import pickle
import streamlit as st
import requests

# Set page title and sidebar properties
st.set_page_config(page_title="Insightly")

st.markdown(
    """
    <style>
    .image-container {
        margin-bottom: 60px;
    }
    .sidebar-link {
        display: flex;
        justify-content: left;
        font-size: 28px;
        margin-top: 10px; /* Adjust margin-top value to control space on the top */
        margin-left: 20px; /* Adjust margin-left value to add space from the left */
    }
    .vertical-space {
        height: 20px;
    }
    .movie-title {
        font-size: 18px;
        font-weight: bold;
    }
    .row-padding {
        padding-bottom: 40px;
    }
    </style>
    """,
    unsafe_allow_html=True,
)

# Sidebar contents
with st.sidebar:
    st.image("https://i.ibb.co/bX6GdqG/insightly-wbg.png", use_column_width=True)
    st.markdown("<p class='sidebar-link'>📈 <a href='https://insightly-csv-bot.hf.space/'>  CSV Bot</a></p>", unsafe_allow_html=True)
    st.markdown("<p class='sidebar-link'>📚  <a href='https://chandrakalagowda-demo2.hf.space/'>  PDF Bot </a></p>", unsafe_allow_html=True)
    st.markdown("<p class='sidebar-link'>📸  <a href='https://insightly-frame-capturer.hf.space/'>  Frame Capturer</a></p>", unsafe_allow_html=True)
    st.markdown("<p class='sidebar-link'>🖼️  <a href='https://insightly-image-reader.hf.space/'>  Image Reader</a></p>", unsafe_allow_html=True)
    st.markdown("<div class='vertical-space'></div>", unsafe_allow_html=True)



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

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


st.title('Movie Recommender 🎬')

# Provide the correct absolute paths to the pickled data
movie_list_path = "https://drive.google.com/file/d/1OmueIayrvczEQKRWIOfEkKH5zLWGxCn1/view?usp=drive_link"
similarity_path = "https://drive.google.com/file/d/1RI6XgtbaNxlBqZM0cznOZXN88tQWo98a/view?usp=drive_link"

movies = pickle.load(open(movie_list_path, 'rb'))
similarity = pickle.load(open(similarity_path, 'rb'))


movies = pickle.load(open(movie_list_path, 'rb'))
similarity = pickle.load(open(similarity_path, 'rb'))

movie_list = movies['title'].values
selected_movie = st.selectbox(
    "Type or select a movie from the dropdown",
    movie_list
)

if st.button('Show Recommendation'):
    recommended_movie_names, recommended_movie_posters = recommend(selected_movie)

    # Create columns based on the number of recommended movies
    num_recommendations = len(recommended_movie_names)
    num_columns = 3
    num_rows = (num_recommendations + num_columns - 1) // num_columns  # Calculate the number of rows required

    # Create a list of columns
    cols = [st.columns(num_columns) for _ in range(num_rows)]

    # Loop through recommended movies and posters and display them in the columns
    for i, movie_name in enumerate(recommended_movie_names):
        col_index = i // num_columns
        row_index = i % num_columns
        cols[col_index][row_index].markdown(f"<span class='movie-title'>{movie_name}</span>", unsafe_allow_html=True)
        cols[col_index][row_index].image(recommended_movie_posters[i])

    # Add padding between the rows
    st.markdown("<br>", unsafe_allow_html=True)
    st.write('<div class="row-padding"></div>', unsafe_allow_html=True)