Update app.py

app.py

@@ -1,50 +0,0 @@
-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
-tokenizer.eos_token = tokenizer.sep_token
-
-config = EncoderDecoderConfig.from_pretrained(model_ckpt)
-config.early_stopping = True
-
-model = EncoderDecoderModel.from_pretrained(model_ckpt, config=config)
-
-text = st.text('Enter an article to summarize:')
-
-if text:
-    input_ids = tokenizer.encode(custom_text, return_tensors='pt', padding=True, truncation=True, max_length=512)
-    summary_ids = model.generate(input_ids,
-            min_length=40,
-            max_length=200,
-            num_beams=10,
-            repetition_penalty=2.0,
-            length_penalty=1.0,
-            no_repeat_ngram_size=3,
-            use_cache=True,
-            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)
-
-