AnalyzeMyCV

AI-powered resume analysis engine built with a FastAPI backend, Streamlit frontend, and Azure OpenAI (GPT-5 Mini). Analyzes resumes against job descriptions to provide semantic scores and gap analysis, hosted on Azure App Service.

Production URL: https://tinyurl.com/analyzemycv

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Tech Stack & Architecture

• Frontend UI: Streamlit (Python-driven reactive web interface)
• Backend API: FastAPI (High-performance API server for robust model orchestration)
• AI Engine: Azure OpenAI API Integration (GPT-5 Mini for contextual parsing and semantic processing using the Responses API)
• Infrastructure / Hosting: Azure App Services (Linux Environment)
• CI/CD Pipeline: GitHub Actions (master_analyzemycv.yml automated zip deployment via Oryx)

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Local Quickstart & Installation Steps

Follow these exact steps to set up and run the application on your local machine.

Prerequisites

• Python: Ensure you have Python 3.9, 3.10, or 3.11 installed. You can check your version by running:

  python3 --version

• Git: Installed and configured on your local terminal.

Step 1: Clone the Repository Clone the codebase to your local system and navigate straight into the project root directory:

git clone https://github.com/ToastCoder/AnalyzeMyCV.git
cd AnalyzeMyCV

Step 2: Create an Isolated Virtual Environment Create a local virtual environment (.venv) to keep the project packages isolated from your global system installations:

python3 -m venv .venv

Step 3: Activate the Virtual Environment Activate the environment before running installations. This ensures packages are bound strictly to this workspace.

• On macOS / Linux Distributions (Ubuntu, Debian, Fedora, Arch)

  source .venv/bin/activate

• On Windows (Command Prompt)

  .venv\Scripts\activate.bat

• On Windows (PowerShell)

  .venv\Scripts\Activate.ps1

(Once activated, you will see (.venv) prepended to your terminal prompt line.)

Step 4: Install Project Dependencies Upgrade the base package installer tool and fetch all the core packages listed in the requirements manifest:

pip install --upgrade pip
pip install -r requirements.txt

Step 5: Configure Environment Variables Locally Create a .env file in the root folder of your project to securely pass your Azure OpenAI endpoints and keys to the application:

AZURE_OPENAI_API_KEY=your_actual_api_key_here
AZURE_OPENAI_ENDPOINT=https://your-resource.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-5-mini

Step 6: Launch the Local Application Services The application architecture is decoupled into a backend service and a frontend client. Boot up both services concurrently using the provided shell script:

chmod +x ./entrypoint.sh
./entrypoint.sh

Alternatively, for hot-reloading during active development, you can run them in separate isolated terminal windows:

• Terminal 1 (Backend): uvicorn api.main:app --host 0.0.0.0 --port 8080 --reload
• Terminal 2 (Frontend): streamlit run client/streamlit_client.py --server.port 8000

Once executed, open your browser to http://localhost:8000 to view the application live.

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Production Deployment (Azure App Service)

The application is deployed securely as a custom Python web container behind an automated reverse proxy structure on Azure.

Critical App Settings (Environment Variables)

To allow heavy binary payloads and clear persistent state processing, the following variables must be configured under Settings -> Environment Variables in the Azure Portal:

• AZURE_OPENAI_API_KEY: Secure token validation endpoint for processing workflows.
• AZURE_OPENAI_ENDPOINT: Full URI string for the Azure API Gateway routing.
• AZURE_OPENAI_DEPLOYMENT_NAME: Set to gpt-5-mini (the exact deployment identity configured in Azure).
• STREAMLIT_SERVER_MAX_UPLOAD_SIZE: Set to 200 to prevent mobile proxy timeouts on multi-page PDF documents.
• SCM_DO_BUILD_DURING_DEPLOYMENT: Set to true to invoke clean automation routines during continuous delivery runs.

Production Startup Command

To ensure both the backend API and frontend client start concurrently and properly bind to the Azure Linux container environment, the startup configuration is managed through a bash script. This command must be defined symmetrically inside the Azure Portal Configuration settings and the GitHub Workflow YAML manifest:

chmod +x ./entrypoint.sh && ./entrypoint.sh

CI/CD Pipeline

Continuous deployment is triggered on every manual or automated merge request to the master integration timeline.

name: Deploy AnalyzeMyCV to Azure

on:
  push:
    branches:
      - master

jobs:
  build-and-deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'