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from transformers import pipeline, AutoTokenizer
import streamlit as st
import torch
import time
# Limit CPU threads for performance
torch.set_num_threads(2)
st.title("AI Humanizer Lite (CPU Friendly) — Unlimited Text Detection")
@st.cache_resource(show_spinner=False)
def load_models():
detect_pipe = pipeline("text-classification", model="roberta-base-openai-detector", device=-1)
tokenizer = AutoTokenizer.from_pretrained("roberta-base-openai-detector")
return detect_pipe, tokenizer
detect_pipe, tokenizer = load_models()
def chunk_text(text, max_tokens=256):
tokens = tokenizer.tokenize(text)
chunks = []
for i in range(0, len(tokens), max_tokens):
chunk_tokens = tokens[i:i+max_tokens]
chunk_text = tokenizer.convert_tokens_to_string(chunk_tokens)
chunks.append(chunk_text)
return chunks
def detect_ai(text):
chunks = chunk_text(text)
scores = []
for chunk in chunks:
outputs = detect_pipe(chunk)
# Find "AI" label score or 0.0 fallback
ai_score = 0.0
for out in outputs:
if out["label"].lower() == "ai":
ai_score = out["score"]
scores.append(ai_score)
# Aggregate results: max score means any AI-like chunk triggers high detection
final_score = max(scores) if scores else 0.0
return final_score
def humanize_text(text):
# Use a smaller summarization or rewriting model
humanizer_pipe = pipeline("text2text-generation", model="sshleifer/distilbart-cnn-12-6", device=-1)
prompt = f"Rewrite this text naturally: {text}"
result = humanizer_pipe(prompt, max_length=128, num_beams=3)
return result[0]["generated_text"]
text = st.text_area("Paste AI-generated text here", height=300)
if st.button("Detect & Humanize"):
if not text.strip():
st.warning("Please enter some text!")
else:
with st.spinner("Detecting AI content on chunks..."):
start = time.time()
score = detect_ai(text)
duration = time.time() - start
st.write(f"AI detection score: {score:.2%} (Processed in {duration:.1f}s)")
if score < 0.5:
st.success("This text looks human already! No need to rewrite.")
st.write(text)
else:
with st.spinner("Rewriting text for better human likeness..."):
start = time.time()
rewritten = humanize_text(text)
duration = time.time() - start
st.success(f"Rewritten text in {duration:.1f} seconds")
st.write(rewritten)
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