import streamlit as st import pickle import requests import pandas as pd st.set_page_config(page_title=None, page_icon=None, layout="wide", initial_sidebar_state="auto", menu_items=None) movies = pickle.load(open("movies_list.pkl",'rb')) similarity = pickle.load(open("similarity.pkl",'rb')) def fetch_poster(movie_id): url = "https://api.themoviedb.org/3/movie/{}?api_key=8735d1698444eb906584226ecb77307b&language=en-US".format(movie_id) data = requests.get(url) data = data.json() poster_path = data['poster_path'] full_path = "https://image.tmdb.org/t/p/w500/" + poster_path return full_path def recommend(movie): index = movies[movies['title'] == movie].index[0] distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1]) recommended_movie_names = [] recommended_movie_posters = [] for i in distances[1:6]: # fetch the movie poster movie_id = movies.iloc[i[0]].movie_id recommended_movie_posters.append(fetch_poster(movie_id)) recommended_movie_names.append(movies.iloc[i[0]].title) return recommended_movie_names,recommended_movie_posters st.header('Movie Recommendation System (Content Based)') st.text("A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users' film preferences ") movie_list = movies['title'].values selected_movie = st.selectbox( "Type or select a movie from the dropdown", movie_list ) if st.button('Show Recommendation'): recommended_movie_names,recommended_movie_posters = recommend(selected_movie) col1, col2, col3, col4, col5 = st.columns(5) with col1: st.text(recommended_movie_names[0]) st.image(recommended_movie_posters[0]) with col2: st.text(recommended_movie_names[1]) st.image(recommended_movie_posters[1]) with col3: st.text(recommended_movie_names[2]) st.image(recommended_movie_posters[2]) with col4: st.text(recommended_movie_names[3]) st.image(recommended_movie_posters[3]) with col5: st.text(recommended_movie_names[4]) st.image(recommended_movie_posters[4])