Create app.py

app.py

@@ -0,0 +1,41 @@
+import torch
+from transformers import T5ForConditionalGeneration,T5Tokenizer, AutoTokenizer, AutoModelForSeq2SeqLM
+import streamlit as st
+st.title("Paraphrase")
+
+model = AutoModelForSeq2SeqLM.from_pretrained("eugenesiow/bart-paraphrase")
+tokenizer = AutoTokenizer.from_pretrained("geckos/pegasus-fined-tuned-on-paraphrase")  
+
+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 =  text + " </s>"
+  encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
+  input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
+  beam_outputs = model.generate(
+    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=number_of_outputs,
+  )
+  for beam_output in beam_outputs:
+    sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
+    print(sent)
+    translated_text.append(sent)
+  return translated_text
+    
+text = st.text_input("Okay")
+st.text("What you wrote: ")
+st.write(text)
+st.text("Output: ")
+if text:
+    translated_text = translate_to_english(model, tokenizer, text)
+    st.write(translated_text if translated_text else "No translation found")