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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", 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() * 100
if predicted_class == 24:
prediction_label = f"✅ - The text is <span class='highlight-human'>**{confidence:.2f}%** likely <b>Human written</b>.</span>"
model_info = ""
else:
prediction_label = f"🤖 - The text is <span class='highlight-ai'>**{confidence:.2f}%** likely <b>AI generated</b>.</span>"
model_info = f"**Identified AI Model:** {label_mapping[predicted_class]}"
result_message = f"**Result:**\n\n{prediction_label}"
if model_info:
result_message += f"\n\n{model_info}"
return result_message
title = "Detect AI Generated Texts!"
description = """
Detect AI-generated texts with precision using the new **ModernBERT** model, fine-tuned for machine-generated text detection, and capable of identifying 40 different models.
-- 🤖 - **Identify AI Models:** Reveals which LLM generated the text if detected as AI.
-- ✅ - **Human Verification:** Marks human-written text with a green checkmark.
**Note:** The longer the text, the better the detection accuracy.
"""
bottom_text = "**AI detection tool by SzegedAI**"
iface = gr.Blocks(css="""
#text_input_box {
border-radius: 10px;
border: 2px solid #4CAF50;
font-size: 18px;
padding: 15px;
margin-bottom: 20px;
width: 70%;
box-sizing: border-box;
margin: auto;
background-color: #1E1E2F;
}
#result_output_box {
border-radius: 10px;
border: 2px solid #4CAF50;
font-size: 18px;
padding: 15px;
background-color: #2E2E3F;
margin-top: 20px;
width: 50%;
box-sizing: border-box;
text-align: center;
margin: auto;
}
body {
background: #1E1E2F;
color: #E1E1E6;
font-family: 'Aptos', sans-serif;
padding: 20px;
display: flex;
justify-content: center;
align-items: center;
height: 100vh;
}
.gradio-container {
border: 2px solid #4CAF50;
border-radius: 15px;
padding: 30px;
box-shadow: 0px 0px 20px rgba(0,255,0,0.6);
max-width: 700px;
margin: auto;
}
h1 {
text-align: center;
font-size: 36px;
font-weight: bold;
}
h2 {
text-align: left;
font-size: 28px;
}
.highlight-human {
color: #4CAF50;
font-weight: bold;
background: rgba(76, 175, 80, 0.2);
padding: 5px;
border-radius: 8px;
}
.highlight-ai {
color: #FF5733;
font-weight: bold;
background: rgba(255, 87, 51, 0.2);
padding: 5px;
border-radius: 8px;
}
#bottom_text {
text-align: center;
margin-top: 50px;
font-weight: bold;
font-size: 20px;
color: #E1E1E6;
}
""")
with iface:
gr.Markdown(f"# {title}")
gr.Markdown(description)
text_input = gr.Textbox(label="Enter Text for Analysis", placeholder="Type or paste your content here...", elem_id="text_input_box", lines=5)
result_output = gr.Markdown("**Results will appear here...**", elem_id="result_output_box")
text_input.change(classify_text, inputs=text_input, outputs=result_output)
gr.Markdown(bottom_text, elem_id="bottom_text")
iface.launch(share=True)
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