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| import pickle | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| def recommend(movie): | |
| index = movies[movies["title"]== movie].index[0] | |
| distances = sorted(list(enumerate(similarity[index])), reverse = True, key = lambda x: x[1]) | |
| recommended_movies_names =[] | |
| #recommended_movies_poster =[] | |
| for i in distances[1:6]: | |
| movie_id = movies.iloc[i[0]].movie_id | |
| # recommended_movies_poster.append(fetch_poster(movie_id)) | |
| recommended_movies_names.append(movies.iloc[i[0]].title) | |
| return recommended_movies_names | |
| st.header("Movie Recommendation System Project ") | |
| movies = pickle.load(open(r"movielist.pkl",'rb')) | |
| similarity = pickle.load(open(r"similarity.pkl",'rb')) | |
| movie_list = movies['title'].values | |
| selected_movie = st.selectbox( | |
| "Type or select a movie to get recommendations", | |
| movie_list | |
| ) | |
| if st.button("Show Recommendation"): | |
| recommended_movies_names = recommend(selected_movie) | |
| st.text(recommended_movies_names[0]) | |
| st.text(recommended_movies_names[1]) | |
| st.text(recommended_movies_names[2]) | |
| st.text(recommended_movies_names[3]) | |
| st.text(recommended_movies_names[4]) | |