import all functions and classes form helper_function.py

app.py

@@ -21,6 +21,65 @@ print('Libraries called succesfully!!!!"')
 
 
 
+class keyWordExtractor():
+    
+    def __init__(self, 
+                 article_text,
+                 similarity_model,
+                 n_gram = 1,
+                 top_n = 3,
+                 french_stopwords = None,
+                 ner= None,
+                 ):
+        self.article_text = article_text
+        self.french_stopwords = french_stopwords
+        self.candidates = self.count_vectorizer(n_gram)
+        self.noun_candidates, self.proper_noun_candidates = self.slice_only_noun_token(ner, self.candidates)
+        self.top_n_keywords = self.top_n_extractor(similarity_model, top_n)
+    
+    def count_vectorizer(self, n_gram):
+        n_gram_range = (n_gram, n_gram)
+        # Extract candidate words/phrases
+        count = CountVectorizer(ngram_range=n_gram_range,
+                        stop_words = self.french_stopwords).fit([self.article_text]) #Main change
+        candidates = count.get_feature_names_out()
+
+        return candidates
+
+    def slice_only_noun_token(self, ner, token_list):
+        """
+        Given the tokenized list, this function returns only the "NOUN" token
+            Args:
+                ner (spacy): The NER class to detect the `token.pos_`
+                token_list (list): List of token from the full article
+            Returns:
+                slice_list (list): List of token containing only "NOUN" part of speech
+        """
+
+        noun_slice_list = []
+        proper_noun_slice_list = []
+        for word_idx in range(len(token_list)):
+            doc = ner(token_list[word_idx])
+
+            for token in doc:
+                if token.pos_ == 'NOUN':
+                    noun_slice_list.append(token.text)
+                elif token.pos_ == 'PROPN':
+                    proper_noun_slice_list.append(token.text)
+
+        return noun_slice_list, proper_noun_slice_list
+
+    def top_n_extractor(self, model, top_n):
+        doc_embedding = model.encode([self.article_text])
+        candidate_embeddings = model.encode(self.noun_candidates)
+        distances = cosine_similarity(doc_embedding, candidate_embeddings)
+        keywords = [self.noun_candidates[index] for index in distances.argsort()[0][-top_n:]]
+
+        return keywords
+
+def clear_input():
+    return ("", "")
+
 def t5_generate_summary(article_text):
     input_ids = t5_tokenizer(
     [WHITESPACE_HANDLER(article_text)],