import streamlit as st import nltk st.header("New York Times Book recomendations") st.markdown("Takes a book that user has read as a input and gives recomendation based on top 70 NYT bestsellers.") book=st.text_input("Enter the book you have read") def gsearch(book): a=book.split() if len(a)==1: return(a[0]) else: abc="" for i in a: abc=abc+i+"+" return(abc[0:len(abc)-1]) gs=gsearch(book) import requests if st.button("Recommend"): breakk=0 data_id=requests.get("https://www.googleapis.com/books/v1/volumes?q="+gs).json() try: booka=str(data_id['items'][0]['volumeInfo']['authors'][0]) desc=str(data_id['items'][0]['volumeInfo']['description']) genres=str(data_id['items'][0]['volumeInfo']['categories'][0]) except: st.markdown("No data for this book. Sorry please try again with another one.") breakk=1 if(breakk==0): tag=genres+" "+booka+" "+desc tag=tag.lower() import pickle ps=pickle.load(open("ps.pkl","rb")) def stem(a): y=[] for i in a.split(): y.append(ps.stem(i)) return(" ".join(y)) tag=stem(tag) cv=pickle.load(open("cv.pkl","rb")) vec=pickle.load(open("vec.pkl","rb")) vec1=cv.transform([tag]) from scipy.sparse import vstack fvec=vstack([vec1, vec]) from sklearn.metrics.pairwise import cosine_similarity similarity=cosine_similarity(fvec) distances=similarity[0] book_list_index=sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[1] import pandas as pd from PIL import Image from io import BytesIO data=pd.read_csv("streamlitdata.csv") if (str(data.iloc[book_list_index[0]-1]['title']).lower())==(book.lower()): st.text("**") book_list_index=sorted(list(enumerate(distances)),reverse=True,key=lambda x:x[1])[2] st.text("Book recomended-"+data.iloc[book_list_index[0]-1]['title']) response = requests.get(str(data.iloc[book_list_index[0]-1]['img'])) img = Image.open(BytesIO(response.content)) st.image(img) st.text("Similarity percentage="+str(round(book_list_index[1]*100,2))) st.text("-------------------------------------") st.text("SYNOPSIS") st.text(data.iloc[book_list_index[0]-1]['description']) else: st.text("Book recomended-"+data.iloc[book_list_index[0]-1]['title']) response = requests.get(str(data.iloc[book_list_index[0]-1]['img'])) img = Image.open(BytesIO(response.content)) st.image(img) st.text("Similarity percentage="+str(round(book_list_index[1]*100,2))) st.text("-------------------------------------") st.text("SYNOPSIS") st.markdown(data.iloc[book_list_index[0]-1]['description'])