import pandas as pd
import matplotlib.pyplot as plt
import string
import re
import nltk
import numpy as np
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from prefect import task, Flow
from pandas import DataFrame
from typing import List

nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')


# remove urls, handles, and the hashtag from hashtags (taken from https://stackoverflow.com/questions/8376691/how-to-remove-hashtag-user-link-of-a-tweet-using-regular-expression)
def remove_urls(text):
    new_text = ' '.join(re.sub("(@[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",text).split())
    return new_text


# make all text lowercase
def text_lowercase(text):
    return text.lower()


# remove numbers
def remove_numbers(text):
    result = re.sub(r'\d+', '', text)
    return result


# remove punctuation
def remove_punctuation(text):
    translator = str.maketrans('', '', string.punctuation)
    return text.translate(translator)


# tokenize
def tokenize(text):
    text = word_tokenize(text)
    return text


# remove stopwords
stop_words = set(stopwords.words('english'))
def remove_stopwords(text):
    text = [i for i in text if not i in stop_words]
    return text

# lemmatize
lemmatizer = WordNetLemmatizer()
def lemmatize(text):
    text = [lemmatizer.lemmatize(token) for token in text]
    return text


def preprocessing(text):
    text = text_lowercase(text)
    text = remove_urls(text)
    text = remove_numbers(text)
    text = remove_punctuation(text)
    text = tokenize(text)
    text = remove_stopwords(text)
    text = lemmatize(text)
    text = ' '.join(text)
    return text


@task
def get_train_set() -> DataFrame:
    train = pd.read_csv('train.csv')
    train = train.drop(['keyword', 'location'], axis=1)
    return train


@task
def get_test_set() -> DataFrame:
    return pd.read_csv('test.csv')


@task
def preprocess_train(train: DataFrame) -> DataFrame:
    pp_text_train = []
    for text_data in train['text']:
        pp_text_data = preprocessing(text_data)
        pp_text_train.append(pp_text_data)
    train['pp_text'] = pp_text_train
    return train


@task
def preprocess_test(test: DataFrame) -> DataFrame:
    pp_text_test = []
    for text_data in test['text']:
        pp_text_data = preprocessing(text_data)
        pp_text_test.append(pp_text_data)
    test['pp_text'] = pp_text_test
    return test


@task
def prepare_vectorizer(train_data: DataFrame, test_data: DataFrame) -> TfidfVectorizer:
    train_text_data = list(train_data['pp_text'])
    test_text_data = list(test_data['pp_text'])
    corpus = train_text_data + test_text_data
    tf = TfidfVectorizer()
    fitted_vectorizer = tf.fit(corpus)
    return fitted_vectorizer


@task
def transform_train(vectorizer: TfidfVectorizer, train_set: DataFrame) -> DataFrame:
    return vectorizer.transform(train_set)


@task
def transform_test(vectorizer: TfidfVectorizer, test_set: DataFrame) -> DataFrame:
    return vectorizer.transform(test_set)


@task
def split_test_set(X: DataFrame, Y: DataFrame) -> dict:
    X_train, X_test, y_train, y_test = train_test_split(X, Y)
    return {'X_train': X_train, 'X_test': X_test, 'y_train': y_train, 'y_test': y_test}


@task
def train_model(X: DataFrame, Y: DataFrame) -> LogisticRegression:
    scikit_log_reg = LogisticRegression()
    model = scikit_log_reg.fit(X, Y)
    return model


@task
def evaluate(model: LogisticRegression, X: DataFrame, Y: DataFrame) -> None:
    predictions = model.predict(X)
    count = 0
    for guess, answer in zip(predictions, Y):
        if guess == answer:
            count += 1
    print("> model score: ", count/len(Y))


if __name__ == "__main__":
    with Flow("My First Flow!") as flow:
        train_data = get_train_set()
        test_data = get_test_set()

        train_data = preprocess_train(train_data)
        test_data = preprocess_test(test_data)

        vectorizer = prepare_vectorizer(train_data, test_data)

        vectorized_train_data = transform_train(vectorizer, train_data['pp_text'])
        vectorized_test_data = transform_test(vectorizer, train_data['pp_text'])

        splitted_data = split_test_set(vectorized_train_data, train_data['target'])
        model = train_model(splitted_data['X_train'], splitted_data['y_train'])
        evaluate(model, splitted_data['X_test'], splitted_data['y_test'])

    flow.validate()
    # flow.visualize()
    flow.run()