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import torch
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from PIL import Image
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import gradio as gr
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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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