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app.py
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app.py
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from flask import Flask, render_template,request
import pandas as pd
import numpy as np
#from sklearn.metrics.pairwise import cosine_similarity
#similarity_score = cosine_similarity()
import pickle
app = Flask(__name__)
popular_df = pd.read_pickle('popular.pkl')
pt = pd.read_pickle('pt.pkl')
books = pd.read_pickle('books.pkl')
similarity_score = pd.read_pickle('similarity_score.pkl')
#popular_df = pickle.load(open('popular.pkl','rb'))
@app.route('/')
def index():
return render_template('index.html',
book_name=list(popular_df['Book-Title'].values),
author=list(popular_df['Book-Author' ].values),
image=list(popular_df['Image-URL-M'].values),
votes=list(popular_df['num_ratings'].values),
rating=list(popular_df['avg_ratings'].values))
@app.route('/recommend')
def recommend_ui():
return render_template('recommend.html')
@app.route('/recommend_books', methods=['post'])
def recommend():
user_input = request.form.get('user_input')
if user_input not in pt.index:
return 'Book not found'
index = np.where(pt.index == user_input)[0][0]
similar_items = sorted(list(enumerate(similarity_score[index])), key=lambda x: x[1], reverse=True)[1:5]
Data = []
for i in similar_items:
item = []
temp_df = books[books['Book-Title'] == pt.index[i[0]]]
item.extend(list(temp_df.drop_duplicates('Book-Title')['Book-Title'].values))
item.extend(list(temp_df.drop_duplicates('Book-Title')['Book-Author'].values))
item.extend(list(temp_df.drop_duplicates('Book-Title')['Image-URL-M'].values))
Data.append(item)
print(Data)
return render_template('recommend.html',Data=Data)
if __name__ == '__main__':
app.run(debug=True)