Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import re | |
| from tokenizers import normalizers | |
| from tokenizers.normalizers import Sequence, Replace, Strip | |
| from tokenizers import Regex | |
| import os | |
| model1_path = "https://huggingface.co/spaces/SzegedAI/AI_Detector/resolve/main/modernbert.bin" | |
| model2_path = "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed12" | |
| model3_path = "https://huggingface.co/mihalykiss/modernbert_2/resolve/main/Model_groups_3class_seed22" | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| print(f"Using device: {device}") | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") | |
| model_1 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41) | |
| model_1.load_state_dict(torch.hub.load_state_dict_from_url(model1_path, map_location=device, progress=True)) | |
| model_1.to(device).eval() | |
| model_2 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41) | |
| model_2.load_state_dict(torch.hub.load_state_dict_from_url(model2_path, map_location=device, progress=True)) | |
| model_2.to(device).eval() | |
| model_3 = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base", num_labels=41) | |
| model_3.load_state_dict(torch.hub.load_state_dict_from_url(model3_path, map_location=device, progress=True)) | |
| model_3.to(device).eval() | |
| except Exception as e: | |
| print(f"Error during model loading: {e}") | |
| print("Please ensure all model paths are correct, dependencies are installed, and you have an internet connection for remote models.") | |
| # Handle the error, e.g., by exiting or displaying an error in the UI if Gradio is already running. | |
| # For simplicity, we'll let it potentially crash if models can't load before Gradio starts. | |
| # If Gradio is already running, you'd need a more sophisticated error display. | |
| # For now, we'll just make sure the Gradio interface doesn't try to use non-existent models. | |
| tokenizer = None # Prevent further errors if tokenizer failed | |
| model_1, model_2, model_3 = None, None, None | |
| 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 clean_text(text: str) -> str: | |
| text = re.sub(r'\s{2,}', ' ', text) | |
| text = re.sub(r'\s+([,.;:?!])', r'\1', text) | |
| return text | |
| if tokenizer: | |
| newline_to_space = Replace(Regex(r'\s*\n\s*'), " ") | |
| join_hyphen_break = Replace(Regex(r'(\w+)[--]\s*\n\s*(\w+)'), r"\1\2") | |
| tokenizer.backend_tokenizer.normalizer = Sequence([ | |
| tokenizer.backend_tokenizer.normalizer, | |
| join_hyphen_break, | |
| newline_to_space, | |
| Strip() | |
| ]) | |
| title_md = """ | |
| <h1 style="text-align: center; margin-bottom: 5px;">AI Text Detector</h1> | |
| <p style="text-align: center; font-size: 0.9em; color: var(--text-secondary); margin-top: 0; margin-bottom: 20px;">Developed by SzegedAI</p> | |
| """ | |
| description = """ | |
| <div class="app-description"> | |
| <p>This tool utilizes the <b>ModernBERT</b> model to decide whether a given text is human-authored or AI-generated. It employs a soft voting ensemble of <b>three</b> models to improve detection accuracy.</p> | |
| <ul class="features-list"> | |
| <li><span class="icon">✅</span> <strong>Human Verification: </strong> Clearly identifies human-written content.</li> | |
| <li><span class="icon">🔍</span> <strong>Model Detection: </strong> Capable of identifying content from over 40 AI models.</li> | |
| <li><span class="icon">📈</span> <strong>Accuracy: </strong> Performs optimally with more extensive text inputs.</li> | |
| <li><span class="icon">📄</span> <strong>Read more: </strong> Our methodology is detailed in our research paper: | |
| <a href="https://aclanthology.org/2025.genaidetect-1.15/" target="_blank" class="learn-more-link"> <b> LINK </b></a>. | |
| </li> | |
| </ul> | |
| <p class="instruction-text">Paste your text into the field below to analyze its origin.</p> | |
| </div> | |
| """ | |
| bottom_text = "<p class='footer-text'>SzegedAI - Mihaly Kiss</p>" | |
| AI_texts = [ | |
| "Camels are remarkable desert animals known for their unique adaptations to harsh, arid environments. Native to the Middle East, North Africa, and parts of Asia, camels have been essential to human life for centuries, serving as a mode of transportation, a source of food, and even a symbol of endurance and survival. There are two primary species of camels: the dromedary camel, which has a single hump and is commonly found in the Middle East and North Africa, and the Bactrian camel, which has two humps and is native to Central Asia. Their humps store fat, not water, as commonly believed, allowing them to survive long periods without food by metabolizing the stored fat for energy. Camels are highly adapted to desert life. They can go for weeks without water, and when they do drink, they can consume up to 40 gallons in one sitting. Their thick eyelashes, sealable nostrils, and wide, padded feet protect them from sand and help them walk easily on loose desert terrain.", | |
| ] | |
| Human_texts = [ | |
| "To make BERT handle a variety of down-stream tasks, our input representation is able to unambiguously represent both a single sentence and a pair of sentences (e.g., h Question, Answeri) in one token sequence. Throughout this work, a “sentence” can be an arbitrary span of contiguous text, rather than an actual linguistic sentence. A “sequence” refers to the input token sequence to BERT, which may be a single sentence or two sentences packed together. We use WordPiece embeddings (Wu et al., 2016) with a 30,000 token vocabulary. The first token of every sequence is always a special classification token ([CLS]). The final hidden state corresponding to this token is used as the aggregate sequence representation for classification tasks. Sentence pairs are packed together into a single sequence." | |
| ] | |
| def classify_text_interface(text): | |
| if not all([tokenizer, model_1, model_2, model_3]): | |
| return "<p style='text-align: center; color: var(--ai-color);'><strong>Error: Models not loaded. Please check the console.</strong></p>" | |
| cleaned_text = clean_text(text) | |
| if not cleaned_text.strip(): | |
| result_message = "<p style='text-align: center; color: var(--text-secondary);'>Please enter some text to analyze.</p>" | |
| return result_message | |
| inputs = tokenizer(cleaned_text, return_tensors="pt", truncation=True, padding=True, max_length=512).to(device) # Added max_length | |
| with torch.no_grad(): | |
| logits_1 = model_1(**inputs).logits | |
| logits_2 = model_2(**inputs).logits | |
| logits_3 = model_3(**inputs).logits | |
| softmax_1 = torch.softmax(logits_1, dim=1) | |
| softmax_2 = torch.softmax(logits_2, dim=1) | |
| softmax_3 = torch.softmax(logits_3, dim=1) | |
| averaged_probabilities = (softmax_1 + softmax_2 + softmax_3) / 3 | |
| probabilities = averaged_probabilities[0] | |
| ai_probs = probabilities.clone() | |
| human_label_index = -1 | |
| for k, v in label_mapping.items(): | |
| if v.lower() == 'human': | |
| human_label_index = k | |
| break | |
| if human_label_index != -1: | |
| ai_probs[human_label_index] = 0 | |
| human_prob_value = probabilities[human_label_index].item() * 100 | |
| else: | |
| human_prob_value = 0 | |
| print("Warning: 'human' label not found in label_mapping.") | |
| ai_total_prob = ai_probs.sum().item() * 100 | |
| ai_argmax_index = torch.argmax(ai_probs).item() | |
| ai_argmax_model = label_mapping.get(ai_argmax_index, "Unknown AI") | |
| if human_prob_value > ai_total_prob : | |
| result_message = ( | |
| f"<p><strong>The text is</strong> <span class='highlight-human'><strong>{human_prob_value:.2f}%</strong> likely <b>Human written</b>.</span></p>" | |
| ) | |
| else: | |
| result_message = ( | |
| f"<p><strong>The text is</strong> <span class='highlight-ai'><strong>{ai_total_prob:.2f}%</strong> likely <b>AI generated</b>.</span></p>" | |
| f"<p style='margin-top: 10px; font-size: 0.95em;'><strong>Most Likely AI Source:</strong> {ai_argmax_model} (with {probabilities[ai_argmax_index].item()*100:.2f}% confidence among AI models)</p>" | |
| ) | |
| return result_message | |
| modern_css = """ | |
| @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap'); | |
| :root { | |
| --primary-bg: #F8F9FA; | |
| --app-bg: #FFFFFF; | |
| --text-primary: #2C3E50; | |
| --text-secondary: #7F8C8D; | |
| --accent-color: #1ABC9C; | |
| --accent-color-darker: #16A085; | |
| --border-color: #E0E0E0; | |
| --input-bg: #FFFFFF; | |
| --input-focus-border: var(--accent-color); | |
| --human-color: #2ECC71; | |
| --human-bg: rgba(46, 204, 113, 0.1); | |
| --ai-color: #E74C3C; | |
| --ai-bg: rgba(231, 76, 60, 0.1); | |
| --shadow-color: rgba(44, 62, 80, 0.1); | |
| --container-max-width: 800px; | |
| --border-radius-md: 8px; | |
| --border-radius-lg: 12px; | |
| } | |
| .features-list strong::after { | |
| content: " "; | |
| display: inline-block; | |
| width: 0.2em; | |
| } | |
| body { | |
| font-family: 'Inter', sans-serif; | |
| background: linear-gradient(135deg, #f5f7fa 0%, #eef2f7 100%); | |
| color: var(--text-primary); | |
| margin: 0; | |
| padding: 20px; | |
| display: flex; | |
| justify-content: center; | |
| align-items: flex-start; | |
| min-height: 100vh; | |
| box-sizing: border-box; | |
| overflow-y: auto; | |
| } | |
| .gradio-container { | |
| background-color: var(--app-bg); | |
| border-radius: var(--border-radius-lg); | |
| padding: clamp(25px, 5vw, 40px); | |
| box-shadow: 0 8px 25px var(--shadow-color); | |
| max-width: var(--container-max-width); | |
| width: 100%; | |
| margin: 20px auto; | |
| border: none; | |
| } | |
| .form.svelte-633qhp, .block.svelte-11xb1hd, .gradio-html .block { | |
| background: none !important; | |
| border: none !important; | |
| box-shadow: none !important; | |
| padding: 0 !important; | |
| } | |
| h1 { | |
| color: var(--text-primary); | |
| font-size: clamp(24px, 5vw, 30px); | |
| font-weight: 700; | |
| text-align: center; | |
| margin-bottom: 20px; | |
| letter-spacing: -0.5px; | |
| } | |
| .app-description p { | |
| color: var(--text-secondary); | |
| font-size: clamp(14px, 2.5vw, 16px); | |
| line-height: 1.7; | |
| margin-bottom: 15px; | |
| } | |
| .app-description .instruction-text { | |
| font-weight: 500; | |
| color: var(--text-primary); | |
| margin-top: 20px; | |
| text-align: center; | |
| } | |
| .features-list { | |
| list-style: none; | |
| padding-left: 0; | |
| margin: 20px 0; | |
| } | |
| .features-list li { | |
| display: flex; | |
| align-items: center; | |
| font-size: clamp(14px, 2.5vw, 16px); | |
| color: var(--text-secondary); | |
| margin-bottom: 12px; | |
| line-height: 1.6; | |
| } | |
| .features-list .icon { | |
| margin-right: 12px; | |
| font-size: 1.2em; | |
| color: var(--accent-color); | |
| } | |
| .learn-more-link, .learn-more-link b { | |
| color: var(--accent-color) !important; | |
| text-decoration: none; | |
| font-weight: 600; | |
| } | |
| .learn-more-link:hover, .learn-more-link:hover b { | |
| color: var(--accent-color-darker) !important; | |
| text-decoration: underline; | |
| } | |
| #text_input_box textarea { | |
| background-color: var(--input-bg); | |
| border: 1px solid var(--border-color); | |
| border-radius: var(--border-radius-md); | |
| font-size: clamp(15px, 2.5vw, 16px); | |
| padding: 15px; | |
| width: 100%; | |
| box-sizing: border-box; | |
| color: var(--text-primary); | |
| transition: border-color 0.3s ease, box-shadow 0.3s ease; | |
| min-height: 120px; | |
| box-shadow: 0 2px 4px rgba(0,0,0,0.05); | |
| } | |
| #text_input_box textarea::placeholder { | |
| color: #B0BEC5; | |
| } | |
| #text_input_box textarea:focus { | |
| border-color: var(--input-focus-border); | |
| box-shadow: 0 0 0 3px rgba(26, 188, 156, 0.2); | |
| outline: none; | |
| } | |
| #result_output_box { | |
| background-color: var(--input-bg); | |
| border: 1px solid var(--border-color); | |
| border-radius: var(--border-radius-md); | |
| padding: 20px; | |
| margin-top: 25px; | |
| width: 100%; | |
| box-sizing: border-box; | |
| text-align: center; | |
| font-size: clamp(16px, 3vw, 17px); | |
| box-shadow: 0 4px 8px rgba(0,0,0,0.05); | |
| min-height: 80px; | |
| display: flex; | |
| flex-direction: column; | |
| justify-content: center; | |
| } | |
| #result_output_box p { | |
| margin-bottom: 8px; | |
| line-height: 1.6; | |
| } | |
| #result_output_box p:last-child { | |
| margin-bottom: 0; | |
| } | |
| .highlight-human, .highlight-ai { | |
| font-weight: 600; | |
| padding: 5px 10px; | |
| border-radius: var(--border-radius-md); | |
| display: inline-block; | |
| font-size: 1.05em; | |
| } | |
| .highlight-human { | |
| color: var(--human-color); | |
| background-color: var(--human-bg); | |
| /* border: 1px solid var(--human-color); | |
| } | |
| .highlight-ai { | |
| color: var(--ai-color); | |
| background-color: var(--ai-bg); | |
| /* border: 1px solid var(--ai-color); | |
| } | |
| .tabs > div:first-child button { | |
| background-color: transparent !important; | |
| color: var(--text-secondary) !important; | |
| border: none !important; | |
| border-bottom: 2px solid transparent !important; | |
| border-radius: 0 !important; | |
| padding: 10px 15px !important; | |
| font-weight: 500 !important; | |
| transition: color 0.3s ease, border-bottom-color 0.3s ease !important; | |
| } | |
| .tabs > div:first-child button.selected { | |
| color: var(--accent-color) !important; | |
| border-bottom-color: var(--accent-color) !important; | |
| font-weight: 600 !important; | |
| } | |
| .gr-examples { | |
| padding: 15px !important; | |
| border: 1px solid var(--border-color) !important; | |
| border-radius: var(--border-radius-md) !important; | |
| background-color: #fdfdfd !important; | |
| margin-top: 10px; | |
| } | |
| .gr-sample-textbox { | |
| border: 1px solid var(--border-color) !important; | |
| border-radius: var(--border-radius-md) !important; | |
| font-size: 14px !important; | |
| } | |
| .gr-accordion > .label-wrap button { | |
| font-weight: 500 !important; | |
| color: var(--text-primary) !important; | |
| } | |
| .footer-text, #bottom_text { | |
| text-align: center; | |
| margin-top: 40px; | |
| font-size: clamp(13px, 2vw, 14px); | |
| color: var(--text-secondary); | |
| } | |
| #bottom_text p { | |
| margin: 0; | |
| } | |
| @media (max-width: 768px) { | |
| body { | |
| padding: 10px; | |
| align-items: flex-start; | |
| } | |
| .gradio-container { | |
| padding: 20px; | |
| margin: 10px; | |
| } | |
| h1 { font-size: 22px; } /* Adjust for custom title markdown */ | |
| .app-description p, .features-list li { font-size: 14px; } | |
| #text_input_box textarea { font-size: 15px; min-height: 100px; } | |
| #result_output_box { font-size: 15px; padding: 15px; } | |
| } | |
| """ | |
| iface = gr.Blocks(css=modern_css, theme=gr.themes.Base(font=[gr.themes.GoogleFont("Inter"), "sans-serif"])) | |
| with iface: | |
| gr.Markdown(title_md) | |
| gr.Markdown(description) | |
| text_input = gr.Textbox( | |
| label="", | |
| placeholder="Type or paste your content here...", | |
| elem_id="text_input_box", | |
| lines=10 | |
| ) | |
| result_output = gr.HTML(elem_id="result_output_box") | |
| if all([tokenizer, model_1, model_2, model_3]): | |
| text_input.change(classify_text_interface, inputs=text_input, outputs=result_output) | |
| else: | |
| gr.HTML("<div id='result_output_box'><p style='color: var(--ai-color); text-align: center;'><strong>Application Error: Models could not be loaded. Please check the server console for details.</strong></p></div>") | |
| with gr.Accordion("AI Text Examples", open=False): | |
| gr.Examples( | |
| examples=AI_texts, | |
| inputs=text_input, | |
| label="", | |
| ) | |
| with gr.Accordion("Human Text Examples", open=False): | |
| gr.Examples( | |
| examples=Human_texts, | |
| inputs=text_input, | |
| label="", | |
| ) | |
| gr.Markdown(bottom_text, elem_id="bottom_text") | |
| if __name__ == "__main__": | |
| iface.launch(share=False) |