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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import gradio as gr
import zipfile
import requests
import io
# Download and extract the MovieLens dataset
url = 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip'
response = requests.get(url)
with zipfile.ZipFile(io.BytesIO(response.content)) as z:
with z.open('ml-latest-small/movies.csv') as f:
movies = pd.read_csv(f)
# Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'
tfidf = TfidfVectorizer(stop_words='english')
# Replace NaN with an empty string
movies['genres'] = movies['genres'].fillna('')
# Construct the required TF-IDF matrix by fitting and transforming the data
tfidf_matrix = tfidf.fit_transform(movies['genres'])
# Compute the cosine similarity matrix
cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
# Construct a reverse map of indices and movie titles
indices = pd.Series(movies.index, index=movies['title']).drop_duplicates()
# Function that takes in movie title as input and outputs most similar movies
def get_recommendations(title, cosine_sim=cosine_sim):
# Get the index of the movie that matches the title
idx = indices[title]
# Get the pairwise similarity scores of all movies with that movie
sim_scores = list(enumerate(cosine_sim[idx]))
# Sort the movies based on the similarity scores
sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
# Get the scores of the 20 most similar movies
sim_scores = sim_scores[1:21]
# Get the movie indices
movie_indices = [i[0] for i in sim_scores]
# Return the top 20 most similar movies with their scores
recommendations = [(movies['title'].iloc[i], sim_scores[idx][1]) for idx, i in enumerate(movie_indices)]
return recommendations
# Gradio interface
def recommend_movies(movie):
recommendations = get_recommendations(movie)
max_length = movies['title'].str.len().max()
print(f"The longest movie name length is: {max_length}")
headers = "Score{:10}Title".format("")
return headers + "\n" + "\n".join([f"{score:>10.2f} {title:<20} " for title, score in recommendations])
# Create the Gradio interface
movie_list = movies['title'].tolist()
iface = gr.Interface(fn=recommend_movies, inputs=gr.Dropdown(movie_list), outputs="text", title="Movie Recommender - Content-Based Filtering", description="Select a movie to get recommendations based on content filtering.")
# Launch the app
iface.launch()
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