app.py

from flask import Flask, request, render_template
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
from sklearn.neighbors import NearestNeighbors

app = Flask(__name__)

movies = pd.read_csv("./dataset/movies.csv")
ratings = pd.read_csv("./dataset/ratings.csv")

final_dataset = ratings.pivot(
    index='movieId', columns='userId', values='rating')

final_dataset.fillna(0, inplace=True)

no_user_voted = ratings.groupby('movieId')['rating'].agg('count')
no_movies_voted = ratings.groupby('userId')['rating'].agg('count')

final_dataset = final_dataset.loc[:,
                                  no_movies_voted[no_movies_voted > 50].index]

csr_data = csr_matrix(final_dataset.values)
final_dataset.reset_index(inplace=True)

knn = NearestNeighbors(metric='cosine', algorithm='brute',
                       n_neighbors=20, n_jobs=-1)
knn.fit(csr_data)

def get_movie_recommendation(movie_name):
    n_movies_to_reccomend = 10
    movie_list = movies[movies['title'].str.contains(movie_name)]
    if len(movie_list):
        movie_idx = movie_list.iloc[0]['movieId']
        movie_idx = final_dataset[final_dataset['movieId']
                                  == movie_idx].index[0]
        distances, indices = knn.kneighbors(
            csr_data[movie_idx], n_neighbors=n_movies_to_reccomend+1)
        rec_movie_indices = sorted(list(zip(indices.squeeze().tolist(
        ), distances.squeeze().tolist())), key=lambda x: x[1])[:0:-1]
        recommend_frame = []
        for val in rec_movie_indices:
            movie_idx = final_dataset.iloc[val[0]]['movieId']
            idx = movies[movies['movieId'] == movie_idx].index
            recommend_frame.append(
                {'Title': movies.iloc[idx]['title'].values[0], 'Distance': val[1]})
        df = pd.DataFrame(recommend_frame, index=range(
            1, n_movies_to_reccomend+1))
        return df
    else:
        return "No movie found. Please Try again"


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


@app.route('/', methods=['POST'])
def recommendation():
    text = request.form['title']
    text = text.title()
    try:
        result = get_movie_recommendation(text)
        name = []
        distance = []
        for i in range(len(result)):
            name.append(result.iloc[i][0])
            distance.append(result.iloc[i][1])
        return render_template('recommendation.html', movie_names=name, movie_date=distance, title=text)
    except AttributeError:
        return render_template('error.html', title=text)

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