Description

TrafficJamAnalyzer is an advanced tool designed to help monitor and analyze traffic conditions by processing images from CCTV cameras around the roads of Tenerife. Tenerife CCTV Cameras Source

By utilizing artificial intelligence (AI) with Semantic Kernel and OpenAI, the application accurately assesses traffic density and identifies locations with potential traffic jams.

• Features
• Architecture diagram
• Getting started
• Deploying to Azure
• Run solution
  • Run locally
  • Run the solution
  • Local dev using an existing model
• Resources
• Video Recordings
• Guidance
  • Costs
  • Security Guidelines
• Resources

Features

GitHub CodeSpaces: This project is designed to be opened in GitHub Codespaces as an easy way for anyone to deploy the solution entirely in the browser.

This is the Camera Traffic Jam Aplication running:

The Aspire Dashboard to check the running services:

There Azure Resource Group with all the deployed services:

Architecture diagram

Getting Started

The solution is in the root folder, the main solution is TrafficJamAnalyzer.sln.

Deploying

Once you've opened the project in Codespaces, or locally, you can deploy it to Azure.

From a Terminal window, open the folder with the clone of this repo and run the following commands.

1. Login to Azure:

   azd auth login

2. Provision and deploy all the resources:

   azd up

   It will prompt you to provide an azd environment name (like "trafficjamdev"), select a subscription from your Azure account, and select a location where OpenAI is available (like "eastus2").

3. When azd has finished deploying, you'll see the list of resources created in Azure and a set of URIs in the command output.

4. Visit the webfrontend URI, and you should see the Traffic Jam Camera Analyzer app! 🎉

5. This is an example of the command output:

1. You can check this video with a 5 minutes overview of the deploy process from codespaces: Deploy Your AI Traffic Jam Analyzer to Azure in Minutes! 🚦🚀.

GitHub CodeSpaces

1. Create a new Codespace using the Code button at the top of the repository.

1. The Codespace creation process can take a couple of minutes.

2. Once the Codespace is loaded, it should have all the necessary requirements to deploy the solution.

Run Locally

To run the project locally, you'll need to make sure the following tools are installed:

• .NET 9
• Git
• Azure Developer CLI (azd)
• Visual Studio Code or Visual Studio
  • If using Visual Studio Code, install the C# Dev Kit
• .NET Aspire workload: Installed with the Visual Studio installer or the .NET CLI workload.
• An OCI compliant container runtime, such as:
  • Docker Desktop or Podman.

Run the solution

Follow these steps to run the project, locally or in CodeSpaces:

• Navigate to the Aspire Host folder project using the command:

cd ./TrafficJamAnalyzer.AppHost/

• Run the project:

dotnet run

• You can expect an output similar to this one:

Local development using an existing gpt-4o model

In order to use an existing gpt-4o model, you need to define the specific connection string in the AIApiService project.

Add a user secret with the configuration:

{
  "ConnectionStrings:openaiTraffic": "Endpoint=https://<endpoint>.openai.azure.com/;Key=<modelkey>;"
}

Otherwise, set openAiClientName to openai, in the program.cs.

var openAiClientName = "openai";
builder.AddAzureOpenAIClient(openAiClientName);

Guidance

Costs

For Azure OpenAI Services, pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers. However, Azure Container Registry has a fixed cost per registry per day.

You can try the Azure pricing calculator for the resources:

• Azure OpenAI Service: S0 tier, gpt-4o model. Pricing is based on token count. Pricing
• Azure Container App: Consumption tier with 0.5 CPU, 1GiB memory/storage. Pricing is based on resource allocation, and each month allows for a certain amount of free usage. Pricing
• Azure Container Registry: Basic tier. Pricing
• Log analytics: Pay-as-you-go tier. Costs based on data ingested. Pricing

⚠️ To avoid unnecessary costs, remember to take down your app if it's no longer in use, either by deleting the resource group in the Portal or running azd down.

Security Guidelines

Samples in this templates uses Azure OpenAI Services with ApiKey and Managed Identity for authenticating to the Azure OpenAI service.

The Main Sample uses Managed Identity](https://learn.microsoft.com/entra/identity/managed-identities-azure-resources/overview) for authenticating to the Azure OpenAI service.

Additionally, we have added a GitHub Action that scans the infrastructure-as-code files and generates a report containing any detected issues. To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the Github secret scanning setting is enabled.

You may want to consider additional security measures, such as:

• Protecting the Azure Container Apps instance with a firewall and/or Virtual Network.

Resources

• This project is based on the original work from Emiliano MontesdeOca, in the repo TrafficJamAnalyzer

• Deploy a .NET Aspire project to Azure Container Apps using the Azure Developer CLI (in-depth guide)

• Aspiring .NET Applications with Azure OpenAI

Video Recordings

• .NET AI Community Standup - Beat Traffic Jams with .NET Aspire and GPT vision models

• .NET AI Community Standup - Level Up with .NET 9, AI, Aspire and Azure! 🚦

• Deploy Your AI Traffic Jam Analyzer to Azure in Minutes! 🚦