According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?
{{ prediction_text }}
```
このファイルのテンプレートに目を通してください。アプリによって提供される変数の周りの「マスタッシュ」構文、例えば予測テキスト：`{{}}`. 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`を追加します：
```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"]
return render_template(
"index.html", prediction_text="Likely country: {}".format(countries[output])
)
if __name__ == "__main__":
app.run(debug=True)
```
> 💡 ヒント：[`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`:
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.
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.
Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction.
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`を追加したときに、アプリのエラーを特定するのが簡単になります。この方法の利点と欠点は何ですか？
## [講義後クイズ](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/18/)
## まとめと自己学習
MLモデルを消費するためのWebアプリを構築する方法はいくつかあります。JavaScriptまたはPythonを使用して機械学習を活用するWebアプリを構築する方法のリストを作成してください。アーキテクチャを考慮してください：モデルはアプリ内に留まるべきか、それともクラウドに存在するべきか？ 後者の場合、どのようにアクセスしますか？ 実用的なML Webソリューションのアーキテクチャモデルを描いてみてください。
## 課題
[別のモデルを試す](assignment.md)
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