Add minimal usage snippets (Kotlin + Python)

README.md

@@ -29,6 +29,41 @@ face[1,3,448,448] (ImageNet-normalized) →[GPU: ResNet50]→ yaw[1,90], pitch[1
 
 The 90 bins span [-180,180]° (4° each); gaze angle = softmax expectation `Σ p_i·i · 4 − 180` (softmax baked in).
 
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
+
+```kotlin
+val model = CompiledModel.create(context.assets, "gaze_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(chw)              // [1,3,448,448] ImageNet-normalized RGB, NCHW
+model.run(inputs, outputs)
+val yawProbs = outputs[0].readFloat()   // [1,90] softmax over 4-deg bins
+val pitchProbs = outputs[1].readFloat() // [1,90]; deg = sum(p_i * i) * 4 - 180
+```
+
+**Python (desktop verification)**
+
+```python
+MEAN = np.array([0.485, 0.456, 0.406], np.float32)
+STD  = np.array([0.229, 0.224, 0.225], np.float32)
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+
+img = Image.open("face.jpg").convert("RGB").resize((448, 448))   # centered face crop
+x = ((np.asarray(img, np.float32) / 255 - MEAN) / STD).transpose(2, 0, 1)[None]
+
+it = Interpreter(model_path="gaze_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
+od = it.get_output_details()                       # output 0 = yaw, 1 = pitch (both [1,90])
+deg = lambda p: float((p * np.arange(90)).sum() * 4 - 180)
+yaw, pitch = (deg(it.get_tensor(o["index"])[0]) for o in od)
+print(f"yaw {yaw:+.1f} deg, pitch {pitch:+.1f} deg")
+```
+
 ## How it converts (litert-torch)
 
 Pure CNN (ResNet50 + 2 FC heads). Two numerically-exact ResNet fixes: