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import gradio as gr
from functools import lru_cache
@lru_cache(maxsize=1)
def get_rewriter():
"""
Use a lightweight instruction-following model that can run on CPU.
flan-t5-small is generally more reliable for "rewrite" than GPT2-style models.
"""
from transformers import pipeline
return pipeline(
task="text2text-generation",
model="google/flan-t5-small",
device=-1, # CPU
)
def build_prompt(text: str, style: str) -> str:
text = (text or "").strip()
if not text:
return ""
if style == "More formal":
return (
"Rewrite the text in a more formal tone. "
"Keep the original meaning. Output only the rewritten text.\n\n"
f"Text: {text}"
)
if style == "More friendly":
return (
"Rewrite the text in a friendly, warm tone. "
"Keep the original meaning. Output only the rewritten text.\n\n"
f"Text: {text}"
)
return (
"Rewrite the text to be shorter and clearer. "
"Keep the original meaning. Output only the rewritten text.\n\n"
f"Text: {text}"
)
def rewrite(text: str, style: str) -> str:
text = (text or "").strip()
if not text:
return "Please enter some text."
prompt = build_prompt(text, style)
try:
rewriter = get_rewriter()
out = rewriter(
prompt,
max_new_tokens=128,
do_sample=False,
)
result = (out[0].get("generated_text") or "").strip()
return result if result else "No output. Try a shorter input."
except Exception as e:
return (
"Error: failed to run the model.\n"
"If this is the first run, the Space may still be downloading the model.\n\n"
f"Details: {type(e).__name__}: {e}"
)
with gr.Blocks(title="AI Text Rewriter") as demo:
gr.Markdown(
"AI Text Rewriter\n"
"Paste a sentence or short paragraph, choose a style, then rewrite with AI."
)
with gr.Row():
style = gr.Radio(
["More formal", "More friendly", "Shorter"],
value="More friendly",
label="Rewrite style",
)
text_input = gr.Textbox(
label="Your text",
placeholder="Type or paste text here...",
lines=5,
)
with gr.Row():
btn = gr.Button("Rewrite with AI")
clear = gr.Button("Clear")
output = gr.Textbox(label="Result", lines=6)
btn.click(fn=rewrite, inputs=[text_input, style], outputs=output)
clear.click(fn=lambda: ("", ""), inputs=None, outputs=[text_input, output])
demo.launch()
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