import streamlit as st import pickle import pandas as pd import requests original_title = '

CinePicks

' st.markdown(original_title, unsafe_allow_html=True) st.markdown( f""" """, unsafe_allow_html=True ) def fetch_poster(movie_id): url='https://api.themoviedb.org/3/movie/{}?api_key=4e9dfb203bf90ff6a4fc33522c802f3b'.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 similarity=pickle.load(open('similarity.pkl','rb')) def recommend(movie): movie_index = movies[movies['title'] == movie].index[0] distances = similarity[movie_index] movies_list = sorted(list(enumerate(distances)), reverse=True, key=lambda x: x[1])[1:6] recommended_movies=[] recommended_movie_posters = [] for i in movies_list: movie_id=movies.iloc[i[0]].movie_id # Fetch poster from API recommended_movies.append(movies.iloc[i[0]].title) recommended_movie_posters.append(fetch_poster(movie_id)) return recommended_movies,recommended_movie_posters movies_dict=pickle.load(open('movies_dict.pkl','rb')) movies=pd.DataFrame(movies_dict) selected_movie_name=st.selectbox('Recently Watched',movies['title'].values) if st.button('Show Recommendations'): recommended_movie_names, recommended_movie_posters = recommend(selected_movie_name) 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])