import mlflow
import pandas as pd
import os
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn. preprocessing import LabelEncoder
import pickle
import sys



def datasets_preparation():
    df_1 = pd.read_csv("spotify_songs.csv")
    df_2 = pd.read_csv("Spotify_Dataset.csv", sep=";")

    df_1 = df_1.dropna()
    df_2 = df_2.dropna()
    df_2 = df_2.rename(columns={'Title': 'track_name'})

    columns_to_remove_df_1 = ['track_id', 'track_album_id', 'track_album_name', 'track_album_release_date',
                              'playlist_id', 'playlist_subgenre']
    columns_to_remove_df_2 = ['Date','# of Artist', 'Artist (Ind.)', '# of Nationality',
                              'Nationality', 'Continent', 'Points (Total)',
                              'Points (Ind for each Artist/Nat)', 'id', 'Song URL']

    df_1 = df_1.drop(columns=columns_to_remove_df_1)
    df_2 = df_2.drop(columns=columns_to_remove_df_2)
    df_1 = df_1.drop_duplicates(subset=['track_name'])
    df_2 = df_2.drop_duplicates(subset=['track_name'])

    le = LabelEncoder()

    unique_names_df2 = df_2['track_name'].unique()
    diff_df = df_1[~df_1['track_name'].isin(unique_names_df2)]
    diff_df = diff_df.iloc[:10000]

    diff_df['track_artist'] = le.fit_transform(diff_df.track_artist)
    diff_df['playlist_name'] = le.fit_transform(diff_df.playlist_name)
    diff_df['playlist_genre'] = le.fit_transform(diff_df.playlist_genre)

    if "docker_test_dataset.csv" not in os.listdir():
        diff_df.to_csv("docker_test_dataset.csv", index=False)

    result_df = pd.merge(df_1, df_2, on='track_name', how='inner') 
    result_df = result_df.drop_duplicates(subset=['track_name'])
    columns_to_remove_result_df = ['Rank', 'Artists', 'Danceability', 'Energy', 'Loudness',
                                   'Speechiness', 'Acousticness', 'Instrumentalness', 'Valence']
    result_df = result_df.drop(columns=columns_to_remove_result_df)

    result_df['track_artist'] = le.fit_transform(result_df.track_artist)
    result_df['playlist_name'] = le.fit_transform(result_df.playlist_name)
    result_df['playlist_genre'] = le.fit_transform(result_df.playlist_genre)
       
    return result_df

def train_model_and_log(max_iter):

    result_df = datasets_preparation()
    Y = result_df[['playlist_genre']]
    X = result_df.drop(columns='playlist_genre')
    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.10, random_state=42)
    Y_train = np.ravel(Y_train)
    Y_test = np.ravel(Y_test)

    scaler = StandardScaler()
    numeric_columns = X_train.select_dtypes(include=['int', 'float']).columns
    X_train_scaled = scaler.fit_transform(X_train[numeric_columns])
    X_test_scaled = scaler.transform(X_test[numeric_columns])

    model = LogisticRegression(max_iter=max_iter)
    model.fit(X_train_scaled, Y_train)

    Y_pred = model.predict(X_test_scaled)

    accuracy = accuracy_score(Y_test, Y_pred)
    print("Accuracy:", accuracy)

    with mlflow.start_run():
        mlflow.log_param("model_type", "LogisticRegression")
        mlflow.log_param("test_size", 0.1)
        mlflow.log_param("max_iter", max_iter)
        mlflow.log_metric("accuracy", accuracy)
        mlflow.sklearn.log_model(model, "model")

if __name__ == "__main__":
    max_iter = int(sys.argv[1]) if len(sys.argv) > 1 else 1000
    mlflow.set_tracking_uri("http://localhost:5000") 
    train_model_and_log(max_iter)