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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_path = "modernbert.bin"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)
model.eval()
label_mapping = {
0: '13B', 1: '30B', 2: '65B', 3: '7B', 4: 'GLM130B', 5: 'bloom_7b',
6: 'bloomz', 7: 'cohere', 8: 'davinci', 9: 'dolly', 10: 'dolly-v2-12b',
11: 'flan_t5_base', 12: 'flan_t5_large', 13: 'flan_t5_small',
14: 'flan_t5_xl', 15: 'flan_t5_xxl', 16: 'gemma-7b-it', 17: 'gemma2-9b-it',
18: 'gpt-3.5-turbo', 19: 'gpt-35', 20: 'gpt4', 21: 'gpt4o',
22: 'gpt_j', 23: 'gpt_neox', 24: 'human', 25: 'llama3-70b', 26: 'llama3-8b',
27: 'mixtral-8x7b', 28: 'opt_1.3b', 29: 'opt_125m', 30: 'opt_13b',
31: 'opt_2.7b', 32: 'opt_30b', 33: 'opt_350m', 34: 'opt_6.7b',
35: 'opt_iml_30b', 36: 'opt_iml_max_1.3b', 37: 't0_11b', 38: 't0_3b',
39: 'text-davinci-002', 40: 'text-davinci-003'
}
def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
inputs = {key: value.to(device) for key, value in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.softmax(outputs.logits, dim=1)[0]
predicted_class = torch.argmax(probabilities).item()
confidence = probabilities[predicted_class].item()
if predicted_class == 24:
prediction_label = "✅ **Human Written**"
confidence_message = f"🔒 **Confidence:** {confidence:.2f}"
if confidence > 0.8:
confidence_message += " (Highly Likely Human)"
else:
prediction_label = f"🤖 **AI Generated by {label_mapping[predicted_class]}**"
confidence_message = f"🔒 **Confidence:** {confidence:.2f}"
if confidence > 0.8:
confidence_message += " (Highly Likely AI)"
return f"**Result:**\n\n{prediction_label}\n\n{confidence_message}"
title = "🧠 SzegedAI ModernBERT Text Detector"
description = (
"""
**AI Detection Tool by SzegedAI**
**Detect AI-generated texts with precision.** This tool uses the new **ModernBERT** model, fine-tuned for machine-generated text detection, and able to detect 40 different models.
- **🤖 Identify AI Models**: If detected as AI-generated, the system will reveal which LLM was responsible for the text generation.
- **✅ Human Verification**: If confidently human, the result will be marked with a **green checkmark**.
**Press the button below to classify your text!**
"""
)
iface = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(
label="✏️ Enter Text for Analysis",
placeholder="Type or paste your content here...",
lines=5,
elem_id="text_input_box"
),
outputs=gr.Textbox(
label="Detection Results",
lines=4,
elem_id="result_output_box"
),
title=title,
description=description,
theme="dark",
allow_flagging="never",
live=False,
submit_button="🎯 Analyze Now",
css="""
#text_input_box, #result_output_box {
border-radius: 10px;
border: 2px solid #4CAF50;
font-size: 18px;
}
body {
background: #1E1E2F;
color: #E1E1E6;
font-family: 'Aptos', sans-serif;
padding: 20px;
}
.gradio-container {
border: 2px solid #4CAF50;
border-radius: 15px;
padding: 20px;
box-shadow: 0px 0px 20px rgba(0,255,0,0.6);
}
h1, h2 {
text-align: center;
font-size: 32px;
font-weight: bold;
}
"""
)
if __name__ == "__main__":
iface.launch(share=True)
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