In this lesson, you will train a ML model on a dataset that's out of this world: UFO sightings over the past century, sourced from [NUFORC's database](https://www.nuforc.org). We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app. To do this, you need to build a web app using Flask.
In this lesson, you will train an ML model on a data set that's out of this world: _UFO sightings over the past century_, sourced from [NUFORC's database](https://www.nuforc.org).
We will continue our use of notebooks to clean data and train our model, but you can take the process one step further by exploring using a model 'in the wild', so to speak: in a web app.
To do this, you need to build a web app using Flask.
There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app. There are many questions you need to ask: Is it a web app, or a mobile app? Where will the model reside, in the cloud or locally? Does the app have to work offline? And what technology was used to train the model, because that may influence the tooling you need to use?
## Building an app
There are several ways to build web apps to consume machine learning models. Your web architecture may influence the way your model is trained. Imagine that you are working in a business where the data science group has trained a model that they want you to use in an app.
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/). 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.
### Considerations
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.
There are many questions you need to ask:
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.
- **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?
- **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.
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. For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app.
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.
## Tools
For our purposes, since we have been working with Python-based notebooks, let's explore the steps you need to take to export a trained model from such a notebook to a format readable by a Python-built web app.
## Tool
For this task, you need two tools: Flask and Pickle, both of which run on Python.
@ -21,92 +36,106 @@ For this task, you need two tools: Flask and Pickle, both of which run on Python
✅ What's [Pickle](https://docs.python.org/3/library/pickle.html)? Pickle 🥒 is a Python module that serializes and de-serializes a Python object structure. When you 'pickle' a model, you serialize or flatten its structure for use on the web. Be careful: pickle is not intrinsically secure, so be careful if prompted to 'un-pickle' a file. A pickled file has the suffix `.pkl`.
## Clean your data
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 "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" or simply "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.
## Exercise - clean your data
In the blank [notebook](notebook.ipynb) included in this lesson, import pandas, matplotlib, and numpy as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
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:
```python
import pandas as pd
import numpy as np
- **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".
ufos = pd.read_csv('../data/ufos.csv')
ufos.head()
```
Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the Country field.
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`.
In the blank [notebook](notebook.ipynb) included in this lesson:
ufos.Country.unique()
```
1. import `pandas`, `matplotlib`, and `numpy` as you did in previous lessons and import the ufos spreadsheet. You can take a look at a sample data set:
Now, you can reduce the amount of data we need to deal with by dropping any null values and only importing sightings between 1-60 seconds:
```python
import pandas as pd
import numpy as np
ufos = pd.read_csv('../data/ufos.csv')
ufos.head()
```
```python
ufos.dropna(inplace=True)
1. Convert the ufos data to a small dataframe with fresh titles. Check the unique values in the `Country` field.
Now you can get ready to train a model by diving the data into the training and testing group. 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.
Now you can get ready to train a model by diving the data into the training and testing group.
```python
from sklearn.model_selection import train_test_split
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.
The model you created isn't very revolutionary as it's you should be able to infer a `Country` from its `Latitude` and `Longitude`, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.
The accuracy isn't bad (around 95%), unsurprisingly, as country and latitude/longitude correlate. The model you created isn't very revolutionary as it's obvious you should be able to infer a country from its latitude and longitude, but it's a good exercise to try to train from raw data that you cleaned, exported, and then use this model in a web app.
## Pickle your model
## Exercise - 'pickle' your model
Now, it's time to pickle your model! You can do that in just a few lines of code. Once it's pickled, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,
Now, it's time to _pickle_ your model! You can do that in a few lines of code. Once it's _pickled_, load your pickled model and test it against a sample data array containing values for seconds, latitude and longitude,
The model returns '3', which is the country code for the UK. Wild! 👽
## Build a Flask app
Now you can build a Flask app to call your model and return similar results, but in a more visually pleasing way.
Start by creating a folder called web-app next to the _notebook.ipynb_ file where your _ufo-model.pkl_ file resides. In that folder create three more folders: `static`, with a folder `css` inside it, and `templates`.
✅ Refer to the solution folder for a view of the finished app
The first file to create in `web-app` is a `requirements.txt` file. Like `package.json` in a JavaScript app, this file lists dependencies required by the app. In `requirements.txt` add the lines:
```text
scikit-learn
pandas
numpy
flask
```
Now, run this file by navigating to `web-app` (`cd web-app`) in your terminal and typing `pip install -r requirements.txt`.
Now, you're ready to create three more files to finish the app:
<buttontype="submit"class="btn">Predict country where the UFO is seen</button>
</form>
<p>{{ prediction_text }}</p>
</div>
</div>
</body>
</html>
```
Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.
Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:
In `app.py` add:
```python
import numpy as np
from flask import Flask, request, render_template
import pickle
app = Flask(__name__)
model = pickle.load(open("../ufo-model.pkl", "rb"))
@app.route("/")
def home():
return render_template("index.html")
@app.route("/predict", methods=["POST"])
def predict():
int_features = [int(x) for x in request.form.values()]
final_features = [np.array(int_features)]
prediction = model.predict(final_features)
The model returns **'3'**, which is the country code for the UK. Wild! 👽
output = prediction[0]
## Exercise - build a Flask app
countries = ["Australia", "Canada", "Germany", "UK", "US"]
Now you can build a Flask app to call your model and return similar results, but in a more visually pleasing way.
> 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
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:
```output
web-app/
static/
css/
templates/
notebook.ipynb
ufo-model.pk1
```
✅ Refer to the solution folder for a view of the finished app
1. The first file to create in _web-app_ folder is **requirements.txt** file. Like _package.json_ in a JavaScript app, this file lists dependencies required by the app. In **requirements.txt** add the lines:
```text
scikit-learn
pandas
numpy
flask
```
1. Now, run this file by navigating to _web-app_:
```bash
cd web-app
```
1. In your terminal type `pip install`, to install the libraries listed in _reuirements.txt_:
```bash
pip install -r requirements.txt
```
1. Now, you're ready to create three more files to finish the app:
1. Create **app.py** in the root
2. Create **index.html** in _templates_ directory.
3. Create **styles.css** in _static/css_ directory.
1. Build out the _styles.css__ file with a few styles:
<buttontype="submit"class="btn">Predict country where the UFO is seen</button>
</form>
<p>{{ prediction_text }}</p>
</div>
</div>
</body>
</html>
```
Take a look at the templating in this file. Notice the 'mustache' syntax around variables that will be provided by the app, like the prediction text: `{{}}`. There's also a form that posts a prediction to the `/predict` route.
Finally, you're ready to build the python file that drives the consumption of the model and the display of predictions:
1. In `app.py` add:
```python
import numpy as np
from flask import Flask, request, render_template
import pickle
app = Flask(__name__)
model = pickle.load(open("../ufo-model.pkl", "rb"))
@app.route("/")
def home():
return render_template("index.html")
@app.route("/predict", methods=["POST"])
def predict():
int_features = [int(x) for x in request.form.values()]
final_features = [np.array(int_features)]
prediction = model.predict(final_features)
output = prediction[0]
countries = ["Australia", "Canada", "Germany", "UK", "US"]
> 💡 Tip: when you add [`debug=True`](https://www.askpython.com/python-modules/flask/flask-debug-mode) while running the web app using Flask, any changes you make to your application will be reflected immediately without the need to restart the server. Beware! Don't enable this mode in a production app.
If you run `python app.py` or `python3 app.py` - your web server starts up, locally, and you can fill out a short form to get an answer to your burning question about where UFOs have been sighted!
Before doing that, take a look at the parts of `app.py`.
Before doing that, take a look at the parts of `app.py`:
1. First, dependencies are loaded and the app starts.
1. Then, the model is imported.
1. Then, index.html is rendered on the home route.
First, dependencies are loaded and the app starts. Then, the model is imported. Then, index.html is rendered on the home route. On the `/predict` route, several things happen when the form is posted:
On the `/predict` route, several things happen when the form is posted:
1. The form variables are gathered and converted to a numpy array. They are then sent to the model and a prediction is returned.
2. The Countries that we want displayed are re-rendered as readable text from their predicted country code, and that value is sent back to index.html to be rendered in the template.
@ -266,6 +324,7 @@ Using a model this way, with Flask and a pickled model, is relatively straightfo
In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
---
## 🚀 Challenge:
Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
softchris
3 years ago