app.py c

app.py

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+import gradio as gr
+from transformers import AutoModelForImageClassification, ViTImageProcessor
+from PIL import Image
+import torch
+
+# Load model once (global)
+model_id = "jacoballessio/ai-image-detect-distilled"
+
+processor = ViTImageProcessor.from_pretrained(model_id)
+model = AutoModelForImageClassification.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32
+)
+model.eval()
+
+device = "cpu"
+model.to(device)
+
+
+def predict(image: Image.Image):
+    if image is None:
+        return "Please upload an image", ""
+
+    # Preprocess
+    inputs = processor(image, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Softmax
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    confidence = probs.max().item()
+    predicted_label = model.config.id2label[probs.argmax().item()]
+
+    # Result text
+    if predicted_label.lower() == "fake":
+        result = f"⚠️ AI-GENERATED\nConfidence: {confidence:.3f}"
+    else:
+        result = f"✅ REAL IMAGE\nConfidence: {confidence:.3f}"
+
+    return result, probs.squeeze().tolist()
+
+
+# UI
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Image"),
+    outputs=[
+        gr.Textbox(label="Prediction"),
+        gr.Label(label="Confidence Scores")
+    ],
+    title="🖼️ AI vs Real Image Detector",
+    description="Upload an image to check if it's AI-generated or real."
+)
+
+if __name__ == "__main__":
+    app.launch(server_name="0.0.0.0", server_port=7860)