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| import pandas as pd | |
| import plotly.express as px | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| from math import sqrt | |
| import matplotlib | |
| matplotlib.use("Agg") | |
| genre_df = pd.read_csv("ml-100k/ml-100k/u.genre", | |
| sep="|", names=["genreName", "count"]) | |
| user_df = pd.read_csv("ml-100k/ml-100k/u.user", sep="|", | |
| names=["userID", "age", "gender", "occupation", "zip_code"]) | |
| movie_df = pd.read_csv("ml-100k/ml-100k/u.item", sep="|", names=["itemID", "title", "release_date", "video_release_date", "IMDb_URL", "unknown", "Action", "Adventure", "Animation", "Children's", "Comedy", "Crime", "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"],encoding='latin-1') | |
| prediction_df = pd.read_csv(r"pred.csv", sep=",") | |
| def mappingMovie(mid): | |
| return movie_df.loc[movie_df["itemID"].values==mid]["title"].values[0] | |
| prediction_df["Movie Title"] = prediction_df["itemID"].apply(mappingMovie) | |
| df = pd.read_csv("ml-100k/ml-100k/u.data", sep="\t", names=["userID", "itemID", "rating", "timestamp"]) | |
| rating_df = df[df["rating"]>=1.0] | |
| rating_df["Movie Title"] = rating_df["itemID"].apply(mappingMovie) | |
| print(rating_df.head(7)) | |
| num_users = len(prediction_df["userID"].unique()) | |
| def get_top_rated_movies_from_user(id): | |
| entire_df= rating_df[rating_df["userID"]==id].sort_values(by="rating", ascending=False).head(10) | |
| return entire_df | |
| def get_recommendations(id): | |
| entire_df= prediction_df[prediction_df["userID"]==id].sort_values(by="prediction", ascending=False).head(10) | |
| entire_df.drop(columns=["Unnamed: 0"], inplace=True) | |
| return entire_df | |
| def update_user(id): | |
| return get_top_rated_movies_from_user(id), get_recommendations(id) | |
| def random_user(): | |
| return update_user(np.random.randint(0, num_users-1)) | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown(""" | |
| <div> | |
| <h1 style='text-align: center'>Movie Recommender</h1> | |
| LightGCN++ is used to predict the top 10 recommended movies for a particular user from the dataset based on that user and previous movies they have rated. | |
| </div> | |
| """) | |
| with gr.Box(): | |
| gr.Markdown( | |
| """ | |
| ### Input | |
| #### Select a user to get recommendations for. | |
| """) | |
| inp1 = gr.Slider(0, num_users-1, value=0, label='User') | |
| # btn1 = gr.Button('Random User') | |
| # top_rated_from_user = get_top_rated_from_user(0) | |
| gr.Markdown( | |
| """ | |
| <br> | |
| """) | |
| gr.Markdown( | |
| """ | |
| #### Movies with the Highest Ratings from this user | |
| """) | |
| df1 = gr.DataFrame(headers=["title", "genres"], datatype=["str", "str"], interactive=False) | |
| with gr.Box(): | |
| # recommendations = get_recommendations(0) | |
| gr.Markdown( | |
| """ | |
| ### Output | |
| #### Top 10 movie recommendations | |
| """) | |
| df2 = gr.DataFrame(headers=["title", "genres"], datatype=["str", "str"], interactive=False) | |
| gr.Markdown(""" | |
| <p style='text-align: center'> | |
| <a href='https://github.com/Jimut123/FML_Project' target='_blank' style='text-decoration: underline'></a> | |
| <br> | |
| Space by Prateek Chanda (prateekiiest) | |
| </p> | |
| """) | |
| inp1.change(fn=update_user, | |
| inputs=inp1, | |
| outputs=[df1, df2]) | |
| demo.launch() |