const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
const feeds = { float_input: input };
const results = await session.run(feeds);
// feed inputs and run
// read from results
alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
const results = await session.run(feeds);
} catch (e) {
console.log(`failed to inference ONNX model: ${e}.`);
}
}
else alert("Please check an ingredient")
// read from results
alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
} catch (e) {
console.log(`failed to inference ONNX model: ${e}.`);
}
}
else alert("Please check an ingredient")
}
init();
</script>
</script>
```
In this code, there are several things happening:
1. You created an array of 380 possible values (1 or 0) to be set and sent to the model for inference, depending on whether an ingredient checkbox is checked.
1. You created an array of 380 possible values (1 or 0) to be set and sent to the model for inference, depending on whether an ingredient checkbox is checked.
2. You created an array of checkboxes and a way to determine whether they were checked in an `init` function that is called when the application starts. When a checkbox is checked, the `ingredients` array is altered to reflect the chosen ingredient.
3. You created a `testCheckboxes` function that checks whether any checkbox was checked.
4. You use that function when the button is pressed and, if any checkbox is checked, you start inference.
4. You use `startInference` function when the button is pressed and, if any checkbox is checked, you start inference.
5. The inference routine includes:
1. Setting up an asynchronous load of the model
2. Creating a Tensor structure to send to the model
Vico Chu
4 years ago
committed by
GitHub