Create app.py

app.py

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+# 🔄 Text Paraphraser | CPU-only HF Space
+
+import gradio as gr
+from transformers import pipeline
+
+# Load the paraphrase pipeline once at startup
+paraphraser = pipeline(
+    "text2text-generation",
+    model="Vamsi/T5_Paraphrase_Paws",
+    device=-1,                # CPU
+)
+
+def paraphrase(text: str, num_variations: int):
+    if not text.strip():
+        return []
+    # T5 paraphrase prompt
+    prompt = "paraphrase: " + text.strip()
+    outputs = paraphraser(
+        prompt,
+        max_length=128,
+        num_return_sequences=num_variations,
+        do_sample=True,
+        top_k=120,
+        top_p=0.95
+    )
+    # Extract generated_text
+    return [out["generated_text"].strip() for out in outputs]
+
+with gr.Blocks(title="🔄 Text Paraphraser") as demo:
+    gr.Markdown(
+        "# 🔄 Text Paraphraser\n"
+        "Enter a sentence and get multiple alternative rewrites—all on CPU."
+    )
+
+    with gr.Row():
+        input_text = gr.Textbox(
+            label="Input Sentence",
+            placeholder="Type something to paraphrase…",
+            lines=3
+        )
+        variations = gr.Slider(
+            1, 5, value=3, step=1,
+            label="Number of Variations"
+        )
+    run_btn = gr.Button("Paraphrase 🔁", variant="primary")
+
+    output_df = gr.Dataframe(
+        label="Paraphrases",
+        headers=[f"Variant #{i}" for i in range(1, 6)],
+        datatype=["str"]*5,
+        interactive=False,
+        row_count=1
+    )
+
+    def format_for_dataframe(results):
+        # Pad out to 5 columns
+        variants = results + [""]*(5 - len(results))
+        return [variants]
+
+    run_btn.click(
+        fn=lambda text, n: format_for_dataframe(paraphrase(text, n)),
+        inputs=[input_text, variations],
+        outputs=output_df
+    )
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0")