Update app.py

app.py

@@ -17,7 +17,7 @@ model = from_pretrained_keras("keras-io/text-generation-miniature-gpt")
 a = []
 word_to_index = {}
 
-a_file = open("imdb.vocab")
+a_file = open("imdb.vocab") # get vocab tokens
 for line in a_file:
   a.append(line.strip())
 print(len(a))
@@ -25,9 +25,11 @@ print(len(a))
 for index, word in enumerate(a):
     word_to_index[index] = word
 
-def text_process_pipeline(text): #pipeline
-  tokenizer = Tokenizer(num_words=80, split=' ')
-  tokenizer.fit_on_texts(word_to_index.values())
+tokenizer = Tokenizer(num_words=80, split=' ')
+tokenizer.fit_on_texts(word_to_index.values()) # fit tokenizer on vocab tokens
+  
+def text_process_pipeline(start_prompt): #pipeline
+
   processed_text = tokenizer.texts_to_sequences(start_prompt)
   processed_text = pad_sequences(processed_text, maxlen=80, padding='post')
   return processed_text