37 lines
3.1 KiB
Markdown
37 lines
3.1 KiB
Markdown
# Day 47 - Azure logic app, low / no code
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[](https://www.youtube.com/watch?v=pEB4Kp6JHfI)
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It seems like you have successfully created an end-to-end workflow using Azure Logic Apps that processes a grocery receipt image, identifies food items, fetches recipes for those foods, and sends an email with the list of recipes.
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To continue with the next step, follow these instructions:
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1. Save your workflow in your GitHub repository (if you haven't already) so you can access it later.
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2. To run the workflow, you need to authenticate each connector as mentioned during the explanation:
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- Azure Blob Storage: You will need to provide authentication for the storage account where the receipt image is stored.
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- Computer Vision API (OCR): Provide authentication for your Computer Vision resource.
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- Outlook API: Authenticate with your Outlook account to send emails.
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3. To test the workflow, upload a new grocery receipt image in the specified storage account.
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4. Wait for an email with the list of potential recipes based on the items detected in the receipt.
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5. Review and make changes as needed to improve the workflow or add more features (such as adding JavaScripts, Python functions, etc.).
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6. Share your experiences, improvements, feedback, and new ideas using Azure Logic Apps in the comments section.
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7. Enjoy learning and exploring the possibilities of this powerful tool!
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In this session, we explored creating a workflow using Azure Logic Apps with minimal code knowledge. The goal was to automate a process that takes a receipt as input, extracts relevant information, and sends an email with potential recipes based on the food items purchased.
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The workflow consisted of several steps:
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1. Blob Trigger: A blob trigger was set up to capture new receipts uploaded to a storage account.
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2. JSON Output: The receipt content was passed through OCR (Optical Character Recognition) and computer vision, which converted the text into a JSON format.
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3. Schema Classification: The JSON output was then classified using a schema, allowing us to extract specific properties or objects within the JSON.
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4. Filtering and Looping: An array of food-related texts was created by filtering the original JSON output against a food word list. A loop was used to iterate through each recipe, extracting its name, URL, and image (or thumbnail).
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5. Email Body: The email body was constructed using variables for the food labels and URLs, listing out potential recipes for the user.
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The final step was sending the email with the recipe list using the Outlook connector.
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Key takeaways from this session include:
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* Azure Logic Apps can be used to simplify workflows without requiring extensive coding knowledge.
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* The platform provides a range of connectors and actions that can be combined to achieve specific business outcomes.
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* Creativity and experimentation are encouraged, as users can add their own custom code snippets or integrate with other services.
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The GitHub repository accompanying this session provides the complete code view of the workflow, allowing users to copy and modify it for their own purposes.
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