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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
import math
import pickle
app = Flask(__name__)
popular_df = pickle.load(open('popular.pkl','rb'))
books = pickle.load(open('books.pkl','rb'))
pt = pickle.load(open('pt.pkl','rb'))
similarity_score = pickle.load(open('similarity_score.pkl','rb'))
@app.route('/')
def index1():
return render_template("index.html")
@app.route('/other_page')
def other_page():
rounded_ratings = [math.ceil(rating) for rating in popular_df['avg_ratings']]
return render_template('mustread.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=rounded_ratings
)
@app.route('/lists')
def lists():
return render_template('list.html',
book_name=list(books['Book-Title'].values),
author=list(books['Book-Author'].values)
)
@app.route('/results')
def results():
return render_template('recommend.html')
def compute_similarity(pt):
ratings_matrix = pt.to_numpy()
similarity_score = np.zeros((ratings_matrix.shape[0], ratings_matrix.shape[0]))
for i in range(ratings_matrix.shape[0]):
for j in range(ratings_matrix.shape[0]):
if i == j:
continue
ratings_vec_i = ratings_matrix[i]
ratings_vec_j = ratings_matrix[j]
dot_product = np.dot(ratings_vec_i, ratings_vec_j)
norm_vec_i = np.linalg.norm(ratings_vec_i)
norm_vec_j = np.linalg.norm(ratings_vec_j)
similarity_score[i, j] = dot_product / (norm_vec_i * norm_vec_j)
return similarity_score
def recommend(book_name, pt, books, similarity_score):
index = np.where(pt.index == book_name)[0][0]
similar_items = sorted(list(enumerate(similarity_score[index])), key=lambda x: x[1], reverse=True)[1:13]
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)
return data
@app.route('/recommend', methods=['POST'])
def recommend_books():
# Get user input from the form
user_input = request.form.get('user_input')
# Check if user input is missing or empty
if not user_input:
error_message = '**Please provide a valid book name**'
return render_template('recommend.html', error_message=error_message)
try:
similarity_score = compute_similarity(pt)
recommended_books = recommend(user_input, pt, books, similarity_score)
if not recommended_books:
error_message = 'Book not found'
return render_template('recommend.html', error_message=error_message, user_input=user_input)
else:
return render_template('recommend.html', recommended_books=recommended_books, user_input=user_input)
except IndexError:
error_message = '**Book not found**'
return render_template('recommend.html', error_message=error_message, user_input=user_input)
# @app.route('/recommend', methods=['POST'])
# def recommend_books():
# # Get user input from the form
# user_input = request.form.get('user_input')
#
# # Check if user input is missing or empty
# if not user_input:
# error_message = 'Please provide a valid book name.'
# return render_template('recommend.html', error_message=error_message)
#
# # Compute similarity score
# similarity_score = compute_similarity(pt)
#
# # Get recommended books
# recommended_books = recommend(user_input, pt, books, similarity_score)
#
# if not recommended_books:
# error_message = 'Book not found'
# return render_template('recommend.html', error_message=error_message, user_input=user_input)
# else:
# return render_template('recommend.html', recommended_books=recommended_books, user_input=user_input)
# @app.route('/recommend', methods=['POST'])
# def recommend():
# user_input = request.form.get('user_input')
#
# try:
# index = np.where(pt.index == user_input)[0][0]
# except IndexError:
# error_message = '**Book not found**'
# return render_template('recommend.html', error_message=error_message,user_input=user_input)
#
# # similar_items = sorted(list(enumerate(similarity_score[index])), key=lambda x: x[1], reverse=True)[1:13]
# #
# # 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)
# #
# # return render_template('recommend.html', data=data, user_input=user_input)
#
@app.route('/autosuggest', methods=['POST'])
def autosuggest():
query = request.form.get('query', '')
suggestions = [book_name for book_name in pt.index if query.lower() in book_name.lower()]
suggestions = suggestions[:5]
suggestions_string = ', '.join(suggestions)
return suggestions_string
trend_df = pickle.load(open('trending.pkl','rb'))
@app.route('/trend')
def trend():
rounded_ratings = [math.ceil(rating) for rating in trend_df['avg-ratings']]
return render_template('trending.html',
book_name=list(trend_df['Book-Title'].values),
author=list(trend_df['Book-Author'].values),
image=list(trend_df['Image-URL-M'].values),
genre=list(trend_df['Genre'].values),
des=list(trend_df['Description'].values),
votes=list(trend_df['num-ratings'].values),
rating=rounded_ratings
)
f_df = pickle.load(open('f.pkl','rb'))
nf_df = pickle.load(open('nf.pkl','rb'))
s_df = pickle.load(open('s.pkl','rb'))
art_df = pickle.load(open('art.pkl','rb'))
h_df = pickle.load(open('h.pkl','rb'))
poetry_df = pickle.load(open('poetry.pkl','rb'))
@app.route('/f')
def f():
return render_template('fiction.html',
book_name=list(f_df['title'].values),
author=list(f_df['author'].values),
image=list(f_df['img'].values),
des=list(f_df['desc'].values),
rating=list(f_df['rating'].values)
)
@app.route('/nf')
def nf():
return render_template('nfiction.html',
book_name=list(nf_df['title'].values),
author=list(nf_df['author'].values),
image=list(nf_df['img'].values),
des=list(nf_df['desc'].values),
rating=list(nf_df['rating'].values)
)
@app.route('/h')
def h():
return render_template('history.html',
book_name=list(h_df['title'].values),
author=list(h_df['author'].values),
image=list(h_df['img'].values),
des=list(h_df['desc'].values),
rating=list(h_df['rating'].values)
)
@app.route('/s')
def s():
return render_template('science.html',
book_name=list(s_df['title'].values),
author=list(s_df['author'].values),
image=list(s_df['img'].values),
des=list(s_df['desc'].values),
rating=list(s_df['rating'].values)
)
@app.route('/art')
def art():
return render_template('art.html',
book_name=list(art_df['title'].values),
author=list(art_df['author'].values),
image=list(art_df['img'].values),
des=list(art_df['desc'].values),
rating=list(art_df['rating'].values)
)
@app.route('/poetry')
def poetry():
return render_template('poetry.html',
book_name=list(poetry_df['title'].values),
author=list(poetry_df['author'].values),
image=list(poetry_df['img'].values),
des=list(poetry_df['desc'].values),
rating=list(poetry_df['rating'].values)
)
if __name__ == ('__main__'):
app.run(debug=True)