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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])
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