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| import gradio as gr | |
| import numpy as np | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline | |
| def load_model(model_name): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| return pipeline('text-classification', model=model, tokenizer=tokenizer, truncation=True, max_length=512, top_k=4) | |
| classifier = load_model("ngocminhta/authscan-baseline") | |
| classifier2 = load_model("ngocminhta/authscan-baseline-machine") | |
| TEXT_CLASS_MAPPING_MACHINE = { | |
| 'LABEL_0': 'Gemini 1.5 Family', | |
| 'LABEL_1': 'Gemini 2.0 Family', | |
| 'LABEL_2': 'GPT-4o Family', | |
| 'LABEL_3': 'Llama 3.1 Family' | |
| } | |
| TEXT_CLASS_MAPPING = { | |
| 'LABEL_0': 'Human-Written', | |
| 'LABEL_1': 'Machine-Generated' | |
| } | |
| def update_language(language): | |
| if language == 'Java' or language == 'Python': | |
| return gr.update(language='python') | |
| elif language == 'C': | |
| return gr.update(language='c') | |
| elif language == 'C++': | |
| return gr.update(language='cpp') | |
| return gr.update(language='python') | |
| def process_result_detection_tab(text, language): | |
| result = classifier(f"Language: {language}\n\n{text}")[0] | |
| result_machine = classifier2(f"Language: {language}\n\n{text}")[0] | |
| labels = [TEXT_CLASS_MAPPING[x['label']] for x in result] | |
| labels_machine = [TEXT_CLASS_MAPPING_MACHINE[x['label']] for x in result_machine] | |
| scores = list(np.array([x['score'] for x in result])) | |
| scores_machine = list(np.array([x['score'] for x in result_machine])) | |
| final_results = dict(zip(labels, scores)) | |
| if max(final_results, key=final_results.get) == 'Machine-Generated': | |
| final_results_machine = dict(zip(labels_machine, scores_machine)) | |
| else: | |
| final_results_machine = None | |
| return final_results, final_results_machine | |
| def clear_detection_tab(): | |
| return "", gr.update(interactive=False) | |
| theme = gr.themes.Soft( | |
| primary_hue="teal", | |
| font=[gr.themes.GoogleFont('Open Sans'), 'ui-sans-serif', 'system-ui', 'sans-serif'], | |
| font_mono=[gr.themes.GoogleFont('Roboto Mono'), 'ui-monospace', 'Consolas', 'monospace'], | |
| ) | |
| with gr.Blocks(theme=theme) as demo: | |
| gr.Markdown("""<h1><center>AuthScan</center></h1>""") | |
| with gr.Tab("Code Detection"): | |
| with gr.Row(): | |
| language = gr.Dropdown( | |
| choices=["C", "C++", "Java", "Python"], | |
| label="Select Programming Language", | |
| value="C" | |
| ) | |
| with gr.Row(): | |
| input_text = gr.Code( | |
| label="Enter code here", | |
| language="python", | |
| elem_id="code_input", | |
| ) | |
| with gr.Row(): | |
| check_button = gr.Button("Check Origin", variant="primary") | |
| clear_button = gr.Button("Clear", variant="stop") | |
| out = gr.Label(label='Result') | |
| out_machine = gr.Label(label='Detailed Information') | |
| # When language is changed, update the code component's language | |
| language.change(update_language, inputs=language, outputs=input_text) | |
| check_button.click(process_result_detection_tab, inputs=[input_text, language], outputs=[out, out_machine]) | |
| # out_machine.change(lambda x: gr.update(visible=True) if out_machine else gr.update(visible=False), inputs=out_machine, outputs=out_machine) | |
| clear_button.click(clear_detection_tab, inputs=[], outputs=[input_text, check_button]) | |
| with gr.Tab("Text Detection"): | |
| gr.Markdown("""Under development!""") | |
| demo.launch(share=True) |