Update app.py

app.py

@@ -4,7 +4,7 @@ import time
 from transformers import pipeline
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 from transformers import BartTokenizer, BartForConditionalGeneration
-from transformers import AutoTokenizer, EncoderDecoderModel
+#from transformers import AutoTokenizer, EncoderDecoderModel
 #from transformers import AutoTokenizer, LEDForConditionalGeneration
 #from transformers import AutoTokenizer, FlaxLongT5ForConditionalGeneration
 
@@ -38,15 +38,15 @@ def bart_summarize(text):
     return pp
 
 #Encoder-Decoder
-def encoder_decoder(text):
-    model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
-    tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
-    # let's perform inference on a long piece of text
-    input_ids = tokenizer(text, return_tensors="pt").input_ids
-    # autoregressively generate summary (uses greedy decoding by default) 
-    generated_ids = model.generate(input_ids)
-    generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-    return generated_text
+# def encoder_decoder(text):
+#     model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
+#     tokenizer = AutoTokenizer.from_pretrained("patrickvonplaten/bert2bert_cnn_daily_mail")
+#     # let's perform inference on a long piece of text
+#     input_ids = tokenizer(text, return_tensors="pt").input_ids
+#     # autoregressively generate summary (uses greedy decoding by default) 
+#     generated_ids = model.generate(input_ids)
+#     generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+#     return generated_text
     
 # Result
 def result(summary):
@@ -90,13 +90,13 @@ if button:
                 result(summary)
             except Exception:
                 st.warning("🚨 Your input text is quite lengthy. For better results, consider providing a shorter text or breaking it into smaller chunks.")
-        elif model == "Encoder-Decoder":
-            st.write("You have selected Encoder-Decoder model.")
-            try:
-                summary = encoder_decoder(text)
-                result(summary)
-            except Exception:
-                st.warning("🚨 Your input text is quite lengthy. For better results, consider providing a shorter text or breaking it into smaller chunks.")
+        # elif model == "Encoder-Decoder":
+        #     st.write("You have selected Encoder-Decoder model.")
+        #     try:
+        #         summary = encoder_decoder(text)
+        #         result(summary)
+        #     except Exception:
+        #         st.warning("🚨 Your input text is quite lengthy. For better results, consider providing a shorter text or breaking it into smaller chunks.")
 
         #st.toast("Please wait while we summarize your text.")
         #with st.spinner("Summarizing..."):