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import gradio as gr
import numpy as np
import pandas as pd
from scipy.sparse import csr_matrix
from sklearn.neighbors import NearestNeighbors
def create_matrix(df):
N = len(df['userId'].unique())
M = len(df['movieId'].unique())
# Map Ids to indices
user_mapper = dict(zip(np.unique(df["userId"]), list(range(N))))
movie_mapper = dict(zip(np.unique(df["movieId"]), list(range(M))))
# Map indices to IDs
user_inv_mapper = dict(zip(list(range(N)), np.unique(df["userId"])))
movie_inv_mapper = dict(zip(list(range(M)), np.unique(df["movieId"])))
user_index = [user_mapper[i] for i in df['userId']]
movie_index = [movie_mapper[i] for i in df['movieId']]
X = csr_matrix((df["rating"], (movie_index, user_index)), shape=(M, N))
return X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper
def find_similar_movies(movie_id, X, k, metric='cosine', show_distance=False):
neighbour_ids = []
movie_ind = movie_mapper[movie_id]
movie_vec = X[movie_ind]
k += 1
kNN = NearestNeighbors(n_neighbors=k, algorithm="brute", metric=metric)
kNN.fit(X)
movie_vec = movie_vec.reshape(1,-1)
neighbour = kNN.kneighbors(movie_vec, return_distance=show_distance)
for i in range(0,k):
n = neighbour.item(i)
neighbour_ids.append(movie_inv_mapper[n])
neighbour_ids.pop(0)
return neighbour_ids
def recommend_movies(movie_id):
similar_ids = find_similar_movies(movie_id, X, k=10)
movie_title = movie_titles[movie_id]
recommendations = []
for i in similar_ids:
recommendations.append(movie_titles[i])
return recommendations
# Load data
ratings = pd.read_csv("https://s3-us-west-2.amazonaws.com/recommender-tutorial/ratings.csv")
movies = pd.read_csv("https://s3-us-west-2.amazonaws.com/recommender-tutorial/movies.csv")
n_ratings = len(ratings)
n_movies = len(ratings['movieId'].unique())
n_users = len(ratings['userId'].unique())
user_freq = ratings[['userId', 'movieId']].groupby('userId').count().reset_index()
user_freq.columns = ['userId', 'n_ratings']
mean_rating = ratings.groupby('movieId')[['rating']].mean()
lowest_rated = mean_rating['rating'].idxmin()
highest_rated = mean_rating['rating'].idxmax()
movie_stats = ratings.groupby('movieId')[['rating']].agg(['count', 'mean'])
movie_stats.columns = movie_stats.columns.droplevel()
X, user_mapper, movie_mapper, user_inv_mapper, movie_inv_mapper = create_matrix(ratings)
movie_titles = dict(zip(movies['movieId'], movies['title']))
# Set up Gradio interface
movie_id = gr.inputs.Number(default=3, label="Movie ID")
iface = gr.Interface(
fn=recommend_movies,
inputs=movie_id,
outputs="text",
title="Movie Recommender System",
description="Enter a movie ID and get recommendations for similar movies."
)
iface.launch()
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