import streamlit as st
import pickle
import requests
import os

movies = pickle.load(open('movies.pkl','rb'))
similarity = pickle.load(open('similarity.pkl','rb'))
movie_list = movies['title'].values
tmdb_api_token = os.environ["TMDB_API_TOKEN"]

def poster_fetcher(id):
    url = "https://api.themoviedb.org/3/movie/{}?api_key={}".format(id,tmdb_api_token)
    response = requests.get(url)
    obj  = response.json()
    return "https://image.tmdb.org/t/p/w500/" + obj['poster_path']



def recommend(movie):
    movie_names = []
    posters = []
    movie_index = movies[movies['title']==movie].index[0]
    distance =  similarity[movie_index]
    movie_list = sorted((list(enumerate(distance))),reverse=True,key=lambda x : x[1])[1:6]
    for i in movie_list:
        # movie_id = i[0]
        movie_names.append(movies.iloc[i[0]].title)
        posters.append(poster_fetcher(movies.iloc[i[0]].movie_id))
    return movie_names,posters




st.title("movie recommendation system")

selected_movie_name = st.selectbox("select any movie that you like",movie_list)

if st.button('recommend'):
    names,posters = recommend(selected_movie_name)
    st.title("recommendations:")
    # for (i,j) in zip(names,posters):
    #     st.write(i)
    #     st.image(j)


    # col1, col2, col3, col4, col5 = st.columns(5)

    # with col1:
    #     st.text(names[0])
    #     st.image(posters[0])

    # with col1:
    #     st.text(names[1])
    #     st.image(posters[1])

    # with col1:
    #     st.text(names[2])
    #     st.image(posters[2])

    # with col1:
    #     st.text(names[3])
    #     st.image(posters[3])

    # with col1:
    #     st.text(names[4])
    #     st.image(posters[4])

    

    col = st.columns(len(names))[0]

# Use a loop to add images horizontally
    for (i,j) in zip(names,posters):
        with col:
            st.divider()
            st.text(i)
            st.image(j)


    st.divider()
    st.text('source - TMDB')