import streamlit as st
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity

# DATA:
movies = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/movies.csv')
ratings = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/recommender-systems/main/movie_data/ratings.csv')

# create "database" to use for recommendations
user_item_matrix = (
                    ratings
                        .merge(movies, on='movieId')[['title', 'rating', 'userId']]
                        .pivot_table(index='userId', columns='title', values='rating')
                        .fillna(0)
                    )

similarities_users = pd.DataFrame(cosine_similarity(user_item_matrix),
                                  index=user_item_matrix.index,
                                  columns=user_item_matrix.index)

# INSTRUCTIONS:
st.title("User-Based Recommender")
st.write("""
### Instructions
Type in the user-ID you want to receive recommendations for.  
Move the slider to the desired number of recommendations you wish to receive. 
""")
st.write("""
Optional: You can narrow down the recommendations by picking one or several genre(s).  
  
Note: The more genres you choose, the fewer movies will be recommended.
""")

# FUNCTIONS:

def get_user_recommendations(user_id, n, genres):
    
    user_id = int(user_id)
    # calculate weights for ratings
    weights = similarities_users.loc[similarities_users.index != user_id, user_id] / sum(similarities_users.loc[similarities_users.index != user_id, user_id])
    
    # get unwatched movies for recommendations
    unwatched_movies = (
        user_item_matrix
            .loc[user_item_matrix.index != user_id, user_item_matrix.loc[user_id,:] == 0]
            .T
        )

    # compute weighted averages and return the n movies with the highest predicted ratings
    weighted_averages = pd.DataFrame(unwatched_movies.dot(weights), columns = ["predicted_rating"])
    recommendations = (
        weighted_averages
            .sort_values("predicted_rating", ascending=False)
            .head(n)
            .merge(movies, how= 'left', left_index = True, right_on = 'title')
            [lambda df: df["genres"].str.contains(genres, regex=True)]
    )

    return recommendations[['title', 'genres']]

def transform_genre_to_regex(genres):
    regex = ""
    for genre in genres:
        regex += f"(?=.*{genre})"
    return regex

# USER INPUT:
user_id_input = st.text_input('User-ID')

number_of_recommendations = st.slider("Number of recommendations", 1, 10, 5)

genre_list = set([inner for outer in movies.genres.str.split('|') for inner in outer])
genres = st.multiselect('Optional: Select one or more genres', genre_list, default=None, key=None, help=None, on_change=None, args=None, kwargs=None, disabled=False)
genres_regex = transform_genre_to_regex(genres)

# EXECUTION:
if st.button("Recommend!"):
    st.write(get_user_recommendations(user_id_input, number_of_recommendations, genres_regex))