What You Are Building
In this chapter, you are evolving your OCR tool into a cloud-hosted service that can be accessed from anywhere.
Instead of just extracting data locally, you will expose your logic as an API endpoint that returns structured JSON.
This means:
- Your tool is no longer tied to your machine
- Other systems (including SAP) can consume your results
- You are now building a reusable backend service
POST /api/ocr → returns:
{
"invoice": "12345",
"total": "512.45"
}
Workflow
AI Assist
Refactor Code
Deploy
Expose API
Consume
Step 1: Use AI to Refactor Code
Before writing new code manually, we leverage AI tools like ChatGPT, GitHub Copilot, or SAP Joule.
These tools can accelerate development by transforming your existing OCR script into a structured API.
Prompt:
"Convert this OCR script into a Flask API endpoint that accepts a file and returns JSON."
What happens here:
- You provide context (your OCR script)
- AI generates structured backend code
- You validate and refine the output
AI is not replacing you — it is accelerating boilerplate and repetitive tasks.
Learn more:
- https://platform.openai.com/docs/guides/prompt-engineering
- https://docs.github.com/en/copilot
- https://help.sap.com (search "Joule AI")
Step 2: Create API Endpoint
Now we convert our OCR logic into an API route. This allows external systems to send a file and receive structured output.
@app.route("/api/ocr", methods=["POST"])
def ocr_api():
file = request.files["file"]
file.save("temp.pdf")
pages = convert_from_path("temp.pdf")
text = ""
for page in pages:
text += pytesseract.image_to_string(page)
invoice = re.search(r'INVOICE NUMBER:\s*(\d+)', text)
total = re.search(r'TOTAL.*?\$(\d+\.\d+)', text)
return {
"invoice": invoice.group(1) if invoice else None,
"total": total.group(1) if total else None
}
Key concepts:
- POST request: allows file uploads
- request.files: retrieves uploaded file
- return JSON: creates reusable service output
Step 3: Test API Locally
Before deploying, always test your API locally. This ensures your logic works before introducing cloud complexity.
curl -X POST http://127.0.0.1:5000/api/ocr \
-F "file=@invoice.pdf"
{
"invoice": "12345",
"total": "512.45"
}
You are simulating a real client calling your API.
Step 4: Prepare for Cloud Foundry
Now we package the application so Cloud Foundry knows how to run it.
requirements.txt:
flask
pytesseract
pdf2image
pillow
Procfile:
web: python app.py
manifest.yml:
applications:
- name: ocr-api-app
memory: 512M
buildpacks:
- python_buildpack
These files define:
- Dependencies
- Startup command
- Cloud runtime configuration
Step 5: Deploy
Deploy your application to the cloud using Cloud Foundry CLI.
cf push
During deployment:
- Your code is uploaded
- Dependencies are installed
- The app is started in a container
Step 6: Consume Your API
Once deployed, your API can be called from any system — including Python scripts, SAP integrations, or automation tools.
import requests
url = "https://your-app.cfapps.../api/ocr"
files = {"file": open("invoice.pdf", "rb")}
response = requests.post(url, files=files)
print(response.json())
This is the moment your tool becomes enterprise-ready.
Expand Your AI Usage
AI can help you beyond code generation:
- Generate regex patterns from sample OCR text
- Debug failing API calls
- Optimize Flask structure
- Suggest improvements for scalability
Prompt:
"Given this OCR output, generate a regex to extract invoice and total"
Checkpoint
- You used AI to enhance development
- You built an API endpoint
- You deployed to cloud
- You created a reusable service