# main code

import streamlit as st
import pickle
import requests

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")
selectvalue = st.selectbox("Select movie from dropdown", movies_list)

def fetch_poster(movie_id):
    try:
        url = "https://api.themoviedb.org/3/movie/{}?api_key=8cfe8dff1a6fff88fe27b573ee65c035&language=en-US".format(movie_id)
        # Disablinb SSL verification for development
        data = requests.get(url, verify=False)
        data = data.json()
        poster_path = data['poster_path']
        if poster_path:
            full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
            return full_path
        else:
            st.warning(f"No poster found for movie ID {movie_id}")
            return None
    except Exception as e:
        st.error(f"Error fetching poster: {str(e)}")
        return None

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)
        poster = fetch_poster(movies_id)
        recommend_poster.append(poster)
    return recommend_movie, recommend_poster

if st.button("Show Recommend"):
    movie_name, movie_poster = recommend(selectvalue)
    cols = st.columns(5)
    for idx, (col, name, poster) in enumerate(zip(cols, movie_name, movie_poster)):
        with col:
            st.text(name)
            if poster:  # showing image if url exists
                st.image(poster)
            else:
                st.write("No poster available")