import streamlit as st
import pandas as pd
import re
import requests

# DATA:
movies = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/movies.csv')
ratings = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/ratings.csv')
links = pd.read_csv('https://raw.githubusercontent.com/tobiasaurer/movie-recommender-streamlit/main/data/links.csv')

# clean titles column by moving "The" and "A" to the beginning of the string
# this makes it more searchable for users
movies.loc[lambda df: df["title"].str.contains(", The", regex=True), 'title'] = 'The ' + movies['title']
movies.loc[lambda df: df["title"].str.contains(", The", regex=True), 'title'] = movies['title'].str.replace(", The", '', regex=True)

movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = 'A ' + movies['title']
movies.loc[lambda df: df["title"].str.contains(", A", regex=True), 'title'] = movies['title'].str.replace(", A", '', regex=True)

# INSTRUCTIONS:
st.title("Popularity-Based Recommender")
st.write("""
### Instructions
Move the slider to the desired number of recommendations you wish to receive.  
  
__Optional__: You can receive links for popular streaming services for each  recommendation (if available) by selecting in your countrycode.  
Leave this field empty if you don't want to get streaming links.  
  
__Optional__: You can narrow down the recommendations by picking one or several genre(s).  
However, the more genres you choose, the fewer movies will be recommended.
  
Afterwards, simply click the "Get Recommendations" button to receive recommendations based on the most popular movies in our database.  
""")

# FUNCTIONS:

def get_popular_recommendations(n, genres):
    return (
        ratings
            .groupby('movieId')
            .agg(avg_rating = ('rating', 'mean'), num_ratings = ('rating', 'count'))
            .merge(movies, on='movieId')
            .assign(combined_rating = lambda x: x['avg_rating'] * x['num_ratings']**0.5)
            [lambda df: df["genres"].str.contains(genres, regex=True)]
            .sort_values('combined_rating', ascending=False)
            .head(n)
            [['title', 'avg_rating', 'genres']]
    )

def get_popular_recommendations_streaming(n, genres, country, url, headers):
    recommendations = (
        ratings
            .groupby('movieId')
            .agg(avg_rating = ('rating', 'mean'), num_ratings = ('rating', 'count'))
            .merge(movies, on='movieId')
            .assign(combined_rating = lambda x: x['avg_rating'] * x['num_ratings']**0.5)
            [lambda df: df["genres"].str.contains(genres, regex=True)]
            .sort_values('combined_rating', ascending=False)
            .head(n)
            [['title', 'avg_rating', 'genres']]
    )
    # merge recommendations with links df to get imdbIds for the API calls
    recommendations_ids =   (
                            recommendations
                                .merge(movies, how = 'left', on = 'title')
                                .merge(links, how = 'left', on = 'movieId')
                                [['title', 'genres', 'imdbId']]
                            )
    recommendations_ids['imdbId'] = 'tt0' + recommendations_ids['imdbId'].astype('str')
    imdb_ids = list(recommendations_ids['imdbId'])

    # create new column for streaming links
    recommendations_ids['Streaming Availability'] = ""

    # loop through imdb_ids to make one api call for each to get available streaming links
    for id in imdb_ids:

        # make api call
        querystring = {"country":country,"imdb_id":id,"output_language":"en"}
        response = requests.request("GET", url, headers=headers, params=querystring)
        streaming_info = response.json()

        for streaming_service in streaming_info['streamingInfo']:
            recommendations_ids.loc[recommendations_ids['imdbId'] == id, 'Streaming Availability'] += f"{streaming_service}: {streaming_info['streamingInfo'][streaming_service][country]['link']} \n" 

        return recommendations_ids[['title', 'genres', 'Streaming Availability']]

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

# USER INPUT:
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)

streaming_country = st.selectbox('Optional: Country for streaming information', ('de', 'us'))

# API INFORMATION:
url = "https://streaming-availability.p.rapidapi.com/get/basic"
headers = {
	"X-RapidAPI-Key": st.secrets["api_key"],
	"X-RapidAPI-Host": "streaming-availability.p.rapidapi.com"
}

# EXECUTION:

if st.button("Get Recommendations"):
    if streaming_country == '':
        st.write(get_popular_recommendations(number_of_recommendations, genres_regex))
    else: 
        st.write(get_popular_recommendations_streaming(number_of_recommendations, genres_regex, streaming_country, url, headers))