@ -22,12 +22,12 @@ There are several ways to build web apps to consume machine learning models. You
There are many questions you need to ask:
- **Is it a web app or a mobile app?** If you are building a mobile app or need to use the model in an IoT context, you could use [TensorFlow Lite](https://www.tensorflow.org/lite/) and use the model in an Android or iOS app.
- **Where will the model reside**? In the cloud or locally?
- **Offline support**. Does the app have to work offline?
- **Where will the model reside?** In the cloud or locally?
- **Offline support.** Does the app have to work offline?
- **What technology was used to train the model?** The chosen technology may influence the tooling you need to use.
- **Using Tensor flow**. If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).
- **Using PyTorch**. If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
- **Using Lobe.ai or Azure Custom vision**. If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
- **Using Tensor flow.** If you are training a model using TensorFlow, for example, that ecosystem provides the ability to convert a TensorFlow model for use in a web app by using [TensorFlow.js](https://www.tensorflow.org/js/).
- **Using PyTorch.** If you are building a model using a library such as [PyTorch](https://pytorch.org/), you have the option to export it in [ONNX](https://onnx.ai/) (Open Neural Network Exchange) format for use in JavaScript web apps that can use the [Onnx Runtime](https://www.onnxruntime.ai/). This option will be explored in a future lesson for a Scikit-learn-trained model.
- **Using Lobe.ai or Azure Custom Vision.** If you are using an ML SaaS (Software as a Service) system such as [Lobe.ai](https://lobe.ai/) or [Azure Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-15963-cxa) to train a model, this type of software provides ways to export the model for many platforms, including building a bespoke API to be queried in the cloud by your online application.
You also have the opportunity to build an entire Flask web app that would be able to train the model itself in a web browser. This can also be done using TensorFlow.js in a JavaScript context.
@ -45,8 +45,8 @@ For this task, you need two tools: Flask and Pickle, both of which run on Python
In this lesson you'll use data from 80,000 UFO sightings, gathered by [NUFORC](https://nuforc.org) (The National UFO Reporting Center). This data has some interesting descriptions of UFO sightings, for example:
- **Long example description**. "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
- **Short example description**. "the lights chased us".
- **Long example description.** "A man emerges from a beam of light that shines on a grassy field at night and he runs towards the Texas Instruments parking lot".
- **Short example description.** "the lights chased us".
The [ufos.csv](./data/ufos.csv) spreadsheet includes columns about the `city`, `state` and `country` where the sighting occurred, the object's `shape` and its `latitude` and `longitude`.
@ -58,7 +58,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
import pandas as pd
import numpy as np
ufos = pd.read_csv('../data/ufos.csv')
ufos = pd.read_csv('./data/ufos.csv')
ufos.head()
```
@ -105,7 +105,7 @@ In the blank [notebook](notebook.ipynb) included in this lesson:
## Exercise - build your model
Now you can get ready to train a model by diving the data into the training and testing group.
Now you can get ready to train a model by dividing the data into the training and testing group.
1. Select the three features you want to train on as your X vector, and the y vector will be the `Country`. You want to be able to input `Seconds`, `Latitude` and `Longitude` and get a country id to return.
@ -159,7 +159,7 @@ Now you can build a Flask app to call your model and return similar results, but
1. Start by creating a folder called **web-app** next to the _notebook.ipynb_ file where your _ufo-model.pkl_ file resides.
1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates`**. You should now have the following files and directories:
1. In that folder create three more folders: **static**, with a folder **css** inside it, and **templates**. You should now have the following files and directories:
```output
web-app/
@ -195,7 +195,7 @@ Now you can build a Flask app to call your model and return similar results, but
1. Now, you're ready to create three more files to finish the app:
1. Create **app.py** in the root
1. Create **app.py** in the root.
2. Create **index.html** in _templates_ directory.
3. Create **styles.css** in _static/css_ directory.
@ -258,10 +258,10 @@ Now you can build a Flask app to call your model and return similar results, but
<buttontype="submit"class="btn">Predict country where the UFO is seen</button>
</form>
<p>{{ prediction_text }}</p>
</div>
</div>
</body>
@ -343,5 +343,3 @@ There are many ways to build a web app to consume ML models. Make a list of the
Jen Looper
3 years ago
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GitHub