Add minimal usage snippets (Kotlin + Python)

README.md

@@ -37,14 +37,34 @@ motion deblur.
 image[1,3,256,256] (RGB [0,1]) →[GPU: NAFNet U-Net]→ restored[1,3,256,256]
 ```
 
-## Usage (Android, LiteRT CompiledModel)
+## Minimal usage
+
+**Android (Kotlin, CompiledModel GPU)**
 
 ```kotlin
-val model = CompiledModel.create(modelPath, CompiledModel.Options(Accelerator.GPU), null)
-val input = model.createInputBuffers(); val output = model.createOutputBuffers()
-input[0].writeFloat(chw)             // [1,3,256,256] RGB in [0,1], NCHW
-model.run(input, output)
-val restored = output[0].readFloat()  // [1,3,256,256] in [0,1]
+val model = CompiledModel.create(context.assets, "nafnet_fp16.tflite",
+    CompiledModel.Options(Accelerator.GPU), null)
+val inputs = model.createInputBuffers()
+val outputs = model.createOutputBuffers()
+inputs[0].writeFloat(chw)            // [1,3,256,256] RGB in [0,1], NCHW
+model.run(inputs, outputs)
+val restored = outputs[0].readFloat()    // [1,3,256,256] in [0,1]
+```
+
+**Python (desktop verification)**
+
+```python
+import numpy as np
+from PIL import Image
+from ai_edge_litert.interpreter import Interpreter
+
+img = Image.open("blurry.jpg").convert("RGB").resize((256, 256))
+x = (np.asarray(img, np.float32) / 255.0).transpose(2, 0, 1)[None]   # [1,3,256,256]
+
+it = Interpreter(model_path="nafnet_fp16.tflite"); it.allocate_tensors()
+it.set_tensor(it.get_input_details()[0]["index"], x); it.invoke()
+y = it.get_tensor(it.get_output_details()[0]["index"])[0]            # [3,256,256], [0,1]
+Image.fromarray((y.transpose(1, 2, 0).clip(0, 1) * 255).astype(np.uint8)).save("restored.png")
 ```
 
 A complete Android sample (image picker + before/after) is in the official