Add upwork_github_action_app.md file and update types.d.ts

Signed-off-by: Muhammad Umer Farooq <[email protected]>

Author: lablnet

.astro/types.d.ts

@@ -524,6 +524,13 @@ declare module 'astro:content' {
   collection: "projects";
   data: InferEntrySchema<"projects">
 } & { render(): Render[".md"] };
+"upwork_github_action_app.md": {
+	id: "upwork_github_action_app.md";
+  slug: "github-action-and-github-app";
+  body: string;
+  collection: "projects";
+  data: InferEntrySchema<"projects">
+} & { render(): Render[".md"] };
 "upwork_leadcrm.md": {
 	id: "upwork_leadcrm.md";
   slug: "leadcrm";

src/content/projects/upwork_github_action_app.md

@@ -0,0 +1,87 @@
+---
+title: GitHub Action & GitHub App
+slug: github-action-and-github-app
+startDate: 2024-02-01
+endDate: 2024-02-18
+stacks: [Python, Lambda, DynamoDB, GitHub, GitHub-Action, GitHub-App]
+company: upwork
+
+---
+
+This project evolved through several iterations, each marked by specific enhancements and the introduction of new features. Below is a detailed account of these versions, highlighting the key concepts, changes, and technical implementations.
+
+### Version 1 (Feb 1, 2024 - Feb 2, 2024)
+<details>
+<summary>Key concepts in Version 1</summary>
+
+In the initial version, I developed a GitHub Action workflow designed to activate upon every push to the repository and each pull request. The primary objectives were to execute coverage and linter checks for a Rust project, capture these outputs, and automatically comment on the pull request with the results. This workflow was successfully implemented and rigorously tested within the repository. <sup>[1](https://github.com/yubrew/ac-outpost/blob/main/.github/workflows/pr-tasks.yml)</sup>.
+</details>
+
+### Version 2 (Feb 4, 2024 - Feb 10, 2024)
+<details>
+<summary>Key Changes in Version 2</summary>
+
+- Significant advancements were made in this iteration:
+- A new GitHub Action workflow, `ai-audit.yml`, was created to operate on every push and pull request, enhancing our project's automation and integration capabilities. <sup>[2](https://github.com/yubrew/ac-outpost/blob/main/.github/workflows/ai-audit.yml)</sup>
+
+- A key task was to aggregate all Rust files following a specified schema, excluding any that matched defined patterns ('test', 'schema'). This was achieved through a straightforward shell script, which efficiently processed and prepared these files for further analysis.
+    - Note: I am sharing these files because they are on a public repo.
+    - I've done this with a simple shell script below:
+
+        <details>
+        <summary><strong>rust_file_aggregator.sh</strong></summary>
+
+        ```sh
+        #!/bin/bash
+
+        # Initialize the file
+        echo "" > rust.md
+
+        # Find all the Rust files in the current directory 
+        # and its subdirectories.
+        # and iterate over the files
+        find . -name "*.rs" | while read file; do
+            # Check if the file name matches the exclusion patterns
+            # ['test', 'schema']
+            if [[ $file != *test* && $file != *schema* ]]; then
+                # Print the file name
+                echo "Processing file: $file"
+                # Append the file name to the output file
+                echo "### FILE: $(basename $file)" >> rust.md
+                # Append the file content to the output file
+                cat $file >> rust.md
+            fi
+        done
+        ```
+        </details>
+
+    - These files were then transmitted to a mock API endpoint (Which I created), crafted using AWS Lambda and DynamoDB, demonstrating a practical application of serverless technologies in automating code review processes.
+
+- Additionally, a `webhook` workflow was set up to trigger upon receiving webhook events <sup>[3](https://github.com/yubrew/ac-outpost/blob/main/.github/workflows/webhook.yml)</sup>, further integrated with two AWS Lambda functions for dynamic API simulation `API-CRON` and repository data management `Webhook`.
+    - `API-CRON` Which was part of Mock API to randomly simulate the API behavior.
+    - `Webhook` Which reads data from the DynamoDB and add comment to the PR. 
+
+
+</details>
+
+### Version 3 (Feb 12, 2024- Feb 18, 2024)
+<details>
+<summary>Key Changes in Version 3</summary>
+
+This phase marked a significant shift in the project's direction, with the introduction of a GitHub App and Webhook, both hosted on AWS Lambda, showcasing a complex, integrated development environment:
+
+- The flow of that GitHub was as follow:
+    - The user will install the GitHub App on their repository.
+    - The GitHub App webhook will be triggered by the GitHub event.
+    - The GitHub App will send the data to the AWS Lambda.
+    - The AWS Lambda will process the data, save to the DynamoDB and commit the required file and secrets to the repository.
+        - `ai-audit.yml` and `rust_file_aggregator.sh` will be committed to the repository.
+        - The `MOCK API` url will be saved to the repository secrets.
+- The `version 2` Lambda was used as it is just updated the following:
+    - The `webhook` Lambda updated to update the comment on the PR with the data from the Mock API.
+- The new Lambda `GithubAppWebhook` added to handle the GitHub App webhook event.
+
+</details>
+
+### Lessons Learned
+- How to create GitHub App and seemelessly integrate webhook with it that hosted on AWS Lambda.