Update app.py

app.py

@@ -1,12 +1,16 @@
 import torch
 from transformers import T5ForConditionalGeneration,T5Tokenizer, AutoTokenizer, AutoModelForSeq2SeqLM
 import streamlit as st
+st.title("Paraphrase")
 model_name = st.text_input("Pick a Model", "seduerr/t5-pawraphrase")
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained("t5-base")  
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = model.to(device)
+temp = st.sidebar.slider("Temperature", 0.7, 1.5)
+number_of_outputs = st.sidebar.slider("Number of Outputs", 1, 10)
+
 def translate_to_english(model, tokenizer, text):
   translated_text = []
   text = "paraphrase: " + text + " </s>"
@@ -16,10 +20,11 @@ def translate_to_english(model, tokenizer, text):
     input_ids=input_ids, attention_mask=attention_masks,
     do_sample=True,
     max_length=256,
+    temperature = temp,
     top_k=120,
     top_p=0.98,
     early_stopping=True,
-    num_return_sequences=10
+    num_return_sequences=number_of_outputs,
   )
   for beam_output in beam_outputs:
     sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
@@ -27,11 +32,10 @@ def translate_to_english(model, tokenizer, text):
     translated_text.append(sent)
   return translated_text
     
-st.title("Auto Translate (To English)")
 text = st.text_input("Okay")
 st.text("What you wrote: ")
 st.write(text)
-st.text("English Translation: ")
+st.text("Output: ")
 if text:
     translated_text = translate_to_english(model, tokenizer, text)
     st.write(translated_text if translated_text else "No translation found")