import streamlit as st
import pickle
import requests


# function to fetch the poster
def fetch_poster(movie_id):
    response = requests.get(f"https://api.themoviedb.org/3/movie/{movie_id}?api_key=bb1678d9f4f581a1a07e8161ffe487a0&language=en-US%27")
    data = response.json()
    if 'poster_path' in data:
        return "https://image.tmdb.org/t/p/w500/" + data['poster_path']
    else:
        return None



movielist_of_5000_movies = pickle.load(open('movies.pkl', 'rb'))
similarityArray = pickle.load(open('similarityArray.pkl', 'rb'))


def recommended(movie):
    movie_index = movielist_of_5000_movies[movielist_of_5000_movies['title'] == movie].index[0]
    distances = similarityArray[movie_index]
    movie_list = sorted(enumerate(distances), reverse=True, key=lambda x: x[1])[1:11]

    recommended_movies = []
    recommended_movies_posters = []

    for i in movie_list:
        recommended_movies.append(movielist_of_5000_movies.iloc[i[0]].title)
        recommended_movies_posters.append(fetch_poster(movielist_of_5000_movies.iloc[i[0]].movie_id))

    return recommended_movies, recommended_movies_posters


st.title("Movie Recommendation System")
st.subheader("The is a Movie Recommendation System. I have created this for fun")
st.caption("I hope you all will like it!!!")

selected_movie = st.selectbox(
    'Please Select the Movie you like to get Recommendation ',
    movielist_of_5000_movies['title'].values)

if st.button('Recommend'):
    st.write('The Recommended Movies for ', selected_movie, " are: ")
    recommendations, posters = recommended(selected_movie)



    col1, col2 = st.columns(2)
    for i in range(5):
        with col1:
            st.text(recommendations[i])
            st.image(posters[i])

    for i in range(5, 10):
        with col2:
            st.text(recommendations[i])
            st.image(posters[i])