Update app.py

app.py

@@ -1,38 +1,38 @@
-# import torch
-# from transformers import PegasusForConditionalGeneration, PegasusTokenizer
+import torch
+from transformers import PegasusForConditionalGeneration, PegasusTokenizer
 
-# model_name = 'tuner007/pegasus_paraphrase'
-# torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-# tokenizer = PegasusTokenizer.from_pretrained(model_name)
-# model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
+model_name = 'tuner007/pegasus_paraphrase'
+torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+tokenizer = PegasusTokenizer.from_pretrained(model_name)
+model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)
 
-# def get_response(input_text,num_return_sequences):
-#   batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
-#   translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)
-#   tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
-#   return tgt_text
+def get_response(input_text,num_return_sequences):
+  batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device)
+  translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5)
+  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)
+  return tgt_text
 
-# from sentence_splitter import SentenceSplitter, split_text_into_sentences
+from sentence_splitter import SentenceSplitter, split_text_into_sentences
 
-# splitter = SentenceSplitter(language='en')
+splitter = SentenceSplitter(language='en')
 
-# def paraphraze(text):
-#   sentence_list = splitter.split(text)
-#   paraphrase = []
+def paraphraze(text):
+  sentence_list = splitter.split(text)
+  paraphrase = []
 
-#   for i in sentence_list:
-#     a = get_response(i,1)
-#     paraphrase.append(a)
-#     paraphrase2 = [' '.join(x) for x in paraphrase]
-#     paraphrase3 = [' '.join(x for x in paraphrase2) ]
-#   paraphrased_text = str(paraphrase3).strip('[]').strip("'")
-#   return paraphrased_text
+  for i in sentence_list:
+    a = get_response(i,1)
+    paraphrase.append(a)
+    paraphrase2 = [' '.join(x) for x in paraphrase]
+    paraphrase3 = [' '.join(x for x in paraphrase2) ]
+  paraphrased_text = str(paraphrase3).strip('[]').strip("'")
+  return paraphrased_text
 
 
-# def summarize(text):
+def summarize(text):
 
-#   paraphrased_text = paraphraze(text)
-#   return paraphrased_text
+  paraphrased_text = paraphraze(text)
+  return paraphrased_text
 ########################################################################################################
 
 import torch
@@ -84,16 +84,17 @@ def app():
     with col1:
         user_input = st.text_area('Enter text','', height=300)
         
-        paraphraseNo = st.slider('Number of Parapharases',1,10,2)
+        #paraphraseNo = st.slider('Number of Parapharases',1,10,2)
         if st.button('Paraphrase'):
             with st.spinner(text="This may take a moment..."):
-                output = get_paraphrased_sentences(model, tokenizer, user_input, num_beams=10, num_return_sequences=paraphraseNo)    
+                
+                output = summarize(user_input)    
     
     #with spacer:
     
     with col2:
         for x, element in enumerate(output):
-            user_output = st.text_area(label="", value=output[x], height=200 )
+            user_output = st.text_area(label="", value=output, height=200 )
 
 # st.markdown(
 #     '''<style>