app.py

import streamlit as st
from fastai.collab import *
import torch
from torch import nn
import pickle
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import sentencepiece
import string
import requests


@st.cache_resource
def load_stuff():
    # Load the data loader
    dls = pd.read_pickle("dataloader.pkl")
    # Create an instance of the model
    learn = collab_learner(dls, use_nn=True, layers=[20, 10], y_range=(0, 10.5))
    # Load the saved state dictionary
    state_dict = torch.load("myModel.pth", map_location=torch.device("cpu"))
    # Assign the loaded state dictionary to the model's load_state_dict() method
    learn.model.load_state_dict(state_dict)
    # load books dataframe
    books = pd.read_csv("./data/BX_Books.csv", sep=";", encoding="latin-1")
    # load tokenizer
    tokenizer = AutoTokenizer.from_pretrained("pszemraj/pegasus-x-large-book-summary")
    # load model
    model = AutoModelForSeq2SeqLM.from_pretrained(
        "pszemraj/pegasus-x-large-book-summary"
    )
    return dls, learn, books, tokenizer, model


dls, learn, books, tokenizer, model = load_stuff()


# function to get recommendations
def get_3_recs(book):
    book_factors = learn.model.embeds[1].weight
    idx = dls.classes["title"].o2i[book]
    distances = nn.CosineSimilarity(dim=1)(book_factors, book_factors[idx][None])
    idxs = distances.argsort(descending=True)[1:4]
    recs = [dls.classes["title"][i] for i in idxs]
    return recs


# function to get descriptions from Google Books
def search_book_description(title):
    # Google Books API endpoint for book search
    url = "https://www.googleapis.com/books/v1/volumes"
    # Parameters for the book search
    params = {"q": title, "maxResults": 1}
    # Send GET request to Google Books API
    response = requests.get(url, params=params)
    # Check if the request was successful
    if response.status_code == 200:
        # Parse the JSON response to extract the book description
        data = response.json()

        if "items" in data and len(data["items"]) > 0:
            book_description = data["items"][0]["volumeInfo"].get(
                "description", "No description available."
            )
            return book_description
        else:
            print("No book found with the given title.")
            return None
    else:
        # If the request failed, print the error message
        print("Error:", response.status_code, response.text)
        return None


# function to ensure summaries end with punctuation
def cut(sum):
    last_punc_idx = max(sum.rfind("."))
    output = sum[: last_punc_idx + 1]
    return output


# function to summarize
def summarize(des_list):
    if "No description available." in des_list:
        idx = des_list.index("No description available.")
        des = des_list.copy()
        des.pop(idx)
        rest = summarize(des)
        rest.insert(idx, "No description available.")
        return rest
    else:
        # Tokenize all the descriptions in the list
        encoded_inputs = tokenizer(
            des_list, truncation=True, padding="longest", return_tensors="pt"
        )

        # Generate summaries for all the inputs
        summaries = model.generate(**encoded_inputs, max_new_tokens=100)

        # Decode the summaries and process them
        outputs = tokenizer.batch_decode(summaries, skip_special_tokens=True)
        outputs = list(map(cut, outputs))
        return outputs


# function to get cover images
def get_covers(recs):
    imgs = [books[books["Book-Title"] == r]["Image-URL-L"].tolist()[0] for r in recs]
    return imgs


# streamlit app construction
st.title("Your digital librarian")
st.markdown(
    "Hi there! I recommend you books based on one you love (which might not be in the same genre because that's boring) and give you my own synopsis of each book. Enjoy!"
)
options = books["Book-Title"].tolist()
input = st.selectbox("Select your favorite book", options)
if st.button("Get recommendations"):
    recs = get_3_recs(input)
    descriptions = list(map(search_book_description, recs))
    des_sums = summarize(descriptions)
    imgs = get_covers(recs)

    col1, col2, col3 = st.columns(3)
    col1.image(imgs[0])
    col1.markdown(f"**{recs[0]}**")
    col1.write(des_sums[0])

    col2.image(imgs[1])
    col2.markdown(f"**{recs[1]}**")
    col2.write(des_sums[1])

    col3.image(imgs[2])
    col3.markdown(f"**{recs[2]}**")
    col3.write(des_sums[2])