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Model Builder Generated Code for Web Apps

Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.

The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.

Proposed Flows

1. Web app

User has an existing web project and wants model and consumption to happen in their web app.

  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model. Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs

2. Web app via new web API

User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).

  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.

3. Web API

User has an existing Web API and wants model and consumption to happen here.

  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.

About

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3 stars

Watchers

4 watching

Forks

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" + '

Skip to content
BranchesTags
Repository files navigation
Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
3 stars
Watchers
4 watching
Forks
0 forks
Report repository
Releases
Packages
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
Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
3 stars
Watchers
4 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
Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
3 stars
Watchers
4 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
Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
3 stars
Watchers
4 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
Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
3 stars
Watchers
4 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('^' + ".*" + '

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Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
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, '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
Repository files navigation
Model Builder Generated Code for Web Apps
Currently, Model Builder only generates code for offline scenarios (e.g. console, desktop) which uses the non-scalable Prediction Engine.
The proposed feature is to detect the app type, and if it is web app or web api, Model Builder will generate different consumption code using the Prediction Engine Pool.
Proposed Flows
1. Web app
User has an existing web project and wants model and consumption to happen in their web app.
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis (Text classification) model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file.
  5. User is presented with links to docs to see example of how to implement / consume model in their web app. They also have the option to add a sample web app project which utilizes their model.
Ex: https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Startup.cs#L27, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml, https://github.com/briacht/MBGeneratedCode/blob/main/WebAppWithModelConsumption/Pages/Index.cshtml.cs
2. Web app via new web API
User has an existing web project and wants model and consumption to happen in a web API (which does not yet exist).
  1. User right-clicks on their web app project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web app project.
  5. After model evaluation, Model Builder asks the user if they would like to generate a new ASP.NET Core Web API for model consumption. If the user chooses this, then the Web API is added to their solution, and .mbconfig + code-behind (including the model) is moved to web API project.
3. Web API
User has an existing Web API and wants model and consumption to happen here.
  1. User right-clicks on their web API project in Solution Explorer and hits Add > New Item.
  2. In the Add New Item dialog, user selects Machine Learning Model (ML.NET) and names it SentimentModel.
  3. A new file is added to the project called "SentimentModel.mbconfig" and the Model Builder UI "wizard" opens in a new tool window.
  4. User goes through the steps for training a sentiment analysis model using Model Builder. After training, the model and consumption code are added as code-behind the SentimentModel.config file as part of the web API project.
  5. User is presented with links to docs to see example of how to implement / consume model in their web API. They also have the option to add a sample web API project which utilizes their model.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
3 stars
Watchers
4 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages