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tensorflask

This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.

Setup

  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.

Usage

Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:

curl http://localhost:8000?file=/home/pete/mypoodle.jpg

Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:

[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]

And you can see that the model is 99% sure that the image is a poodle.

License

Licensed under the Apache 2.0 license. See LICENSE file for details.

About

Simple example hosting TensorFlow model as Flask service

Resources

Readme
Apache-2.0 license

Security policy

Security policy
Activity
Custom properties

Stars

20 stars

Watchers

22 watching

Forks

15 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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" + '

Skip to content
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tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
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
tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
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
tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
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
tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
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
tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
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); }
})();
})();

Skip to content
BranchesTags
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tensorflask
This is a simple example of hosting a TensorFlow model as Flask service for inference. It provides the "Poodle, Pug or Weiner Dog?" image identification service using a retrained MobileNet model. The retrained model/labels are provided here to let you run the service locally.
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
Once you've started the service, you can query it on localhost:8000. You can either hit it via a web browser, or use curl from the commandline. It takes a single parameter file which specifies the full path to a local image, so for example:
curl http://localhost:8000?file=/home/pete/mypoodle.jpg
Will send the photo to the service, which will run the model and return JSON identifying the probabilties of each type of dog breed. In this case you'll get results like:
[
[
"poodle", "pug", "dachshund"
], [
0.9994891881942749, 1.1696176443365403e-05, 0.0004991634050384164
]
]
And you can see that the model is 99% sure that the image is a poodle.
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Security policy
Security policy
Activity
Custom properties
Stars
20 stars
Watchers
22 watching
Forks
15 forks
Report repository
Releases
Packages
Used by
Contributors
Languages