Update app.py

app.py

@@ -4,8 +4,8 @@ import torch
 
 @st.cache_resource
 def load_model():
-    tokenizer = T5Tokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
-    model = T5ForConditionalGeneration.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+    tokenizer = T5Tokenizer.from_pretrained("ramsrigouthamg/t5_paraphraser")
+    model = T5ForConditionalGeneration.from_pretrained("ramsrigouthamg/t5_paraphraser")
     return tokenizer, model
 
 def humanize_text(input_text):
@@ -13,24 +13,31 @@ def humanize_text(input_text):
     device = "cuda" if torch.cuda.is_available() else "cpu"
     model = model.to(device)
 
-    text = "paraphrase: " + input_text + " </s>"
-    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)
+    prompt = f"paraphrase this text to make it sound more human and natural: {input_text} </s>"
+
+    encoding = tokenizer.encode_plus(
+        prompt,
+        padding="max_length",
+        return_tensors="pt",
+        max_length=512,
+        truncation=True
+    )
+
+    input_ids = encoding["input_ids"].to(device)
+    attention_mask = encoding["attention_mask"].to(device)
 
     output = model.generate(
         input_ids=input_ids,
         attention_mask=attention_mask,
-        max_length=256,
-        do_sample=True,
-        top_k=120,
-        top_p=0.95,
-        early_stopping=True,
-        num_return_sequences=1
+        max_length=512,
+        num_beams=5,
+        no_repeat_ngram_size=3,
+        early_stopping=True
     )
 
     return tokenizer.decode(output[0], skip_special_tokens=True)
 
-# Streamlit Interface
+# Streamlit UI
 st.set_page_config(page_title="Humanize AI Text", layout="centered")
 st.title("🧠 Humanize AI Text")
 st.write("Make AI-generated text sound more human to evade detection.")