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python-flask-docker-sklearn-template

A simple example of python api for real time machine learning. On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.
For more information read this post

requirements

docker installed

Run on docker - local

docker build . -t {some tag name} -f ./Dockerfile_local
detached : docker run -p 3000:5000 -d {some tag name}
interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}

Run on docker - production

Using uWSGI and nginx for production
docker build . -t {some tag name}
detached : docker run -p 3000:80 -d {some tag name}
interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}

Run on local computer

python -m venv env
source env/bin/activate
python -m pip install -r ./requirements.txt
python main.py

Use sample api

127.0.0.1:3000/isAlive
127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4

About

A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker

Resources

Readme
Activity

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0 stars

Watchers

1 watching

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0 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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BranchesTags
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python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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BranchesTags
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python-flask-docker-sklearn-template
A simple example of python api for real time machine learning.
On init, a simple linear regression model is created and saved on machine. On request arrival for prediction, the simple model is loaded and returning prediction.

For more information read this post
requirements
docker installed
Run on docker - local
docker build . -t {some tag name} -f ./Dockerfile_local

detached : docker run -p 3000:5000 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:5000 -it {some tag name}
Run on docker - production
Using uWSGI and nginx for production

docker build . -t {some tag name}

detached : docker run -p 3000:80 -d {some tag name}

interactive (recommended for debug): docker run -p 3000:80 -it {some tag name}
Run on local computer
python -m venv env

source env/bin/activate

python -m pip install -r ./requirements.txt

python main.py
Use sample api
127.0.0.1:3000/isAlive

127.0.0.1:3000/prediction/api/v1.0/some_prediction?f1=4&f2=4&f3=4
About
A simple example of python api for real time machine learning, using scikit-learn, Flask and Docker
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages