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import gradio as gr |
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import torch |
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from PIL import Image |
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from transformers import ViTForImageClassification, ViTImageProcessor, CLIPProcessor, CLIPModel |
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"""Load models""" |
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deepfake_model = ViTForImageClassification.from_pretrained("prithivMLmods/Deep-Fake-Detector-v2-Model") |
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deepfake_processor = ViTImageProcessor.from_pretrained("prithivMLmods/Deep-Fake-Detector-v2-Model") |
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clip_model_path = "openai/clip-vit-base-patch32" |
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clip_model = CLIPModel.from_pretrained(clip_model_path) |
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clip_processor = CLIPProcessor.from_pretrained(clip_model_path) |
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clip_model.eval() |
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deepfake_model.eval() |
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clip_labels = ["photo of a real person or scene", "synthetic or AI generated image"] |
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def hybrid_classifier(img: Image.Image): |
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df_inputs = deepfake_processor(images=img, return_tensors="pt") |
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with torch.no_grad(): |
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df_outputs = deepfake_model(**df_inputs) |
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df_probs = torch.nn.functional.softmax(df_outputs.logits, dim=1) |
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df_real_score = df_probs[0][deepfake_model.config.label2id["Realism"]].item() |
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df_fake_score = df_probs[0][deepfake_model.config.label2id["Deepfake"]].item() |
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clip_inputs = clip_processor(text=clip_labels, images=img, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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clip_outputs = clip_model(**clip_inputs) |
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clip_probs = torch.softmax(clip_outputs.logits_per_image, dim=1).squeeze() |
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clip_real_score = clip_probs[0].item() |
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clip_fake_score = clip_probs[1].item() |
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final_real_score = (0.3 * df_real_score + 0.7 * clip_real_score) |
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final_fake_score = (0.3 * df_fake_score + 0.7 * clip_fake_score) |
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decision = "Generated" |
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if abs(final_real_score - final_fake_score) > 0.3: |
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decision = "Real" if final_real_score > final_fake_score else decision |
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return decision |
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"""Preset and launch Gradio """ |
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iface = gr.Interface( |
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fn=hybrid_classifier, |
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inputs=gr.Image(type="pil"), |
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outputs=gr.Textbox(label="Final Decision"), |
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title="RealifyAI", |
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description="Figure out if image real or generated", |
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examples=[["examples/image00.jpg"], |
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["examples/image01.jpg"], |
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["examples/image02.jpg"]]) |
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if __name__ == "__main__": |
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iface.launch() |
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