Update app.py

app.py

@@ -3,15 +3,15 @@ import re
 import torch
 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
 
-# Move model to CPU (remove if using GPU)
+# Move model to CPU
 model.to("cpu")
 
-# Initialize paraphrase pipeline with optimized settings
+# Initialize paraphrase pipeline
 paraphrase_pipeline = pipeline(
     "text2text-generation",
     model=model,
@@ -35,14 +35,16 @@ def paraphrase_text(text):
         [f"paraphrase: {sentence} </s>" for sentence in sentences if sentence], 
         max_length=50,
         do_sample=True,  
-        temperature=0.9,  # Increase randomness
-        top_p=0.92,  # Nucleus sampling
-        top_k=50,  # Limits next-word options
+        temperature=0.9,
+        top_p=0.92,
+        top_k=50,
         num_return_sequences=2,  # Generate 2 variations per sentence
-        batch_size=8  # Faster processing
+        batch_size=8
     )
 
-    paraphrased_sentences = [result['generated_text'] for result in paraphrased_results]
+    # Fix: Unpack the nested list correctly
+    paraphrased_sentences = [result['generated_text'] for results in paraphrased_results for result in results]
+
     return " ".join(paraphrased_sentences)
 
 # Define Gradio Interface