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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# ================= LOAD MODEL =================
MODEL_NAME = "distilbert-base-multilingual-cased"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(
MODEL_NAME,
num_labels=2
)
model.to(device)
model.eval()
# ================= AI FUNCTION =================
def classify_text(text):
if not text or text.strip() == "":
return "❌ Please enter some text"
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
padding=True
).to(device)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=1)[0]
human = probs[0].item() * 100
ai = probs[1].item() * 100
return f"""
✅ Human Probability: {human:.2f} %
🤖 AI Probability: {ai:.2f} %
"""
# ================= UI =================
with gr.Blocks(theme=gr.themes.Soft()) as app:
gr.Markdown(
"<h1 style='text-align:center;'>AI vs Human Text Detector</h1>"
)
gr.Markdown(
"<p style='text-align:center;'>Professional NLP Web Application</p>"
)
with gr.Tab("📝 Text Input"):
text_input = gr.Textbox(
lines=8,
placeholder="Paste text here..."
)
analyze_btn = gr.Button("Analyze Text")
result = gr.Textbox(label="Result")
analyze_btn.click(classify_text, text_input, result)
gr.Markdown(
"<p style='text-align:center; font-size:12px;'>"
"Created by Manar | AI Project 2025</p>"
)
app.launch()
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