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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py

Repository files navigation

CS-Project

The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py

Overview of the Notebooks / Python files & Dataframes:

  1. Original Dataset Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design

About

The group project for the Computer Science course at the University of St. Gallen.

Resources

Readme
Activity

Stars

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1 watching

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0 forks
Report repository

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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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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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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('^' + ".*" + '

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42 Commits
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NameName
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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
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
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42 Commits
Folders and files
NameName
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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
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" + '

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42 Commits
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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
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('^' + ".*" + '

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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
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, '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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1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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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
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42 Commits
Folders and files
NameName
Last commit message
Last commit date
1_Notebook.ipynb
1_Notebook.ipynb
2 api.py
2 api.py
AppraiSet_complete_dataset.csv
AppraiSet_complete_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_testing_dataset.csv
AppraiSet_training_dataset.csv
AppraiSet_training_dataset.csv
Artiste_name(streamlitteam).py
Artiste_name(streamlitteam).py
README.md
README.md
StreamlitAPI_combined.py
StreamlitAPI_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
Streamlit_DataDiscovery_Prediction_combined.py
appfinal.py
appfinal.py
artist_name.ipynb
artist_name.ipynb
artsy_google_api.py
artsy_google_api.py
cleaned.csv
cleaned.csv
cleaned_tow.csv
cleaned_tow.csv
fiona_update.py
fiona_update.py
myapp.py
myapp.py
myappfinal.py
myappfinal.py
test_björn.py
test_björn.py
test_lorenzo.py
test_lorenzo.py
Repository files navigation
CS-Project
The group project for the Computer Science course at the University of St. Gallen. Our final product can be found in myappfinal.py
Overview of the Notebooks / Python files & Dataframes:
  1. Original Dataset
Source: https://data.mendeley.com/datasets/2nfvz8g27c/1
  • AppraiSet_complete_dataset.csv: the complete original dataset used for our data cleaning.
  • AppraiSet_testing_dataset.csv: the orginial testing dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  • AppraiSet_training_dataset.csv: the original training dataset, not actively used -> we wanted to clean the data ourselves to better fit our project needs
  1. Data Clearning and the Machine Learning Model
  • 1_Notebook.ipynb: majority of the data cleaning including material category
  • artist_name.ipynb: final data cleaning including arist_rank and the development of our ML model
  • Artiste_name(streamlitteam).py: integration of the machine learning model into streamlit
  • cleaned.csv: Our cleaned dataset used in final project
  • cleaned_tow.csv: Our cleaned dataset + column title of work
  1. First Attemps at Streamlit
  • myapp.py : first attempt at making a streamlit app, not used in final project
  • test_björn.py: testing streamlit functions, not used in final project
  • test_lorenzo.py: testing streamlit functions, not used in final project
  • fiona_update.py: improving sliders and filters with streamlit
  1. API Inclusion
  • StreamlitAPI_combined.py : not used in final project, first attempt at the integration of the two apis (artsy and google)
  • 2 api.py : combined google api and artsy api, artsy to search for artist info and google for image search
  • artsy_google_api.py : final version of the two apis, used in final project
  1. Creating a Final Product
  • Streamlit_DataDiscovery_Prediction_combined.py: First attempt at combining prediction and data discovery with ML model, and combined API
  • myappfinal.py: Our final streamlit product, including the Prediction, Data Discovery, API usage and Visualization pages plus the final design
About
The group project for the Computer Science course at the University of St. Gallen.
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages