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app.py
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app.py
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"""
# My first app
Here's our first attempt at using data to create a table:
"""
import streamlit as st
import pandas as pd
import numpy as np
import crate.client as client
import os
import json
import boto3
def get_cratedb_password():
secret_name = "dome_cratedb_password"
region_name = "eu-west-3"
# Create a Secrets Manager client
session = boto3.session.Session()
client = session.client(service_name="secretsmanager", region_name=region_name)
try:
get_secret_value_response = client.get_secret_value(SecretId=secret_name)
secret = get_secret_value_response["SecretString"]
secret = json.loads(secret)["cratedb_password"]
except:
secret = os.getenv("CRATEDB_PASSWORD")
return secret
def get_matching_entry(author: str, book: str):
select_stmt = f"""
select *
from dome.books_search
where
match(title, '{book}')
and match(author, '{author}')
limit 1"""
with get_connection() as conn:
cursor = conn.cursor()
cursor.execute(select_stmt)
entry = cursor.fetchall()
return entry
def get_connection() -> client.connection.Connection:
connection = client.connect(
"https://beige-nute-gunray.aks1.eastus2.azure.cratedb.net:4200",
username="admin",
password=get_cratedb_password(),
)
return connection
def recommend(selected_author: str, selected_book: str, top_k: int):
# standardize
selected_author = selected_author.lower()
selected_book = selected_book.lower()
# load ratings
ratings = pd.read_csv("/data/Ratings.csv", encoding="cp1251", sep=";")
ratings = ratings[ratings["Book-Rating"] != 0]
# load books
books = pd.read_csv(
"/data/Books.csv", encoding="cp1251", sep=";", on_bad_lines="skip"
)
# users_ratigs = pd.merge(ratings, users, on=['User-ID'])
dataset = pd.merge(ratings, books, on=["ISBN"])
dataset_lowercase = dataset.apply(
lambda x: x.str.lower() if (x.dtype == "object") else x
)
selected_author_readers = dataset_lowercase["User-ID"][
(dataset_lowercase["Book-Title"] == selected_book)
& (dataset_lowercase["Book-Author"].str.contains(selected_author))
]
selected_author_readers = selected_author_readers.tolist()
selected_author_readers = np.unique(selected_author_readers)
# final dataset
books_of_selected_author_readers = dataset_lowercase[
(dataset_lowercase["User-ID"].isin(selected_author_readers))
]
# Number of ratings per other books in dataset
number_of_rating_per_book = (
books_of_selected_author_readers.groupby(["Book-Title"])
.agg("count")
.reset_index()
)
# select only books which have actually higher number of ratings than threshold
books_to_compare = number_of_rating_per_book["Book-Title"][
number_of_rating_per_book["User-ID"] >= 8
]
books_to_compare = books_to_compare.tolist()
if selected_book not in books_to_compare:
return None
ratings_data_raw = books_of_selected_author_readers[
["User-ID", "Book-Rating", "Book-Title"]
][books_of_selected_author_readers["Book-Title"].isin(books_to_compare)]
# group by User and Book and compute mean
ratings_data_raw_nodup = ratings_data_raw.groupby(["User-ID", "Book-Title"])[
"Book-Rating"
].mean()
# reset index to see User-ID in every row
ratings_data_raw_nodup = ratings_data_raw_nodup.to_frame().reset_index()
dataset_for_corr = ratings_data_raw_nodup.pivot(
index="User-ID", columns="Book-Title", values="Book-Rating"
)
result_list = []
# Take out the Lord of the Rings selected book from correlation dataframe
dataset_of_other_books = dataset_for_corr.copy(deep=False)
dataset_of_other_books.drop([selected_book], axis=1, inplace=True)
# empty lists
book_titles = []
correlations = []
avgrating = []
# corr computation
for book_title in list(dataset_of_other_books.columns.values):
book_titles.append(book_title)
correlations.append(
dataset_for_corr[selected_book].corr(dataset_of_other_books[book_title])
)
tab = (
ratings_data_raw[ratings_data_raw["Book-Title"] == book_title]
.groupby(ratings_data_raw["Book-Title"])["Book-Rating"]
.mean()
)
avgrating.append(tab.values[0])
# final dataframe of all correlation of each book
corr_fellowship = pd.DataFrame(
list(zip(book_titles, correlations, avgrating)),
columns=["book", "corr", "avg_rating"],
)
corr_fellowship.head()
# top 10 books with highest corr
result_list.append(corr_fellowship.sort_values("corr", ascending=False).head(top_k))
result_list = result_list[0]["book"].values
return result_list
# rec_list = recommend(
# "tolkien", "the fellowship of the ring (the lord of the rings, part 1)", 5
# )
# print(rec_list)
if "author" not in st.session_state:
st.session_state.author = ""
if "book" not in st.session_state:
st.session_state.book = ""
if "top_k" not in st.session_state:
st.session_state.top_k = 5
st.session_state.author = st.text_input(
label="Who is the author of the book?", value=st.session_state.author
)
st.session_state.book = st.text_input(
label="What is your favorite book?", value=st.session_state.book
)
st.session_state.top_k = st.slider(
label="How many books should I recommend?",
min_value=0,
max_value=10,
value=0,
)
run = st.button("Submit")
if run:
entry = get_matching_entry(st.session_state.author, st.session_state.book)
if entry:
author = entry[0][2]
book = entry[0][1]
st.write(f"Found book: {author}: {book}")
rec_list = recommend(author, book, st.session_state.top_k)
if rec_list is None:
st.write(
"Sorry, as there are less than 8 ratings for the selected book,"
" we cannot recommend based on it."
)
else:
s = ""
for i in rec_list:
s += "- " + i + "\n"
st.write(f"Here are the first {st.session_state.top_k} recommended books:")
st.markdown(s)
else:
st.write(
"It looks like there is not matching book,"
" please try with different input!"
)