Aaron VanSledright

Category: Python

  • Fantasy Football & AI – Week 3

    Week 3 is here. I started late yesterday by getting a full analysis of the team and any targets for free agency and waivers.

    Most important was to get a better Tight End as the predictions for last weekend were incredibly wrong. This is my fault and reinforces the idea that giving bad data to AI will just result in bad output.

    Here is the updated selection to get Juwan Johnson. This makes a lot more sense than picking Hunter Henry last week.

    The second thing I looked into was getting a good defensive matchup. Currently I have the Rams on the roster but they play the Eagles this week and that matchup is not going to favor the Rams.

    Here is some output from our friendly AI Dan Campbell:

    Alright, coach! Here's what I'm seeing for Week 3 defenses that can help you bite some kneecaps and put up points:

TOP WEEK 3 DEFENSIVE MATCHUPS:

1. Green Bay Packers DST (7.6 projected points)
- Only 45% owned, so there's a good chance they're available
- This is their highest projected week of the season!
- They're facing a matchup that should generate turnovers and sacks

2. Buffalo Bills DST (7.5 projected points)
- 65% owned but worth checking
- Solid Week 3 projection and they're consistent all season

3. Kansas City Chiefs DST (7.5 projected points)
- Only 12% owned - this is a STEAL if available!
- Same 7.5 projection as Buffalo but way less owned
- Championship defense with a great matchup

Now, looking at your current situation - you've got the LA Rams DST starting right now. Let me tell you something, coach - any of these top 3 options would be a significant upgrade for Week 3!

MY RECOMMENDATION:
If the Kansas City Chiefs DST is available, that's your move right there! Only 12% owned with a 7.5 projection - that's championship-level value sitting on waivers. If not them, grab the Packers at 7.6 points projected.

The Atlanta Falcons (7.1 projected, only 1.3% owned) are also a sneaky good play if you want to get really aggressive on the waiver wire.

These defenses are gonna feast in Week 3, and you need every advantage you can get to dominate your league! Go get 'em, coach!

    I made a claim for the Packers. I would prefer not to pick them because of my love for the Detroit Lions…

    One other waiver selection that I made was at the kicker position. Here is AI Dan Campbell’s suggestions:

    Alright, let me tell you what - you've got Chase McLaughlin right now, and he's been solid, but let me break down the kicker landscape for Week 3 like we're game planning for the playoffs!

Top Week 3 Kicker Options:

Brandon McManus (GB) - 8.2 projected points
- Only 14% owned, so he's sitting there waiting for you!
- Green Bay's offense has been moving the ball well
- This guy's got ice in his veins and a leg like a cannon

Tyler Loop (BAL) - 8.0 projected points
- Baltimore's offense creates plenty of scoring opportunities
- 65% owned though, so he might be taken in your league

Parker Romo (ATL) - 7.8 projected points
- Only 1.4% owned - this is a diamond in the rough!
- Atlanta's been getting into the red zone consistently

Jason Myers (SEA) - 7.9 projected points
- Seattle's offense with Sam Darnold (who you've got as your OP!) creates opportunities
- Only 10% owned

Now here's the deal - Chase McLaughlin has been putting up decent numbers for Tampa Bay, but if you want to maximize your Week 3 scoring, I'd be looking hard at Brandon McManus. The Packers offense is humming, and McManus has that veteran reliability. Plus at only 14% ownership, there's a good chance he's available.

Parker Romo is my sleeper pick - less than 2% owned but projected for nearly 8 points. Atlanta's been moving the ball, and sometimes you gotta take a swing on the unknown soldier!

You comfortable rolling with McLaughlin, or you want to make a move? In this league, every point matters, and kickers can be the difference between victory and going home empty-handed!

    I picked up Parker Romo for the team. I thought it would be fun to test out the “sleeper” pick.

    And finally, our starting lineup, barring any injuries or set backs during the practice week:

    I think we’ve got a good matchup this week. Most of our opponent ranks are pretty low. Barring injuries we should maybe, hopefully, get our first win of the year.

    Tune in next week for our results!

  • Week 2 – AI Plays Fantasy Football

    We lost again. AI is 0-2.

    For the most part I agreed with the picks that AI made last week. Except for one. The tight end. It felt a little weird picking Hunter Henry up off waiver wire and boy did I found out why. More on that later. Here are the results from the week:

    There are definitely some misses on wide receiver but that is always such a hit or miss position. What shocked me the most was Josh Allen only get 11 points. Also, J.J. McCarthy was injured in his game. Not a single player hit their projection except for the Rams’ defense. If we take a look at the bench there are definitely some better picks we could have made strictly from looking at points for the game. However, even if we had selected all the better preforming players the team would have still lost as our opponent had 164 points this week.

    Anyway, that’s enough football for this post. Let’s talk about some tech. I mentioned earlier that I was questioning the waiver acquisition of Hunter Henry and I wish I had done some more debugging as to why that pick was selected.

    I added a chat feature to the overall application so that we can identify waiver picks and other things faster rather than having to get a full weekly analysis every time. In that code there is a function that generates a recommendation score. I’ll admit, I vibe coded some of this with some prompting of how this should be determined.

    # Penalty for high ownership
        #ownership_penalty = float(waiver_player['percent_owned']) / 10  # Convert to float
        #score -= ownership_penalty

    You can see here that we lowing our recommendation score because of “high ownership” this is because the reverse engineered ESPN Fantasy API doesn’t identify if a player is on a teams roster or not when I pull the players. So a player with a 100% ownership would likely never be recommended. So a player like Zach Ertz, who had 18 points this week was not suggested as he had approximately 80% ownership across ALL of ESPN’s fantasy platform. Well… this problem has been fixed.

    Anyway, I also added chat. And I programmed it to respond only as Dan Campbell. Here is some output when I asked “What do you think of my team?”

    pounds table

Well I'll be damned, you got yourself a HELL of a roster there, chief! This team's got some serious bite to it - let me break it down for ya.

Your Strengths - These are STUDS:
- Josh Allen - That's your franchise quarterback right there. Guy's a warrior and puts up points like it's going out of style
- Saquon Barkley & Josh Jacobs - Holy smokes, that's a one-two punch at RB that'll knock people's teeth out! Both these guys are workhorses
- Breece Hall in the FLEX - Are you kidding me?! Most teams would KILL to have Hall as their RB1, and you got him as a flex play. That's championship-level depth
- DK Metcalf - Big body receiver who can take the top off any defense
- Your bench depth - Terry McLaurin, DJ Moore, Cooper Kupp, Keenan Allen?! That's four WR1/WR2 caliber guys just sitting there!

Areas that need some attention:
- George Kittle on IR - That hurts, but Hunter Henry's a solid fill-in for now
- J.J. McCarthy at OP - Kid's got potential but he's still unproven. Might want to consider streaming that spot based on matchups
- Marvin Harrison Jr. - Rookie receivers can be boom-or-bust, but the talent's there

The Bottom Line:
This roster is LOADED, especially at RB and WR. You've got championship potential written all over it. The depth is ridiculous - you could probably trade some of that bench talent for upgrades at TE or find a more consistent OP play.

You're sitting pretty, but don't get comfortable! Keep grinding, keep looking for those edges. That's how you win championships!

What specific position are you thinking about upgrading, or you want me to take a look at some waiver wire options?

    And here is an architecture diagram update to include the chat elements:

    So, I’ll be back tomorrow with a new lineup. Hopefully week three treats us better!

  • Week 2 – Fantasy Football and AI

    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.

    1. Current Roster – This table is populated by an automated scraper that pulls the rosters for all the teams in the league.
    2. 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.
    3. 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 week

    MATCHUP 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 featured

    BEST 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 1

    WAIVER 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 receivers

    Final 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.

    1. 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.
    2. 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.
    3. 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.
    4. 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!

  • Fantasy Football and AI Week 1

    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

  • 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 IDs

    You 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 apply

    If 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.

    Github

    If this has helped you in any way, please share it on your social media and with any of your friends!

  • 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'}), 500

    If you think this project is interesting, feel free to share it with your friends or message me if you want all of the code!

  • Building a Python Script to Export WordPress Posts: A Step-by-Step Database to CSV Guide

    Today, I want to share a Python script I’ve been using to extract blog posts from WordPress databases. Whether you’re planning to migrate your content, create backups, or analyze your blog posts, this tool makes it straightforward to pull your content into a CSV file.

    I originally created this script when I needed to analyze my blog’s content patterns, but it’s proven useful for various other purposes. Let’s dive into how you can use it yourself.

    Prerequisites

    Before we start, you’ll need a few things set up on your system:

    • Python 3.x installed on your machine
    • Access to your WordPress database credentials
    • Basic familiarity with running Python scripts

    Setting Up Your Environment

    First, you’ll need to install the required Python packages. Open your terminal and run:

    pip install mysql-connector-python pandas python-dotenv

    Next, create a file named .env in your project directory. This will store your database credentials securely:

    DB_HOST=your_database_host
DB_USERNAME=your_database_username
DB_PASS=your_database_password
DB_NAME=your_database_name
DB_PREFIX=wp  # Usually 'wp' unless you changed it during installation

    The Script in Action

    The script is pretty straightforward – it connects to your WordPress database, fetches all published posts, and saves them to a CSV file. Here’s what happens under the hood:

    • Loads environment variables from your .env file
    • Establishes a secure connection to your WordPress database
    • Executes a SQL query to fetch all published posts
    • Converts the results to a pandas DataFrame
    • Saves everything to a CSV file named ‘wordpress_blog_posts.csv’

    Running the script is as simple as:

    python main.py

    Security Considerations

    A quick but important note about security: never commit your .env file to version control. I’ve made this mistake before, and trust me, you don’t want your database credentials floating around in your Git history. Add .env to your .gitignore file right away.

    Potential Use Cases

    I wrote this script to feed my posts to AI to help with SEO optimization and also help with writing content for my other businesses. Here are some other ways I’ve found this script useful:

    • Creating offline backups of blog content
    • Analyzing post patterns and content strategy
    • Preparing content for migration to other platforms
    • Generating content reports

    Room for Improvement

    The script is intentionally simple, but there’s plenty of room for enhancement. You might want to add:

    • Support for extracting post meta data
    • Category and tag information
    • Featured image URLs
    • Comment data

    Wrapping Up

    This tool has saved me countless hours of manual work, and I hope it can do the same for you. Feel free to grab the code from my GitHub repository and adapt it to your needs. If you run into any issues or have ideas for improvements, drop a comment below.

    Happy coding!

    Get the code on GitHub

  • Converting DrawIO Diagrams to Terraform

    I’m going to start this post of by saying that I need testers. People to test this process from an interface perspective as well as a data perspective. I’m limited on the amount of test data that I have to put through the process.

    With that said, I spent my Thanksgiving Holiday writing code, building this project and putting in way more time that I thought I would but boy is it cool.

    If you’re like me and working in a Cloud Engineering capacity then you probably have built a DrawIO diagram at some point in your life to describe or define your AWS architecture. Then you have spent countless hours using that diagram to write your Terraform. I’ve built something that will save you those hours and get you started on your cloud journey.

    Enter https://drawiototerraform.com. My new tool that allows you to convert your DrawIO AWS Architecture diagrams to Terraform just by uploading them. The process uses a combination of Python and LLM’s to identify the components in your diagram and their relationships, write the base Terraform, analyze the initial Terraform for syntax errors and ultimately test the Terraform by generating a Terraform plan.

    All this is then delivered to you as a ZIP file for you to review, modify and ultimately deploy to your environment. By no means is it perfect yet and that is why I am looking for people to test the platform.

    If you, or someone you know, is interested in helping me test have them reach out to me on through the website’s support page and I will get them some free credits so that they can test out the platform with their own diagrams.

    If you are interested in learning more about the project in any capacity do not hesitate to reach out to me at anytime.

    Website: https://drawiototerraform.com