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import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
import re
# Load model
model_name = "ravindravala/ravi-v0.1"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Clean up output (remove duplicates, preserve meaning)
def clean_output(text):
sentences = re.split(r'(?<=[.!?])\s+', text.strip())
seen = set()
unique_sentences = []
for s in sentences:
s_clean = s.strip()
if s_clean and s_clean not in seen:
seen.add(s_clean)
unique_sentences.append(s_clean)
return " ".join(unique_sentences).strip()
# Process a single paragraph
def process_block(block, temperature, top_k, top_p):
if not block.strip():
return block
input_text = "paraphrase: " + block
input_ids = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).input_ids.to(device)
input_length = input_ids.shape[1]
min_len = max(30, int(input_length * 0.7))
max_len = min(512, int(input_length * 1.3))
output_ids = model.generate(
input_ids,
max_length=max_len,
min_length=min_len,
do_sample=True,
top_k=top_k,
top_p=top_p,
temperature=temperature,
repetition_penalty=1.3,
no_repeat_ngram_size=3,
eos_token_id=tokenizer.eos_token_id
)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return clean_output(output_text)
# Handle multi-line text with formatting preserved
def humanize(text, temperature, top_k, top_p):
if not text.strip():
return "Please enter some text."
parts = re.split(r'(\n+)', text) # Keep newlines
processed_parts = [process_block(p, temperature, top_k, top_p) if not p.isspace() and p.strip() else p for p in parts]
return "".join(processed_parts)
# Gradio UI
demo = gr.Interface(
fn=humanize,
inputs=[
gr.Textbox(lines=8, placeholder="Enter text to paraphrase...", label="Text to Humanize"),
gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature"),
gr.Slider(10, 100, value=50, step=1, label="Top-K"),
gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-P"),
],
outputs=gr.Textbox(label="Humanized Output", lines=8),
title="AI → Human Paraphraser",
description="Rewrites AI-like responses into natural, human-like text while preserving paragraphs and formatting."
)
demo.launch()
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