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app.py

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+import os
+import numpy as np
+import torch
+import joblib
+import torch.nn as nn
+from transformers import AutoImageProcessor, AutoModel
+from PIL import Image
+import requests
+import gradio as gr
+
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# -----------------------
+# Your model class (unchanged)
+# -----------------------
+class ImageAuthenticityClassifier(nn.Module):
+  def __init__(self, backbone, w, b):
+    super().__init__()
+    self.backbone = backbone
+
+    d = w.shape[0]
+    self.head = nn.Linear(d, 1)
+
+    # Load my trained classifier head
+    with torch.no_grad():
+      self.head.weight.copy_(
+          w.unsqueeze(0).to(dtype=self.head.weight.dtype,
+                            device=self.head.weight.device)
+      )
+      bias_tensor = torch.tensor(
+          [b],
+          dtype=self.head.bias.dtype,
+          device=self.head.bias.device,
+      )
+      self.head.bias.copy_(bias_tensor)
+
+
+  def forward(self, pixel_values, return_tokens: bool = False):
+    outputs = self.backbone(pixel_values=pixel_values)
+    hidden = outputs.last_hidden_state
+
+    patch_tokens = hidden[:, 1:, :]
+    emb = patch_tokens.mean(dim = 1)
+
+    # Apply classifier head to mean patch token embeddings
+    logits = self.head(emb)
+    prob = torch.sigmoid(logits)
+
+    if (return_tokens):
+      return logits, prob, emb, patch_tokens
+
+    return logits, prob, emb
+
+
+# -----------------------
+# Load linear classifier head for logistic regression
+# -----------------------
+model_save_path = "logisticRegressionClassifier.joblib"
+logisticRegressionClassifier = joblib.load(model_save_path)
+
+coef = logisticRegressionClassifier.coef_
+w = torch.from_numpy(coef.squeeze(0)).float()
+intercept = logisticRegressionClassifier.intercept_
+b = float(intercept[0])
+
+
+# -----------------------
+# Load DinoV3 backbone + processor (gated repo via token)
+# -----------------------
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+backbone = AutoModel.from_pretrained("facebook/dinov3-vitb16-pretrain-lvd1689m", token=HF_TOKEN).to(device)
+processor = AutoImageProcessor.from_pretrained("facebook/dinov3-vitb16-pretrain-lvd1689m", token=HF_TOKEN,)
+image_auth_model = ImageAuthenticityClassifier(backbone, w, b).to(device)
+
+
+# -----------------------
+# Inference helper functions (unchanged)
+# -----------------------
+def load_image(online_image_url):
+  img = Image.open(requests.get(online_image_url, stream=True).raw).convert("RGB")
+  return img
+
+def prepare_pixel_values(img):
+  inputs = processor(images=img, return_tensors="pt")
+  pixel_values = inputs["pixel_values"].to(device)
+  return pixel_values
+
+def predict_from_online_url(online_image_url):
+  img = load_image(online_image_url)
+  pixel_values = prepare_pixel_values(img)
+
+  with torch.no_grad():
+      logits, prob, emb = image_auth_model(pixel_values)
+  return float(prob[0][0].item())
+
+
+# -----------------------
+# Gradio interface exposing ui_predict as a web UI/API. (AI Generated lol)
+# -----------------------
+
+def ui_predict(image_url: str):
+    if not image_url:
+        return None, "Awaiting input", "Enter an image URL to run a prediction."
+
+    try:
+        img = load_image(image_url)
+        ai_prob = float(predict_from_online_url(image_url))
+        percent = ai_prob * 100.0
+
+        verdict = "AI-generated" if ai_prob >= 0.5 else "Not AI-generated"
+        headline = verdict
+        detail = f"{percent:.1f}% probability the image is AI-generated"
+
+        return img, headline, detail
+    except Exception as e:
+        return None, "Error", str(e)
+
+demo = gr.Interface(
+    fn=ui_predict,
+    inputs=gr.Textbox(
+        label="Image URL",
+        placeholder="https://example.com/image.jpg",
+    ),
+    outputs=[
+        gr.Image(label="Preview"),
+        gr.Textbox(label="Verdict"),
+        gr.Textbox(label="Details"),
+    ],
+    title="Image Authenticity",
+    description="Paste an image URL to estimate how likely it is AI-generated.",
+)
+
+if __name__ == "__main__":
+    demo.launch()

logisticRegressionClassifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c3162743076d6b843efa03f16727093be0c4b95aed4c9b5c550e208812fbafc9
+size 7007

requirements.txt

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+torch
+torchvision
+transformers
+scikit-learn
+joblib
+numpy
+pillow
+gradio
+requests