Update app.py

app.py

@@ -3,21 +3,26 @@ import streamlit as st
 from PIL import Image
 import os
 
-auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
-
-tokenizer_en = T5Tokenizer.from_pretrained(
-    "Voicelab/vlt5-base-keywords-v4_3-en", use_auth_token=auth_token
-)
-model_en = T5ForConditionalGeneration.from_pretrained(
+@st.cache
+def load_model_cache():
+    auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
+    tokenizer_en = T5Tokenizer.from_pretrained(
     "Voicelab/vlt5-base-keywords-v4_3-en", use_auth_token=auth_token
-)
+    )
+    model_en = T5ForConditionalGeneration.from_pretrained(
+        "Voicelab/vlt5-base-keywords-v4_3-en", use_auth_token=auth_token
+    )
+    
+    tokenizer_pl = T5Tokenizer.from_pretrained(
+        "Voicelab/vlt5-base-keywords-v4_3", use_auth_token=auth_token
+    )
+    model_pl = T5ForConditionalGeneration.from_pretrained(
+        "Voicelab/vlt5-base-keywords-v4_3", use_auth_token=auth_token
+    )
 
-tokenizer_pl = T5Tokenizer.from_pretrained(
-    "Voicelab/vlt5-base-keywords-v4_3", use_auth_token=auth_token
-)
-model_pl = T5ForConditionalGeneration.from_pretrained(
-    "Voicelab/vlt5-base-keywords-v4_3", use_auth_token=auth_token
-)
+    return tokenizer_en, model_en, tokenizer_pl, model_pl
+
+tokenizer_en, model_en, tokenizer_pl, model_pl = load_model_cache()
 
 img_full = Image.open("images/vl-logo-nlp-blue.png")
 img_short = Image.open("images/sVL-NLP-short.png")
@@ -92,3 +97,4 @@ if __name__ == "__main__":
     if result:
         generated_keywords = get_predictions(text=user_input, language=language)
         st.text_area("Generated keywords", generated_keywords)
+        print(f"Input: {user_input}---> Keywords: {generated_keywords}")