ElCorrecto

ElCorrecto is a resume-to-role analysis application. Upload a CV as a PDF, paste a job description, and receive an AI-powered comparison showing the match score, matched skills, missing skills, and suggested improvements.

The application uses a React and TypeScript frontend with Ant Design and Tailwind CSS. Its Azure Functions backend uploads the resume to Azure Blob Storage, extracts text with Azure Document Intelligence, and sends the extracted resume and job description to Azure Foundry for analysis.

Project structure

api/       Azure Functions API and Azure service integrations
backend/   Express API serving job listings and user profiles (TypeORM on Azure SQL + Blob Storage)
frontend/  React, TypeScript, Vite, Ant Design, and Tailwind UI
bruno/     Bruno collection for testing the API

Application architecture

flowchart LR
	User[User] --> Frontend[React frontend\nAzure Static Web Apps]
	Frontend -->|Axios multipart/form-data| Function[Azure Function\nAnalyzeResume]
	Function -->|Store PDF| Blob[Azure Blob Storage]
	Function -->|Extract text| Document[Azure Document Intelligence]
	Function -->|Resume text + job description| Foundry[Azure AI Foundry\nGPT deployment]
	Foundry -->|Structured JSON analysis| Function
	Function -->|Analysis response| Frontend

Loading

The application is split into two deployable layers:

• Frontend: A Vite-powered React and TypeScript single-page application hosted on Azure Static Web Apps. It handles PDF selection, job-description input, loading and error states, and result presentation. The Azure Function URL is supplied through VITE_AZURE_FUNCTION_URL, and the jobs/profile backend through VITE_BACKEND_BASE_URL (the /api/jobs and /api/profile paths are appended in code — see frontend/src/constants/api.ts).
• API: A Node.js Azure Functions v4 HTTP API hosted in the analyze-resume Function App. It validates the multipart request, uploads the PDF, extracts resume text, calls Azure AI Foundry, and returns typed JSON.
• Storage and AI services: Blob Storage retains the uploaded PDF, Document Intelligence performs OCR and text extraction, and Azure AI Foundry evaluates the resume against the job description.

The browser communicates only with the HTTP Function endpoint. Azure credentials remain server-side in Function App environment variables; the frontend exposes only its public API URL.

How it works

1. The user drops a PDF resume into the frontend.
2. The user pastes the target job description.
3. The frontend sends both fields as multipart/form-data with Axios.
4. The Azure Function stores the PDF in Blob Storage.
5. Document Intelligence extracts the resume text.
6. Azure Foundry compares the resume with the job description.
7. The frontend displays the structured analysis.

Setup

Install dependencies in both workspaces:

npm install --prefix api
npm install --prefix backend
npm install --prefix frontend

Copy the API settings template and add your Azure credentials:

cp api/local.settings.example.json api/local.settings.json

The frontend Function URL is configured in frontend/.env. Use frontend/.env.example as a template when setting up another environment. Never commit real secrets or local settings files.

Copy the jobs/profile backend's env template and add your credentials:

cp backend/.env.example backend/.env

backend/.env needs the Azure SQL Database connection details (SQL_SERVER, SQL_DATABASE, SQL_USER, SQL_PASSWORD) and the same Blob Storage connection string/container used by the api workspace, since profile resumes are stored in the same resumes container. Before running the backend for the first time, create the Profiles and Jobs tables by running backend/sql/create-profiles-table.sql and backend/sql/create-jobs-table.sql against the database (e.g. via the Azure Portal's Query Editor) and populate Jobs with your own listings. Both tables are accessed through TypeORM — see backend/src/entities/. The SQL server's firewall must allow the connecting IP — either your own machine's, or "Allow Azure services" for Azure-hosted deployments.

Required Azure services:

• Azure Storage Account with a resumes Blob container
• Azure SQL Database (for job listings and profile records)
• Azure Document Intelligence resource
• Azure AI Foundry model deployment
• Azure Function App

Run locally

Start the Azure Functions API from the project root:

npm start

The API runs at http://localhost:7071/api/analyze-resume.

Start the jobs backend in a second terminal:

npm run start:backend

The jobs API runs at http://localhost:4000/api/jobs and the profile API at http://localhost:4000/api/profile. The frontend reads VITE_BACKEND_BASE_URL (e.g. http://localhost:4000/) and appends each path itself.

Start the frontend in another terminal:

npm run start:frontend

Open http://localhost:5173/ in your browser.

Build and checks

Build both applications:

npm run build

Run frontend checks:

npm --prefix frontend run lint
npm --prefix frontend run format:check

Format the frontend with Prettier:

npm --prefix frontend run format

Deployment

Frontend deploys automatically via GitHub Actions on every push to main. See frontend/README.md for the manual swa deploy alternative.

Backend (backend/) deploys automatically via GitHub Actions on every push to main that touches backend/**.

To deploy it manually instead:

cd backend
rm -rf dist
npm run build

rm -rf deploy && mkdir deploy
cp package.json package-lock.json deploy/
cp -r dist deploy/dist
(cd deploy && npm ci --omit=dev)

cd deploy
zip -r ../deploy.zip . -x "*.DS_Store"
cd ..

az webapp deploy \
  --resource-group ai-103-rg \
  --name elcorrectobackend \
  --src-path deploy.zip \
  --type zip

This builds a production-only package (compiled dist/ plus node_modules installed without dev dependencies) and pushes it to the elcorrectobackend App Service. Requires being logged in via az login with access to the ai-103-rg resource group.

TODO

• Replace browser-based Auth0 token persistence with a server-side authentication flow using an HttpOnly session cookie.

API request

The API expects a POST request with multipart/form-data:

• resume: PDF file
• jobDescription: job description text

The deployed Function endpoint is:

https://analyze-resume-ebgeb5bkaubjchhu.westus3-01.azurewebsites.net/api/analyze-resume

A Bruno request is available in bruno. The API-specific setup notes are in api/README.md.