Update app.py

app.py

@@ -4,7 +4,16 @@ from sentence_transformers import SentenceTransformer, util
 import random
 
 # Load models
-paraphraser = pipeline("text2text-generation", model="Vamsi/T5_Paraphrase_Paws")
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws", use_fast=False)
+model = T5ForConditionalGeneration.from_pretrained("Vamsi/T5_Paraphrase_Paws")
+
+def generate_paraphrase(text):
+    input_ids = tokenizer.encode("paraphrase: " + text, return_tensors="pt", max_length=256, truncation=True)
+    output_ids = model.generate(input_ids, max_length=80, num_return_sequences=1, do_sample=True, top_k=120, top_p=0.98)
+    return tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
 similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # Tone modifier prompts
@@ -21,7 +30,8 @@ def humanize_text(input_text, tone):
 
     style_prompt = tone_prompts.get(tone, "Paraphrase:")
     result = paraphraser(f"{style_prompt} {input_text}", max_length=80, num_return_sequences=1)
-    output_text = result[0]['generated_text']
+    output_text = generate_paraphrase(input_text)
+
 
     # Semantic similarity score
     emb1 = similarity_model.encode(input_text, convert_to_tensor=True)