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app.py and reqs.txt

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  1. app.py +110 -0
  2. requirements.txt +3 -0
app.py ADDED
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+ import gradio as gr
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+
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+ model_path = "modernbert.bin"
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+ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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+
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+ tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
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+ model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41)
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+ model.load_state_dict(torch.load(model_path, map_location=device))
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+ model.to(device)
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+ model.eval()
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+
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+ label_mapping = {
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+ 0: '13B', 1: '30B', 2: '65B', 3: '7B', 4: 'GLM130B', 5: 'bloom_7b',
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+ 6: 'bloomz', 7: 'cohere', 8: 'davinci', 9: 'dolly', 10: 'dolly-v2-12b',
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+ 11: 'flan_t5_base', 12: 'flan_t5_large', 13: 'flan_t5_small',
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+ 14: 'flan_t5_xl', 15: 'flan_t5_xxl', 16: 'gemma-7b-it', 17: 'gemma2-9b-it',
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+ 18: 'gpt-3.5-turbo', 19: 'gpt-35', 20: 'gpt4', 21: 'gpt4o',
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+ 22: 'gpt_j', 23: 'gpt_neox', 24: 'human', 25: 'llama3-70b', 26: 'llama3-8b',
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+ 27: 'mixtral-8x7b', 28: 'opt_1.3b', 29: 'opt_125m', 30: 'opt_13b',
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+ 31: 'opt_2.7b', 32: 'opt_30b', 33: 'opt_350m', 34: 'opt_6.7b',
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+ 35: 'opt_iml_30b', 36: 'opt_iml_max_1.3b', 37: 't0_11b', 38: 't0_3b',
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+ 39: 'text-davinci-002', 40: 'text-davinci-003'
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+ }
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+
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+ def classify_text(text):
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+ inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
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+ inputs = {key: value.to(device) for key, value in inputs.items()}
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+
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+ with torch.no_grad():
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+ outputs = model(**inputs)
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+ probabilities = torch.softmax(outputs.logits, dim=1)[0]
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+ predicted_class = torch.argmax(probabilities).item()
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+ confidence = probabilities[predicted_class].item()
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+
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+ if predicted_class == 24:
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+ prediction_label = "βœ… **Human Written**"
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+ confidence_message = f"πŸ”’ **Confidence:** {confidence:.2f}"
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+ if confidence > 0.8:
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+ confidence_message += " (Highly Likely Human)"
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+ else:
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+ prediction_label = f"πŸ€– **AI Generated by {label_mapping[predicted_class]}**"
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+ confidence_message = f"πŸ”’ **Confidence:** {confidence:.2f}"
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+ if confidence > 0.8:
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+ confidence_message += " (Highly Likely AI)"
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+
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+ return f"**Result:**\n\n{prediction_label}\n\n{confidence_message}"
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+
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+ title = "🧠 SzegedAI ModernBERT Text Detector"
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+ description = (
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+ """
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+ **AI Detection Tool by SzegedAI**
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+
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+ **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.
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+
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+ - **πŸ€– Identify AI Models**: If detected as AI-generated, the system will reveal which LLM was responsible for the text generation.
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+ - **βœ… Human Verification**: If confidently human, the result will be marked with a **green checkmark**.
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+
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+ **Press the button below to classify your text!**
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+ """
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+ )
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+
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+ iface = gr.Interface(
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+ fn=classify_text,
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+ inputs=gr.Textbox(
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+ label="✏️ Enter Text for Analysis",
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+ placeholder="Type or paste your content here...",
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+ lines=5,
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+ elem_id="text_input_box"
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+ ),
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+ outputs=gr.Textbox(
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+ label="Detection Results",
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+ lines=4,
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+ elem_id="result_output_box"
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+ ),
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+ title=title,
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+ description=description,
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+ theme="dark",
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+ allow_flagging="never",
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+ live=False,
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+ submit_button="🎯 Analyze Now",
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+ css="""
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+ #text_input_box, #result_output_box {
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+ border-radius: 10px;
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+ border: 2px solid #4CAF50;
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+ font-size: 18px;
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+ }
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+ body {
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+ background: #1E1E2F;
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+ color: #E1E1E6;
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+ font-family: 'Aptos', sans-serif;
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+ padding: 20px;
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+ }
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+ .gradio-container {
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+ border: 2px solid #4CAF50;
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+ border-radius: 15px;
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+ padding: 20px;
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+ box-shadow: 0px 0px 20px rgba(0,255,0,0.6);
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+ }
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+ h1, h2 {
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+ text-align: center;
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+ font-size: 32px;
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+ font-weight: bold;
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+ }
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+ """
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+ )
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+
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+ if __name__ == "__main__":
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+ iface.launch(share=True)
requirements.txt ADDED
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+ gradio
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+ torch
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+ git+https://github.com/huggingface/transformers