Initial commit: Plant Disease Identification app

- Next.js 16 App Router project with Tailwind CSS
- Plant disease knowledge base (93 diseases, 25 plants)
- Image upload with client+server preprocessing
- ML inference pipeline with mock/demo fallback
- Responsive results page with disease cards and treatment
- Full test suite (285 passing tests)

apps/web/src/lib/ml/inference.ts

@@ -0,0 +1,141 @@
+/**
+ * ML inference pipeline for plant disease classification.
+ *
+ * Accepts a preprocessed image tensor, runs it through the model,
+ * applies softmax, extracts top-K predictions, and returns results
+ * with timing metadata.
+ */
+
+import type { InferenceResult, RawPrediction } from "@/lib/types";
+import { getModel } from "./model-loader";
+import { softmaxFloat32, getTopKFloat32 } from "./confidence";
+
+// ─── Configuration ───────────────────────────────────────────────────────────
+
+/** Number of top predictions to return */
+export const DEFAULT_TOP_K = 5;
+
+/** Input tensor shape: [batch=1, channels=3, height=224, width=224] */
+export const INPUT_SHAPE: [number, number, number, number] = [1, 3, 224, 224];
+
+/** Expected input tensor length */
+export const INPUT_SIZE = INPUT_SHAPE[1] * INPUT_SHAPE[2] * INPUT_SHAPE[3]; // 3 * 224 * 224 = 150528
+
+// ─── Main Inference ──────────────────────────────────────────────────────────
+
+/**
+ * Run the full inference pipeline on a preprocessed image tensor.
+ *
+ * @param imageTensor - Normalized Float32Array of shape [1, 3, 224, 224] (NCHW)
+ * @param topK - Number of top predictions to return (default 5)
+ * @returns InferenceResult with top-K predictions and timing
+ */
+export async function runInference(
+  imageTensor: Float32Array,
+  topK = DEFAULT_TOP_K,
+): Promise<InferenceResult> {
+  const startTime = performance.now();
+
+  // Validate input
+  validateInput(imageTensor);
+
+  // Get model (lazy loads on first call)
+  const model = await getModel();
+
+  // Run model forward pass
+  const { logits, inferenceTimeMs } = await model.predict(imageTensor);
+
+  // Apply softmax to convert logits to probabilities
+  const probabilities = softmaxFloat32(logits);
+
+  // Extract top-K predictions
+  const predictions = getTopKFloat32(probabilities, topK);
+
+  const totalTime = performance.now() - startTime;
+
+  return {
+    predictions,
+    inferenceTimeMs: Math.round(totalTime),
+  };
+}
+
+// ─── Input Validation ────────────────────────────────────────────────────────
+
+/**
+ * Validate that the input tensor has the expected shape and type.
+ *
+ * @param tensor - Input tensor to validate
+ * @throws Error if tensor is invalid
+ */
+export function validateInput(tensor: Float32Array): void {
+  if (!(tensor instanceof Float32Array)) {
+    throw new Error(
+      `Expected Float32Array input, got ${typeof tensor}`,
+    );
+  }
+
+  if (tensor.length !== INPUT_SIZE) {
+    throw new Error(
+      `Expected tensor of length ${INPUT_SIZE} (shape ${INPUT_SHAPE.join("×")}), ` +
+        `got ${tensor.length}`,
+    );
+  }
+
+  // Check for NaN/Infinity values
+  for (let i = 0; i < tensor.length; i++) {
+    if (!Number.isFinite(tensor[i])) {
+      throw new Error(
+        `Tensor contains non-finite value at index ${i}: ${tensor[i]}`,
+      );
+    }
+  }
+}
+
+// ─── Batch Inference ─────────────────────────────────────────────────────────
+
+/**
+ * Run inference on multiple images.
+ *
+ * Currently runs sequentially. For true batching, the model itself would need
+ * to support batch input.
+ *
+ * @param tensors - Array of preprocessed image tensors
+ * @param topK - Number of top predictions per image
+ * @returns Array of inference results
+ */
+export async function runBatchInference(
+  tensors: Float32Array[],
+  topK = DEFAULT_TOP_K,
+): Promise<InferenceResult[]> {
+  const results: InferenceResult[] = [];
+
+  for (const tensor of tensors) {
+    results.push(await runInference(tensor, topK));
+  }
+
+  return results;
+}
+
+// ─── Utility ─────────────────────────────────────────────────────────────────
+
+/**
+ * Create a zero-filled input tensor for testing.
+ *
+ * @returns Float32Array of shape [1, 3, 224, 224]
+ */
+export function createZeroTensor(): Float32Array {
+  return new Float32Array(INPUT_SIZE);
+}
+
+/**
+ * Create a random input tensor for testing.
+ *
+ * @returns Float32Array of shape [1, 3, 224, 224] with random values
+ */
+export function createRandomTensor(): Float32Array {
+  const tensor = new Float32Array(INPUT_SIZE);
+  for (let i = 0; i < tensor.length; i++) {
+    tensor[i] = (Math.random() * 2 - 1) * 2; // Range roughly -2 to 2
+  }
+  return tensor;
+}