Update app.py

app.py

@@ -1,7 +1,6 @@
 import streamlit as st
-import torch
-from transformers import pipeline
-# from transformers import T5Tokenizer, T5ForConditionalGeneration
+#from transformers import pipeline
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 # from transformers import BartTokenizer, BartForConditionalGeneration
 # from transformers import AutoTokenizer, EncoderDecoderModel
 #from transformers import AutoTokenizer, LEDForConditionalGeneration
@@ -17,21 +16,21 @@ Kathmandu, Nepal's capital, is set in a valley surrounded by the Himalayan mount
 ##initializing models
 
 #Transformers Approach
-def transform_summarize(text):
-  summary = pipeline("summarization")
-  k=summary(text,max_length=100,do_sample=False)
-  return k
+# def transform_summarize(text):
+#   summary = pipeline("summarization")
+#   k=summary(text,max_length=100,do_sample=False)
+#   return k
 
 #T5
-# #def t5_summarize(text):
-#     tokenizer = T5Tokenizer.from_pretrained("t5-small")
-#     model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
-#     input_text = "summarize: " + text
-#     inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
-#     outputs = model.generate(inputs, max_length=200, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
-#     summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#     return summary
+def t5_summarize(text):
+    tokenizer = T5Tokenizer.from_pretrained("t5-small")
+    model = T5ForConditionalGeneration.from_pretrained("t5-small")
+
+    input_text = "summarize: " + text
+    inputs = tokenizer.encode(input_text, return_tensors="pt", max_length=1024, truncation=True)
+    outputs = model.generate(inputs, max_length=200, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
 
 #BART
 # def bart_summarize(text):
@@ -55,8 +54,8 @@ def transform_summarize(text):
 #   return generated_text
 
 #st.write("Generated Summaries are: ")
-l=transform_summarize(text)
+# l=transform_summarize(text)
+l=t5_summarize(text)
 st.write(l)
-# print(t5_summarize(text))
 # print(bart_summarize(text))
 # print(encoder_decoder(text))