🚀 Building a Sentiment Analysis App with Azure DevOps CI/CD, ACR, and ACI
🌟 Introduction
🚀 Building a Sentiment Analysis App with Azure DevOps CI/CD, ACR, and ACI
🌟 Introduction
In today’s world, showcasing AI applications to clients often requires more than just code — you need a working demo, a clean UI, and a professional deployment pipeline. In this article, I’ll walk you through building a sentiment analysis web app with Python Flask, containerizing it with Docker, and deploying it to the cloud using Azure DevOps CI/CD, Azure Container Registry (ACR), and Azure Container Instance (ACI).
🧠 The AI Concept
The app demonstrates Sentiment Analysis — a Natural Language Processing (NLP) technique that evaluates text to determine:
- Polarity → whether the text is positive, negative, or neutral.
- Subjectivity → whether the text is factual or opinionated.
We use the TextBlob library to keep things simple and lightweight.
📂 Project Structure
Code. ├── app.py # Flask application ├── requirements.txt # Python dependencies ├── Dockerfile # Container build instructions ├── templates/ │ └── index.html # Front-end UI └── azure-pipelines.yaml # CI/CD pipeline definition
app.py
from flask import Flask, request, render_template from textblob import TextBlob
app = Flask(name)
@app.route(“/”) def home(): return render_template(“index.html”)
@app.route(“/sentiment”, methods=[“POST”]) def sentiment(): text = request.form.get(“text”, “”) analysis = TextBlob(text) result = { “polarity”: analysis.sentiment.polarity, “subjectivity”: analysis.sentiment.subjectivity } return render_template(“index.html”, text=text, result=result)
if name == “main”: app.run(host=”0.0.0.0", port=5000)
templates/index.html
<!DOCTYPE html> <html> <head> <title>Sentiment Analyzer</title> </head> <body> <h1>Sentiment Analyzer</h1> <form method=”POST” action=”/sentiment”> <input type=”text” name=”text” placeholder=”Enter text here” required /> <button type=”submit”>Analyze</button> </form>
{% if result %} <h2>Result for: “{{ text }}”</h2> <p>Polarity: {{ result.polarity }}</p> <p>Subjectivity: {{ result.subjectivity }}</p> {% endif %} </body> </html>
Dockerfile FROM python:3.9-slim WORKDIR /app COPY requirements.txt . RUN pip install — no-cache-dir -r requirements.txt COPY . . EXPOSE 5000 CMD [“python”, “app.py”]
requirements.txt
flask textblob
azure-pipelines.yml
trigger:
- main
pool: vmImage: ‘ubuntu-latest’
variables: imageName: ‘sentiment-ui-app’
steps:
- task: Docker@2 inputs: containerRegistry: ‘your-acr-service-connection’ repository: ‘$(imageName)’ command: ‘buildAndPush’ Dockerfile: ‘**/Dockerfile’ tags: | $(Build.BuildId)
- task: AzureCLI@2 inputs: azureSubscription: ‘your-azure-subscription’ scriptType: ‘bash’ scriptLocation: ‘inlineScript’ inlineScript: | az container update \ — resource-group your-resource-group \ — name your-existing-aci-name \ — image youracr.azurecr.io/$(imageName):$(Build.BuildId) az container restart \ — resource-group your-resource-group \ — name your-existing-aci-name
🌐 Demo Flow
- Push code → Pipeline runs automatically.
- Image stored in ACR.
- Existing ACI updated with new image.
- Client opens the app in browser.
- Types text → Sees instant sentiment analysis results.
📝 Conclusion
This project demonstrates a complete AI + DevOps workflow:
- AI concept: Sentiment Analysis.
- Backend: Python Flask.
- Frontend: HTML UI.
- Containerization: Docker.
- Deployment: Azure ACR + ACI.
- Automation: Azure DevOps CI/CD
메타데이터
- post_id
- 980eb8cf70be
- slug
- building-a-sentiment-analysis-app-with-azure-devops-ci-cd-acr-and-aci-980eb8cf70be
- url
- https://medium.com/@dineshOffl/building-a-sentiment-analysis-app-with-azure-devops-ci-cd-acr-and-aci-980eb8cf70be
- canonical_url
- https://medium.com/@dineshOffl/building-a-sentiment-analysis-app-with-azure-devops-ci-cd-acr-and-aci-980eb8cf70be
- author_url
- https://medium.com/@dineshOffl
- status
- ok
- fetched_at
- 2026-06-23 21:39:52
