from typing import List
from enum import Enum
import pandas as pd
import nltk
from nltk.stem import WordNetLemmatizer


PHRASE_NO_PROBLEMS = ['got food',
                      'got food and clothes',
                     'got food and covers']

KEYS_HOUSE = [
    "shelters",
    "mattresses",
    "pillows",
    "blankets",
    "shelter",
    "tentes",
    "housing",
    "couvertures",
    "tents",
    "covers",
    "sdader",
    "housing_shelter",
]
KEYS_FOOD = [
    "groceries",
    "nouriture",
    "food",
    "water",
    "gaz",
    "dishes",
    "oil",
    "sugar",
    "tea",
    "hungry",
]
KEYS_CLOTHES = [
    "clothes",
    "clothing",
    "hygiene",
]
KEYS_MEDICAL = [
    "betadine",
    "medical",
    "diabetics",
    "medicaments",
    "diabetes",
    "doliprane",
    "vitamines",
    "drugs",
]

class HelpCategory(Enum):
    HOUSE = 'house'
    FOOD = 'food'
    CLOTHES = 'clothes'
    MEDICAL = 'medical'
    UNKNOW = 'unknow'


nltk.download('wordnet')
nltk.download('omw-1.4')
lemmatizer = WordNetLemmatizer()

lemmatize_house = [lemmatizer.lemmatize(word) for word in KEYS_HOUSE]
lemmatize_food = [lemmatizer.lemmatize(word) for word in KEYS_FOOD]
lemmatize_clothes = [lemmatizer.lemmatize(word) for word in KEYS_CLOTHES]
lemmatize_medical = [lemmatizer.lemmatize(word) for word in KEYS_MEDICAL]

def to_category(text: str) -> List[HelpCategory]:
    if text in PHRASE_NO_PROBLEMS:
        return []

    words = text.split()
    categories = []
    for word in words:
        if word in KEYS_HOUSE:
            categories.append(HelpCategory.HOUSE)
        elif word in KEYS_FOOD:
            categories.append(HelpCategory.FOOD)
        if word in KEYS_CLOTHES:
            categories.append(HelpCategory.CLOTHES)
        if word in KEYS_MEDICAL:
            categories.append(HelpCategory.MEDICAL)
        if lemmatizer.lemmatize(word) in lemmatize_house:
            categories.append(HelpCategory.HOUSE)
        if lemmatizer.lemmatize(word) in lemmatize_food:
            categories.append(HelpCategory.FOOD)
        if lemmatizer.lemmatize(word) in lemmatize_clothes:
            categories.append(HelpCategory.CLOTHES)
        if lemmatizer.lemmatize(word) in lemmatize_medical:
            categories.append(HelpCategory.MEDICAL)
    if len(categories) == 0:
        categories = [HelpCategory.UNKNOW]
    return categories


def clean(text: str) -> str:
    text = text.replace('Housing/Shelter', 'housing_shelter')
    text = text.replace('/', ',')
    text = text.lower()
    text = text.strip()
    return text


def to_list(text: str) -> List[str]:
    helps = text.split(',')
    helps = [help_string.replace('.', ' ').strip() for help_string in helps]
    return helps


def help_text_to_help_category(helps: List[str]) -> List[str]:
    all_categories = set()
    for help_string in helps:
        categories = to_category(help_string)
        all_categories.update(categories)
    return list(all_categories)


def add_category(df:pd.DataFrame) -> pd.DataFrame:
    df['help_category'] = df['Help Details'].apply(clean).apply(to_list).apply(help_text_to_help_category)
    return df

def string_category(df:pd.DataFrame) -> pd.DataFrame:
    df['help_category'] = df['help_category'].apply(lambda x : ','.join([category.value for category in x]))
    return df