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import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import re
import pandas as pd
import gradio as gr
MODEL_NAME = "dejanseo/ai-cop"
# --- Load model ---
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if (device.type=="cuda" and torch.cuda.is_bf16_supported()) else torch.float32
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, torch_dtype=dtype)
model.to(device).eval()
def sent_tokenize(text):
return [s for s in re.split(r'(?<=[\.!?])\s+', text.strip()) if s]
# --- Inference function ---
def classify_text(text):
if not text.strip():
return "⚠️ Please enter some text.", None, None
sentences = sent_tokenize(text)
if not sentences:
return "⚠️ No sentences detected.", None, None
inputs = tokenizer(
sentences,
return_tensors="pt",
padding=True,
truncation=True,
max_length=model.config.max_position_embeddings
).to(device)
with torch.no_grad():
logits = model(**inputs).logits
probs = F.softmax(logits, dim=-1).cpu()
preds = torch.argmax(probs, dim=-1).cpu()
results = []
highlighted_sentences = []
for i, s in enumerate(sentences):
p = preds[i].item()
conf = probs[i, p].item()
label = "AI" if p == 0 else "Human"
results.append([s, label, f"{conf:.2f}"])
if label == "AI":
highlighted_sentences.append(f"<span style='color:red; font-weight:bold'>{s}</span>")
else:
highlighted_sentences.append(f"<span style='color:green; font-weight:bold'>{s}</span>")
# Overall AI likelihood
avg = torch.mean(probs, dim=0)
model_ai = avg[0].item() * 100
highlighted_text = " ".join(highlighted_sentences)
df = pd.DataFrame(results, columns=["Sentence", "Classification", "Confidence"])
return f"⚖️ AI Likelihood: {model_ai:.1f}%", highlighted_text, df
# --- Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("## 🧠 AI Article Detection by Writenix")
with gr.Row():
text_input = gr.Textbox(label="Enter text", lines=10, placeholder="Paste your text here…")
classify_btn = gr.Button("Classify")
ai_score = gr.Label(label="Overall AI Likelihood")
highlighted = gr.HTML()
table = gr.Dataframe(headers=["Sentence", "Classification", "Confidence"], wrap=True)
classify_btn.click(classify_text, inputs=text_input, outputs=[ai_score, highlighted, table])
if __name__ == "__main__":
demo.launch()
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