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app.py

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+import gradio as gr
+from transformers import pipeline
+
+classifier = pipeline("image-classification", model="XenArcAI/AIRealNet")
+
+def recognize(img):
+    results = classifier(img)
+    return {r["label"]: round(r["score"], 3) for r in results}
+
+with gr.Blocks() as demo:   # removed theme for compatibility
+    gr.Markdown(
+        """
+        # **XenArcAI**
+        # 🖼️ AIRealNet Image Recognition
+        Upload an image and let **AIRealNet** identify what's inside.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            inp = gr.Image(type="pil", label="Upload Image")
+            btn = gr.Button("Run Recognition")
+        with gr.Column():
+            out = gr.Label(num_top_classes=2, label="Predictions")
+
+    examples = gr.Examples(
+        examples=["examples/cat.jpg", "examples/dog.jpg", "examples/car.jpg"],
+        inputs=inp
+    )
+
+    btn.click(fn=recognize, inputs=inp, outputs=[out])
+
+demo.launch()

requirements.txt

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+torch
+transformers
+safetensors
+gradio