Update app.py

app.py

@@ -10,23 +10,32 @@ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model = model.to(device)
 model.eval()
 
-# Paraphrasing Function
+# Paraphrasing Function with Paragraph Splitting
 def paraphrase_t5(text, temperature=0.9, top_p=0.92):
     if not text.strip():
         return "⚠️ Please enter some text"
 
-    inputs = tokenizer([f"paraphrase: {text}"], return_tensors="pt", truncation=True, padding=True).to(device)
-    
-    outputs = model.generate(
-        **inputs,
-        max_new_tokens=256,
-        do_sample=True,
-        top_p=float(top_p),       # nucleus sampling
-        temperature=float(temperature),  # creativity
-        num_return_sequences=1
-    )
-
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Split by paragraphs
+    paragraphs = [p.strip() for p in text.split("\n") if p.strip()]
+    paraphrased_paragraphs = []
+
+    for p in paragraphs:
+        inputs = tokenizer([f"paraphrase: {p}"], return_tensors="pt", truncation=True, padding=True).to(device)
+
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            do_sample=True,
+            top_p=float(top_p),       # nucleus sampling
+            temperature=float(temperature),  # creativity
+            num_return_sequences=1
+        )
+
+        paraphrased = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        paraphrased_paragraphs.append(paraphrased)
+
+    # Join paraphrased paragraphs with newlines
+    return "\n\n".join(paraphrased_paragraphs)
 
 # Gradio UI
 iface = gr.Interface(