Update app.py

app.py

@@ -1,25 +1,21 @@
 # 🔄 Text Paraphraser | CPU-only HF Space
 
 import gradio as gr
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSeq2SeqLM,
-    pipeline,
-)
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 
-# 1️⃣ Load model + slow tokenizer explicitly
+# 1️⃣ Model & Tokenizer setup
 MODEL_ID = "Vamsi/T5_Paraphrase_Paws"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
 model     = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID)
 
-# 2️⃣ Create paraphrase pipeline with our slow tokenizer
 paraphraser = pipeline(
     "text2text-generation",
     model=model,
     tokenizer=tokenizer,
-    device=-1,                # CPU
+    device=-1,        # CPU
 )
 
+# 2️⃣ Paraphrase function
 def paraphrase(text: str, num_variations: int):
     if not text.strip():
         return []
@@ -30,10 +26,11 @@ def paraphrase(text: str, num_variations: int):
         num_return_sequences=num_variations,
         do_sample=True,
         top_k=120,
-        top_p=0.95
+        top_p=0.95,
     )
     return [out["generated_text"].strip() for out in outputs]
 
+# 3️⃣ Gradio UI
 with gr.Blocks(title="🔄 Text Paraphraser") as demo:
     gr.Markdown(
         "# 🔄 Text Paraphraser\n"
@@ -55,18 +52,18 @@ with gr.Blocks(title="🔄 Text Paraphraser") as demo:
     output_df = gr.Dataframe(
         label="Paraphrases",
         headers=[f"Variant #{i}" for i in range(1, 6)],
-        datatype=["str"]*5,
+        datatype=["str"] * 5,
         interactive=False,
         row_count=1
     )
 
     def format_for_dataframe(results):
         # Pad out to 5 columns
-        variants = results + [""]*(5 - len(results))
+        variants = results + [""] * (5 - len(results))
         return [variants]
 
     run_btn.click(
-        fn=lambda text, n: format_for_dataframe(paraphrase(text, n)),
+        fn=lambda txt, n: format_for_dataframe(paraphrase(txt, n)),
         inputs=[input_text, variations],
         outputs=output_df
     )