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Simplify

Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.


Dataset Stats

ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100

Intent Classification

Comparison among Several Approachs for Intent Classification.

Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021

Custom-NER-using-Spacy

Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model

Get Started

Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder

Download entity recognition model folder and place it inside "./simplify/models" folder

Activate your environment

conda activate codify_env

Run Python Server

python server.py

Run React App

cd client/
npm start

Credits

About

Final Year Major Projct

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Contributors

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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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Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
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("// 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
Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
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
Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
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
Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
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
Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
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
Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Simplify
Simplify stands for a smart intelligent system that can code like a human being for a data science application. It enables data scientists to perform all the tedious and time-consuming tasks such as EDA (exploratory data analysis), data cleaning, data pre-processing, data visualization, modeling, and evaluation in the data-science life cycle, by only conveying the logic of the task in natural language (English query) and the system will automatically give out all the relevant python code snippets, or in other words the user just needs to type what they want in the form of a natural language query (English), and our system will automatically give out all the relevant code snippets in python for it.

Dataset Stats
ParameterStatistics
Total Number of Users Queries525
Total Number of Unique Intents20
Total Number of Unique Entity10
Total Number of Unique Python Code Snippets100
Intent Classification
Comparison among Several Approachs for Intent Classification.
Sr. NoMethodAccuracyPaperYear
1.Sum of Word Embedding (Citation Word Embedding)88.60%PaperJan 2021
3.Facebook InferSent87.34%PaperJul 2018
2.Semantic Subword Hashing78.48%PaperSep 2019
4.TF-IDF (Citation Word Embedding)74.68%PaperJan 2021
Custom-NER-using-Spacy
Custom Named Entity Recognition annotated using NER Annotated by tecoholic and Spacy for training the model
Get Started
Download glove embeddings folder and place it inside "./simplify/intent_word_emb" folder
Download entity recognition model folder and place it inside "./simplify/models" folder
Activate your environment
conda activate codify_env
Run Python Server
python server.py
Run React App
cd client/
npm start
Credits
About
Final Year Major Projct
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages