app.py

import pickle
import numpy as np
from flask import Flask, render_template, request


app = Flask(__name__)

books = pickle.load(open('./models/books.pkl', 'rb'))
popular_df = pickle.load(open('./models/popular.pkl', 'rb'))
pivot_table = pickle.load(open('./models/pivot_table.pkl', 'rb'))
similarity_scores = pickle.load(open('./models/similarity_scores.pkl', 'rb'))


@app.route('/')
def index():
    return render_template(
        'index.html',
        author=list(popular_df['Book-Author'].values),
        votes=list(popular_df['Rating Count'].values),
        rating=list(popular_df['Rating Avg'].values),
        image=list(popular_df['Image-URL-L'].values),
        book_name=list(popular_df['Book-Title'].values),
    )


@app.route('/recommendation')
def recommend_ui():
    return render_template('recommendation.html')


@app.route('/recommendation', methods=['POST'])
def recommend_books():
    user_input = request.form.get('user_input', '').strip().lower()

    indices = np.where(pivot_table.index.str.lower() == user_input)[0]
    idx = indices[0] if indices.size > 0 else None

    if idx is None:
        return render_template('recommendation.html', error="Book not found. Please try again.")

    top_indices = np.argsort(similarity_scores[idx])[::-1][1:7]
    recommendations = []

    for i in top_indices:
        book_title = pivot_table.index[i]

        book_info = books[
            books['Book-Title'] == book_title
        ].drop_duplicates('Book-Title')

        if book_info.empty:
            continue

        title = book_info['Book-Title'].values[0]
        author = book_info['Book-Author'].values[0]
        image_url = book_info[f'Image-URL-L'].values[0]

        if image_url.startswith('http://'):
            image_url = 'https://' + image_url[7:]

        recommendations.append([title, author, image_url])

    if not recommendations:
        return render_template('recommendation.html', error="No recommendations found. Please try again.")

    return render_template('recommendation.html', data=recommendations, query=user_input)


if __name__ == '__main__':
    app.run(debug=True)