| 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']) |