_csv('./amazon-books-titles.csv')
+
+    current_user_book_ids = titles[titles['title'].isin([t.strip() for t in y])]['asin'].values
+    current_user_ratings = pd.DataFrame({
+        'reviewerID': [x] * len(current_user_book_ids),
+        'asin': current_user_book_ids,
+        'overall': [5] * len(current_user_book_ids)
+    })
+    st.write(current_user_ratings)
+
+    books = books.append(current_user_ratings)
+
+    data = Dataset.load_from_df(books[['reviewerID', 'asin', 'overall']], Reader(line_format='user item rating', rating_scale=(1, 5)))
+    trainset = data.build_full_trainset()
+    best_params = {
+        'n_epochs': 15,
+        'lr_all': 0.004760245463611792,
+        'reg_all': 0.40040712444861504,
+        'random_state': 42
+    }
+    
+    algo = SVDpp(**best_params)
+    algo.fit(trainset)
+
+    with open('./model.pkl', 'wb') as f:
+        pickle.dump(algo, f)
+
+
+with open('./titles.txt', 'r', encoding='utf8') as f:
+    options = f.readlines()
+
+x = st.text_input('uid')
+
+y = st.multiselect('Select book', options)
+
+btn = st.button('Submit', on_click=train_model)
+
+btn2 = st.button('Predict', on_click=predict)