Update app.py

app.py

@@ -1,11 +1,11 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 import torch
 
 @st.cache_resource
 def load_model():
-    tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5_paraphraser")
-    model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5_paraphraser")
+    tokenizer = T5Tokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+    model = T5ForConditionalGeneration.from_pretrained("Vamsi/T5_Paraphrase_Paws")
     return tokenizer, model
 
 def humanize_text(input_text):
@@ -14,10 +14,10 @@ def humanize_text(input_text):
     model = model.to(device)
 
     text = "paraphrase: " + input_text + " </s>"
-    encoding = tokenizer.encode_plus(text, max_length=256, padding='max_length', return_tensors="pt", truncation=True)
+    encoding = tokenizer.encode_plus(text, padding="max_length", return_tensors="pt", max_length=256, truncation=True)
     input_ids, attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
 
-    outputs = model.generate(
+    output = model.generate(
         input_ids=input_ids,
         attention_mask=attention_mask,
         max_length=256,
@@ -28,14 +28,12 @@ def humanize_text(input_text):
         num_return_sequences=1
     )
 
-    paraphrased_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return paraphrased_text
+    return tokenizer.decode(output[0], skip_special_tokens=True)
 
-# Streamlit UI
+# Streamlit Interface
 st.set_page_config(page_title="Humanize AI Text", layout="centered")
-
-st.title("🧠 Humanize AI Text to Evade Detection")
-st.write("Paste your AI-generated text and we'll paraphrase it to make it sound more natural and human-like.")
+st.title("🧠 Humanize AI Text")
+st.write("Make AI-generated text sound more human to evade detection.")
 
 input_text = st.text_area("Enter AI-Generated Text", height=300)