import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import linear_kernel
import gradio as gr

# Load the MovieLens dataset
movies = pd.read_csv('https://files.grouplens.org/datasets/movielens/ml-latest-small.zip', compression='zip')

# 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 10 most similar movies
    sim_scores = sim_scores[1:11]

    # Get the movie indices
    movie_indices = [i[0] for i in sim_scores]

    # Return the top 10 most similar movies
    return movies['title'].iloc[movie_indices]

# Gradio interface
def recommend_movies(movie):
    recommendations = get_recommendations(movie)
    return recommendations.tolist()

# Create the Gradio interface
movie_list = movies['title'].tolist()
iface = gr.Interface(fn=recommend_movies, inputs=gr.inputs.Dropdown(movie_list), outputs="text", title="Movie Recommender", description="Select a movie to get recommendations based on content filtering.")

# Launch the app
iface.launch()