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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e 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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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

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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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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

About

No description, website, or topics provided.

Resources

Readme
Activity

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2 stars

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1 watching

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0 forks
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, '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 \u003e 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('^' + ".*" + '
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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

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, '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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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

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0 forks
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, '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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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

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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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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

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Mon1 - by Just4Fun

1. Introduction

"Mon1(stands for "LG1 Canteen Monitor")" is a real-time data-prediction-based food ordering service that balances the number of dine-in and mobile-phone-order customers with the aim of improving hygiene and customer experience during post-pandemic era.

2. Components

Our project consists of 4 parts:

  • Website: under /lg1-monitor folder, please refer to readme file for website. we've also deployed it on our AWS server, as you can see at http://mon1-j4f.site/ or http://54.89.142.90/.

    Assisted by our website, customers are encouraged to order online(via phone or computer) and take away food, and they are also able to monitor the current state of restaurant.(real-time customer no., future people density forecasting, kitchen monitoring, as well as personal order history)

  • People Detection Program: under /ObjectDetection/Image-Test folder, please refer to readme file for detection program.

    Through this program, we adopted an open source package, enabling us to detect how many people are there in a picture/video. By doing this, we made it possible to monitor real-time people density in canteen, and produce data for later use.

    We have already captured some photos of LG1 canteen to generate basic data to test both of our People Detection Program and People Density Forecasting Program(see below), which can be found under /ObjectDetection/CanteenImages folder, with original images as well as their results after detection.

  • People Density Forecasting Program: under /forecasting folder.

    This program is designed to forecast people density in certain future timestamps(every 20 minutes) based on past data, with help of our self-programmed forecasting algorithms. For example, if we would like to know the no. of people at 12pm next Friday, this program will analyze past fridays' data, i.e., no. of people at 12pm on previous Fridays, and then produce an accurate forecast.

  • Auto Send-Email Program: under /EmailTest folder, please refer to readme file for auto-email program

    This program is used to generate and send emails to customers automatically using a Python package. This allows us to send emails before peak time(normally lunchtime and dinnertime) and provides customers with useful information such as their order preference(based on his/her order history), and ask whether he/she would like to order via phone in advance. The emails also offer varities of coupons for customers, encouraging them to use Online Order/Canteen Monitor System that we've built.

3. Usage

Please refer to readme file for each program.

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