adding pytest/unittest first version

MarkoBrie · Feb 22, 2024 · dde9236 · dde9236
1 parent d364545
commit dde9236
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diff --git a/.DS_Store b/.DS_Store
diff --git a/.ipynb_checkpoints/2_Model_selection-checkpoint.ipynb b/.ipynb_checkpoints/2_Model_selection-checkpoint.ipynb
diff --git a/2_Model_selection.ipynb b/2_Model_selection.ipynb
diff --git a/3_FastAPI/__init__.py b/3_FastAPI/__init__.py
diff --git a/3_FastAPI/main.py b/3_FastAPI/main.py
@@ -0,0 +1,68 @@
+import uvicorn
+from fastapi import FastAPI
+import numpy as np
+#import pickle # pipfile does not lock
+import mlflow
+import lightgbm
+import os
+from typing import List
+from pydantic import BaseModel # for data validation
+
+# load environment variables
+port = os.environ["PORT"]
+
+# initialize FastAPI
+app = FastAPI(title="Automatic Credit Scoring",
+              description='''Obtain a credit score (0,1) for ClientID.
+                           Visit this URL at port 8501 for the streamlit interface.''',
+              version="0.1.0",)
+
+# Pydantic model for the input data
+class DataPoint(BaseModel):
+    data_point: List[float]
+
+# 3. Expose the prediction functionality, make a prediction from the passed
+#    JSON data and return the predicted flower species with the confidence
+@app.post('/predict')
+def predict_credit_score(data: DataPoint):
+    """ Endpoint for ML model
+
+    Args:
+                    list (float): one data point of 239 floats
+                    
+    Returns:
+                float: prediction probability
+                int: prediction score
+    """
+    print("predict_credit_score function")
+    #print(data)
+    print([data.data_point])
+
+    #if len(data) != 239:
+    #    raise HTTPException(status_code=400, detail="Expected 239 data points")
+
+    #data_point = {"data_point": data_point}
+
+    #data_point = np.array(data_point) #.reshape(1, -1)
+
+    sklearn_pyfunc = mlflow.lightgbm.load_model(model_uri="LightGBM")
+    #data = [[0, 0, 1, 1, 63000.0, 310500.0, 15232.5, 310500.0, 0.026392, 16263, -214.0, -8930.0, -573, 0.0, 1, 1, 0, 1, 1, 0, 2.0, 2, 2, 11, 0, 0, 0, 0, 1, 1, 0.0, 0.0765011930557638, 0.0005272652387098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, True, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, False, False, False, False, True, False, False, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]]
+
+    prediction = sklearn_pyfunc.predict_proba([data.data_point]).max()
+    #print(prediction)
+    #prediction = 0.7
+
+    return {
+        'prediction': prediction,
+        'probability': 0.8
+    }
+
+@app.get("/")
+def index():
+    return {"data": "Application ran successfully - FastAPI release v4.2 with Github Actions no staging: cloudpickle try environment pipenv",
+
+    }
+    #return {st.title("Hello World")}
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=port, reload=False)
diff --git a/3_STREAMlit_dashboard.py b/3_STREAMlit_dashboard.py
@@ -21,10 +21,8 @@ def request_prediction(model_uri, data):
 #
 
 def main():
-    #MLFLOW_URI = 'https://fastapi-cd-webapp.azurewebsites.net/predict'
-    MLFLOW_URI = 'http://0.0.0.0:8000/predict'
-
-
+    MLFLOW_URI = 'https://fastapi-cd-webapp.azurewebsites.net/predict'
+    #MLFLOW_URI = 'http://0.0.0.0:8000/predict'
 
     api_choice = st.sidebar.selectbox(
         'Quelle API souhaitez vous utiliser',

diff --git a/5_unittest.py b/5_unittest.py
@@ -0,0 +1,84 @@
+import unittest
+import requests
+from fastapi.testclient import TestClient
+import httpx
+from main import app
+import pytest
+
+
+client = TestClient(app)
+
+class TestConnection(unittest.TestCase):
+    def test_connection_functionality(self):
+        """
+        Test that connection is working and 
+        """
+        try:
+            test_location = "local"
+            if (test_location == "local"):
+                host = '127.0.0.1'
+                port = '8000'
+                # endpoint
+                url = f'http://{host}:{port}/predict'
+            else:
+                url = 'https://fastapi-cd-webapp.azurewebsites.net/predict'
+
+            data_for_request =  [0, 0, 1, 1, 63000.0, 310500.0, 15232.5, 310500.0, 0.026392, 16263, -214.0, -8930.0, -573, 0.0, 1, 1, 0, 1, 1, 0, 2.0, 2, 2, 11, 0, 0, 0, 0, 1, 1, 0.0, 0.0765011930557638, 0.0005272652387098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, True, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, False, False, False, False, True, False, False, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,
+                                                                    False]
+            # Send the POST request with the data
+            response = requests.post(url, json={"data_point": data_for_request})
+            assert response.status_code == 200
+        except Exception as e:
+            pytest.fail(f"Test failed: {e}")
+
+    def test_response(self):
+        """
+        TEST model output with fixture test data 
+        """
+        try:
+            test_location = "local"
+            if (test_location == "local"):
+                host = '127.0.0.1'
+                port = '8000'
+                # endpoint
+                url = f'http://{host}:{port}/predict'
+            else:
+                url = 'https://fastapi-cd-webapp.azurewebsites.net/predict'
+
+            # fixture simulation with test data
+            data_for_request =  [0, 0, 1, 1, 63000.0, 310500.0, 15232.5, 310500.0, 0.026392, 16263, -214.0, -8930.0, -573, 0.0, 1, 1, 0, 1, 1, 0, 2.0, 2, 2, 11, 0, 0, 0, 0, 1, 1, 0.0, 0.0765011930557638, 0.0005272652387098, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, True, False, False, False, False, False, False, False, True, False, False, False, False, False, False, True, False, False, False, False, True, False, False, False, True, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,
+                                                                    False]
+            # Send the POST request with the data
+            response = requests.post(url, json={"data_point": data_for_request})
+            assert response.status_code == 200
+            assert response.json() == {"prediction":0.857982822560715,"probability":0.8}
+            # Unit tests for response status codes
+        except Exception as e:
+            pytest.fail(f"Test failed: {e}")
+
+    def test_response(self):
+        """
+        TEST "post" with empty data 
+        """
+        try:
+            test_location = "local"
+            if (test_location == "local"):
+                host = '127.0.0.1'
+                port = '8000'
+                # endpoint
+                url = f'http://{host}:{port}/predict'
+            else:
+                url = 'https://fastapi-cd-webapp.azurewebsites.net/predict'
+
+            # fixture simulation with test data
+            data_for_request =  []
+
+            # Send the POST request with the data
+            response = requests.post(url, json={"data_point": data_for_request})
+            assert response.status_code == 500
+            assert response.json() == {"detail":"An error occurred during prediction: Found array with 0 feature(s) (shape=(1, 0)) while a minimum of 1 is required."}
+        except Exception as e:
+            pytest.fail(f"Test failed: {e}")
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/Dockerfile b/Dockerfile
@@ -17,8 +17,9 @@ RUN pip install uvicorn
 RUN pip install fastapi
 RUN pip install mlflow
 RUN pip install lightgbm 
-#RUN pip install cloudpickle
+RUN pip install pydantic
 RUN pip install streamlit
+RUN pip install typing
 #RUN pipenv install --system --deploy --ignore-pipfile
 
 # expose the port that uvicorn will run the app on

diff --git a/Pipfile b/Pipfile
@@ -12,8 +12,12 @@ streamlit = "*"
 mlflow = "2.9.2"
 lightgbm = "4.1.0"
 pydantic = "*"
+httpx = "*"
+pytest = "*"
 
 [dev-packages]
 
 [requires]
 python_version = "3.8"
+
+[scripts]