Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+
+from uei_core.models import ModelPortfolio
+from uei_core.uncertainty import UncertaintyEstimator
+from uei_core.energy import EnergyProfiler
+from uei_core.policy import UEIPolicy
+
+device = "cpu"  # M1 CPU is optimized enough; GPU optional
+
+models = ModelPortfolio(device=device)
+unc = UncertaintyEstimator()
+energy = EnergyProfiler()
+policy = UEIPolicy()
+
+
+def uei_infer(img):
+
+    x = models.preprocess(img)
+
+    # Step 1: Small model
+    logits_s, E_s = energy.measure(models.infer_small, x)
+    U_s = unc.estimate(logits_s)
+
+    # Step 2: Large model (to measure marginal utility)
+    logits_l, E_l = energy.measure(models.infer_large, x)
+    U_l = unc.estimate(logits_l)
+
+    decision = policy.decide(U_s, U_l, E_s, E_l)
+
+    if decision == "small":
+        final_logits = logits_s
+        energy_used = E_s
+        unc_used = U_s
+        model_name = "Low-Energy Small Model"
+    else:
+        final_logits = logits_l
+        energy_used = E_l
+        unc_used = U_l
+        model_name = "High-Energy Large Model"
+
+    probs = torch.softmax(final_logits, dim=1).squeeze()
+    top_idx = torch.argmax(probs).item()
+    confidence = float(probs[top_idx])
+
+    return {
+        "Predicted Class Index": top_idx,
+        "Confidence": confidence,
+        "Selected Model": model_name,
+        "Uncertainty": float(unc_used),
+        "Energy (proxy units)": energy_used
+    }
+
+
+demo = gr.Interface(
+    fn=uei_infer,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.JSON(),
+    title="Uncertainty-Elastic Inference (UEI)",
+    description="Energy-efficient inference by dynamically selecting models based on uncertainty-energy tradeoffs."
+)
+
+if __name__ == "__main__":
+    demo.launch()