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| import pandas as pd | |
| import numpy as np | |
| import sklearn | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| # Load movie metadata into a pandas DataFrame | |
| movie_data = pd.read_csv('movies_metadata.csv', usecols=['title', 'budget', 'revenue', 'vote_count', 'vote_average']) | |
| # Load movie ratings into a pandas DataFrame | |
| ratings_data = pd.read_csv('ratings_small.csv') | |
| # Collaborative Filtering | |
| def collaborative_filtering(user_id, num_recommendations=5): | |
| user_ratings = ratings_data[ratings_data['userId'] == user_id] | |
| user_ratings = user_ratings.merge(movie_data[['title']], left_on='movieId', right_index=True) | |
| user_ratings = user_ratings.drop_duplicates(subset=['title'], keep='first') | |
| # Get the mean ratings for each movie | |
| movie_ratings_mean = ratings_data.groupby('movieId')['rating'].mean().reset_index() | |
| # Merge with movie data to get movie titles | |
| movie_ratings_mean = movie_ratings_mean.merge(movie_data[['title']], left_on='movieId', right_index=True) | |
| movie_ratings_mean = movie_ratings_mean.drop_duplicates(subset=['title'], keep='first') | |
| # Sort movies by mean rating in descending order | |
| top_movies = movie_ratings_mean.sort_values(by='rating', ascending=False)['title'][:num_recommendations] | |
| return top_movies.tolist() | |
| import gradio as gr | |
| with gr.Blocks() as demo: | |
| gr.Markdown("Flip text or image files using this demo.") | |
| title="Movie Recommender System" | |
| user_id = gr.Textbox(placeholder='user_id') | |
| #input2 = gr.Textbox(placeholder='second film name...') | |
| #input3 = gr.Textbox(placeholder='third film name...') | |
| text_output=gr.Textbox( ) | |
| text_button = gr.Button("Submit") | |
| text_button.click(collaborative_filtering, inputs=user_id, outputs=text_output) | |
| demo.launch() | |