Solution

.github/workflows/cd.yml

@@ -0,0 +1,65 @@
+name: 'Continuous Delivery'
+
+on:
+  push:
+    branches:
+      - main
+      - release/*
+  pull_request:
+    branches:
+      - main
+      - release/*
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Cache dependencies
+      uses: actions/cache@v2
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        pip install -r requirements-dev.txt
+        pip install -r requirements-test.txt
+
+    - name: Set up Google Cloud SDK
+      uses: google-github-actions/auth@v1
+      with:
+        credentials_json: ${{ secrets.GCP_CREDENTIALS }}
+
+    - name: Configure Docker
+      run: gcloud auth configure-docker
+
+    - name: Setup gcloud CLI
+      uses: google-github-actions/setup-gcloud@v1
+      with:
+        version: '390.0.0'
+        service_account_key: ${{ secrets.GCP_CREDENTIALS }}
+
+    - name: Download Model from GCS
+      run: gsutil cp gs://delay-models/source/${{ secrets.MODEL_VERSION }}.pkl delay_model.pkl
+
+    - name: Submit Build
+      run: gcloud builds submit --region ${{ secrets.GCP_REGION }} --tag ${{ secrets.GCP_REGION }}-docker.pkg.dev/${{ secrets.GCP_PROJECT_ID }}/challenge/${{ secrets.GCP_IMAGE_NAME }}:latest
+
+    - name: Deploy to Cloud Run
+      run: gcloud run deploy ${{ secrets.GCP_IMAGE_NAME }} --image ${{ secrets.GCP_REGION }}-docker.pkg.dev/${{ secrets.GCP_PROJECT_ID }}/challenge/${{ secrets.GCP_IMAGE_NAME }}:latest --allow-unauthenticated --region ${{ secrets.GCP_REGION }}
+
+    - name: Run Stress Test
+      run: make stress-test

.github/workflows/ci.yml

@@ -0,0 +1,45 @@
+name: 'Continuous Integration'
+
+on:
+  push:
+    branches:
+      - main
+      - develop
+  pull_request:
+    branches:
+      - main
+      - develop
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Cache dependencies
+      uses: actions/cache@v2
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-
+
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -r requirements.txt
+        pip install -r requirements-dev.txt
+        pip install -r requirements-test.txt
+
+    - name: Run model tests
+      run: make model-test
+
+    - name: Run API tests
+      run: make api-test

.gitignore

@@ -0,0 +1,6 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+
+# Unit test / coverage reports
+.coverage
+reports/

Dockerfile

@@ -1,3 +1,21 @@
-# syntax=docker/dockerfile:1.2
-FROM python:latest
-# put you docker configuration here
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements files into the container
+COPY requirements.txt requirements.txt
+COPY requirements-dev.txt requirements-dev.txt
+
+# Install the required Python packages
+RUN pip install -r requirements.txt
+RUN pip install -r requirements-dev.txt
+
+# Copy all files from the current directory to the working directory in the container
+COPY . .
+
+# Expose port 8080 of the container to external network
+EXPOSE 8080
+
+# Command to run the FastAPI application with Uvicorn
+CMD ["uvicorn", "challenge.api:app", "--host", "0.0.0.0", "--port", "8080"]

Makefile

@@ -23,7 +23,7 @@ install:		## Install dependencies
 	pip install -r requirements-test.txt
 	pip install -r requirements.txt
 
-STRESS_URL = http://127.0.0.1:8000 
+STRESS_URL = https://paz-challenge-tryolabs-latam-6fru3wsz3q-uc.a.run.app
 .PHONY: stress-test
 stress-test:
 	# change stress url to your deployed app 

challenge/api.py

@@ -1,13 +1,69 @@
-import fastapi
+from fastapi import FastAPI, HTTPException
+import pandas as pd
+
+from challenge.model import DelayModel
+
+from pydantic import BaseModel, validator
+from typing import List
+
+app = FastAPI()
+delay_model = DelayModel()
+
+
+class Flight(BaseModel):
+    OPERA: str
+    MES: int
+    TIPOVUELO: str
+
+    @validator('MES')
+    def validate_month(cls, v):
+        if v < 1 or v > 12:
+            raise HTTPException(
+                status_code=400,
+                detail='MES must be between 1 and 12'
+            )
+        return v
+
+    @validator('TIPOVUELO')
+    def validate_flight_type(cls, v):
+        if v not in ['I', 'N']:
+            raise HTTPException(
+                status_code=400,
+                detail='TIPOVUELO must be either "I" or "N"'
+            )
+        return v
+
+
+class PredictionInfo(BaseModel):
+    flights: List[Flight]
 
-app = fastapi.FastAPI()
 
 @app.get("/health", status_code=200)
 async def get_health() -> dict:
-    return {
-        "status": "OK"
-    }
+    return {"status": "OK"}
+
+
+@app.post("/predict", response_model=dict, status_code=200)
+async def post_predict(input: PredictionInfo) -> dict:
+    try:
+        data = [
+            {
+                "OPERA": flight.OPERA,
+                "MES": flight.MES,
+                "TIPOVUELO": flight.TIPOVUELO
+            } for flight in input.flights
+        ]
+        df = pd.DataFrame(data)
+
+        preprocessed_data = delay_model.preprocess(df)
+        predictions = delay_model.predict(preprocessed_data)
+
+        return {"predict": predictions}
 
-@app.post("/predict", status_code=200)
-async def post_predict() -> dict:
-    return
+    except ValueError as ve:
+        raise HTTPException(status_code=400, detail=str(ve))
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"An error occurred while processing the prediction: {str(e)}"
+        )