structural changes

Not_used/preprocessing.py

@@ -1,62 +0,0 @@
-import pandas as pd
-import string, re
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-import nltk
-from nltk.stem import PorterStemmer, WordNetLemmatizer
-from sentence_transformers import SentenceTransformer
-import pkg_resources
-from symspellpy.symspellpy import SymSpell, Verbosity
-
-nltk.download('wordnet')
-nltk.download('punkt_tab')
-nltk.download('stopwords')
-
-stop_words = set(stopwords.words('english'))
-sym_spell = SymSpell(max_dictionary_edit_distance=2, prefix_length=7)
-
-stemmer = PorterStemmer()
-lemmatizer = WordNetLemmatizer()
-
-# model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
-# df = pd.read_excel('C:\\Users\\ishaa\\OneDrive\\Documents\\MSU\\Spring 2026\\Data mining\\Project\\sample_data.xlsx', engine='openpyxl')
-
-def clean_plot(text):
-    text = text.lower()
-    text = text.translate(str.maketrans('', '', string.punctuation))  # Remove punctuation
-    text = re.sub(r'\W', ' ', text)
-    suggestions = sym_spell.lookup_compound(text, max_edit_distance=2)
-    if suggestions:
-        text = suggestions[0].term
-    text = ([word for word in word_tokenize(text) if word not in stop_words])
-    text = [stemmer.stem(word) for word in text]
-    text = ' '.join(lemmatizer.lemmatize(word) for word in text)
-    return text
-
-def get_genre(row):
-    movie = row['Title']
-    print(movie)
-    text = row['Genre']
-    text = text.split(".")[0]
-    text = text.replace(movie, "")
-    text = text.lower()
-    match = re.search(r'is a ((?:\S+\s+){4}\S+)', text)
-    if match:
-        words = match.group(1).split()
-        text = ' '.join(words[1:])
-    text = text.translate(str.maketrans('', '', string.punctuation))  # Remove punctuation
-    text = re.sub(r'\W', ' ', text)  # Remove special characters
-    text = ([word for word in word_tokenize(text) if word not in stop_words])
-    text = ' '.join(text)
-
-    return text
-
-# print(df.columns)
-
-# df['preprocessed'] = df['Plot'].apply(clean_text)
-# sample_plot = df['preprocessed'][0]
-# print(sample_plot)
-
-# embeddings = model.encode(sample_plot)
-# print(embeddings)