Update app.py

app.py

@@ -1,9 +1,5 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-
-# Load T5 model
-model_name = "t5-base"
+# Load better paraphraser model
+model_name = "Vamsi/T5_Paraphrase_Paws"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
@@ -11,14 +7,7 @@ def humanize_text(text, tone):
     if not text.strip():
         return "⚠️ Please enter text.", "❌ Empty input"
 
-    # Better instruction for T5 model
-    prefix = {
-        "Academic": "paraphrase this sentence into a refined academic style:",
-        "Professional": "rephrase this text in a formal and professional tone:",
-        "Conversational": "rewrite this text in a natural conversational style:"
-    }[tone]
-
-    prompt = f"{prefix} {text}"
+    prompt = f"paraphrase: {text} </s>"
 
     try:
         inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
@@ -34,22 +23,4 @@ def humanize_text(text, tone):
     except Exception as e:
         return f"⚠️ Error: {str(e)}", "❌ Model error"
 
-# Build interface
-iface = gr.Interface(
-    fn=humanize_text,
-    inputs=[
-        gr.Textbox(lines=8, label="✍️ Enter your text"),
-        gr.Radio(["Academic", "Professional", "Conversational"], label="Tone", value="Academic")
-    ],
-    outputs=[
-        gr.Textbox(lines=10, label="🧠 Refined Academic Rewrite"),
-        gr.Textbox(label="System Status")
-    ],
-    title="🕌 Zawiyah AI Collective – Attention-Based Humanizer AI",
-    description="Transforms ordinary text into refined, academic, or professional language using a T5 transformer model."
-)
-
-if __name__ == "__main__":
-    iface.launch()
-