import pandas as pd
import os
import ast

DATA_FOLDER_LOCATION = "course_project/data/"
MOVIES_METADATA_FILE_LOCATION = DATA_FOLDER_LOCATION + "movies_metadata.csv"
CREDITS_FILE_LOCATION = DATA_FOLDER_LOCATION + "credits.csv"
RATINGS_FILE_LOCATION = DATA_FOLDER_LOCATION + "title.ratings.tsv"
MOVIES_DATA_FILE_LOCATION = DATA_FOLDER_LOCATION + "javiers_movies_data.csv"

def turn_numeric_or_warn(df, column_name):
    try:
        df.loc[:, column_name] = pd.to_numeric(df[column_name])
    except ValueError as e:
        # Captura el error y muestra los registros problemáticos
        problematic_datapoints = df[pd.to_numeric(df[column_name], errors='coerce').isnull()]
        print("Problematic Datapoints:")
        print(problematic_datapoints)

def turn_boolean_or_warn(df, column_name):
    try:
        df.loc[:, column_name] = df[column_name].astype(bool)
    except ValueError as e:
        # Captura el error y muestra los registros problemáticos
        problematic_datapoints = df[~df[column_name].isin(['True', 'False', True, False])]
        print("Problematic Datapoints:")
        print(problematic_datapoints)

def get_genres_set(df):
    unique_combinations = pd.Series(df["genres"].astype(str).unique()).apply(ast.literal_eval)
    combinations_exploded = unique_combinations.explode().dropna()
    unique_values_str = combinations_exploded.astype("str").unique()
    return unique_values_str

def prepare_movies_metadata(movies_metadata):
    # Dropping registers without required fields
    needed_fields = ["adult", "budget", "genres", "id", "imdb_id", "original_title",
                    "overview", "popularity", "poster_path", "revenue", "runtime", "title"]
    movies_metadata = movies_metadata[needed_fields].dropna()

    turn_numeric_or_warn(movies_metadata, "popularity")
    turn_numeric_or_warn(movies_metadata, "budget")
    turn_numeric_or_warn(movies_metadata, "id")
    turn_boolean_or_warn(movies_metadata, "adult")

    movies_metadata.loc[:, "genres"] = format_genres(movies_metadata)

    return movies_metadata

def load_movies_metadata():
    movies_metadata = pd.read_csv(MOVIES_METADATA_FILE_LOCATION, low_memory=False)
    return prepare_movies_metadata(movies_metadata)

def prepare_credits(credits):
    # Leaving only cast names
    credits["cast"] = credits["cast"].apply(ast.literal_eval).apply(lambda cast: [actor["name"] for actor in cast])

    # Retrieving director information
    credits["director"] = credits["crew"].apply(ast.literal_eval).apply(lambda crew: [crewmate["name"] for crewmate in crew if crewmate["job"].lower() == "director"])
    
    # Crew column is not needed anymore
    credits = credits.drop(["crew"], axis=1)

    # Dropping NANs and register without a Director
    credits = credits.dropna()
    credits = credits[credits["director"].apply(len) > 0]
    return credits

def load_credits():
    credits = pd.read_csv(CREDITS_FILE_LOCATION)
    return prepare_credits(credits)

def prepare_ratings(ratings):
    ratings = ratings.rename(columns={'tconst': 'imdb_id', 'averageRating': 'rating'}).drop(["numVotes"], axis=1)
    return ratings

def load_ratings():
    ratings = pd.read_csv(RATINGS_FILE_LOCATION, delimiter='\t')
    return prepare_ratings(ratings)

def generate_movies_data():
    print("Loading Movies Metadata")
    movies_metadata = load_movies_metadata()
    print("Loading Credits")
    credits = load_credits()
    print("Merging")
    movies_data = pd.merge(movies_metadata, credits, on="id", how="inner")
    print("Loading ratings")
    ratings = load_ratings()
    movies_data = pd.merge(movies_data, ratings, on="imdb_id", how="inner")
    movies_data = movies_data.drop_duplicates(subset=['id'])
    return movies_data

def format_genres(df):
    genres_dict = df["genres"].apply(ast.literal_eval)
    return genres_dict.apply(lambda genre_list: [genre["name"] for genre in genre_list if genre["name"] != "nan"])

def read_or_generate_movies_data():
    if os.path.exists(MOVIES_DATA_FILE_LOCATION):
        movies_data = pd.read_csv(MOVIES_DATA_FILE_LOCATION)
        print("Movies Database retrieved successfully.")
    else:
        movies_data = generate_movies_data()
        movies_data.to_csv(MOVIES_DATA_FILE_LOCATION, index=False)
        print("Movies Database generated and saved successfully.")
    return movies_data

def format_list_of_strings(register):
    return (str(register)).replace("', '", ", ")[2:-2]

if __name__ == "__main__":
    movies_data = read_or_generate_movies_data()
    print("Data info: ")
    print(movies_data.info())
    print("\n\n----------\n\n")
    print("Data description: ")
    print(movies_data.describe())
    print("\n\n----------\n\n")
    print("Data preview: ")
    print(movies_data)
    # print("\n\n----------\n\n")
    # print("The Show Rating: ")
    # print(get_imdb_rating_by_id("0114435"))
    # print("\n\n----------\n\n")
    # print("Movies metadata. ID 56088: ")
    # print(movies_metadata[movies_metadata["id"] == 56088])
    # print(movies_metadata[movies_metadata["id"] == 56088]["imdb_id"])