Deploying a Python Script as an API on Azure

Goal: Take a local PDF metadata extraction script and deploy it as a production-ready REST API on Azure.

📋 What This Blog Covers

flowchart LR A["📄 Local Python Script"] --> B["🌐 REST API
(FastAPI/Flask)"] B --> C["🐳 Docker Container"] C --> D["☁️ Azure Deployment"] D --> E["🔒 Auth + Monitoring"]

Sample local script to extract content from PDF
Available options to deploy the script as an API
Designing the deployment architecture
Implementation and testing

Script resources: GitHub Repo

🔧 Background & Prerequisites

1. PDF Content Extraction — The Script

Library	Strengths	Best For
PyMuPDF (fitz)	Fastest, handles complex layouts	General text extraction
pdfplumber	Excellent table extraction	Tabular data
PyPDF2	Lightweight, basic extraction	Simple PDFs
Tesseract OCR	Open-source OCR for scanned PDFs	Image-based PDFs
Azure Doc Intelligence	Cloud-based, layout analysis	Enterprise extraction

Key extraction targets:

📝 Text — Full-text extraction from each page
📊 Tables — Structured table data
🏷️ Metadata — Title, author, creation date, page count (PdfReader(file).metadata)
🔍 OCR fallback — Detect if PDF has extractable text; if not, route to OCR

2. API Design

sequenceDiagram participant Client participant API as FastAPI Server participant Extractor as PDF Extractor Client->>API: POST /api/extract
(multipart/form-data) API->>API: Validate file type & size API->>Extractor: Extract text + metadata Extractor-->>API: Structured result API-->>Client: 200 JSON response
{text, metadata, pages, time_ms}

Aspect	Design Decision
Endpoint	`POST /api/extract` — upload PDF, get extracted data
Input	`multipart/form-data` file upload (or URL download)
Output	JSON: `{text, metadata, page_count, tables, processing_time_ms}`
Validation	File type check, size limits, malware considerations
Status Codes	`200` success, `400` bad request, `413` too large, `500` error
Docs	OpenAPI/Swagger auto-generated (built into FastAPI)
Auth	API key in header or Azure Entra ID token

3. Framework Choice — Flask vs FastAPI

Feature	Flask	FastAPI
Style	WSGI (sync)	ASGI (async)
Auto docs	Extension needed	Built-in Swagger UI
Validation	Manual	Pydantic models
Performance	Good	Excellent
Learning curve	Minimal	Minimal
Verdict	✅ Use if integrating into existing Flask app	✅ Recommended for new APIs

4. Containerization with Docker

# Example multi-stage Dockerfile
FROM python:3.12-slim AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

FROM python:3.12-slim
WORKDIR /app
COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY . .
EXPOSE 8000
HEALTHCHECK CMD curl -f http://localhost:8000/health || exit 1
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

💡 Tips: Use multi-stage builds to reduce image size. Install system deps like poppler-utils for pdftotext. Pass secrets via environment variables — never hardcode.

5. Azure Deployment Options

graph TD Script["🐍 Python Script"] --> Container["🐳 Docker Image"] Container --> ACR["📦 Azure Container Registry"] ACR --> ACA["⭐ Container Apps
(Recommended)"] ACR --> AppService["🌐 App Service"] ACR --> Functions["⚡ Azure Functions"] ACR --> ACI["📦 Container Instances"] ACR --> AKS["☸️ AKS"]

Option	Scale to Zero	Complexity	Best For	Monthly Cost
Container Apps ⭐	✅	Low	Variable-traffic APIs	Pay per use
App Service	❌	Low	Steady-traffic APIs	~$13+ (B1)
Azure Functions	✅	Low	Infrequent calls	Free tier available
Container Instances	❌	Minimal	Testing/one-off jobs	Pay per second
AKS	❌	High	Multi-service architectures	$$$$

⭐ Recommendation: Azure Container Apps — best balance of simplicity, cost, and zero-to-scale capabilities.

6. CI/CD Pipeline

flowchart LR Push["📤 Git Push"] --> Build["🏗️ GitHub Actions"] Build --> Image["🐳 Build Docker Image"] Image --> ACR["📦 Push to ACR"] ACR --> Deploy["🚀 Deploy to
Container Apps"]

Use GitHub Actions for automated build → push → deploy
Store Azure credentials and API keys in GitHub Secrets
Maintain separate staging and production environments

✅ TODO — Remaining Work

#	Task	Priority
1	Write PDF extraction script (PyMuPDF: metadata + text + tables)	🔴 High
2	Wrap in FastAPI with endpoints, validation, error handling	🔴 High
3	Add OpenAPI/Swagger documentation	🔴 High
4	Write Dockerfile and test locally	🔴 High
5	Push image to Azure Container Registry	🟡 Medium
6	Deploy to Azure Container Apps	🟡 Medium
7	Set up GitHub Actions CI/CD pipeline	🟡 Medium
8	Add authentication (API key or Azure Entra ID)	🟡 Medium
9	Load test with sample PDFs and document performance	🟢 Low
10	Create full architecture diagram with all components	🟢 Low

Deploying scripts as an API in Azure