Merge pull request #1 from renansantosmendes/four_lecture

Four lecture

.github/workflows/pipeline.yml

@@ -0,0 +1,47 @@
+name: FastAPI-GitHub-Heroku
+
+on:
+  push:
+    branches:
+    - main
+    - four_lecture
+  pull_request:
+    branches:
+    - main
+
+jobs:
+  ci_pipeline:
+      runs-on: ubuntu-latest
+      steps:
+          - uses: actions/checkout@v1
+            with:
+              fetch-depth: 0
+
+          - name: Set up Python 3.9
+            uses: actions/setup-python@v1
+            with:
+                python-version: 3.9
+
+          - name: Install dependencies
+            run: |
+                python -m pip install --upgrade pip
+                if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+
+          - name: Test
+            run: |
+              python -m pytest test.py
+
+  cd_pipeline:
+
+      runs-on: ubuntu-latest
+      needs: [ci_pipeline]
+      steps:
+        - uses: actions/checkout@v2
+        - name: Deploy to Heroku
+          env:
+            HEROKU_AUTH_TOKEN: ${{ secrets.HEROKU_AUTH_TOKEN }}
+            HEROKU_APP_NAME: ${{ secrets.HEROKU_APP_NAME }}
+          if: github.ref == 'refs/heads/master' && job.status == 'success'
+          run: |
+            git remote add heroku https://heroku:$HEROKU_AUTH_TOKEN@git.heroku.com/$HEROKU_APP_NAME.git 
+            git push heroku HEAD:master -f

main.py

@@ -1,8 +1,49 @@
+import os
+import pickle
+import numpy as np
 from fastapi import FastAPI
+from pydantic import BaseModel
 
+
+class Data(BaseModel):
+    baseline_value: float
+    accelerations: float
+    fetal_movement: float
+    uterine_contractions: float
+    light_decelerations: float
+    severe_decelerations: float
+    prolongued_decelerations: float
+
+
+def load_models():
+    model = pickle.load(open(os.path.join(os.getcwd(),
+                                          'models',
+                                          'model.pkl'), 'rb'))
+    scaler = pickle.load(open(os.path.join(os.getcwd(),
+                                           'models',
+                                           'scaler.pkl'), 'rb'))
+    return scaler, model
+
+
+scaler, model = load_models()
 app = FastAPI()
 
 
 @app.get('/')
 def home():
     return {"Hello": "World"}
+
+
+@app.post('/api/predict')
+def predict(request: Data):
+    received_data = np.array([
+        request.baseline_value,
+        request.accelerations,
+        request.fetal_movement,
+        request.uterine_contractions,
+        request.light_decelerations,
+        request.severe_decelerations,
+        request.prolongued_decelerations
+    ]).reshape(1, -1)
+    prediction = model.predict(scaler.transform(received_data))[0]
+    return {'prediction': prediction}

test.py

@@ -1,10 +1,17 @@
 import pytest
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler
 from fastapi.testclient import TestClient
-from main import app
+from main import app, scaler, model
 
 client = TestClient(app)
 
 
 def test_home():
     response = client.get('/')
-    assert response.json() == {"Hello": "World"}
+    assert response.json() == {"Hello": "World"}
+
+
+def test_models():
+    assert isinstance(scaler, StandardScaler)
+    assert isinstance(model, RandomForestClassifier)