load nltk

app.py

@@ -83,6 +83,8 @@ def predict(brakes, transcript):
  
 if __name__ == "__main__":
 
+    metrics.load_nltk()
+
     title = "Deep Punkt App"
     description = """
 <b>Description</b>: <br>

metrics.py

@@ -1,8 +1,17 @@
 # Import nltk library for natural language processing
 import nltk
-
+import os
 from transformers import AutoTokenizer
 
+def load_nltk():
+    nltk_file = '/home/user/nltk_data/tokenizers/punkt.zip'
+    if os.path.exists(nltk_file):
+        print('nltk punkt file exists in ', nltk_file)
+    else:
+        print("downloading punkt file")
+        nltk.download('punkt')
+
+
 # Define a function that takes some text as input and returns the number of tokens
 def token_count(text):
     # Import the Encoder class from bpe