🚀 FastAPI + Docker + CI/CD Demo

A hands-on project to understand Docker and CI/CD pipelines using FastAPI and GitHub Actions — with a real AI Sentiment Analyzer!

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🎯 What This Project Does

• A real FastAPI app with a working AI Sentiment Analyzer endpoint
• Docker — packages the app so it runs identically everywhere
• GitHub Actions CI/CD — automatically tests and builds Docker on every push

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📁 Project Structure

docker-demo/
├── .github/
│   └── workflows/
│       └── ci.yml          ← GitHub Actions pipeline
├── main.py                 ← FastAPI app with Sentiment Analyzer
├── test_main.py            ← pytest tests
├── Dockerfile              ← Docker instructions
└── requirements.txt        ← dependencies

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🧠 Key Concepts Learned

What is CI/CD?

You push code to GitHub
        ↓
GitHub Actions runs automatically
        ↓
❌ Tests fail → stops, tells you the bug → app stays safe
✅ Tests pass → builds Docker image → ready to deploy

What is Docker?

WITHOUT DOCKER
Your Windows PC  →  Cloud Server (Linux) = ❌ breaks
Different Python versions, different libraries

WITH DOCKER
Pack everything into one box 📦
Your code + Python 3.11 + all libraries
Same box runs on ANY machine ✅

How They Work Together

You write code
      ↓
git push  ← only thing YOU do
      ↓
CI/CD automatically:
  ✅ Runs tests
  ✅ Builds Docker image
  ✅ Deploys to cloud (when configured)
      ↓
🌍 Live API — zero manual work

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🔗 API Endpoints

Endpoint	What it does	Example
GET /	Returns welcome message	{"message": "Welcome to Sentiment Analyzer API!"}
GET /analyze?text=your text	Analyzes sentiment of text	{"sentiment": "positive", "emoji": "😊"}

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😊 Sentiment Analyzer in Action

Input  → "I love coding!"
Output → {"sentiment": "positive", "confidence": "95%", "emoji": "😊"}

Input  → "This is terrible!"
Output → {"sentiment": "negative", "confidence": "89%", "emoji": "😞"}

Input  → "Today is okay"
Output → {"sentiment": "neutral", "confidence": "0%", "emoji": "😐"}

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⚙️ Files Explained

main.py — FastAPI + AI Sentiment Analyzer

from fastapi import FastAPI
from textblob import TextBlob

app = FastAPI()

@app.get("/")
def home():
    return {"message": "Welcome to Sentiment Analyzer API!"}

@app.get("/analyze")
def analyze(text: str):
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity

    if polarity > 0:
        sentiment = "positive"
        emoji = "😊"
    elif polarity < 0:
        sentiment = "negative"
        emoji = "😞"
    else:
        sentiment = "neutral"
        emoji = "😐"

    confidence = str(round(abs(polarity) * 100)) + "%"

    return {
        "text": text,
        "sentiment": sentiment,
        "confidence": confidence,
        "emoji": emoji
    }

test_main.py — Automated Tests

from fastapi.testclient import TestClient
from main import app

client = TestClient(app)

def test_home():
    response = client.get("/")
    assert response.status_code == 200

def test_positive_sentiment():
    response = client.get("/analyze?text=I love this!")
    assert response.json()["sentiment"] == "positive"

def test_negative_sentiment():
    response = client.get("/analyze?text=I hate this!")
    assert response.json()["sentiment"] == "negative"

Dockerfile — Docker Instructions

FROM python:3.11        # use Python 3.11 as base
WORKDIR /app            # set working folder inside container
COPY requirements.txt . # copy requirements first
RUN pip install -r requirements.txt  # install all dependencies
COPY . .                # copy all your code
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

requirements.txt — Dependencies

fastapi      ← web framework
uvicorn      ← server to run FastAPI
httpx        ← for API testing
pytest       ← automated testing
textblob     ← AI library for sentiment analysis

ci.yml — GitHub Actions Pipeline

name: CI Pipeline - FastAPI Docker

on:
  push:
    branches: [main]

jobs:
  test-and-build:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout code
        uses: actions/checkout@v3

      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: "3.11"

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: Run tests
        run: pytest

      - name: Build Docker image
        run: docker build -t docker-demo .

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💻 All Commands Reference

Git Commands

git add .                        # stage all files
git commit -m "your message"    # save changes
git push                         # push to GitHub → triggers CI/CD
git pull                         # sync GitHub changes to your PC
git remote -v                    # check GitHub connection
git status                       # check current status

Run FastAPI Locally (without Docker)

pip install -r requirements.txt  # install dependencies
uvicorn main:app --reload        # start the app
# Open → http://localhost:8000

Run Tests Locally

pytest       # run all tests
pytest -v    # run with details

Docker Commands

# Build Docker image
docker build --network=host -t docker-demo .

# Run the container
docker run -p 8000:8000 docker-demo

# See running containers
docker ps

# Stop a container
docker stop <container_id>

Test Sentiment Analyzer in Browser

http://localhost:8000
http://localhost:8000/docs                              ← interactive API docs
http://localhost:8000/analyze?text=I love coding!
http://localhost:8000/analyze?text=This is awful!
http://localhost:8000/analyze?text=Today is okay

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✅ How to Test the CI/CD Pipeline

Break it — see red ❌

# In test_main.py — change to wrong value
def test_positive_sentiment():
    assert response.json()["sentiment"] == "negative"  # wrong!

git add . && git commit -m "break test" && git push
# Actions tab → ❌ red failure → Docker never builds

Fix it — see green ✅

def test_positive_sentiment():
    assert response.json()["sentiment"] == "positive"  # correct!

git add . && git commit -m "fix test" && git push
# Actions tab → ✅ green success → Docker builds

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🔄 What Happens on Every Push

git push
   ↓
GitHub Actions starts automatically
   ↓
✅ Checkout code
✅ Setup Python 3.11
✅ Install dependencies (fastapi, textblob, pytest...)
✅ Run pytest (3 tests)
   ↓
❌ Any test fails → pipeline stops → Docker never builds → app stays safe
✅ All tests pass → Docker image builds → ready for deployment

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🧱 What's Next

Step	What to learn	Tool
✅ Done	CI + Docker build	GitHub Actions + Docker
🔜 Next	Auto deploy to cloud	Render / Railway
🔜 Later	Secrets management	GitHub Secrets
🔜 Later	Real AI model API	HuggingFace / OpenAI

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🛠️ Tech Used

• Python — programming language
• FastAPI — modern Python web framework for building APIs
• TextBlob — AI library for sentiment analysis
• pytest — automated testing
• Docker — containerization
• GitHub Actions — CI/CD automation

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💡 Real World Value

Without CI/CD + Docker	With CI/CD + Docker
Manual testing every time	Automatic on every push
"Works on my machine" problem	Runs same everywhere
Bugs reach production	Bugs caught before deploy
Manual deployment	Auto deploy on green tests
Hours of setup for new team member	5 minutes with Docker