--- a
+++ b/utils/vocabulary.py
@@ -0,0 +1,69 @@
+# tensorflow imports
+import tensorflow
+from tensorflow.keras.preprocessing.text import Tokenizer
+
+
+class Vocabulary:
+    def __init__(self, texts:list, threshold:int = 3):
+        """ Vocabulary class we utilised in CNN-RNN models. This class creates a vocabulary for our captions with a cut-off frequency.
+
+        Args:
+            texts (list): All training captions from which we extract our vocabulary
+            threshold (int, optional): The cut-off frequency in our dictionary. Defaults to 3.
+        """
+        self.texts = texts
+        # add <pad> abd <unk> tokens in vocabulary. <unk> refer to the OOV (out-of-vocabulary) tokens.
+        self.pad_token = "<pad>"
+        self.unk_token = "<unk>"
+        self.threshold = threshold
+        # init the tokenizer
+        self.tokenizer = Tokenizer(oov_token="<unk>")
+        # dictionaries
+        self.word2idx = {}
+        self.idx2word = {}
+
+    def build_vocab(self) ->tuple[Tokenizer, dict, dict]:
+        """ This function creates the Vocabulary we want to employ for our CNN-RNN model.
+
+        Returns:
+            tuple[Tokenizer, dict, dict]: The fitted tokenizer, the word-to-idx dictionary, and idx-to-word dictionary. 
+            The latters are mappers for words and index respectively
+        """
+        # fit in training captions
+        self.tokenizer.fit_on_texts(self.texts)
+        # create the vocabulary in sorted way
+        sorted_vocab = dict(
+            sorted(self.tokenizer.word_counts.items(), key=lambda t: t[1], reverse=True)
+        )
+
+        word_index_threshold, index_word_threshold = {}, {}
+
+        # add pad and unk token to tokenizer dictionary
+        word_index_threshold[self.pad_token] = 0
+        index_word_threshold[0] = self.pad_token
+        word_index_threshold[self.unk_token] = 1
+        index_word_threshold[1] = self.unk_token
+
+        # begin from index=2
+        idx = 2
+        for k, v in sorted_vocab.items():
+            # if the occurence of current word is less than the threshold, continue.
+            if v >= self.threshold:
+                word_index_threshold[k] = idx
+                index_word_threshold[idx] = k
+                idx += 1
+
+        # get the dictionaries
+        self.tokenizer.word_index = word_index_threshold
+        self.tokenizer.index_word = index_word_threshold
+
+        dictionary = self.tokenizer.word_index
+
+        # append to the global ones
+        for k, v in dictionary.items():
+            self.word2idx[k] = v
+            self.idx2word[v] = k
+
+        print(f"Made a vocabulary with {len(self.word2idx)} words!")
+        
+        return self.tokenizer, self.word2idx, self.idx2word