Learn to Build a Photo Captioning Streamlit Application with Amazon Bedrock LLMs

In this article, we will walk through how to create an AI-powered photo captioning app. First, let's create our app.

Creating LLM integrated Steamlit app

You can download the code for the app from the following GitHub repository:

https://github.com/Vivek-Rathee/PhotoCaptioner

But before deploying, let's understand what's happening inside the AIPCWA.py file.

First, you import the necessary packages mentioned in the file. You will need to pip install these packages if you don't already have them on your system.

Then set the page configurations for Streamlit, and load the CLIP model and processor. Next, add an AWS profile that has the necessary permissions to access AWS Rekognition and Bedrock, and create clients for the respective services.

Add a modelID of the LLM you would like to use, which you can find on the AWS Bedrock models page.

Create a function that converts the uploaded images to bytes and calls Rekognition to detect labels.

Next, refine the caption based on the user's choice of platform. Create the body as a dictionary, then serialize it to JSON and encode it as bytes. Read and decode the response, extract the refined caption from the response, and remove any prefatory text.

At last, create your Streamlit app, and with it, you have a functioning code for your AI-enhanced photo captioning app.

Test locally using VS Code with "streamlit run yourAppName.py". You'll need to configure the profile you mentioned in the code. You can use the AWS extension for configuring your profile in VS Code.

Hosting your Streamlit app on your custom domain.

Create an EC2 instance with the following configuration:

OS: Amazon AMI Linux 2023
Network/Security Group: Allow internet access on ports 80, 443, and 8501 (port on which streamlit runs)
Storage: 100GB (as dependencies need space)

System Update and Python Environment Setup

Switch to the root user:
```
 sudo su
```
Update all installed packages to their latest version:
```
 yum update -y
```
Upgrade the operating system to the latest release:
```
 yum upgrade -y
```
Create a Python virtual environment:
```
 python3 -m venv venv
```
Activate the Python virtual environment:
```
 source venv/bin/activate
```

Install Python Packages

Install the Python package installer (pip):
```
 yum install -y python3-pip
```

Install specific versions of PyTorch and related libraries:

 python3 -m pip install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --index-url https://download.pytorch.org/whl/cpu

Install Streamlit, ignoring any previously installed versions:
```
 python3 -m pip install --ignore-installed streamlit
```
Install Boto3, the Amazon Web Services (AWS) SDK for Python:
```
 python3 -m pip install boto3
```
Install Pillow, a Python Imaging Library (PIL) fork:
```
python3 -m pip install pillow
```
Install the Transformers library:
```
python3 -m pip install transformers
```

Git and Project Setup

Install Git:
```
yum install git
```

Clone the PhotoCaptioner repository:

git clone https://github.com/Vivek-Rathee/PhotoCaptioner.git

Navigate to the project directory:
```
cd PhotoCaptioner
```

AWS Configuration

Configure AWS CLI with your user profile:

aws configure --profile YourUserProfile

Follow the prompts to enter your AWS credentials:

Access Key ID [None]: Your Access Key ID
AWS Secret Access Key [None]: Your Secret Access Key
Default region name [None]: us-east-1
Default output format [None]: json

Setting up HTTP nginx web server

Install nginx:
```
yum install nginx -y
```
Install Certbot:
```
yum install certbot -y
```
Stop nginx:
```
sudo systemctl stop nginx
```

Obtain SSL certificates for your domain:

sudo certbot certonly --standalone -d yourdomain.com

Clear and edit nginx configuration file:

sudo sh -c 'echo -n > /etc/nginx/nginx.conf'
nano /etc/nginx/nginx.conf

Edit the nginx configuration file:

user nginx;
worker_processes auto;
error_log /var/log/nginx/error.log;
pid /run/nginx.pid;

events {
    worker_connections 1024;
}

http {
    include /etc/nginx/mime.types;
    default_type application/octet-stream;

    log_format main '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_user_agent" "$http_x_forwarded_for"';

    access_log /var/log/nginx/access.log main;

    sendfile on;
    keepalive_timeout 65;
    include /etc/nginx/conf.d/*.conf;

    map $http_upgrade $connection_upgrade {
        default upgrade;
        '' close;
    }

    upstream backend {
        server 127.0.0.1:8501;
        keepalive 64;
    }

    server {
        listen 80;
        server_name aipc.vivekrathee.com;

        location / {
            return 301 https://$host$request_uri;
        }
    }

    server {
        listen 443 ssl;
        server_name aipc.vivekrathee.com;

        ssl_certificate /etc/letsencrypt/live/yourdomain.com/fullchain.pem;
        ssl_certificate_key /etc/letsencrypt/live/yourdomain.com/privkey.pem;

        location / {
            proxy_pass http://backend;
            proxy_http_version 1.1;
            proxy_set_header Upgrade $http_upgrade;
            proxy_set_header Connection $connection_upgrade;
            proxy_set_header Host $host;
            proxy_cache_bypass $http_upgrade;
            proxy_set_header X-Real-IP $remote_addr;
            proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
            proxy_set_header X-Forwarded-Proto $scheme;
            client_max_body_size 20M;  # Adjust this value as necessary
        }
    }

    types_hash_max_size 2048;
    types_hash_bucket_size 128;
}

Restart and Enable nginx

Restart nginx:
```
sudo systemctl restart nginx
```
Enable nginx to start on boot:
```
sudo systemctl enable nginx
```

Create the Shell Script

Create a new file namedrun_streamlit.sh in the same directory:

nano run_streamlit.sh

Add the following content to the file:

while true
do
    nohup streamlit run AIPCWA.py &
    wait $!
    sleep 5
done

Make the shell script executable:
```
chmod +x run_streamlit.sh
```

Using tmux to Run the Script

Install tmux:
```
sudo yum install tmux
```
Create a new tmux session namedstreamlit_session:
```
tmux new -s streamlit_session
```

Run the shell script in the background:

nohup ./run_streamlit.sh > streamlit.log 2>&1 &

Detach from the tmux session by pressing Ctrl+b followed by d.

Conclusion: After following the above prcess you should have an AI powered Photo Captioning app running on your custom domain (like the one you can use here https://aipc.vivekrathee.com/).

#AIForTomorrow

Learn to Build a Photo Captioning Streamlit Application with Amazon Bedrock LLMs

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Creating LLM integrated Steamlit app

Hosting your Streamlit app on your custom domain.

Create an EC2 instance with the following configuration:

System Update and Python Environment Setup

Install Python Packages

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AWS Configuration

Setting up HTTP nginx web server

Restart and Enable nginx

Create the Shell Script

Using tmux to Run the Script

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Comments

More from this blog

Creating LLM integrated Steamlit app

Hosting your Streamlit app on your custom domain.

Create an EC2 instance with the following configuration:

System Update and Python Environment Setup

Install Python Packages

Git and Project Setup

AWS Configuration

Setting up HTTP nginx web server

Restart and Enable nginx

Create the Shell Script

Using tmux to Run the Script