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Tag: amazon web services
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Fantasy Football and AI – Week 9
A BIG win in week 9! Our team is now tied for 1st place with 6 wins and 3 losses. We currently have 1318.66 total fantasy points on the season. It was looking pretty grim going into the afternoon games on Sunday. The receivers AI selected were not preforming and other players were barely hitting their projects. Josh Allen sparked some life into the team with his 30 points and then Sam Darnold showed everyone how to play quarter back with a 37.2 point performance! Check out the full results below.
So, now we’re off to week 10. Currently, as I write this, Saquon Barkley is questionable to play. I would expect that he does play but the AI will not put him in the starting lineup. We picked up the Panther’s defense upon request from the AI. I would expect this is because they are playing the Saints who just traded away Rasheed Shaheed. The addition of DJ Moore into the OP slot is going to be a rough choice over playing a quarterback in that position. I’ll be monitoring the roster throughout the week to see if there are any other suggestions we can make. Here is what we are currently fielding into week 10:
Tune in next week for the results! Hopefully AI can get to 7-3!
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Fantasy Football and AI – Week 7
Well, our win streak was too good to be true. Unfortunately we lost a close one in week 6. It came down to the Monday night games and Sam Darnold just wasn’t able to get it going over the Texans even though the Seahawks still pulled out the win.
Our running back group also did not preform well outside of Josh Jacobs. The loss was by a difference of about 7 points so if anyone had put up another touchdown we could have won.
Anyway, on to week week 8. A few byes to contend with but otherwise most of are starts will be playing. The AI suggested grabbing the Colts defense and kicker as they are playing Tennessee. Breece Hall is currently questionable to play so we will have to keep an eye on that but he has a favorable matchup against the Bengals. The current roster is below.
I promised to work on MCP this week but have only made a little bit of progress. I’ve been doing a lot of research on doing it in a cost effective manner as this project makes ZERO dollars and so I can’t afford to setup a bunch of expensive infrastructure. SO – this week I worked on combining the waiver table and the stats table into one table so that we can minimize DynamoDB calls throughout the application. The other thing I did was setup DynamoDB streams which are then converted into text files for each player and placed into an S3 bucket. This is what I think will be the first step in setting up a RAG pipeline so that a model can begin to be more “aware” of current NFL and Fantasy Football landscape.
Here is an updated architecture diagram. You’ll notice the S3 bucket on the right side. This is the eventual start of our knowledgebase.
You’ll also notice the waiver table removed. The new player structure looks like this:
{ "player_id": "George Kittle#TE", "espn_player_id": 3040151, "player_name": "George Kittle", "position": "TE", "seasons": { "2024": { "season_totals": { "MISC_FL": 0, "MISC_FPTS": 158.6, "MISC_FPTS/G": 10.6, "MISC_G": 15, "MISC_ROST": "99.4%", "Player": "George Kittle", "Rank": 1, "RECEIVING_20+": 21, "RECEIVING_LG": 43, "RECEIVING_REC": 78, "RECEIVING_TD": 8, "RECEIVING_TGT": 94, "RECEIVING_Y/R": 14.2, "RECEIVING_YDS": 1106, "RUSHING_ATT": 0, "RUSHING_TD": 0, "RUSHING_YDS": 0 }, "weekly_stats": { "1": { "fantasy_points": 4, "opponent": "NYJ" }, "2": { "fantasy_points": 13.6, "opponent": "MIN" }, "4": { "fantasy_points": 10.5, "opponent": "NE" }, "5": { "fantasy_points": 12.4, "opponent": "ARI" }, "6": { "fantasy_points": 17.8, "opponent": "SEA" }, "7": { "fantasy_points": 9.2, "opponent": "KC" }, "8": { "fantasy_points": 18.8, "opponent": "DAL" }, "10": { "fantasy_points": 11.7, "opponent": "TB" }, "12": { "fantasy_points": 14.2, "opponent": "GB" }, "13": { "fantasy_points": 0.7, "opponent": "BUF" }, "14": { "fantasy_points": 15.1, "opponent": "CHI" }, "15": { "fantasy_points": 6.1, "opponent": "LA" }, "16": { "fantasy_points": 10.6, "opponent": "MIA" }, "17": { "fantasy_points": 11.2, "opponent": "DET" }, "18": { "fantasy_points": 2.7, "opponent": "ARI" } } }, "2025": { "injury_status": "ACTIVE", "jersey_number": "85", "percent_owned": 98.97, "pro_team_id": 25, "season_projections": { "MISC_FL": 0.5, "MISC_FPTS": 147.6, "RECEIVING_REC": 76, "RECEIVING_TD": 7.5, "RECEIVING_YDS": 1036.9 }, "team": "SF", "weekly_outlooks": { "1": "George Kittle is healthy and wealthy for the 49ers' Week 1 matchup against Seattle after signing a big four-year contract extension in the offseason. Kittle's role as a pass catcher should be intensified early on with WR Brandon Aiyuk (ACL) on the PUP list to begin the campaign and Jauan Jennings (calf, contract) uncertain to suit up against the Seahawks. Kittle is coming off a 78-catch, 1,106-yard, eight-TD 2024 campaign, further cementing his place as one of the NFL's elite producers at tight end. The Seahawks were middle-of-the-pack against the position last year, giving up an average of 51.5 receiving yards per game.", "2": "George Kittle won't play in San Francisco's Week 2 matchup against New Orleans due to a hamstring injury that landed him on IR. Luke Farrell and Jake Tonges, who caught a TD in Kittle's absence last week against the Seahawks, will be asked to step in at tight end for the 49ers.", "3": "George Kittle will miss his second straight game for the 49ers in Week 3 against the Cardinals while he remains on IR due to a hamstring injury. Jake Tonges and Luke Farrell should continue to hold down the fort at TE for Kittle until the latter is able to return. Kittle won't be eligible to suit up again until Week 6." }, "weekly_projections": { "5": 12.7, "6": 13.1, "7": 13.4, "8": 11.7, "10": 14.6, "12": 11.6, "13": 13.2, "14": 13.4, "15": 14.1, "16": 14.1, "17": 14.5 }, "weekly_stats": { "1": { "fantasy_points": 12.5, "opponent": "SEA", "team": "SF", "updated_at": "2025-10-15T17:40:58.625370" }, "2": { "fantasy_points": 12.5, "opponent": "NO", "team": "SF", "updated_at": "2025-09-16T17:08:05.179797" }, "3": { "fantasy_points": 12.5, "opponent": "ARI", "team": "SF", "updated_at": "2025-09-23T15:00:13.907272" }, "4": { "fantasy_points": 12.5, "opponent": "JAX", "team": "SF", "updated_at": "2025-09-30T15:00:14.035733" }, "5": { "fantasy_points": 12.5, "opponent": "LAR", "team": "SF", "updated_at": "2025-10-07T15:00:13.665217" }, "6": { "fantasy_points": 12.5, "opponent": "TB", "team": "SF", "updated_at": "2025-10-14T15:00:14.748804" } } } }, "updated_at": "2025-10-22T18:11:05.039158" }I hope to continue to refine this so that it can be used for future seasons. Then we can continue to use the bot into 2026’s season.
Anyway, hopefully I can figure out MCP and the knowledgebase this week. Winter is coming so its time to hunker down and build AWS Architectures!
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Fantasy Football and AI – Week 6
We’re back with the week 6 AI managed fantasy football team and we got another win! The team’s record is now 4-2-0 and sits 3rd in the league.
Here is the final lineup that was fielded for week 6 and the points
There were a couple players on the bench that did better. Keenan Allen and Breece Hall could have been swapped for pretty much anyone on the bench and we would have had a few more points. A win is a win. One thing to note is that we had a game time injury of Dalton Kincaid and I had to pull Zach Ertz in at the last minute to avoid getting a zero.
For week 7 we are starting to see both injury and bye weeks happening. The AI will have to make some pretty deep roster moves in order to fill the gaps. Here is the initial starting lineup:
Because of the BYE weeks we are picking up Aaron Rodgers who put up a good game against Cincinnati last night. DK did not have a great night last night. We picked up the Patriots defense as well as Eddy Pineiro to fill in some slots. I think the Patriots against Tennessee will be an interesting matchup given that the Titans fired their head coach. Mike Vrabel has the Patriots firing again and hopefully he can shut them out and we can put up some big points!
From a tech perspective, I’m slowly putting together an MCP server to help create some efficiencies when working with the DynamoDB tables. Hopefully, if we can handle that, the overall application response time will be faster. If I wasn’t traveling AGAIN this weekend I would have made it a hackathon. Hopefully for week 8!
Subscribe for updates.
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Using Strands to build an AWS Cost Analysis Agent
Taking a break from Fantasy Football today to talk about a quick weekend project I put together.
A friend of mine was chatting about how their AWS costs are getting out of control and they aren’t sure where to start when it comes to cleaning up the account. This prompted me with an idea to utilize AI to build an Agent that can interact with your AWS account to review resources, provide cost analysis and give you clear CLI commands or console instructions to help clean up the account.
In order to do this, I wanted to incur as little cost as possible. So, I built a Docker image in order to run it locally. First, there is a shell script that will build an IAM User in your account that provides read-only access to the account, Cost Explorer access and access to Bedrock (to communicate with an AI model).
The Docker image runs and builds an Agent that interacts with whichever model you want to utilize. I picked Amazon’s Nova model just to keep the costs down. The container then presents a web interface where the account’s bill break down will be displayed:
It will also display some common costly resources and their counts:
The next block is where things get very helpful. The AI will present to you suggestions as to how to save some money as well as some risk calculations. Because I ran this against my real account I had to blur out some information but you get the idea:
So, now you have some actionable activities to work through to help you save money on your AWS bill. But what if you have more questions? I also included a simple chat box to help you work with the bot to come up with other explanations or find other ways to save cost.
So I asked the AI to find the largest instance in my account and then determine the right size for it. Here is the response:
Why would this be important? Well, if you had the AI review all of the instances in your account you could identify EC2 instances that are oversized and have them be changed accordingly. After I implemented a few of the changes that the AI recommended (and verified they didn’t break anything), my account billing decreased by about $100.
If this is something you are interested in running on your own account feel free to reach out! I’d be happy to help you setup up the container on your machine and make suggestions as to how to save some money!
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Week 2 – Fantasy Football and AI
After a heartbreaking (lol) loss in week one, our agent is back with its picks for week two!
But, before we start talking about rosters and picks and how I think AI is going to lose week two, let’s talk about the overall architecture of the application.
Current Architecture diagram You may notice that after my post on Tuesday I have substantially reduced the data storage. I’m now using three DynamoDB tables to handle everything.
- Current Roster – This table is populated by an automated scraper that pulls the rosters for all the teams in the league.
- Player Data Table – This table populates all the historical data from the draft as well as projected stats for the 2025 season. It also holds the actual points received after the week has completed.
- Waiver Table – this is probably the most notable addition to the overall Agent. This table is populated by both ESPN and FantasyPros
The waiver wire functionality is a massive addition to the Agent. It now has the ability to know what players are available for me to add to the team. If we combine that with the player stats in the Player Data Table we can get a clear picture as to how the player MIGHT preform on a week to week basis.
The waiver table is populated by a lambda function that goes out and scrapes the ESPN Fantasy Platform. It is quite involved code as there is no API for ESPN. I’m still not sure why they don’t build one. It seems like an easy win for them especially as they get into more sports gambling. You can read the code here. This Lambda function runs on a CRON every day so that the Agent always has daily updated data.
The other major addition is a web interface. I realized that accessing this via a terminal is great but, it would be way more interesting to have something to look at. Especially if I am away from the computer.
The web interface consists of a display of the roster:
Roster Screenshot There are a couple things I need to fix. You’ll notice that a few players “have points” this is a problem with the data in the Player Data Table from when I was merging all the sources. Ideally, this will display the points the player has received for the week. Eventually I would like to add some live streaming of the point totals.
Bench Players It also displays the bench (notice the same data glitch). On my list of things to do is to make these boxes drag and drop and auto update the roster table so that we can move players around. I also want to add their projections to each block so I can see the projected points per week for each player.
The BEST part (in my opinion) is the prediction functionality. There is a box that we can choose which week to get a prediction for and then it will return output from the agent.
So, let’s take a look at week two!
Optimal Week 2 Lineup Here is a screenshot of the week two optimal lineup. This is the lineup I will be starting for week two unless one of my tight-ends becomes healthy. With George Kittle on injured reserve and Evan Engram also questionable, the AI suggested picking up Hunter Henry.
The Agent will also provide up with some waiver targets as well as matchup analysis
It will also output the full response for each week. Here is the full text of what the Agent has predicted for week two:
LINEUP OPTIMIZATION FOR WEEK 2
Starting Lineup Strategy:
– Josh Allen (QB): Elite QB1 with 38.8 points in Week 1, facing NYJ defense that allowed significant passing yards. His 2024 average of 31.85 and strong 2025 projection make him a must-start.
– Josh Jacobs (RB1): Top projected RB at 18.4 points, facing Washington at home. Green Bay’s new offensive system should benefit Jacobs significantly.
– Saquon Barkley (RB2): Projected 18.1 points against Kansas City. His 2024 average of 15.93 and strong 2025 projection of 273.2 season points make him a core play.
– DK Metcalf (WR1): Highest projected WR at 14.3 points. Pittsburgh vs Seattle should be a competitive game with passing volume.
– Marvin Harrison Jr. (WR2): Rookie with 14.2 projection facing Carolina. His 154.5 season projection suggests breakout potential.
– Hunter Henry (TE): Best available TE option at 9.3 projected points with Kittle on IR and Engram questionable.
– DJ Moore (FLEX): Solid 13.9 projection against Detroit. Chicago’s passing game should see volume in what could be a high-scoring affair.
– Breece Hall (OP): Critical OP selection at 14.9 projected points. Hall offers RB1 upside against Buffalo, and using an RB in the OP slot provides higher floor than WR options.CRITICAL WAIVER WIRE TARGETS:
Roster analysis shows no critical positional needs. Your team has excellent depth across all positions with 19 total players and strong projections.
TOP DEFENSE WAIVER PICKUP:
No specific waiver targets identified in the data, but consider streaming defenses based on matchups. Look for teams facing backup QBs or offenses with high turnover rates.INJURY CONCERNS:
– George Kittle: On Injury Reserve, unavailable for Week 2
– Evan Engram: Listed as Questionable, monitor his status throughout the weekMATCHUP ANALYSIS:
– Josh Allen vs NYJ: Excellent matchup for Allen, Jets defense struggled in Week 1
– Jacobs vs WSH: Home game advantage for Green Bay, Washington’s run defense showed vulnerabilities
– Barkley vs KC: High-scoring game potential, Eagles should stay competitive requiring rushing attempts
– DK Metcalf vs SEA: Revenge game narrative, Pittsburgh’s passing attack should be featuredBEST DEFENSE MATCHUPS
Detroit DST faces Chicago at home, which presents a solid matchup. Other potential streaming options to consider:
– Teams facing rookie QBs or backup quarterbacks
– Defenses at home against struggling offensive lines
– Units facing teams with high turnover rates from Week 1WAIVER PRIORITY:
1. Streaming Defense: Look for defenses with favorable Week 3 matchups
2. Handcuff RBs: Target backup RBs for your elite runners (Jacobs, Barkley, Hall)
3. High-Upside WRs: Monitor snap counts and target shares from Week 2 for emerging receiversFinal Comments:
Your roster is exceptionally deep and well-constructed. The OP position selection of Breece Hall provides excellent upside, as RBs typically offer higher floors than WRs in flex positions. Focus on matchup-based streaming for defense and monitor the waiver wire for breakout players rather than addressing positional needs. Your core lineup projects for strong Week 2 performance with multiple players having 15+ point upside.What’s Next?
So we have a pretty solid week two prediction. Josh Allen and Saquon Barkley I have faith in. The rest of the team is hit or miss. Chicago faces Detroit at Ford Field (Go Lions!) and both teams lost week one. But Ben Johnson facing his old team for the first time has me nervous.
This brings up a few of my to-dos for the overall program.
- Defensive matchups – I need to get data for the Defenses to find the best matchups week to week. Having a good defense play is an easy way to get an advantage every week.
- Add authentication – I added a really simple authentication method to the code just for the time being. But, it would be nice to have a Single Sign On or something a little bit more secure.
- Drag-n-drop interface – I need to add functionality to be able to modify the roster on the web interface. It would be nice if this could also update ESPN.
- Slow Output – I’m always looking for ways to optimize the Agent’s output. Currently it takes about 45 seconds to a minute to return the output.
Thoughts? I hope this series is entertaining. If you have ideas for the Agent please comment below or shoot me a message somewhere!
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Fantasy Football and AI Week 1
The last you heard from me I was building a drafting agent for Fantasy Football. Well, the draft has finished and my roster is set. Behold, the Fantasy Football AI drafted team for my 8 team, PPR league.
STARTING LINEUP
QB – Josh Allen (BUF)
RB – Saquon Barkley (PHI)
RB – Josh Jacobs (GB)
WR – Terry McLaurin (WSH)
WR – DJ Moore (CHI)
TE – George Kittle (SF)
FLEX – Breece Hall (NYJ)
OP – Sam Darnold (SEA)
D/ST – Lions (DET)
K – Chase McLaughlin (TB)
BENCH
WR – DK Metcalf (PIT)
WR – Marvin Harrison Jr. (ARI)
TE – Evan Engram (DEN)
RB – Aaron Jones Sr. (MIN)
WR – Cooper Kupp (SEA)
QB – J.J. McCarthy (MIN)
WR – Keenan Allen (LAC)
RB – Travis Etienne Jr. (JAX)Now, I have also added another feature to the overall solution which is to include a week to week manager I’m calling the “coach”. I built a database that contains the 2024 statistics for each player and who they played against. I’m also scraping FantasyPros.com as well for future projections still.
I added a new Lambda function and API call to my architecture so that I can send a request to the AI Agent to build out my ideal weekly roster.
The roster I posted above is what I will be starting for week 1. The agent also provides some context as to why it selected each player.
🏈 Fantasy Lineup for Team 1, Week 1 ================================================== 🏆 STARTING LINEUP: QB: Josh Allen (BUF) 23.4 pts RB: Saquon Barkley (PHI) 19.9 pts RB: Josh Jacobs (GB) 15.5 pts WR: Terry McLaurin (WAS) 11.1 pts WR: DJ Moore (CHI) 12.5 pts TE: George Kittle (SF) 10.6 pts FLEX: Breece Hall (NYJ) 11.5 pts OP: Sam Darnold (SEA) 18.6 pts K: Chase McLaughlin (TB) 8.5 pts DST: Lions (DET) 9.2 pts 💯 TOTAL PROJECTED: 145.4 points 📋 BENCH (Top 5): WR: Keenan Allen 7.6 pts WR: DK Metcalf 8.6 pts WR: Marvin Harrison Jr. 9.5 pts WR: Cooper Kupp 9.0 pts RB: Aaron Jones Sr. 12.8 pts 💡 COACH ANALYSIS: ================================================== Made one key adjustment to the computed lineup: replaced Keenan Allen with DJ Moore at WR2. While Allen showed decent recent form (9.325 avg last 4 games), DJ Moore is the higher-drafted talent with WR1 upside who should be prioritized in Week 1. Moore's lack of 2024 data likely indicates injury, but his talent level and role in Chicago's offense make him the better play. The rest of the lineup is solid: Allen/Darnold QB combo maximizes ceiling, Barkley/Jacobs/Hall provide strong RB production, McLaurin offers consistency at WR1, and Kittle remains a reliable TE1. Lions DST should perform well at home, and McLaughlin provides steady kicking in Tampa Bay's offense. ==================================================There are still some gaps I need to fill with the data set as D.J. Moore did play in 2024 so I’m likely missing some data sets. I also have plans to build a “general manager” who can scan available players and find maybe some hidden gems on a week to week basis.
Finally, command line tools are fun but, I think the solution needs a web interface so watch for updates on that. The coach will inevitably automated and send me a report on the weeks performance as well as suggestions for the following week.
If you like Fantasy Football and technology follow along to see how this team performs throughout the season!
All the code is available here on GitHub: https://github.com/avansledright/fantasy-football-agent
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An AI Fantasy Football Draft Assistant
Last year I attempted to program a Fantasy Football draft assistant which took live data from ESPN’s Fantasy Platform. Boy was that a mistake…
First of all, shame on ESPN for not having an API for their Fantasy sports applications. The reverse engineered methods were not fast enough nor were they reliable. So, this year I took a new approach to building out a system for getting draft pick recommendations for my team.
I also wanted to put to use the example architecture and code I wrote the other day for the Strands SDK to work so I utilized it to build an API which would utilize the AWS Bedrock platform to analyze data and and ultimately return the best possible picks.
Here is a simple workflow of how the tool works:
I generated this with Claude AI. It is pretty OK. The first problem I encountered was getting data. I needed two things:
1. Historical data for players
2. Projected fantasy data for the upcoming season
The historical data provides information about the players past season and the projections are for the upcoming season, obviously. The projections are useful because of any incoming rookies.
In the repository I link below I put a scripts to scrape FantasyPros for both the historical and projected data. It stores them in separate files in case you want to utilize them in a different way. There is also a script to combine them into one data source and ultimately load them into a DynamoDB table.
The most important piece of the puzzle was actually simulating the draft. I needed to create a program that would be able to track the other team’s draft picks as well as give me the suggestions and track my teams picks. This is the heart of the repository and I will be using it to get suggestions and track the draft for this coming season.Through the application, when you issue the “next” command the application will send a request to the API with the current situation of the draft. The payload looks like this:
payload = { "team_needs": team_needs, "your_roster": your_roster, "already_drafted": all_drafted_players, "scoring_format": self.session.scoring_format if self.session else "ppr", "league_size": self.session.league_size if self.session else 12 }The “team_needs” key represents the current number of players remaining for each position. The “your_roster” position is all of the current players on my team. The other important key is “already_drafted”. This key sends all of the drafted players to the AI agent so it knows who NOT to recommend.
The application goes through all of the picks and you are able to manually enter each of the other teams picks until the draft is complete.
I’ll post an update after my draft on August 24th with the team I end up with! I still will probably lose in my league but this was fun to build. I hope to add in some sort of week-to-week management of my team as well as a trade analysis tool in the future. It would also be cool to add in some sort of analysis that could send updates to my Slack or Discord.
If you have other ideas message me on any platform you can find me on!
GitHub: https://github.com/avansledright/fantasy-football-agent -
Deploying a Strands Agent on AWS Lambda using Terraform
Recently I’ve been exploring the AI space a lot more as I’m sure a lot of you are doing as well. I’ve been looking at the Strands Agent SDK. I see this SDK as being very helpful in building out agents in the future (follow the blog to see what I come up with!).
One thing that is not included in the SDK is the ability to deploy with Terraform. The SDK includes examples of how to package and deploy with Amazon Web Services CDK so I adapted that to utilize Terraform.
I took my adaptation a step further and added an API Gateway layer so that you have the beginnings of a very simple AI agent deployed with the Strands SDK.
Check out the code here: https://github.com/avansledright/terraform-strands-agent-api
The code in the repository is fairly simple and includes everything you need to build an API Gateway, Lambda function, and some other useful resources just to help out.
The key to all of this is packaging the required dependencies inside of the Lambda Layer. Without this the function will not work.
File structure:
terraform-strands-agent-api/
└── lambda_code/
│ ├── lambda_function.py # Your Strands agent logic
│ └── requirements.txt # strands-agents + dependencies
├── api_gateway.tf # API Gateway configuration
├── iam.tf # IAM roles and policies
├── lambda.tf # Lambda function setup
├── locals.tf # Environment variables
├── logs.tf # CloudWatch logging
├── s3.tf # Deployment artifacts
├── variables.tf # Configurable inputs
└── outputs.tf # API endpoints and resource IDsYou shouldn’t have to change much in any of these files until you want to fully start customizing the actual functionality of the agent.
To get started follow the instructions below!
git clone https://github.com/avansledright/terraform-strands-agent-api cd terraform-strands-agent-api # Configure your settings. Add other values as needed echo 'aws_region = "us-west-2"' > terraform.tfvars # Deploy everything terraform init terraform plan terraform applyIf everything goes as planned you should see the output of a curl command which will give you the ability to test the demo code.
If you run into any issues feel free to let me know! I’d be happy to help you get this up and running.
If this has helped you in any way, please share it on your social media and with any of your friends!
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Building out a reusable Terraform framework for Flask Applications
I find myself utilizing the same architecture for deploying demo applications on the great Python library Flask. I’ve been using the same Terraform files over and over again to build out the infrastructure.
Last weekend I decided it was time to build a reusable framework for deploying these applications. So, I began building out the repository. The purpose of this repository is to give myself a jumping off point to quickly deploy applications for demonstrations or live environments.
Let’s take a look at the features:
- Customizable Environments within Terraform for managing the infrastructure across your development and production environments
- Modules for:
- Application Load Balancer
- Elastic Container registry
- Elastic Container Service
- VPC & Networking components
- Dockerfile and Docker Compose file for launching and building the application
- Demo code for the Flask application
- Automated build and deploy for the container upon code changes
This module is built for any developer who wants to get started quickly and deploy applications fast. Using this framework will allow you to speed up your development time by being able to focus solely on the application rather than the infrastructure.
Upcoming features:
- CI/CD features using either GitHub Actions or Amazon Web Services like CodePipeline and Codebuild
- Custom Domain Name support for your application
If there are other features you would like to see me add shoot me a message anytime!
Check out the repository here:
https://github.com/avansledright/terraform-flask-module -
Create an Image Labeling Application using Artificial Intelligence
I have a PowerPoint party to go to soon. Yes you read that right. At this party everyone is required to present a short presentation about any topic they want. Last year I made a really cute presentation about a day in the life of my dog.
This year I have decided that I want to bore everyone to death and talk about technology, Python, Terraform and Artificial Intelligence. Specifically, I built an application that allows a user to upload an image and have it return to them a renamed file that is labeled based on the object or scene in the image.The architecture is fairly simple. We have a user connecting to a load balancer which routes traffic to our containers. The containers connect Bedrock and S3 for image.
If you want to try it out the site is hosted at https://image-labeler.vansledright.com It will be up for some time, I haven’t decided how long I will host it for but at least through this weekend!
Here is the code that interacts with Bedrock and S3 to process the image:
def process_image(): if not request.is_json: return jsonify({'error': 'Content-Type must be application/json'}), 400 data = request.json file_key = data.get('fileKey') if not file_key: return jsonify({'error': 'fileKey is required'}), 400 try: # Get the image from S3 response = s3.get_object(Bucket=app.config['S3_BUCKET_NAME'], Key=file_key) image_data = response['Body'].read() # Check if image is larger than 5MB if len(image_data) > 5 * 1024 * 1024: logger.info("File size to large. Compressing image") image_data = compress_image(image_data) # Convert image to base64 base64_image = base64.b64encode(image_data).decode('utf-8') # Prepare prompt for Claude prompt = """Please analyze the image and identify the main object or subject. Respond with just the object name in lowercase, hyphenated format. For example: 'coca-cola-can' or 'golden-retriever'.""" # Call Bedrock with Claude response = bedrock.invoke_model( modelId='anthropic.claude-3-sonnet-20240229-v1:0', body=json.dumps({ "anthropic_version": "bedrock-2023-05-31", "max_tokens": 100, "messages": [ { "role": "user", "content": [ { "type": "text", "text": prompt }, { "type": "image", "source": { "type": "base64", "media_type": response['ContentType'], "data": base64_image } } ] } ] }) ) response_body = json.loads(response['body'].read()) object_name = response_body['content'][0]['text'].strip() logging.info(f"Object found is: {object_name}") if not object_name: return jsonify({'error': 'Could not identify object in image'}), 422 # Get file extension and create new filename _, ext = os.path.splitext(unquote(file_key)) new_file_name = f"{object_name}{ext}" new_file_key = f'processed/{new_file_name}' # Copy object to new location s3.copy_object( Bucket=app.config['S3_BUCKET_NAME'], CopySource={'Bucket': app.config['S3_BUCKET_NAME'], 'Key': file_key}, Key=new_file_key ) # Generate download URL download_url = s3.generate_presigned_url( 'get_object', Params={ 'Bucket': app.config['S3_BUCKET_NAME'], 'Key': new_file_key }, ExpiresIn=3600 ) return jsonify({ 'downloadUrl': download_url, 'newFileName': new_file_name }) except json.JSONDecodeError as e: logger.error(f"Error decoding Bedrock response: {str(e)}") return jsonify({'error': 'Invalid response from AI service'}), 500 except Exception as e: logger.error(f"Error processing image: {str(e)}") return jsonify({'error': 'Error processing image'}), 500If you think this project is interesting, feel free to share it with your friends or message me if you want all of the code!