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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/image00.jpg filter=lfs diff=lfs merge=lfs -text
+examples/image01.jpg filter=lfs diff=lfs merge=lfs -text
+examples/image02.JPG filter=lfs diff=lfs merge=lfs -text

requirements

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+torch>=2.0.0
+transformers>=4.40.0
+gradio>=4.18.0
+Pillow

run_realify.py

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