import streamlit as st import pickle import requests def fetch_poster(movie_id): url = "https://api.themoviedb.org/3/movie/{}?api_key=c7ec19ffdd3279641fb606d19ceb9bb1&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 movies = pickle.load(open("movies_list.pkl", 'rb')) similarity = pickle.load(open("similarity.pkl", 'rb')) movies_list=movies['title'].values st.header("Movie Recommender System") import streamlit.components.v1 as components imageCarouselComponent = components.declare_component("image-carousel-component", path="frontend/public") imageUrls = [ fetch_poster(1632), fetch_poster(299536), fetch_poster(17455), fetch_poster(2830), fetch_poster(429422), fetch_poster(9722), fetch_poster(13972), fetch_poster(240), fetch_poster(155), fetch_poster(598), fetch_poster(914), fetch_poster(255709), fetch_poster(572154) ] imageCarouselComponent(imageUrls=imageUrls, height=200) selectvalue=st.selectbox("Select movie from dropdown", movies_list) def recommend(movie): index=movies[movies['title']==movie].index[0] distance = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda vector:vector[1]) recommend_movie=[] recommend_poster=[] for i in distance[1:6]: movies_id=movies.iloc[i[0]].id recommend_movie.append(movies.iloc[i[0]].title) recommend_poster.append(fetch_poster(movies_id)) return recommend_movie, recommend_poster if st.button("Show Recommend"): movie_name, movie_poster = recommend(selectvalue) col1,col2,col3,col4,col5=st.columns(5) with col1: st.text(movie_name[0]) st.image(movie_poster[0]) with col2: st.text(movie_name[1]) st.image(movie_poster[1]) with col3: st.text(movie_name[2]) st.image(movie_poster[2]) with col4: st.text(movie_name[3]) st.image(movie_poster[3]) with col5: st.text(movie_name[4]) st.image(movie_poster[4])