Update app.py

app.py

@@ -1,8 +1,8 @@
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch, gradio as gr
 
-# Load Model
-model_name = "alykassem/FLAN-T5-Paraphraser"
+# Load Model (lighter + faster)
+model_name = "humarin/chatgpt_paraphraser_on_T5_base"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
@@ -11,7 +11,7 @@ model = model.to(device)
 model.eval()
 
 # Paraphrasing Function
-def paraphrase_flan(text, diversity=0.7, temperature=0.9):
+def paraphrase_t5(text, diversity=0.7, temperature=0.9):
     if not text.strip():
         return "⚠️ Please enter some text"
 
@@ -23,7 +23,7 @@ def paraphrase_flan(text, diversity=0.7, temperature=0.9):
         do_sample=True,
         top_p=0.92,  # nucleus sampling
         temperature=float(temperature),  # creativity
-        diversity_penalty=float(diversity),  # encourages lexical variety
+        diversity_penalty=float(diversity),  # lexical variety
         num_return_sequences=1
     )
 
@@ -31,15 +31,15 @@ def paraphrase_flan(text, diversity=0.7, temperature=0.9):
 
 # Gradio UI
 iface = gr.Interface(
-    fn=paraphrase_flan,
+    fn=paraphrase_t5,
     inputs=[
         gr.Textbox(lines=8, placeholder="Paste full text here..."),
         gr.Slider(0.0, 1.0, step=0.1, value=0.7, label="Lexical Diversity"),
         gr.Slider(0.5, 1.5, step=0.1, value=0.9, label="Temperature")
     ],
     outputs=gr.Textbox(label="Paraphrased & Humanized Text"),
-    title="FLAN-T5 Paraphraser (Humanizer)",
-    description="High-quality paraphrasing with adjustable diversity and temperature to reduce AI detection."
+    title="T5-Base Paraphraser (Humanizer)",
+    description="Fast, high-quality paraphrasing on T5-base, tuned for human-like rewrites."
 )
 
 iface.launch()