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README.md

@@ -5,48 +5,9 @@ colorFrom: green
 colorTo: blue
 sdk: static
 app_file: index.html
-fullWidth: true
-header: mini
-short_description: Browser waste classification with MS-SE-EfficientNet-B0
-tags:
-  - computer-vision
-  - image-classification
-  - waste-classification
-  - efficientnet
-  - onnx
-  - browser-inference
+pinned: false
 ---
 
 # MS-SE Waste Classifier
 
-A fully static Hugging Face Space for ten-class waste image classification. Inference runs locally in the visitor's browser through ONNX Runtime Web.
-
-## Upload these files to the Space root
-
-```text
-README.md
-index.html
-styles.css
-app.js
-ms_se_efficientnet_b0.onnx
-```
-
-Do not place any file inside a subfolder.
-
-## Model configuration
-
-- Architecture: MS-SE-EfficientNet-B0
-- Input: RGB image resized to 224 × 224
-- Normalization: ImageNet mean `[0.485, 0.456, 0.406]` and standard deviation `[0.229, 0.224, 0.225]`
-- Classes: battery, biological, cardboard, clothes, glass, metal, paper, plastic, shoes, trash
-- Runtime: ONNX Runtime Web with WebAssembly
-
-## Local test
-
-Run a local web server from this folder:
-
-```bash
-python -m http.server 8000
-```
-
-Then open `http://localhost:8000`.
+Browser-based waste image classification using MS-SE-EfficientNet-B0 and ONNX Runtime Web.

app.js

@@ -158,7 +158,7 @@ function renderModelFacts() {
   if (!state.metadata) return;
   const facts = [
     ['Input', `${state.metadata.input.width} × ${state.metadata.input.height} RGB`],
-    ['Classes', String(state.metadata.classes.length)],
+    ['Classes', String(state.labels.length)],
     ['Parameters', `${(state.metadata.complexity.parameters / 1e6).toFixed(3)}M`],
     ['Runtime', 'WebAssembly'],
   ];
@@ -199,11 +199,27 @@ async function initializeModel() {
     state.session = await ort.InferenceSession.create(modelBytes, {
       executionProviders: ['wasm'],
       graphOptimizationLevel: 'all',
-      freeDimensionOverrides: { batch: 1 },
     });
 
-    if (!state.session.inputNames.includes('input') || !state.session.outputNames.includes('logits')) {
-      throw new Error(`Unexpected ONNX interface: inputs ${state.session.inputNames.join(', ')}, outputs ${state.session.outputNames.join(', ')}`);
+    const inputNames = Array.isArray(state.session.inputNames) ? state.session.inputNames : [];
+    const outputNames = Array.isArray(state.session.outputNames) ? state.session.outputNames : [];
+    if (!inputNames.includes('input') || !outputNames.includes('logits')) {
+      throw new Error(`Unexpected ONNX interface: inputs ${inputNames.join(', ') || 'none'}, outputs ${outputNames.join(', ') || 'none'}`);
+    }
+
+    // Run one deterministic smoke test before enabling the interface. This catches
+    // invalid model files, unsupported operators, and output-shape mismatches.
+    setProgress(92, 'Verifying model execution and output shape…');
+    const testData = new Float32Array(3 * APP.imageSize * APP.imageSize);
+    const testTensor = new ort.Tensor('float32', testData, [1, 3, APP.imageSize, APP.imageSize]);
+    const testOutputs = await state.session.run({ input: testTensor });
+    const testLogits = testOutputs.logits && testOutputs.logits.data;
+    if (!testLogits || testLogits.length !== state.labels.length) {
+      const actualLength = testLogits ? testLogits.length : 0;
+      throw new Error(`ONNX smoke test failed: expected ${state.labels.length} logits, received ${actualLength}.`);
+    }
+    if (!Array.from(testLogits).every(Number.isFinite)) {
+      throw new Error('ONNX smoke test failed: output contains non-finite values.');
     }
 
     state.modelReady = true;
@@ -404,7 +420,11 @@ async function predict() {
     const outputs = await state.session.run({ input: inputTensor });
     const inferenceEnd = performance.now();
 
-    const logits = Array.from(outputs.logits.data);
+    const outputTensor = outputs.logits;
+    if (!outputTensor || !outputTensor.data) {
+      throw new Error('The ONNX runtime did not return the expected logits output.');
+    }
+    const logits = Array.from(outputTensor.data);
     if (logits.length !== state.labels.length) {
       throw new Error(`Expected ${state.labels.length} logits but received ${logits.length}.`);
     }

index.html

@@ -165,6 +165,6 @@
   <canvas id="preprocessCanvas" width="224" height="224" class="visually-hidden" aria-hidden="true"></canvas>
 
   <script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.27.0/dist/ort.min.js" crossorigin="anonymous"></script>
-  <script src="app.js"></script>
+  <script src="app.js?v=20260802-2"></script>
 </body>
 </html>