import pandas as pd import streamlit as st import requests def fetch_poster(movie_id): url = "https://api.themoviedb.org/3/movie/{}?api_key=8265bd1679663a7ea12ac168da84d2e8&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 import pickle similarity=pickle.load(open('similarity.pkl','rb')) movies_dict= pickle.load(open('movies_dict.pkl','rb')) movies=pd.DataFrame(movies_dict) st.title('Movie Recommender System ') # def add_bg_from_url(): # st.markdown( # f""" # # """, # unsafe_allow_html=True # ) # add_bg_from_url() 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 option = st.selectbox('How would you like to be continued?', movies['title'].values) if st.button('Recommend') : recommended_movie_names,recommended_movie_posters= recommend(option) 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])