Update app.py

app.py

@@ -1,50 +1,39 @@
 import gradio as gr
 import re
-import torch
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# Load PEGASUS model in optimized mode
+# Load PEGASUS paraphrase model
 model_name = "tuner007/pegasus_paraphrase"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.float16)  # Use half precision
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
 
-# Move model to CPU for consistency
-model.to("cpu")
-
-# Initialize paraphrase pipeline with optimized settings
-paraphrase_pipeline = pipeline(
-    "text2text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    truncation=True
-)
+# Initialize pipeline
+paraphrase_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer, truncation=True)
 
 def split_sentences(text):
-    """Split text into sentences using regex (faster than nltk)."""
-    return re.split(r'(?<=[.!?])\s+', text.strip())
+    """Split text into sentences using regex instead of NLTK."""
+    return re.split(r'(?<=[.!?])\s+', text.strip())  # Split at sentence-ending punctuation
 
 def paraphrase_text(text):
     """Paraphrases input text while maintaining sentence structure."""
     if not text.strip():
         return "⚠️ Please enter some text to paraphrase."
 
-    sentences = split_sentences(text)
-
-    # Batch processing with optimized settings
-    paraphrased_results = paraphrase_pipeline(
-        sentences, max_length=50, do_sample=False, batch_size=8  # Reduced max_length & increased batch_size
-    )
-
-    paraphrased_sentences = [result['generated_text'] for result in paraphrased_results]
-    return " ".join(paraphrased_sentences)
+    sentences = split_sentences(text)  # Use regex to split sentences
+    paraphrased_sentences = [
+        paraphrase_pipeline(sentence, max_length=60, do_sample=False)[0]['generated_text']
+        for sentence in sentences if sentence
+    ]
+    
+    return " ".join(paraphrased_sentences)  # Reassemble into a paragraph
 
 # 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="🚀 Faster PEGASUS Paraphraser",
-    description="Enter text and let AI generate a paraphrased version using an optimized PEGASUS model!",
+    title="📝 PEGASUS Paraphraser",
+    description="Enter text and let AI generate a paraphrased version using the PEGASUS model!",
     theme="huggingface"
 )