from typing import Mapping
from urllib import response
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd
import numpy as np

#tf_idf
#words = whole text
#word the word we finding the score for
#return the score
words = ['this is the first document this is another one this is the final Kaeya of all the docs wow this will just keep going who knew that ther could be this much Madeon - Love You Back (Visualizer)']
doc1 = "I can't fucking take it any more. Among Us has singlehandedly ruined my life. The other day my teacher was teaching us Greek Mythology and he mentioned a pegasus and I immediately thought 'Pegasus? more like Mega Sus!!!!' and I've never wanted to kms more. I can't look at a vent without breaking down and fucking crying. I can't eat pasta without thinking 'IMPASTA??? THATS PRETTY SUS!!!!' Skit 4 by Kanye West. The lyrics ruined me. A Mongoose, or the 25th island of greece. The scientific name for pig. I can't fucking take it anymore. Please fucking end my suffering."
doc2 = "Anyways, um... I bought a whole bunch of shungite rocks, do you know what shungite is? Anybody know what shungite is? No, not Suge Knight, I think he's locked up in prison. I'm talkin' shungite. Anyways, it's a two billion year-old like, rock stone that protects against frequencies and unwanted frequencies that may be traveling in the air. That's my story, I bought a whole bunch of stuff. Put 'em around the la casa. Little pyramids, stuff like that."
word = 'doc'

vect = TfidfVectorizer()
tfidf_matrix = vect.fit_transform(words)
feature_index = tfidf_matrix[0,:].nonzero()[1]
feature_names = vect.get_feature_names_out()
tfidf_scores = zip(feature_index, [tfidf_matrix[0, x] for x in feature_index])
for w, s in [(feature_names[i], s) for (i, s) in tfidf_scores]:
    if w == word: 
        print (s)
    else:
        exit
#--------------------------------- Prints the list of all -----------------------------------#        
# for w, s in [(feature_names[i], s) for (i, s) in tfidf_scores]:
#   print (w, s)

#--------------------------------- Both of these implentations are from this link -----------------------------------------#
# https://stackoverflow.com/questions/34449127/sklearn-tfidf-transformer-how-to-get-tf-idf-values-of-given-words-in-documen
# tfidf = TfidfVectorizer()
# response = tfidf.fit_transform([doc1, doc2])
# print(len(tfidf.vocabulary_))
# print(tfidf.vocabulary_)
# feature_names = tfidf.get_feature_names_out()
# for col in response.nonzero()[1]:
#     print(feature_names[col], ' - ', response[0,col])


# vect = TfidfVectorizer()
# tfidf_matrix = vect.fit_transform(words)
# df = pd.DataFrame(tfidf_matrix.toarray(), columns = vect.get_feature_names_out())
# print(df)