Update app.py

app.py

@@ -3,10 +3,11 @@ import streamlit as st
 from PIL import Image
 import os
 
+
 @st.cache(allow_output_mutation=True)
 def load_model_cache():
     auth_token = os.environ.get("TOKEN_FROM_SECRET") or True
-    
+
     tokenizer_pl = T5Tokenizer.from_pretrained(
         "Voicelab/vlt5-base-rfc-v1_2", use_auth_token=auth_token
     )
@@ -31,18 +32,19 @@ st.set_page_config(
 
 tokenizer_en, model_en, tokenizer_pl, model_pl = load_model_cache()
 
+
 def get_predictions(text):
-        input_ids = tokenizer_pl(text, return_tensors="pt", truncation=True).input_ids
-        output = model_pl.generate(
-            input_ids,
-            no_repeat_ngram_size=1,
-            num_beams=3,
-            num_beam_groups=3,
-            min_length=10,
-            max_length=100,
-        )
-        predicted_kw = tokenizer_pl.decode(output[0], skip_special_tokens=True)
-    return predicted_kw
+    input_ids = tokenizer_pl(text, return_tensors="pt", truncation=True).input_ids
+    output = model_pl.generate(
+        input_ids,
+        no_repeat_ngram_size=1,
+        num_beams=3,
+        num_beam_groups=3,
+        min_length=10,
+        max_length=100,
+    )
+    predicted_rfc = tokenizer_pl.decode(output[0], skip_special_tokens=True)
+    return predicted_rfc
 
 
 def trim_length():