Update app.py

app.py

@@ -1,17 +1,16 @@
 import gradio as gr
 import re
 import torch
+import asyncio
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# Load T5 paraphrase model (faster than PEGASUS)
+# Load T5 paraphrase model
 model_name = "Vamsi/T5_Paraphrase_Paws"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.float16)  # Use fp16 for speed
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.float16)
 
-# Move model to CPU (remove if using GPU)
-model.to("cpu")
 
-# Initialize paraphrase pipeline with optimized settings
+# Initialize paraphrase pipeline
 paraphrase_pipeline = pipeline(
     "text2text-generation",
     model=model,
@@ -23,31 +22,38 @@ def split_sentences(text):
     """Split text into sentences using regex (faster than nltk)."""
     return re.split(r'(?<=[.!?])\s+', text.strip())
 
-def paraphrase_text(text):
-    """Paraphrases input text while maintaining sentence structure."""
+async def paraphrase_text(text):
+    """Paraphrases input text while maintaining sentence structure asynchronously."""
     if not text.strip():
         return "⚠️ Please enter some text to paraphrase."
 
     sentences = split_sentences(text)
 
-    # Apply T5 paraphrasing to each sentence
-    paraphrased_results = paraphrase_pipeline(
+    # Apply T5 paraphrasing with optimized settings
+    paraphrased_results = await asyncio.to_thread(paraphrase_pipeline, 
         [f"paraphrase: {sentence} </s>" for sentence in sentences if sentence], 
-        max_length=50, do_sample=True, batch_size=8, num_return_sequences=1  # Faster settings
+        max_length=80,
+        do_sample=True,  
+        temperature=0.7,  
+        top_p=0.85,  
+        top_k=50,
+        repetition_penalty=1.2,  
+        num_return_sequences=1,  
+        batch_size=8
     )
 
+    # Extract and join paraphrased sentences
     paraphrased_sentences = [result['generated_text'] for result in paraphrased_results]
     return " ".join(paraphrased_sentences)
 
-# Define Gradio Interface
-demo = gr.Interface(
-    fn=paraphrase_text,
-    inputs=gr.Textbox(label="Enter text", placeholder="Type your text to paraphrase...", lines=10),
-    outputs=gr.Textbox(label="Paraphrased Text", lines=10),
-    title="🚀 Fast & Clean T5 Paraphraser",
-    description="Enter text and let AI generate a paraphrased version using an optimized T5 model!",
-    theme="huggingface"
-)
+# Define Gradio Interface (Disable queueing)
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 Fast & Parallel T5 Paraphraser")
+    input_box = gr.Textbox(label="Enter text", placeholder="Type your text to paraphrase...", lines=10)
+    output_box = gr.Textbox(label="Paraphrased Text", lines=10)
+    button = gr.Button("Paraphrase")
+
+    button.click(paraphrase_text, inputs=input_box, outputs=output_box)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True, queue=False)  # Disable queueing