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| import gradio as gr | |
| from transformers import pipeline | |
| pipe = pipeline("image-classification", "umm-maybe/AI-image-detector") | |
| def image_classifier(image): | |
| outputs = pipe(image) | |
| results = {} | |
| for result in outputs: | |
| results[result['label']] = result['score'] | |
| return results | |
| title = "Maybe's AI Art Detector" | |
| description = """ | |
| This app is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI. | |
| It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It still may be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors. | |
| Furthermore the intended scope of this tool is artistic images; that is to say, it is not a deepfake photo detector, and general computer imagery (webcams, screenshots, etc.) may throw it off. | |
| In general, this tool can only serve as one of many potential indicators that an image was AI-generated. Images scoring as very probably artificial (e.g. 90% or higher) could be referred to a human expert for further investigation, if needed. | |
| For more information please see the blog post describing this project at: | |
| https://medium.com/@matthewmaybe/can-an-ai-learn-to-identify-ai-art-545d9d6af226 | |
| """ | |
| demo = gr.Interface(fn=image_classifier, inputs=gr.Image(type="pil"), outputs="label", title=title, description=description) | |
| demo.launch(show_api=False) | |