Update app.py

app.py

@@ -45,7 +45,7 @@ def get_model():
     
 model, model2, tokenizer, tokenizer2 = get_model()
 
-st.text('''For Prompt Templates: https://huggingface.co/BigSalmon/InformalToFormalLincoln63Paraphrase''')
+st.text('''For Prompt Templates: https://huggingface.co/BigSalmon/InformalToFormalLincoln82Paraphrase''')
 
 temp = st.sidebar.slider("Temperature", 0.7, 1.5)
 number_of_outputs = st.sidebar.slider("Number of Outputs", 5, 50)
@@ -122,6 +122,16 @@ def run_generate2(text, bad_words):
     e = e.replace(text, "")
     yo.append(e)
   return yo
+  
+def prefix_format(sentence):
+  words = sentence.split()
+  if "[MASK]" in sentence:
+    words2 = words.index("[MASK]")
+    #print(words2)
+    output = ("<Prefix> " + ' '.join(words[:words2]) + " <Prefix> " + "<Suffix> " + ' '.join(words[words2+1:]) + " <Suffix>" + " <Middle>")
+    st.write(output)
+  else:
+    st.write("Add [MASK] to sentence")
  
 with st.form(key='my_form'):
     text = st.text_area(label='Enter sentence', value=first)
@@ -133,6 +143,8 @@ with st.form(key='my_form'):
     
     submit_button5 = st.form_submit_button(label='Most Prob')
     
+    submit_button6 = st.form_submit_button(label='Turn Sentence with [MASK] into <Prefix> Format')
+    
     if submit_button:
       translated_text = run_generate(text, bad_words)
       st.write(translated_text if translated_text else "No translation found")
@@ -153,16 +165,19 @@ with st.form(key='my_form'):
       translated_text = run_generate2(text, bad_words)
       st.write(translated_text if translated_text else "No translation found")
     if submit_button4:
-        text2 = str(text)
-        print(text2)
-        text3 = tokenizer2.encode(text2)
-        myinput, past_key_values = torch.tensor([text3]), None
-        myinput = myinput
-        logits, past_key_values = model2(myinput, past_key_values = past_key_values, return_dict=False)
-        logits = logits[0,-1]
-        probabilities = torch.nn.functional.softmax(logits)
-        best_logits, best_indices = logits.topk(logs_outputs)
-        best_words = [tokenizer2.decode([idx.item()]) for idx in best_indices]      
-        st.write(best_words)
+      text2 = str(text)
+      print(text2)
+      text3 = tokenizer2.encode(text2)
+      myinput, past_key_values = torch.tensor([text3]), None
+      myinput = myinput
+      logits, past_key_values = model2(myinput, past_key_values = past_key_values, return_dict=False)
+      logits = logits[0,-1]
+      probabilities = torch.nn.functional.softmax(logits)
+      best_logits, best_indices = logits.topk(logs_outputs)
+      best_words = [tokenizer2.decode([idx.item()]) for idx in best_indices]      
+      st.write(best_words)
     if submit_button5:
-      BestProbs5(text)
+      BestProbs5(text)
+    if submit_button6:
+      text2 = str(text)
+      prefix_format(text2)