First small project to explain docker compose #19
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…vide clear guidance for users of all skill levels. The README includes: - Introduction to Ansible and its key concepts (inventory, modules, playbooks, roles, control/managed nodes) - Step-by-step installation instructions for Linux, macOS, and Windows (via WSL) - Basic usage examples and running ad-hoc commands - Writing and running a first playbook - Common use cases and troubleshooting tips - Links to official resources as the Ansible GitHub repository and official documentation. This README aims to provide a complete, professional reference that helps users to understand Ansible, making the project more accessible to beginners while also serving as a practical guide for more experienced users.
WalkthroughThis PR introduces a complete Docker Compose project with a Flask-based UI application and database service, alongside expanded Ansible documentation. The project includes containerized configurations, a Flask web application with SQLite backend, HTML templates, CSS styling, and a docker-compose.yaml orchestration file. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes
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✨ Finishing touches
🧪 Generate unit tests (beta)
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Actionable comments posted: 10
🧹 Nitpick comments (5)
tools/Docker/Docker-Compose/DB/Dockerfile (1)
1-7: Clarify “DB” as a data-only/volume container (and optionally pin the base image).Functionally this works as a data-only container for the shared
/datavolume, but it’s not actually running a database process. For learners, a short comment in the Dockerfile explaining that this service exists just to demonstrate a shared volume would help avoid confusion. Optionally, consider pinningalpineto a major/minor tag for reproducibility (e.g.alpine:3.20).tools/Docker/Docker-Compose/UI/Dockerfile (1)
1-13: Dockerfile is solid; consider small best‑practice tweaks.For a teaching example this is fine as‑is. If you want to align with common Docker best practices, you could:
- Add
EXPOSE 5000to make the intended port explicit.- Use
apt-get install -y --no-install-recommends sqlite3to keep the image slimmer.These are optional and don’t affect correctness.
tools/Docker/Docker-Compose/docker-compose.yaml (1)
1-23: Compose wiring is clear; consider briefly documenting the “DB” service role.The shared
dbdata:/datavolume betweenuianddbnicely demonstrates multi‑container volume sharing. Sincedbjust runssleep infinity, it’s effectively a data‑only container; a short note in README or a comment in this file explaining that this is intentionally not a real DB process would help learners understand the pattern (and that in real setups you’d either use a real DB service or just a named volume).tools/Docker/Docker-Compose/UI/templates/styles.css (1)
1-74: This stylesheet appears unused and duplicates the static CSS.
index.htmlonly links to{{ url_for('static', filename='styles.css') }}, so thistemplates/styles.cssfile is probably never served. Sincestatic/styles.csscontains the same styles, consider removing this file (or the static one) to avoid duplication and confusion.tools/Docker/Docker-Compose/UI/app.py (1)
13-68: Tighten DB resource handling and avoid broadexcept Exceptionwith raw error leaks.Functionally this works well, but you might want to tweak a couple of things:
- Use context managers so connections are always closed, even on errors:
def add_value(): try: data = request.get_json() or {} value = data.get("value") if not value: return jsonify({"error": "Missing value"}), 400 with sqlite3.connect(DB_PATH) as conn: c = conn.cursor() c.execute( "INSERT INTO values_table (value) VALUES (?)", (value,), ) conn.commit() return jsonify({"status": "added", "value": value}), 201 except sqlite3.Error as e: return jsonify({"error": "DB error"}), 500
- Similarly in
init_db()andlist_values(), preferwith sqlite3.connect(DB_PATH) as conn:and catch more specific exception types (e.g.sqlite3.Error) rather thanException, and avoid returningstr(e)directly to clients to limit internal detail leakage.These refinements improve robustness and model good practices for readers.
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📒 Files selected for processing (10)
tools/Ansible/README.md(1 hunks)tools/Docker/Docker-Compose/DB/Dockerfile(1 hunks)tools/Docker/Docker-Compose/README.md(1 hunks)tools/Docker/Docker-Compose/UI/Dockerfile(1 hunks)tools/Docker/Docker-Compose/UI/app.py(1 hunks)tools/Docker/Docker-Compose/UI/requirements.txt(1 hunks)tools/Docker/Docker-Compose/UI/static/styles.css(1 hunks)tools/Docker/Docker-Compose/UI/templates/index.html(1 hunks)tools/Docker/Docker-Compose/UI/templates/styles.css(1 hunks)tools/Docker/Docker-Compose/docker-compose.yaml(1 hunks)
🧰 Additional context used
📓 Path-based instructions (1)
tools/**/README.md
📄 CodeRabbit inference engine (.github/copilot-instructions.md)
tools/**/README.md: Tool README format must follow exact structure: # Tool Name, logo, Overview, Key Features, Getting Started, and Resources sections
Logo references in tool READMEs must use relative paths that match directory nesting level: ../logos/ for direct tool subdirectories, ../../logos/ for nested subdirectories
All tool README files must include Overview, Key Features, Getting Started, and Resources sections
Logo path resolution must account for nesting depth: tools/Category/Tool uses ../logos/, tools/Category/SubCategory/Tool uses ../../logos/
Files:
tools/Ansible/README.mdtools/Docker/Docker-Compose/README.md
🧠 Learnings (1)
📚 Learning: 2025-11-27T18:13:03.122Z
Learnt from: CR
Repo: nirgeier/awesome-devops PR: 0
File: .github/copilot-instructions.md:0-0
Timestamp: 2025-11-27T18:13:03.122Z
Learning: Applies to tools/**/README.md : All tool README files must include Overview, Key Features, Getting Started, and Resources sections
Applied to files:
tools/Ansible/README.md
🪛 ast-grep (0.40.0)
tools/Docker/Docker-Compose/UI/app.py
[warning] 70-70: Running flask app with host 0.0.0.0 could expose the server publicly.
Context: app.run(host="0.0.0.0", port=5000, debug=True)
Note: [CWE-668]: Exposure of Resource to Wrong Sphere [OWASP A01:2021]: Broken Access Control [REFERENCES]
https://owasp.org/Top10/A01_2021-Broken_Access_Control
(avoid_app_run_with_bad_host-python)
[warning] 70-70: Detected Flask app with debug=True. Do not deploy to production with this flag enabled as it will leak sensitive information. Instead, consider using Flask configuration variables or setting 'debug' using system environment variables.
Context: app.run(host="0.0.0.0", port=5000, debug=True)
Note: [CWE-489] Active Debug Code. [REFERENCES]
- https://labs.detectify.com/2015/10/02/how-patreon-got-hacked-publicly-exposed-werkzeug-debugger/
(debug-enabled-python)
🪛 markdownlint-cli2 (0.18.1)
tools/Ansible/README.md
15-15: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🪛 Ruff (0.14.6)
tools/Docker/Docker-Compose/UI/app.py
27-27: Do not catch blind exception: Exception
(BLE001)
51-51: Do not catch blind exception: Exception
(BLE001)
67-67: Do not catch blind exception: Exception
(BLE001)
71-71: Possible binding to all interfaces
(S104)
71-71: Use of debug=True in Flask app detected
(S201)
🔇 Additional comments (2)
tools/Docker/Docker-Compose/UI/requirements.txt (1)
1-1: Flask dependency looks good for this demo.Pinning Flask in a dedicated requirements file is fine here and lines up with the Dockerfile usage.
tools/Docker/Docker-Compose/UI/static/styles.css (1)
1-74: Styling matches the UI needs and is wired correctly.This stylesheet lines up with the structure in
index.htmland is correctly served via Flask’sstaticroute. No issues from a code perspective.
| # Ansible | ||
|
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|  | ||
| <img src="https://logo.svgcdn.com/logos/ansible.svg" alt="Ansible Logo" width="150" /> | ||
|
|
There was a problem hiding this comment.
Use relative logo path per coding guidelines.
The logo reference must use a relative path (../logos/ansible.svg) instead of an absolute CDN URL, as specified in the coding guidelines for tool READMEs.
Apply this diff:
-<img src="https://logo.svgcdn.com/logos/ansible.svg" alt="Ansible Logo" width="150" />
+<img src="../logos/ansible.svg" alt="Ansible Logo" width="150" />📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # Ansible | |
|  | |
| <img src="https://logo.svgcdn.com/logos/ansible.svg" alt="Ansible Logo" width="150" /> | |
| # Ansible | |
| <img src="../logos/ansible.svg" alt="Ansible Logo" width="150" /> | |
🤖 Prompt for AI Agents
In tools/Ansible/README.md around lines 1 to 4, the README uses an absolute CDN
URL for the Ansible logo; replace the img src value with the relative path
../logos/ansible.svg (keep alt and width attributes unchanged) so the logo
follows the project's coding guidelines for relative asset references.
| ## Why Ansible? | ||
|
|
||
| - Feature 1 | ||
| - Feature 2 | ||
| - Feature 3 | ||
| - Simple and human-readable | ||
| - Agentless | ||
| - Scalable from one machine to thousands | ||
| - Large module ecosystem | ||
| - Cloud provider integrations (AWS, Azure, GCP) |
There was a problem hiding this comment.
Rename section to match required structure.
Per the retrieved learnings and coding guidelines, this section should be named "Key Features" rather than "Why Ansible?" to maintain consistency across tool READMEs.
Apply this diff:
-## Why Ansible?
+## Key Features📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ## Why Ansible? | |
| - Feature 1 | |
| - Feature 2 | |
| - Feature 3 | |
| - Simple and human-readable | |
| - Agentless | |
| - Scalable from one machine to thousands | |
| - Large module ecosystem | |
| - Cloud provider integrations (AWS, Azure, GCP) | |
| ## Key Features | |
| - Simple and human-readable | |
| - Agentless | |
| - Scalable from one machine to thousands | |
| - Large module ecosystem | |
| - Cloud provider integrations (AWS, Azure, GCP) |
🧰 Tools
🪛 markdownlint-cli2 (0.18.1)
15-15: Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🤖 Prompt for AI Agents
In tools/Ansible/README.md around lines 13 to 19, the section header "Why
Ansible?" must be renamed to "Key Features" to match the repository's README
structure; edit the markdown header line so the title reads "## Key Features"
and keep the existing bullet list unchanged.
| ## Working with Inventory | ||
| inventory.ini | ||
|
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||
| ``` | ||
| [webservers] | ||
| 192.168.1.10 | ||
| 192.168.1.11 | ||
| [dbservers] | ||
| db01 ansible_host=192.168.1.20 ansible_user=root | ||
| ``` |
There was a problem hiding this comment.
Add language specification to code blocks.
Multiple code blocks are missing language specifications, violating MD040 linting rules. The inventory block at line 76 should specify ini as the language.
Apply this diff:
## Working with Inventory
inventory.ini
-```
+```ini
[webservers]
192.168.1.10
192.168.1.11🤖 Prompt for AI Agents
In tools/Ansible/README.md around lines 73 to 83, the inventory code block is
missing a language spec which violates MD040; update the fenced code block
starting at line ~76 to use an ini language tag (i.e., replace the opening ```
with ```ini) and ensure the inventory content remains the same and correctly
indented so the block becomes
```ini\n[webservers]\n192.168.1.10\n192.168.1.11\n\n[dbservers]\ndb01
ansible_host=192.168.1.20 ansible_user=root\n``` (only change is adding the ini
language identifier on the opening fence).
| YAML Inventory (optional) | ||
|
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| ``` | ||
| all: | ||
| hosts: | ||
| local: | ||
| ansible_connection: local | ||
| ``` |
There was a problem hiding this comment.
Specify language for YAML inventory block.
The YAML inventory code block at line 86 is missing the language tag.
Apply this diff:
YAML Inventory (optional)
-```
+```yaml
all:
hosts:
local:
ansible_connection: local🤖 Prompt for AI Agents
In tools/Ansible/README.md around lines 84 to 91 the fenced code block for the
YAML inventory is missing a language tag; update the opening fence from ``` to
```yaml so the block starts with ```yaml and leave the content unchanged to
enable proper syntax highlighting.
| ## Writing Your First Playbook | ||
|
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||
| ``` | ||
| --- | ||
| - name: Install NGINX on web servers | ||
| hosts: webservers | ||
| become: yes | ||
| tasks: | ||
| - name: Install nginx | ||
| apt: | ||
| name: nginx | ||
| state: present | ||
| update_cache: yes | ||
| - name: Ensure nginx is running | ||
| service: | ||
| name: nginx | ||
| state: started | ||
| enabled: yes | ||
| ``` |
There was a problem hiding this comment.
Specify language for Ansible playbook block.
The playbook code block at line 103 is missing the language tag. It should be yaml.
Apply this diff:
## Writing Your First Playbook
-```
+```yaml
---
- name: Install NGINX on web servers
hosts: webservers🤖 Prompt for AI Agents
In tools/Ansible/README.md around lines 101 to 120, the Ansible playbook code
block is missing a language tag; update the fenced code block opening to ```yaml
so the snippet is properly highlighted (replace the existing ``` with ```yaml
and keep the playbook content unchanged).
| # Docker UI + Database Project | ||
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|  | ||
| This project demonstrates a simple 2-container Docker setup: | ||
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| ## Overview | ||
| - **UI Container (Flask App)** | ||
| Receives values from a user and stores them in a database. | ||
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| Docker Compose is a tool for defining and running multi-container Docker applications using a YAML file (`docker-compose.yml`). It simplifies local development, testing, and simple deployments. | ||
| - **DB Container (Lightweight Alpine)** | ||
| Holds a persistent SQLite database file using a shared Docker volume. | ||
|
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||
| ## Key Features | ||
| --- |
There was a problem hiding this comment.
Title and logo missing: align with required README structure.
Per coding guidelines, tool READMEs must follow: # Tool Name, logo, Overview, Key Features, Getting Started, and Resources sections.
Line 1 should be # Docker Compose (the tool name), not # Docker UI + Database Project (a project example). Add a logo reference immediately after the title using the correct relative path for nesting level: ../logos/docker-compose.png (or appropriate logo file).
Apply this diff to fix the title and add a logo:
-# Docker UI + Database Project
+# Docker Compose
+
+
This project demonstrates a simple 2-container Docker setup:🤖 Prompt for AI Agents
In tools/Docker/Docker-Compose/README.md lines 1 to 11, the README title and
logo are incorrect for the required tool README structure: replace the current
title with "# Docker Compose", add a logo reference immediately after the title
using the relative path "../logos/docker-compose.png", and then reorder/expand
the content to include the required sections in this order: Overview, Key
Features, Getting Started, and Resources; ensure the logo line is a markdown
image reference placed directly below the title and keep the existing brief
project description under Overview while moving container details into Key
Features or Getting Started as appropriate.
| ## How It Works | ||
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| ### UI Container | ||
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| The UI is a Python Flask application that: | ||
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| 1. Initializes a SQLite database (`database.db`) on startup. | ||
| 2. Exposes two API routes: | ||
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| #### POST `/add` | ||
| Adds a new value to the DB. | ||
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| Example request: | ||
| ```bash | ||
| # Install (Linux/macOS with Docker Desktop) - or use Compose V2 as a plugin | ||
| docker compose version | ||
| curl -X POST http://localhost:5000/add \ | ||
| -H "Content-Type: application/json" \ | ||
| -d '{"value": "Hello World"}' | ||
| ``` | ||
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| #### GET `/list` | ||
| Returns all stored values. | ||
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| Example: | ||
| ```bash | ||
| curl http://localhost:5000/list | ||
| ``` | ||
|
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| --- | ||
|
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| ### DB Container | ||
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| This container does not run a database engine. | ||
| Instead, it: | ||
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| - Creates a `/data` directory | ||
| - Hosts the SQLite database file | ||
| - Exposes the folder through a **Docker volume**, making the data persistent | ||
| - Serves as a shared storage backend for the UI container | ||
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| This architecture keeps the DB separate and easily replaceable. | ||
|
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| --- | ||
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| ## Docker Compose Flow | ||
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| # Start services defined in docker-compose.yml | ||
| docker compose up -d | ||
| - A shared volume named **`dbdata`** is created. | ||
| - The **db** container exposes `/data`. | ||
| - The **ui** container mounts the same `/data` folder. | ||
| - SQLite file `database.db` lives inside that shared volume. | ||
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| # View logs | ||
| docker compose logs -f | ||
| --- |
There was a problem hiding this comment.
🛠️ Refactor suggestion | 🟠 Major
Restructure "How It Works" into Overview and Key Features sections.
The current sections do not align with the required README structure. Rename or restructure:
- Lines 36–76 should introduce an Overview section covering the project concept and architecture.
- Extract Key Features (e.g., multi-container orchestration, persistent storage via shared volumes, easy database substitution).
Suggested restructuring:
+## Overview
+
+This project demonstrates a practical 2-container Docker setup using Docker Compose. It combines a Flask-based UI container with a lightweight database container that uses a shared Docker volume for persistent SQLite storage. This architecture showcases multi-container workflows and makes the database layer easily replaceable.
+
+## Key Features
+
+- **Multi-container orchestration** with Docker Compose
+- **Persistent data storage** via shared Docker volumes
+- **Lightweight database container** using Alpine Linux
+- **REST API** for adding and retrieving values
+- **Easy database substitution** (e.g., swap SQLite for PostgreSQL)
+
-## How It Works
-
-### UI Container
+## Project StructureCommittable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
tools/Docker/Docker-Compose/README.md lines 36-86: restructure the "How It
Works" section into two sections named "Overview" and "Key Features"; under
"Overview" (replace lines ~36–76) provide a concise description of the project
concept and architecture (UI is a Flask app, DB container hosts the SQLite file
in /data, shared volume `dbdata`, UI mounts /data so database.db is persisted)
and under "Key Features" list short bullet points (multi-container
orchestration, persistent storage via shared Docker volume, easy DB
substitution, simple REST endpoints POST /add and GET /list) ensuring content is
reorganized (no functionality change) and headings/ordering follow README
structure.
| ## How to Run the Project From the browser: | ||
|
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| ### 1. Clone the project | ||
| ```bash | ||
| git clone <your-repository-url> | ||
| cd project | ||
| ``` | ||
|
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| ### 2. Build and run with Docker Compose | ||
| ```bash | ||
| docker compose up --build | ||
| ``` | ||
|
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| You will see: | ||
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| - `db_service` → starts and waits | ||
| - `ui_service` → starts Flask on port **5000** | ||
|
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| ### 3. Open the browser and run: | ||
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| ``` bash | ||
| http://localhost:5000 | ||
| ``` | ||
| #### In the UI itself: | ||
| - Enter a value in the input field and press "Add" button or press the Enter key. | ||
| - Values are displayed in a table below. | ||
| - The table automatically refreshes every 2 seconds to show all stored values. | ||
|
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| --- | ||
|
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| ## How to Run the Project from the CLI: | ||
|
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| ### 1. Clone the project | ||
| ```bash | ||
| git clone <your-repository-url> | ||
| cd project | ||
| ``` | ||
|
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| ### 2. Build and run with Docker Compose | ||
| ```bash | ||
| docker compose up --build | ||
| ``` | ||
|
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| You will see: | ||
|
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| - `db_service` → starts and waits | ||
| - `ui_service` → starts Flask on port **5000** | ||
|
|
||
| --- |
There was a problem hiding this comment.
🛠️ Refactor suggestion | 🟠 Major
Remove duplicated "How to Run" instructions; consolidate into single Getting Started section.
Lines 88–105 ("How to Run the Project From the browser") and lines 119–136 ("How to Run the Project from the CLI") repeat the same clone and build steps. Consolidate into one Getting Started section with both browser and CLI usage examples immediately following.
Suggested consolidation:
-## How to Run the Project From the browser:
+## Getting Started
### 1. Clone the project
```bash
@@ -104,24 +104,20 @@ git clone <your-repository-url>
-### 3. Open the browser and run:
-
-``` bash
-http://localhost:5000
-```
-#### In the UI itself:
+### 3. Using the Application
+
+#### Via Browser
+
+Open your browser and navigate to:
+```
+http://localhost:5000
+```
+
+In the UI:
- Enter a value in the input field and press "Add" button or press the Enter key.
- Values are displayed in a table below.
- The table automatically refreshes every 2 seconds to show all stored values.
----
-
-
-## How to Run the Project from the CLI:
-
-### 1. Clone the project
-```bash
-git clone <your-repository-url>
-cd project
-```
-
-### 2. Build and run with Docker Compose
-```bash
-docker compose up --build
-```
-
-You will see:
-
-- `db_service` → starts and waits
-- `ui_service` → starts Flask on port **5000**
-
----
+#### Via CLI
### 3. Test the API🤖 Prompt for AI Agents
tools/Docker/Docker-Compose/README.md lines 88-137: the README duplicates "How
to Run" instructions; remove the second block and consolidate into a single
"Getting Started" section that contains the clone/build steps once, then two
subsections: "Via Browser" (point to http://localhost:5000 and include the UI
usage notes about entering values and auto-refresh) and "Via CLI" (briefly
mention docker compose up --build and that db_service and ui_service start, with
Flask on port 5000); ensure numbering is consistent and remove the repeated
clone/build paragraphs so the content reads sequentially: clone → build → Using
the Application (Browser and CLI), then Test the API.
| if __name__ == "__main__": | ||
| app.run(host="0.0.0.0", port=5000, debug=True) |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
Flask debug=True host 0.0.0.0 security implications werkzeug debugger
💡 Result:
Short answer
- Running Flask with debug=True and host='0.0.0.0' makes the development server (and the Werkzeug interactive debugger) reachable from any host that can reach your machine — which is unsafe. [1][2]
What the risk is
- Detailed tracebacks can leak secrets (file paths, env vars, credentials). [1]
- The interactive debugger provides an in-browser Python console that can execute arbitrary code on the server. Although protected by a PIN, it is still a major attack vector and has had remote-exploit issues (e.g., CVE reports against the Werkzeug debugger). Exposing it on 0.0.0.0 lets remote attackers trigger that vector. [1][2]
Immediate mitigations
- Never run debug=True on a publicly reachable host or in production. Use host='127.0.0.1' (default) for local-only access. [1][3]
- If you must run with debug mode on a reachable network for development: disable the interactive evaluator with use_evalex=False (or fully disable the built-in debugger with use_debugger=False) and avoid the auto-reloader. Example: app.run(debug=True, host='127.0.0.1', use_evalex=False) or app.run(debug=True, use_debugger=False, use_reloader=False). [3][2]
Recommended production setup
- Use a production WSGI server (gunicorn/uwsgi) behind a reverse proxy (nginx) and run with debug=False. Don’t use Flask’s built-in run() for production. [3]
Sources
- Flask documentation — Debugging (interactive debugger warning). [1]
- CVE / security write-up on Werkzeug debugger remote-exec issues. [2]
- Flask API / run() docs (host, debug, use_evalex/use_debugger notes). [3]
🌐 Web query:
Flask deployment best practices debug mode security 2024 2025
💡 Result:
Short checklist (must-follow) for 2024–2025 Flask deployments
-
Never run Flask’s dev server / debugger in production — disable debug. Use FLASK_ENV=production or ensure app.debug/FLASK_DEBUG is False. The built‑in Werkzeug debugger can execute arbitrary Python and has had remote‑RCE advisories (e.g., CVE-2024-34069). [1][2]
-
Run behind a production WSGI server + reverse proxy:
- Use Gunicorn, uWSGI, or Waitress to serve the app. Don’t use app.run() in production.
- Put Nginx (or an equivalent) in front for TLS termination, static files, buffering and client limits. [3][4]
-
Secure configuration and secrets:
- Keep SECRET_KEY, DB credentials, API keys out of source control; load from environment or a secrets manager.
- Pin and regularly update dependencies (Werkzeug/Flask fixes are security‑critical). Monitor CVEs. [2][1]
-
TLS, headers, and cookies:
- Enforce HTTPS (HSTS), redirect HTTP→HTTPS.
- Set SESSION_COOKIE_SECURE=True, HttpOnly, SameSite and use secure cookie handling.
- Add security headers (CSP, X-Frame-Options, X-Content-Type-Options, Referrer-Policy). [3]
-
Protect against common web attacks:
- Enable CSRF protection (Flask-WTF or equivalent).
- Escape output (Jinja2 does by default); validate and sanitize inputs to avoid XSS/SQL injection.
- Use parameterized DB queries / ORM with care. [1][3]
-
Observability & failure handling:
- Structured logging (rotation), metrics, error reporting (Sentry/Datadog), health checks and alerts.
- Don’t expose stack traces in responses; capture them in logs only. [1][4]
-
Runtime hardening / infra:
- Run app processes as an unprivileged user, use process supervisor (systemd/PM2), limit resource usage, and employ a firewall.
- Use connection pooling, rate limiting (flask-limiter), and Web Application Firewall (WAF) where appropriate. [3][4]
-
CI/CD, testing & deployment patterns:
- Automated tests, dependency scanning, container image scanning.
- Use staging, blue/green or canary deploys and rollout/rollback procedures. [3]
-
Least‑privilege and secrets rotation:
- Use short‑lived credentials or cloud secret managers; rotate keys regularly. Log and audit access. [3]
-
Practical runtime flags (examples):
- Start Gunicorn: gunicorn --workers N --bind 127.0.0.1:8000 myapp:app
- Nginx reverse proxy to localhost socket or port; terminate TLS at Nginx. [3][4]
References
- Flask docs — Debugging & production warning. [1]
- CVE‑2024‑34069 / Werkzeug debugger remote‑RCE advisory (fixes in Werkzeug releases). [2]
- Deployment patterns: WSGI servers + Nginx, TLS, env‑based config, secrets & CI/CD recommendations. [3][4]
If you want, I can:
- produce a minimal secure systemd + Gunicorn + Nginx example for your app, or
- scan a requirements.txt for known vulnerable packages. Which would you prefer?
Don't ship with debug=True bound to 0.0.0.0.
Combining debug mode with 0.0.0.0 exposes the Werkzeug interactive debugger—which allows arbitrary Python code execution—to any host that can reach port 5000. Even with PIN protection, the debugger has had remote code execution vulnerabilities (CVE-2024-34069). This is unsuitable even for examples.
Use an environment variable to control debug mode:
if __name__ == "__main__":
debug = os.getenv("FLASK_DEBUG", "0") == "1"
app.run(host="0.0.0.0", port=5000, debug=debug)Default to debug=False. If local development requires debug mode, use host="127.0.0.1" instead of 0.0.0.0, or disable the interactive evaluator with use_evalex=False. For production, never use Flask's development server; deploy behind a production WSGI server (Gunicorn/uWSGI) and reverse proxy (Nginx).
🧰 Tools
🪛 ast-grep (0.40.0)
[warning] 70-70: Running flask app with host 0.0.0.0 could expose the server publicly.
Context: app.run(host="0.0.0.0", port=5000, debug=True)
Note: [CWE-668]: Exposure of Resource to Wrong Sphere [OWASP A01:2021]: Broken Access Control [REFERENCES]
https://owasp.org/Top10/A01_2021-Broken_Access_Control
(avoid_app_run_with_bad_host-python)
[warning] 70-70: Detected Flask app with debug=True. Do not deploy to production with this flag enabled as it will leak sensitive information. Instead, consider using Flask configuration variables or setting 'debug' using system environment variables.
Context: app.run(host="0.0.0.0", port=5000, debug=True)
Note: [CWE-489] Active Debug Code. [REFERENCES]
- https://labs.detectify.com/2015/10/02/how-patreon-got-hacked-publicly-exposed-werkzeug-debugger/
(debug-enabled-python)
🪛 Ruff (0.14.6)
71-71: Possible binding to all interfaces
(S104)
71-71: Use of debug=True in Flask app detected
(S201)
🤖 Prompt for AI Agents
In tools/Docker/Docker-Compose/UI/app.py around lines 70-71, the app is started
with debug=True bound to 0.0.0.0 which exposes the interactive debugger; change
startup to read an environment variable (e.g. FLASK_DEBUG) and default to False,
ensure os is imported if missing, and only enable debug mode when
FLASK_DEBUG=="1"; when debug is enabled bind to 127.0.0.1 (or set
use_evalex=False) instead of 0.0.0.0, and document that the Flask dev server
must not be used in production (use a WSGI server + reverse proxy).
| <!DOCTYPE html> | ||
| <html lang="en"> | ||
| <head> | ||
| <meta charset="UTF-8"> | ||
| <title>Flask UI + DB</title> | ||
| <link rel="stylesheet" href="{{ url_for('static', filename='styles.css') }}"> | ||
| </head> | ||
| <body> | ||
| <div class="container"> | ||
| <h1>Flask UI + Database</h1> | ||
|
|
||
| <div class="input-group"> | ||
| <input type="text" id="valueInput" placeholder="Enter value"> | ||
| <button onclick="addValue()">Add</button> | ||
| </div> | ||
|
|
||
| <h2>Stored Values</h2> | ||
| <table id="valuesTable"> | ||
| <tr> | ||
| <th>ID</th> | ||
| <th>Value</th> | ||
| <th>Timestamp</th> | ||
| </tr> | ||
| </table> | ||
| </div> | ||
|
|
||
| <script> | ||
| async function fetchValues() { | ||
| const res = await fetch('/list'); | ||
| const data = await res.json(); | ||
| const table = document.getElementById('valuesTable'); | ||
|
|
||
| // Clear existing rows except header | ||
| table.innerHTML = '<tr><th>ID</th><th>Value</th><th>Timestamp</th></tr>'; | ||
|
|
||
| data.forEach(row => { | ||
| const tr = document.createElement('tr'); | ||
| tr.innerHTML = `<td>${row.id}</td><td>${row.value}</td><td>${row.timestamp}</td>`; | ||
| table.appendChild(tr); | ||
| }); | ||
| } | ||
|
|
||
| async function addValue() { | ||
| const input = document.getElementById('valueInput'); | ||
| const value = input.value.trim(); | ||
| if (!value) return alert('Please enter a value.'); | ||
|
|
||
| await fetch('/add', { | ||
| method: 'POST', | ||
| headers: { 'Content-Type': 'application/json' }, | ||
| body: JSON.stringify({ value }) | ||
| }); | ||
|
|
||
| input.value = ''; | ||
| fetchValues(); | ||
| } | ||
|
|
||
| // Press Enter to add value | ||
| document.getElementById('valueInput').addEventListener('keypress', function(e) { | ||
| if (e.key === 'Enter') { | ||
| addValue(); | ||
| } | ||
| }); | ||
|
|
||
| // Fetch values every 2 seconds | ||
| setInterval(fetchValues, 2000); | ||
| fetchValues(); | ||
| </script> | ||
| </body> | ||
| </html> |
There was a problem hiding this comment.
Avoid using innerHTML with unescaped user content (XSS risk).
row.value comes from user input and is rendered via:
tr.innerHTML = `<td>${row.id}</td><td>${row.value}</td><td>${row.timestamp}</td>`;This allows someone to store HTML/JS (e.g. <script>...</script>) that will then execute in other users’ browsers. Even for a demo, it’s worth showing a safer pattern:
const tr = document.createElement('tr');
const idTd = document.createElement('td');
idTd.textContent = row.id;
const valueTd = document.createElement('td');
valueTd.textContent = row.value;
const tsTd = document.createElement('td');
tsTd.textContent = row.timestamp;
tr.append(idTd, valueTd, tsTd);
table.appendChild(tr);This keeps user data as text, preventing script execution.
🤖 Prompt for AI Agents
tools/Docker/Docker-Compose/UI/templates/index.html lines 1-70: the code uses
tr.innerHTML with row values which allows unescaped user input (XSS); replace
building rows via innerHTML with DOM methods that set textContent for each cell.
Specifically, in fetchValues() clear the table body but keep the header, then
for each row create a tr element, create three td elements, set their
textContent to row.id, row.value and row.timestamp respectively, append the tds
to the tr and append the tr to the table; remove any other innerHTML usage that
injects user data directly.
1 participant
This PR introduces a simple 2-container Docker setup for a Flask-based UI and a persistent SQLite database, demonstrating multi-container orchestration with Docker Compose.
Key Features
With this example, I bring this knowledge:
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