Update app.py

app.py

@@ -1,7 +1,7 @@
 ### STREAMLIT APP ####
 import streamlit as st
-
 from transformers import BertTokenizer, EncoderDecoderModel, EncoderDecoderConfig
+
 model_ckpt = 'ardavey/bert2gpt-indosum'
 tokenizer = BertTokenizer.from_pretrained(model_ckpt)
 tokenizer.bos_token = tokenizer.cls_token
@@ -10,12 +10,15 @@ tokenizer.eos_token = tokenizer.sep_token
 config = EncoderDecoderConfig.from_pretrained(model_ckpt)
 config.early_stopping = True
 
-model = EncoderDecoderModel.from_pretrained(model_ckpt, config=config)
+try:
+    model = EncoderDecoderModel.from_pretrained(model_ckpt, config=config)
+except Exception as e:
+    st.error(f"An error occurred while loading the model: {e}")
 
-text = st.text('Enter an article to summarize:')
+text = st.text_area('Enter an article to summarize:')
 
-if text:
-    input_ids = tokenizer.encode(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
+if text and len(text) > 0:
+    input_ids = tokenizer.encode(text, return_tensors='pt', padding='longest', truncation=True, max_length=512)
     summary_ids = model.generate(input_ids,
             min_length=40,
             max_length=200,
@@ -24,28 +27,31 @@ if text:
             length_penalty=1.0,
             no_repeat_ngram_size=3,
             use_cache=True,
-            do_sample = False,
-            top_k = 50,
+            do_sample=False,
+            top_k=50,
             )
 
     summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    # capitalize the first letter of the summary and after each period
-    def capitalize_sentences(text):
-        sentences = text.split('. ')
-        capitalized_sentences = [sentence[0].upper() + sentence[1:] if sentence else sentence for sentence in sentences]
-        return '. '.join(capitalized_sentences)
-    
-    # correct any wrong terms using the replacement_dict
-    replacement_dict = {
-        "optiglain": "OptiGuard",
-        "telkom university": "Telkom University",
-        "menyerbut": "menyebut"
-    }
     
-    for wrong_term, correct_term in replacement_dict.items():
-        summary_text = summary_text.replace(wrong_term, correct_term)
-    
-    summary_text = capitalize_sentences(summary_text)
-    st.info(summary_text)
-
-
+    # Check if summary is empty
+    if not summary_text.strip():
+        st.warning("The model couldn't generate a summary.")
+    else:
+        # Capitalize the first letter of the summary and after each period
+        def capitalize_sentences(text):
+            sentences = text.split('. ')
+            capitalized_sentences = [sentence[0].upper() + sentence[1:] if sentence else sentence for sentence in sentences]
+            return '. '.join(capitalized_sentences)
+        
+        # Correct any wrong terms using the replacement_dict
+        replacement_dict = {
+            "optiglain": "OptiGuard",
+            "telkom university": "Telkom University",
+            "menyerbut": "menyebut"
+        }
+        
+        for wrong_term, correct_term in replacement_dict.items():
+            summary_text = summary_text.replace(wrong_term, correct_term)
+        
+        summary_text = capitalize_sentences(summary_text)
+        st.info(summary_text)