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app.py
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app.py
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from flask import Flask, render_template ,request
import pickle
import numpy as np
best_df = pickle.load(open('best_df.pkl', 'rb'))
pt = pickle.load(open('pt.pkl', 'rb'))
books = pickle.load(open('books.pkl', 'rb'))
similarity_score = pickle.load(open('similarity_score.pkl', 'rb'))
app = Flask(__name__)
@app.route("/")
def index():
return render_template("index.html",
book_name = list(best_df['Book-Title'].values),
author = list(best_df['Book-Author'].values),
image = list(best_df['Image-URL-S'].values),
votes = list(best_df['num_ratings'].values),
rating = list(best_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")
index = np.where(pt.index == user_input)[0][0]
similar_items = sorted(list(enumerate(similarity_score[index])), key = lambda x:x[1], reverse=True)[1:20]
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-S"].values))
data.append(item)
print(data)
return render_template('recommend.html', data = data)
if __name__ == '__main__':
app.run(debug = True)