Editing Openai/694ab010-3e7c-8000-b0e8-b98c22b7a063 (section)

=== Assistant: Below is a beginner-friendly, end-to-end path that matches what you asked: ===
Below is a beginner-friendly, end-to-end path that matches what you asked:
# Create and run a Docker container on an EC2 instance (to prove your app works in Docker)
# Reuse the same Docker image on AWS ECS (production-ish deployment), using a simple Python LangChain chatbot + Streamlit UI example.

I will assume you are using AWS Console + AWS CLI from your laptop, and an Ubuntu EC2 instance.

==== - Your app: Streamlit web UI that calls a LangChain chatbot function ====
* Docker image: contains the app and all Python dependencies
* Step A (EC2 Docker): build and run the container on one EC2 VM
* Step B (ECS): push image to ECR (Elastic Container Registry) and deploy on ECS (Fargate is simplest)

==== ### ====

<syntaxhighlight>ml-chatbot/
  app.py
  requirements.txt
  Dockerfile

</syntaxhighlight>

===== This uses LangChain but keeps logic very simple. For a real chatbot you would add memory, retrieval, tools, etc. =====

<syntaxhighlight lang="python">import os
import streamlit as st

from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage

st.set_page_config(page_title="ML Chatbot", layout="centered")
st.title("Simple ML Chatbot (LangChain + Streamlit)")

=== Read API key from environment variable ===
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")

if not OPENAI_API_KEY:
    st.warning("OPENAI_API_KEY is not set. The chatbot will not work until you set it.")
else:
    llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.2)

if "history" not in st.session_state:
    st.session_state.history = []

prompt = st.chat_input("Ask something...")

if prompt:
    st.session_state.history.append(("user", prompt))

    if not OPENAI_API_KEY:
        reply = "OPENAI_API_KEY missing. Set it as an environment variable and restart."
    else:
        messages = [SystemMessage(content="You are a helpful assistant.")]
        for role, text in st.session_state.history:
            if role == "user":
                messages.append(HumanMessage(content=text))
            else:
                # assistant messages can be added too, kept simple here
                pass

        resp = llm.invoke(messages)
        reply = resp.content

    st.session_state.history.append(("assistant", reply))

for role, text in st.session_state.history:
    with st.chat_message(role):
        st.write(text)

</syntaxhighlight>

===== <syntaxhighlight lang="txt">streamlit==1.39.0 =====
langchain==0.2.16
langchain-openai==0.1.22

</syntaxhighlight>

===== <syntaxhighlight lang="dockerfile">FROM python:3.11-slim =====

WORKDIR /app

=== System packages (optional but often useful) ===
RUN apt-get update && apt-get install -y --no-install-recommends \
    curl \
  && rm -rf /var/lib/apt/lists/*

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

=== Streamlit runs on 8501 by default ===
EXPOSE 8501

=== Streamlit in container must listen on 0.0.0.0 ===
CMD ["streamlit", "run", "app.py", "--server.address=0.0.0.0", "--server.port=8501"]

</syntaxhighlight>

==== ### ====

In AWS Console:
* EC2 → Launch instance
* Ubuntu 22.04
* Instance type: t3.small is fine for demo
* Create/select a key pair (PEM)
* Security group inbound rules: - SSH 22 from your IP (recommended) - Custom TCP 8501 from your IP (for Streamlit UI)

: 

===== From your laptop: =====

<syntaxhighlight lang="bash">ssh -i /path/to/key.pem ubuntu@EC2_PUBLIC_IP

</syntaxhighlight>

===== On EC2: =====

<syntaxhighlight lang="bash">sudo apt-get update
sudo apt-get install -y docker.io
sudo systemctl enable docker
sudo systemctl start docker
sudo usermod -aG docker ubuntu

</syntaxhighlight>

Log out and log back in so group membership applies:

<syntaxhighlight lang="bash">exit
ssh -i /path/to/key.pem ubuntu@EC2_PUBLIC_IP

</syntaxhighlight>

Check:

<syntaxhighlight lang="bash">docker --version
docker ps

</syntaxhighlight>

===== From your laptop, in the folder that contains ml-chatbot/: =====

<syntaxhighlight lang="bash">scp -i /path/to/key.pem -r ml-chatbot ubuntu@EC2_PUBLIC_IP:~

</syntaxhighlight>

On EC2:

<syntaxhighlight lang="bash">cd ~/ml-chatbot
ls

</syntaxhighlight>

===== <syntaxhighlight lang="bash">docker build -t ml-chatbot:latest . =====

</syntaxhighlight>

===== You must pass your API key as an environment variable: =====

<syntaxhighlight lang="bash">docker run -d \
  --name ml-chatbot \
  -p 8501:8501 \
  -e OPENAI_API_KEY="YOUR_KEY_HERE" \
  ml-chatbot:latest

</syntaxhighlight>

Check logs:

<syntaxhighlight lang="bash">docker logs -f ml-chatbot

</syntaxhighlight>

Now open in browser:

<syntaxhighlight>http://EC2_PUBLIC_IP:8501

</syntaxhighlight>

At this point, you have proven:
* your code works,
* Docker build works,
* the container exposes the correct port,
* the app is reachable.

That is the entire value of the “EC2 Docker” step: it removes complexity before ECS.

==== ECS cannot pull images from your EC2 disk. ECS pulls images from a registry—on AWS the standard is ECR. ====

So the real flow is:

Build image → Tag → Push to ECR → ECS Task Definition uses ECR image → ECS Service runs tasks

===== AWS Console: =====
* ECR → Repositories → Create repository
* Name: ml-chatbot

You will get a repository URI like:

<syntaxhighlight>123456789012.dkr.ecr.us-east-1.amazonaws.com/ml-chatbot

</syntaxhighlight>

===== If you do it on EC2: =====

<syntaxhighlight lang="bash">sudo apt-get install -y awscli
aws --version

</syntaxhighlight>

You need credentials. The cleanest approach:
* Attach an IAM role to the EC2 instance with permissions for ECR push (for learning/demo) OR
* Configure AWS CLI with access keys (aws configure) (less ideal long-term)

For beginner demo, attaching an EC2 IAM role is simplest:
* EC2 → Instance → Security → IAM role → Attach role
* Role permissions: ECR push/pull (AmazonEC2ContainerRegistryFullAccess) is the blunt option for learning.

===== On the machine that will push (EC2 or laptop): =====

<syntaxhighlight lang="bash">aws ecr get-login-password --region us-east-1 \
| docker login --username AWS --password-stdin 123456789012.dkr.ecr.us-east-1.amazonaws.com

</syntaxhighlight>

===== <syntaxhighlight lang="bash">docker tag ml-chatbot:latest 123456789012.dkr.ecr.us-east-1.amazonaws.com/ml-chatbot:latest =====

</syntaxhighlight>

===== <syntaxhighlight lang="bash">docker push 123456789012.dkr.ecr.us-east-1.amazonaws.com/ml-chatbot:latest =====

</syntaxhighlight>

Now the image is in ECR and ECS can pull it.

==== There are two ECS “compute” modes: ====
* ECS on EC2: you manage worker EC2 instances inside your cluster
* ECS on Fargate: AWS runs the worker machines for you (simpler)

For a beginner: use Fargate.

===== AWS Console: =====
* ECS → Clusters → Create cluster
* “Networking only (Fargate)” or equivalent
* Name: ml-chatbot-cluster

===== ECS → Task Definitions → Create =====
* Launch type: Fargate
* CPU/Memory: start small (e.g., 0.25 vCPU / 0.5GB or 1GB)
* Container: - Image URI: 123456789012.dkr.ecr.us-east-1.amazonaws.com/ml-chatbot:latest - Container port: 8501 - Environment variables: - OPENAI_API_KEY = your key (for demo)

Important security note: For real deployments, you should NOT store secrets in plain env vars in the task definition. Use AWS Secrets Manager. For learning, env var is acceptable.

===== In the cluster: =====
* Create Service
* Launch type: Fargate
* Task definition: select your task def
* Desired tasks: 1
* Networking: - VPC: default is fine for demo - Subnets: 2+ subnets recommended - Security group inbound: - allow port 8501 from your IP (or from ALB if you attach one)

===== If you expose Fargate tasks directly, you end up with awkward networking and changing IPs. The standard is: =====

ALB (public) → Target Group → ECS Service tasks

In the service creation wizard, enable Load Balancer:
* Create ALB
* Listener: HTTP 80 (or HTTPS 443 later)
* Target group: port 8501
* Health check path: / (Streamlit returns 200 on /)

Then you access:

<syntaxhighlight>http://ALB_DNS_NAME

</syntaxhighlight>

===== ECS → Tasks → select running task → Logs =====
If you configured CloudWatch logs in the task definition, you will see Streamlit output there.

==== ### ====

Because it isolates problems:
* If the container doesn’t start on EC2, it won’t start on ECS either.
* If port mapping is wrong on EC2, ECS health checks will fail.
* If dependencies are missing, Docker build will fail early.

EC2 is a controlled environment for debugging.

===== ECS gives you: =====
* “keep my service running” (service scheduler)
* easy scaling (desired count 1 → 5)
* load balancer integration
* centralized logs
* repeatable deployment

==== 1. Streamlit binds to localhost ====
* Must run with --server.address=0.0.0.0 inside container (done above)
# Security group missing port
* EC2 needs inbound 8501 (or ALB needs inbound 80/443)
# ECS task cannot pull image
* Fix: ensure image is in ECR and the task execution role has ECR pull permissions
# Hardcoding secrets
* For real use: Secrets Manager → injected into task at runtime
# No health checks / failing health checks
* Use ALB health check path /

==== Once you confirm the above works: ====
* Move OPENAI_API_KEY into Secrets Manager
* Attach IAM roles properly (least privilege)
* Add HTTPS via ACM certificate on the ALB
* Add WAF if this is internet-facing
* Add logging/monitoring (CloudWatch alarms)

If you tell me whether you want ECS Fargate or ECS on EC2 as the final target, I can give you a fully copy-paste set of AWS CLI commands (or Terraform-style steps) for that exact path.