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10,000 | https://devpost.com/software/crazy-cows-game | P1 Cow Sprite
P2 Cow Sprite
Inspiration
Games like Super Smash Bros Brawl and Brawlhallla inspired us.
How we built it
We build it using Unity and C#.
Challenges we ran into
One of the challenges we faced was implementing 2 player interactions.
Accomplishments that we're proud of
We're proud that we created a game that is able to allow its players to bond in an immersive gaming experience.
What we learned
We learned that collaboration is extremely important.
What's next for Crazy Cows Game
We will further develop this game after Bay Hacks by adding extra features to improve our game.
Built With
c#
unity
Try it out
github.com |
10,000 | https://devpost.com/software/covidtrade-1vkgez | COVIDPRO19 is an app that combats the corona virus by enabling hospitals to request members of the community for much needed medical supplies. Many people are unsure on how to help. People who 3-d print face protection and make masks at home can effortlessly contribute towards combating corona virus through the use of this app. Hospitals can simply update their needs in the app when they require much-needed medical supplies. With the CORONAPRO app, another life will not be lost unnecessarily.
Built With
sketch
swift |
10,000 | https://devpost.com/software/remote-patient-monitoring-system-zjfnc5 | Block diagram of remote patient monitoring system
Problem Statement
A potential challenge during the pandemic outbreak like COVID19 is overwhelmed hospitals. At present, the hospitals don’t have the capacity for large number of incoming patients. There is a need for a technology platform which is capable of remote-monitoring and allowing for the engagement of patients in their homes. The capabilities also facilitate communication between quarantined people and the healthcare service and maintain visibility of those recently discharged.
Proposed Solution
Remote Patient Monitoring (RPM) platform offers an ideal way to monitor patients while they are in quarantine or at home. The platform offers an end-to-end solution all the way from devices to central command center dashboard and analytics along with managed services.
The device hub include those that measure vital body temperature, heart rate, blood pressure, SPO2 level in the blood. If the patient clinical situation deteriorates, the system, supported by health-workers oversight, can respond rapidly through central command center and decide if they need more intensive levels of medical care. At the same time, providers are at no risk of exposure to COVID-19 when they manage a patient remotely. It is highly important to preserve a healthy clinician workforce at any time and especially during a health crisis like COVID-19.
During this public health emergency, it is imperative that the Government and healthcare system adapt as the situation warrants to act upon measures to save lives.
Implementation
If small Patient Monitoring could be connected to a network wirelessly, patients would be able to move around freely while their physiological signals are monitored. Thus, medical personnel could be informed about a patient’s critical condition regardless of their whereabouts and they could be treated promptly if an emergency occurs. Furthermore, portable devices can be integrated into the Healthcare environment and used to develop novel applications. Thus, we will develop a portable embedded device that can monitor the condition of patients in real time using biomedical sensor network such as sensor is pulse oximeter (SPO2), thermometer, respiration, blood pressure (BP), and provide various physiological signals via wireless communication so that the physiological signals may be monitored remotely Based on the graphic display (android Smartphone) and web, using Web Server and Database subsystem we can take physiological signals data any where in the word at any time and this device detect emergencies and inform medical personnel when they occur. Thus, medical personnel could be informed about a patient’s critical condition regardless of their whereabouts and they could be treated promptly if an emergency occur
Expected Result
RPM will help to ease of access to patient data. It will help to deliver high quality care to the more patients. It will help to exposure to healthcare staff. RPM is a patient monitoring system. It collects the physiological signals of patients using biomedical sensors network and processes them so they can be interpreted easily by medical personnel. Unlike conventional patient monitoring, RPM is small, portable, battery operated, and based on wireless communication. Thus, our device could be useful in a Healthcare environment where the remote monitoring of patients is essential.
Action Plan after the Hackathon
After the Hackathon, we will develop the idea further and standardize decisions regarding supply and distribution with the platform.
With adequate funding, we will launch this product within 5-6 weeks after the hackathon
Built With
api
iot
web
Try it out
github.com
he-s3.s3.amazonaws.com |
10,000 | https://devpost.com/software/drug-and-food-delivery-robot-for-covid19-patients | DRUG AND FOOD DELIVERY ROBOT FOR COVID19 PATIENTS
We Break the wall of Infecting from Corona we are Built a smart UGV (Unmanned ground vehicle) for Monitoring patients and provide a Food and Medicine to the corona patients. our product prevent people from direct contact to corona patients. And it has a UVC light to kill the Corona virus and infection and Automatic Santizer Dispenser.
The drive subsystem has two autonomously controlled drive wheels with the common axis centered on the robot. A spring suspension ensures that the drive wheels remain in contact with the floor even if it is rough or bumpy and four corner casters offer stability.
The HelpMate is provided with structured and ultrasound light range sensing. This sensing continuously offers a set of range values. These sensors are used for the detection of obstacles and identification of walls helping to determine the orientation and location of the robot.
Ultrasound sensors are also provided at the sides of the robot to offer sufficient information about an obstacle and help in avoiding the same.
Touch sensitive bumpers are also provided at the front and back of HelpMate that may be undetected by the range sensors. An LCD and a keypad act as the user interface. Turn signals and warning lights appear when the HelpMate robot not on the right path.
The HelpMate is provided with a locked backpack for carrying meal trays and lab supplies. An offline CAD system tailored for HelpMate applications generates topographical and geometrical information about the elevator lobbies, hospital hallways, elevators and stations.
Advantages of Automated Delivery Robots
The key benefits of automated delivery robots in hospitals are:
• Medicines are delivered with a higher accountability via an integrated chain of custody systems
• Automated delivery enables pharmacy technicians to focus on performing high-value tasks such as mixing IVs without committing any mistake.
• Delivery of medicines can be more frequent and nurses can concentrate on caring for patients rather than worry about missing medicines and supplies.
• Automated delivery brings down costs and improves on-time reliability.
• Waste can be collected more frequently, improving control of infection and maintaining a neat appearance in the facility.
• Automated waste transport brings down the risk of injuries from the transport of heavy loads.
• Lab test items can be delivered immediately, hence speeding the testing process.
• Some of these systems have call functionality to deliver to departments behind locked doors.
• Accurate tracking of high-priced equipment and supplies ensure that the number of lost items is decreased.
How it Works
The robot receives instructions through a human–machine interface. An installed knowledge base helps the robot to maneuver around the hospital. The robot is equipped to climb stairs, detect obstacles, move in lifts and call for opening or shutting doors.
The robot is loaded with supplies, given specific instructions through the human–machine interface and then it goes to its destination. It offloads the supplies, takes back anything else and moves on.
Built With
hardware
robot
robotalker
uptime-robot
Try it out
smartbackyardcreators.blogspot.com |
10,004 | https://devpost.com/software/filter-point | Impact Networking manages 594 Meraki networks across the nation. Impact has begun to include Umbrella with many of its clients to augment the protection for our roaming end points when those clients are not in the office. One of our field network engineers reached out and asked if there was a way to copy the Filtered Categories, Whitelists, and Blacklists from Meraki to Umbrella. We realized a method to do this did not exist. Shortly after we heard about the Meraki Hackathon and knew exactly what we wanted to work on!
The team was assembled between our Enterprise Development, Managed IT Operations, and Managed Security to make this idea into a reality. The first question was what we are coding this on, with contributors from different backgrounds and different experience this was not a simple question. The network teams were more accustomed to hacking away with Powershell, CURL, PHP and Python, while our enterprise development team was experienced in .Net and low code platforms such as Mendix. After outlining the requirements and the timeframe we opted to go with Mendix!
Built With
css
html
java
javascript
mendix
meraki-api
react |
10,004 | https://devpost.com/software/rapid-retail-android-pandemic-incident-disruptor | API Call
Queue Dashboard
Inspiration
COVID-19. Redefining "normal". Keeping retail space safe for customers and employees.
What it does
COVID-19 has changed everything and we, as human beings, are in the process of redefining "normal". The retail space is one area that needs to rethink the way they operate to ensure the safety of their employees and customers. Team 2 at NTT has developed an idea/application to assist with that. Using Meraki MV cameras and sense API, our clients are able to keep an accurate count of people entering and exiting their store/location. The cameras do the counting while our application feeds the data to a web UI showing the stores capacity, as well as number of people in the store at any given time. Our application also ties in with WebEx Teams to notify employees of thresholds such as reaching capacity, at capacity, or over capacity. This will allow employees to move towards an entrance if capacity is reaching 100% and close doors if capacity is reached. If customers would prefer not to wait outside the entrance we have added a feature where they can scan a QR code and put themselves into the queue.
How we built it
Split the work up as best we could. Abha tackled the MV sense API using Python. Luciano took on the web UI using node.js. Will also used node.js to integrate with WebEx Teams to get notifications. James and myself helped out where we could be as the judges will be able to tell, we have the least amount of coding background in the group.
Challenges we ran into
Two challenges worth noting. First was getting the sense API to work in our environment and second was fixing bugs on our bot when sending out notifications.
Accomplishments that we're proud of
Proud of our global team getting together on such a tight timeline and getting this accomplished
What we learned
API are the future!
What's next for RAPID - Retail Android Pandemic Incident Disruptor
Continue to develop the idea!
Built With
css3
html5
node.js
python
Try it out
c3rb.glitch.me
c3rb.glitch.me |
10,004 | https://devpost.com/software/quickpickup | Operation View 1
Dashboard only view
Operation View 2
Sequence diagram
Inspiration
Hospitality challenges with the current COVID-19 situation, people are waiting for long time for order pickups.
What it does
Contact less order pickup process for restaurant patrons to eliminate the need for phone calls and IVR
How we built it
We used/integrated below mentioned technologies to build our app:
Platform:
Azure Cloud
Docker containers
API:
Meraki MVSense
Meraki Wireless
Cisco Webex Teams
Purple AI
Programming Langauges:
Python
Reactjs
Type-Script
Databases
mongodb
Challenges we ran into
Cross time zones, development working together in collaboration and partner programming
Learning the styles within the team
Accomplishments that We are proud of
Able to work with geographically dispersed team to finish the project in a fast pace manner
Learning the technologies such as Cisco APIs, Purple AI
What we learned
DevNet SDKs
WebEx Teams SDK
MV Sense SDK
APIs with Purple
WebEx Teams for meeting spaces
What's next for QuickPickUp
Diversify for other industries such as Retailers
Improve the security features
Automate deployment for quick spin-up
Built With
azurecloud
docker
merakiwifi
mongodb
mvsenseapi
purpleai
python
react
typescript
webexteamapi
Try it out
40.80.151.67
github.com |
10,004 | https://devpost.com/software/citizen-care-pod | The Insight Connected Platform app can be used on a variety of devices.
A prototype of one of the Citizen Card Pods being built.
A Citizen Care Pod inside the Connected Platform App with a list of hardware deployed in that pod and its status.
A camera reading from one of the Cisco Meraki cameras detecting crowds.
Alerts from that camera notifying a user that the line is getting long at the Citizen Care Pod.
WebEx Teams integration showing a real-time chat about the alert with another employee.
Inspiration
How do we help reopen offices, airports, stadiums, parks, and all public spaces, so people can get back to work (or, more importantly, fun) while also feeling safe? A robust disease prevention strategy is critical to helping create a more protective environment for people and ensuring business continuity.
Insight's Connected Platform can help rapidly evaluate, deploy, test, and manage new technology across our ecosystem of 3,500 partner products and solutions (including Cisco Meraki devices.)
For this hackathon, we focused specifically on Citizen Care Pods, which are portable virus testing centers that can be deployed nearly anywhere, which can be used to aid in detection and screening when you have a large group of people (think construction site, retail or entertainment venue).
What it does
The Citizen Care Pod is outfitted with a variety of Cisco Meraki gear (cameras, SD-WAN, and access points for connectivity) to collect and send data to the Connected Platform so that customers can analyze and be more responsive to real-time data reports.
How we built it
The pods themselves are built with the partnership of a construction company using shipping containers (see prototype image).
The Connected Platform app is built in partnership with Insight's CDCT and Digital Innovation teams. It leverages the Cisco Meraki API, the MV Sense API, and the Webex Teams widget as well an Angular for the front end. It pulls together the data sent from the devices in the pod into a real-time dashboard with insights, alerts, and sensor readings.
Challenges we ran into
We ran into challenges trying to get the Guest Access integration for Webex Teams, allowing people that weren't part of an organization to interact via Teams. We had to use a standard integration allowing only people previously in the organization.
We also hit the limitation with the MV Sense API that only allows still images, not a feed.
Accomplishments that we're proud of
We're proud of getting the Meraki APIs and what we could do with Webex Teams integrated into our Connected Platform in a short amount of time.
What we learned
The Meraki platform is agile and can be integrated into solutions much quicker and offers better insights than other networking providers in this space. The design and API access is accessible for networking novices, or even non-network people such as software developers.
The MV Sense API exposes a lot of potential use for social policy enforcement as well as the health and safety of customers and employees.
What's next for Citizen Care Pod
This is a real go-to-market solution for Insight to take Meraki to our customers. The next phase is to build and deploy these for our customers in our communities in Cisco's fiscal year.
We will use Webex Teams Guest Access so that a person doesn't have to be a member of an organization to interact with someone via Teams. Potential leveraging of DNA spaces for guest access providing customizable captive portals for guest onboarding and location analytics.
Looking at Enterprise Wireless APs and evaluating APIs for Cisco Access Points.
Integrate with DUO for multi-factor authentication.
Built With
angular.js
api
cisco-webex
meraki
mr
mv
mx
sd-wan
webex
Try it out
cisco-meraki-278014.ue.r.appspot.com |
10,004 | https://devpost.com/software/norris | Norris
Inspiration
Malware and Ransomware are becoming all too common. Left unchecked, these threats can devastate corporate networks like what happened to Maersk when the NotPetya attack was unleashed against Ukraine.
While tools like Stealthwatch can help identify which systems have been compromised, a gap exists between identifying the affected clients and taking swift action to prevent a further spread.
What it does
Visualizes the location and threat assessment of wired and wireless clients. Enables single button quarantine of malicious clients using APIs to automate changing vlans and firewall rules to isolate these devices from the rest of the network, until a technician can be dispatched to resolve the situation.
How I built it
We built a containerized application stack with a React front-end and Express back-end. In the front-end, we leveraged Google Maps and placed overlays for the building floor plan and positioned network clients, which we color-coded and provided "quarantine" and "release" actions for suspicious devices. In the back-end, we created collectors that fetch Meraki API data about network clients and firewall rules, listen to the Meraki Scanning API for device locations, and interrogate StealthWatch for the devices which are behaving suspiciously. We then correlated this data to tag the devices with an appropriate risk level in the user interface. When a user "quarantines" a device in the front-end, the back-end uses the Meraki API to apply firewall rules which isolate the device.
Challenges I ran into
The single biggest challenge in trying to create the software was the lack of data in the lab environment. We were quite surprised to find that the lab networks had no clients of any kind, nor was a suitable StealthWatch instance available. We were able to use our in-house CMNA stack to provide the Meraki data. And thanks to
WWT's ATC Lab
, we were able to secure a StealthWatch instance, a significant perk of being part of the WWT ecosystem.
Accomplishments that I'm proud of
The team has thought about this concept for a bit, and, it was fun and satisfying to see a working solution in a matter of a couple days.
What I learned
Stealthwatch works very easily with our Meraki network and provides data we otherwise wouldn't have regarding traffic on the network.
What's next for NORRIS
The Thelios team at WWT builds a product for provisioning and monitoring many Meraki networks. NORRIS is an additional monitoring feature that could be introduced in the near future.
Disclaimer
Chuck Norris has no affiliation nor endorsement with Thelios, WWT, or NORRIS. Chuck, if you're reading this, please don't roundhouse us, we're just big fans. 🙏
Built With
docker
google-maps
meraki
node.js
postgresql
react
recoil
rxjs
stealthwatch
timescaledb |
10,004 | https://devpost.com/software/automated-healthcare-network-commissioning-app | Automated Network Deployment & Support Healthcare App on web browser
Simply enter in the Hospital ID and barcode in the Meraki serial numbers, then there is simply only one button to press
The Meraki Dashboard now shows that the Field Hospital London has been created.
At the same time as the new Meraki Network being provisioned a WebEx Teams space is automatically created for that specific Meraki Network
Various parameters from the spreadsheet are configured on the Meraki MX, MS and MR
Network Tags assigned based on data from spreadsheet.
Meraki MS Switch profiles auto bound to switchports to ensure consistency.
Meraki equipment hostname and addresses are auto configured based on the data from the spreadsheet.
Inspiration
Inspired by recent world events, and in particular the NHS Nightingale Hospital requirements within the UK, CAE has identified a requirement within the healthcare sector to be able to rapidly deploy IT network infrastructure. This infrastructure is required at various locations including pop-up hospitals, COVID-19 testing sites and community outreach support centres that are rapidly provisioned to support shielding of vulnerable people and increase care capacity.
Due to the critical and time sensitive nature of work within the healthcare industry any opportunity to minimise the complexity and time to commission new infrastructure is essential, therefore we believe leveraging the integration available within the Meraki platform can deliver this goal.
What it does
Using the Meraki Dashboard API, the CAE application is a single step Meraki deployment tool which enables the healthcare industry to setup an entire suite of Meraki solutions using a single step action.
As demonstrated in the video submission all a user of the application has to do is simply enter the hospital ID number and barcode in the serial numbers of the Meraki devices (MX/MR/MS) that will be going to site, all of the configuration is then completed automatically in the background by pulling the data variables from a user friendly spreadsheet. This includes the automatic configuration of hostnames, network tags, IP schemas, VLAN assignment, switchport assignment and much more, resulting in saving hours of configuration time and money.
Furthermore, as part of the single step action the application integrates into the WebEx Teams API to automatically create a new Space specifically for that newly created Meraki network on the Dashboard and adds in the on-demand CAE NOC (Network Operations Centre) support group.
This on-demand NOC support group combines a Dialogflow bot, to automate addressing several common deployment issues, with a 24/7 support presence to quickly help support the newly created network and the physical deployment / go-live. This additional support presence is not the standard first line support, instead it consists of a real-time SLA with a dedicated team of specialised Meraki engineering resources at CAE helping to get networks online rapidly and with more agility.
The advantages of this CAE created application is as follows:
• Risk mitigation – through the rapid and consistent deployment of infrastructure leveraging automation and the underlying APIs. In the context of the pandemic, this is absolutely critical as resources can be up and running rapidly with full confidence in their capabilities allowing patients to be treated faster;
• Significantly reduced network deployment times – achieved through automation and integration facilitating the decrease of the touch points from factory to field engineer. As a result, care facilities can be stood up in a shorter timeframe which benefits the primary aim of the facilities;
• Reduced (CapEx) deployment costs – through the removal of the need to prestage hardware and send highly qualified networking-orientated resource to deploy. This allows a right-sized professional services effort to be leveraged, including the use of non-IT skilled resources, widening the pool of available resources which can be used. Ultimately, this also ensures that the available budget for healthcare can be used optimally and with more focus on the care of patients;
• User friendly and simplified interface – which heavily reduces human input errors allowing equipment to be provisioned quicker. In addition to these characteristics, integral support for a handheld barcode scanner allows deployment engineers to scan and register Meraki devices directly into the application with minimal effort;
• Configuration standardisation and efficient updates – which is critically important throughout a large scale
install base and allows confidence in the capabilities of the underlying environment. This is achieved through integration and auto binding of configuration templates, which in turn will assist in future “change at scale” configuration updates;
• Targeted support – allowing on-site deployment resources to access the on-demand NOC group automatically created as part of the deployment. This facilitates faster and more personalised responses coupled with a reduced time to resolution for issues faced whilst deploying the Meraki solution.
How I built it
We utilised the Meraki Dashboard API to automate the process of creating a new network and deploying Meraki infrastructure across this. The application has been built using ASP.net framework. Using RestSharp we built a HTTP client library from which we could then perform REST based API calls and also deserialise REST responses. This logic allows for the automated deployment so long as the hospital/site number and Meraki device serials are inputted. The application itself has been hosted on a Microsoft IIS webserver.
The same framework has been used to integrate with the WebEx Teams API allowing automatic room creation and for members to be dynamically assigned. These WebEx Teams spaces allow field engineering teams or none technical staff to have an immediate dedicated network support team available to them. Members of these WebEx Teams are members of the NOC as well as Chatbots created using Dialogflow API. An extensive knowledgebase of Meraki issues and troubleshooting steps was put together based on CAE’s networking and support experience. Dialogflow offers integration to this knowledgebase allowing for a whole suite of issues to be troubleshooted via chat automatically. The human members of this group offer additional support to primarily assist with any additional issues and can also feedback repeat issues/question into the existing knowledgebase for future automation.
Challenges I ran into
Our team’s skillset is primarily in network support. As such, the main challenge we faced was being presented with an entirely new challenge and area of knowledge we had to upskill in. We utilised existing documentation, guides, and resources in order to produce our application.
When building the Dialogflow chatbot integration a substantial amount of time was required to populate the knowledge base for these bots to leverage. Although time consuming, we were aided by our background within network support.
Accomplishments that I'm proud of
Utilising existing skillset/knowledge and utilising this to our advantage (building on chatbot knowledge base).
Upskilling and knowledge obtained from all the team who participated in putting together our application. Exposed members of the team to; ASP.net, RestSharp, Dialogflow and more.
Confirmed the real-life benefits of our application with those working within the healthcare sector, primarily saving time, money, and man-power. Please see additional evidence “Feedback from Harling & Michael Sobell House Hospice” from an engagement with the healthcare sector during the Hackathon.
What I learned
Gained exposure and additional knowledge in a variety of areas; ASP.net, RestSharp, JSON, Diaglow and more as well as a better understanding of the various API's used.
Explored a variety of different problem-solving models/frameworks to initially decide on the application; SURF, 7 point problem solving model.
The economic and customer benefits associated with the designed application. Following a project from initial idea to a developed concept with feedback from professionals within the target market. How to expand on the existing application and create other projects.
What's next for Automated Network Deployment & Support Healthcare App
We would really like to look at formalising a "Healthcare-as-Code" practise and methodology by using integration with the Meraki Dashboard API and others that were part of this Hackathon.
Also we explored locating key health care workers and also infectious patients within a facility using BLE beacons. This can be used to track contact and can also be placed on equipment such as hand sanitation stations and ventilator equipment. Also integrating with the Cisco Vision Dynamic Signage Solution we could display and alert if certain staff haven't come in contact and close proximity with a BLE beacon tagged sanitation station for a certain number of hours so should be prompted visually on the Ward digital sign.
Additional management of Meraki dashboard from the NOC support group within WebEx Teams space using the API, for example being able to reload and diagnose Meraki equipment from Teams Space.
Built With
.net
asp.net
dialogflow-api
http
iis
meraki-dashboard-api
restsharp
webex-teams-api |
10,004 | https://devpost.com/software/3data-analytics | 2D view from desktop of 3D network graph inside the virtual command center.
In VR view of Apollo and network heatmaps with MV camera locations.
3D rendering of Capital Factory building.
Inspiration
Due to Covid-19, our team now works entirely from home. Working remotely makes it harder to do tasks typically performed in a physical setting. For security analysts accustomed to working in a network operations center (NOC), remotely monitoring the health of an IT Network via Meraki can be a challenge. We saw this as an opportunity to combine 3Data Analytics VR technology and Meraki APIs to create a virtual NOC environment.
What it does
Our hackathon project, named Apollo Insights, is a voice-controlled interface that allows you to visualize and control Meraki endpoints in real-time, keeping operators in sync and simplifying Meraki network monitoring and management. Apollo Insights is an event-driven alert service for Meraki endpoints, pushing alerts through Webex teams and visualizing your Meraki Network alerts in the 3Data Virtual Command environment allowing remote operators to visualize their full Meraki Network in Virtual Reality.
How we built it
We integrated the Meraki Dashboard API by setting up a Meraki webhook to notify the Apollo Insights engine whenever there is an anomaly on the Meraki network. We developed an alert system that allows our Apollo Insights engine to quickly dispatch a notification to all of your Webex enabled devices when an anomaly is detected. This notification provides a link allowing users to seamlessly enter the 3Data Analytics Platform and visualize the related network data in Virtual Reality, giving Meraki operators the full context of the alert.
On top of the 3Data platform, we built a component to get the RTSP feed from the Meraki Dashboard API. The RTSP stream allows us to stream live video from compatible cameras. In Virtual Reality, we texture a 3D object, process the video format and undistort the camera feed, enabling security analysts to view the camera feed as if they were actually standing in the room. Our video component makes polling 360* cameras intuitive. This is something you simply cannot do in 2D.
Technology
A-Frame: A-Frame is a framework for creating VR environments, and we used it especially for the VR Camera view.
Meraki Dashboard API
Meraki Webhooks API
Cisco Webex teams API
Meraki camera feeds
Challenges we ran into
The Meraki Fisheye Camera gives us streams that are distorted. There wasn’t much information online about how to undistort it for use in VR, but eventually, we found a UV geometry mapping that worked well.
Maintaining two application states for managing notifications is difficult in the sense that each state always has to be in sync with the other. This presents challenges when working with a remote server where you have far less control over the environment and how you access it.
A well-known challenge when building software that will run in a virtual environment is how to get users in and out of that application effectively. One of the ways where we attempt to solve this problem is with a link provided by the Apollo Insights service which takes a user straight to a 3D visual representation of their data.
Accomplishments that we're proud of
One of the design principles behind the Meraki Webhooks API is an event driven architecture. Building on top of this pattern, we were able to design a performant application that takes full advantage of Node.js native strengths in asynchronous programming.
It was really satisfying to view camera feeds in VR
What we learned
Most of this project was getting a handle on the various APIs involved, which had its own learning curve.
Learned how to map fisheye camera feed to a sphere in VR
What's next for 3Data Analytics
We were limited by the time frame of a hackathon. One thing we’d like to support in the future is more notifications. Currently, our notification is attached to the Access Point Down webhook, but there’s no reason we couldn’t add other hooks in the future. For events like when Network Usage exceeds a certain amount, we could change the context that Apollo brings up to display network usage over time. Webhooks make this task relatively simple.
Built With
aframe
cisco
cisco-webex
javascript
meraki
node.js
webex |
10,004 | https://devpost.com/software/contact-less-front-desk-enabling-social-distancing | Kios Emulator - First page
Person detected by Meraki camera
Check in confirmed
New webex room created
Inspiration
Our global community is experiencing unprecedented times due to the COVID-19 pandemic and social distancing will remain moving forward, resulting in the need for technologies to evolve to allow for more contactless user experiences. Our idea is to implement a
contactless check-in
experience for hotel guests to protect the health and safety of both the guests and staff.
What it does
Meraki camera detects person as soon as guest enters hotel lobby. Real time snapshot is sent to the backend system for identification. If the person is identified as new guest, prompt will appear on the Kiosk asking user to scan QR code. Once QR code is verified, check-in information is provided to guest. Simultaneously a new Webex Teams Space is created where guests can reach out to various hotel staff. This provides a seamless and contactless user experience at hotels.
How we built it
Using
Meraki MV Sense API
we built the person detector. We also used
Meraki camera API
to take the live snapshot which will be sent to a backend database for identification. We used
Webex Teams API
to create a new Space for every new check-in. Our core application is built in
Python3
. We built the UI to emulate the kiosk using
react, type script, and materials
.
Challenges we ran into
One challenges was using the MV Sense API. We had to create polling app to continuously monitor the camera to detect a person.
Accomplishments that we're proud of
We collaborated with different members for the first time, yet worked in harmony. We are very proud of that and eventually had a lot of fun.
What we learned
Most of the team members got firsthand experience to work with Meraki APIs and webex teams APIs which was great. Some of us were new to programming and this was a great opportunity to learn some programming skills.
What's next for Contact-less Front Desk - Enabling Social Distancing
We will add more features and optimize the check-in process
Built With
apache-tomcat
camera-api
flask
flaskrestplus
material
meraki-sdk
mvsense-api
python
react
typescript
webexteams-api
webexteamssdk |
10,004 | https://devpost.com/software/meraki-app-for-splunk-phantom | Resources
Inspiration
An investment management financial services company has increased their remote workers from 400 to 2,700 agents supported primarily by the Cisco Meraki Z3 Cloud Managed Teleworker Gateway.
The firm requires stringent access controls of the devices (only corporate IP Phones and laptops) connected to the gateway. The security analyst(s) must quarantine the teleworker if unauthorized devices are discovered on the teleworker gateway.
What it does
The Meraki app for Splunk Phantom was enhanced to include a 'bind network' function, allowing the security operations team to specify the target network and the name of the quarantine template to apply to the teleworker.
How I built it
The Meraki app for Splunk Phantom uses the Meraki dashboard API to locate end-user devices within one or more organizations, networks / devices, and to bind a configuration template to a specified network.
By using the REST API of Splunk Phantom, security incidents (containers and artifacts) can be created and playbooks are programmatically initiated invoking the Meraki app functionality.
It is assumed the organization can identify the presence of unauthorized devices by way of log analysis or a host PC agent distributed scan. From these tools, the source MAC address and other supporting information are populated into a Common Event Format (CEF) record. The CEF data is part of the Phantom container and artifact generated by a program using the Phantom Ingest SDK.
Splunk Phantom will invoke a playbook which executes the Meraki app after the container is created on Phantom. The first step is to locate the name of the network where the source MAC address is found. The second step is to bind a quarantine network template to the targeted network name.
The results of these operations are returned to Phantom and logged.
This workflow can execute without human intervention to the point of end-user notification and remediation.
Challenges I ran into
Network with types camera cannot be bound to templates.
Accomplishments that I'm proud of
The quarantine template can be applied without human intervention.
What I learned
The Splunk Phantom instance is deployed as an AWS instance, this app demonstrates integration of cloud managed services.
What's next for Meraki app for Splunk Phantom
Deployment by the WWT Meraki managed services team.
Built With
meraki
phantom
python
splunk
Try it out
github.com
github.com |
10,004 | https://devpost.com/software/meraki-client-search | Sharing of CSV file on webex.
Loggin of database synchronization
Inspiration
Rolling out ISE and access-policies across large organizations has required me to spend a lot of time on creating various scripts to collect client data to build the policies. After and during the rollout it has also been useful as a sanity check.
I've also had customers ask "where are all our videos servers, credit card terminals (and so on) located." This tool gives easy access to all that information, and doesn't required users to look at each network in the organization or even get access to the dashboard.
Waiting for a script to collect all client data can take hours for large organizations, so putting it in a database and query that is a timesaver.
What it does
Provides an interface to search for clients across the entire organization. It also logs all events for the database synchronization to a Webex room as well as allows users to share the search result in the Webex room as a CSV file.
How I built it
Its a Django Application running on Heroku. Bootstrap frontend.
Challenges I ran into
The larges challenge was figuring out to run the database synchronization in Heroku using a worker since it can take so long. Another issue was handling the logging to Webex using threading and a logging table.
Accomplishments that I'm proud of
It looks great!
DEMO
URL: merakisearch.herokuapp.com
user: demo
password: ilovemeraki
Built With
bootstrap
django
heroku
jquery
python
Try it out
merakisearch.herokuapp.com |
10,007 | https://devpost.com/software/spilt-coffee-gwzinv | spilt coffee screens (search history, results, competitive analysis)
spilt coffee logo
GIF
spilt coffee animation
☕️ spilt coffee
Media monitoring and brand reputation trends powered by AI.
We help small businesses use wit.ai to take control of their brand by giving them instant access to and sentiment analysis on mentions across social, reviews, news and more.
✨ Mission
We believe everyone has room to grow and thrive. We commoditize big data for small businesses, reviews are better when they’re heard. No one should be left out because the cost is too great or the technology too complex. So we build easy tools to empower businesses to take control of their brand. Tools that make media monitoring, competitive analysis and reputation tracking effortless—spill the coffee.
📈 Features
SEARCH
We scrape data from Yelp, Twitter and News about your brand (and any brand), bucket reviews by sentiment and display results on user-friendly charts.
MONITOR
We automate searches to easily monitor brand sentiments over time, and display historical trends on this data.
SENTIMENT ANALYSIS
We utilize advanced sentiment detection tools like VADER and wit.ai to segment positive, negative and neutral mentions, and assign overall sentiment to each mention.
COMPETITIVE ANALYSIS
We allow businesses to compare and overlay competitor data with their own, keeping up to date with what the
people
are saying.
🧱 Architecture
A brief overview of our application, with some key features (green) on the left and how we handle them on the backend on the right.
🔮 NLP Model
MODEL
Sentiment analysis was conducted based on an ensemble model aggregating both the
VADER model
and Facebook’s wit.ai NLP model. Train and test data was primarily formed from Yelp’s open data set (>8,000,000 user reviews) and the Sentiment140 Twitter dataset.
IMPLEMENTATION
We utilized wit.ai's sentiment analysis to rate and categorize reviews. We trained entity recognition to pull out and rank the most common entities in "very negative" and "very positive" reviews. We can then see what users tend to have issues with, and provide actionable recommendations to improve these businesses.
ACCURACY
Running our ensemble model on a subset (test) dataset, we achieved an accuracy 73.35% on a set of 2000 test Yelp points as well as a 74.25% for the Sentiment140 set on their given test set of 497 tweets.
NEXT STEPS
We hope to add more robust entity tagging as well as bi- and tri-gram recognition in order to better provide value for businesses. We also hope to extend the pre-trained wit.ai sentiment model to a five category model (very negative, negative, neutral, positive, very positive) for better bucketing.
💻 Tech Stack
UI frameworks:
ElasticUI
Recharts
Frontend:
React.js
Backend:
Django
DB:
PostgreSQL
Authentication:
Auth0
Model:
VADER
wit.ai
✔️ To Do
☐ Provide helpful feedback and insights for businesses (actionable recommendations!).
☐ Perform more in depth competitor sentiment analysis, and ability to recognize competitors.
☐ Allow users to mark wrong sentiments (and correct them). Our models aren't perfect, we have room to grow too!
☐ We already provide a set of content marked as "extremely negative" or "extremely positive". Now, it's time to extrapolate reasons and analyze severity.
☐ Scrape more platforms (Facebook, Instagram, more news sources, etc.)
👻 Fun Facts
In our first (virtual) meeting where we were struggling to decide on our product name, one member spilled coffee on himself—with that, "spilt coffee" was born.
"8 million rows [of yelp review data] is a lot of rows."
Built With
django
firebase
heroku
postgresql
react
square
twitter
wit.ai
yelp
Try it out
spilt-coffee.web.app |
10,007 | https://devpost.com/software/robin-accountant | Robin's Website
A typical conversation with Robin
Robin's State Machine
Inspiration
Budgeting and personal finance is a challenge for many people, and a large percentage of the population lives paycheck-to-paycheck, having little to no savings. Extreme circumstances like the current Covid-19 pandemic affect the most vulnerable people the most.
We have decided to leverage the power of Wit.ai to build an easy-to-use chat bot that assists people with budgeting and tracking expenses in an effort to empower people to stay on top of their finances and to put some fun into personal finance. Wit.ai supports a direct and natural way to interact with technology through language, making book-keeping easy and accessible.
What it does
Robin allows users to quickly set up a budget and keep track of expenses. Users can add expenses whenever they occur just by pulling out their phones and leaving a text or quick voice message. Robin is then able to do calculations on these expenses and tell the users how much of their weekly budget is left, when expenses have been incurred, what expenses have been incurred over what period of time, and so on.
How we built it
Robin lives inside of a TypeScript Cloud Function and is hosted on Firebase. Incoming messages from Messenger and Telegram get forwarded to Robin for processing. Messages are analyzed based on the current state of the conversation (backed by a database and a state machine) and are then forwarded to Wit.ai, which returns a list of intents, entities, and traits (we have also implemented support for voice message and audio conversion). We then process those intents, entities, and traits, and match them against the current state of the conversation. Robin then produces a list of reply messages to be send back to the user and a list of actions to be carried out, along with the new state of the conversation that is then persisted in the database and loaded again once the next message for the particular user arrives.
Challenges we ran into
As mentioned above, the implementation of a chat bot's logic can become very complex very quickly. The major challenge we faced was dealing with that complexity in a way that allows more functionality to be added without increasing complexity exponentially. Another challenge we faced goes hand-in-hand: debugging complex, interwoven state can be difficult and time-consuming. Extensive logging and tracing really helped a lot here and is something to remember for the next project. Finally, we had to come up with a custom solution to support voice messages because Wit.ai does not natively support the voice message formats of Telegram/Messenger.
Accomplishments that we're proud of
We were able to set up an MVP of a chat bot that implements the functionality discussed above. The system works well supports both Messenger and Telegram, and can be extended to other chat clients trivially. We were also able to get voice support running through a custom audio convert solution.
What we learned
Sometimes things that seem simple on the surface turn out to be much more difficult when observed in detail. The first few Wit.ai intents were quickly implemented (e.g. tell_joke and greeting), but the more complicated ones such as add_expense quickly lead to a state explosion that required us to change our approach from a direct implementation of the logic to an indirect solution through a state machine. We also learned that it is a good idea to keep things more generic from the start in order to be able to support multiple back-ends (Messenger/Telegram) without excessive refactoring sessions.
What's next for Robin Accountant
Currently, Robin is in MVP status and the functionality needs to be refined a little more. A feature we'd particularly like to implement is support for sending pictures of receipts that will then be tracked by Robin. This would allow users to keep track of important expenses and come in handy for tax time.
Built With
firebase
messenger
telegram
typescript
wit.ai
Try it out
robin.silentbyte.com
github.com
t.me |
10,007 | https://devpost.com/software/otto-v05m26 | Final stage: Code output and open in Google Collab to try it out
K-Nearest Neighbor training & visualization
Linear Regression training & visualization
Neural Network builder with Otto integration
Preview sample datasets in-browser. These are standard sklearn datasets
Otto - Task Inference and Recommendation
Otto: Your friendly machine learning assistant.
Build machine learning pipelines through natural language conversation
Otto is an intelligent chat application, designed to help aspiring machine learning engineers
go from idea to implementation with zero domain knowledge
. Our website features easy model selection, insightful visualizations, and an intuitive natural language experience guiding you every step of the way. A collection of four Wit backend apps service Otto's conversational abilities and machine learning tools.
We encourage you to explore our
GitHub readme
for an animated look at what Otto offers!
Highlights
Beginner-friendly design.
Otto is made for novices, as it assumes no prior knowledge of machine learning. Users simply describe their end goals to obtain intelligent recommendations, or can choose from sample datasets to harness our models in an instant.
Powerful machine learning tools.
A range of machine learning capabilities are supported, including models for regression, classification and natural language processing, as well as preprocessors tailored to your problem. Play with neural networks, explore data visualizations, and generate ready-made Python code right in your browser!
Educational experience.
Users are walked through each stage of the process, with Otto explaining terminology when needed. Annotated code blocks provide eager learners a high-level understanding of their end-to-end pipeline.
Quick Start
To demo some of Otto's main features, try out the following:
Say:
I want to label flower species by petal length
to watch Otto prefill your pipeline and render a nearest neighbors classification on the popular Iris dataset.
Select:
Regression > Sample Dataset
to preview sample datasets for regression, and discover the strongest predictors using different best fit lines
Say:
Detect fraudulent credit card activity
and select the Custom Dataset option to experience Otto's model recommendation system and interactive neural network designer.
Say:
I'd like to interpret the mood of a review
to query Wit-powered natural language models for live results.
and feel free to get creative! Come up with your own ML goals and see where Otto takes you.
Stages
Below is a step-by-step breakdown intended for the technical reader.
Task
One of the biggest obstacles faced by those just getting started with ML is the abundance of jargon, from “loss functions” to “contour boundaries“ — beginners can't be expected to decide what model to use based on cryptic terminology, let alone develop one from scratch! Otto narrows down your options by inferring the high-level task at hand from a simple objective statement.
Task inference is powered by a Wit application (
Otto-Task
) trained on 300 such statements (e.g. “I want to detect loan applications as fraudulent”, “help me forecast stock prices”, or “let's summarize an article into a paragraph”) derived from real-world machine learning research.
Otto-Task
attempts to categorize the
task
intent as regression, classification, or natural language processing, and additionally extracts a
subject
entity embodying a streamlined form of the objective in order to filter out extraneous words.
The subject is parsed for keyword matches (“tweets”, “housing”, etc) against our database of sample datasets. If a relevant dataset is found, Otto pulls the optimal task, model, and preprocessors for the dataset and pre-selects them for the user throughout the pipeline-building process. Otherwise, Otto issues a task recommendation based on the recognized intent. And if no intent was identified, the user is provided with some tips to help them pick the best task themselves.
Dataset
Users are recommended a specific sample dataset matching their subject, or otherwise offered to preview and choose one themselves. Sample data allows beginners to prototype models quickly and easily, without the complexity of finding a dataset and figuring out the relevant features among dozens. Users may also opt to proceed with their own data, which they can include later on in the generated code.
Model
If the user opted for custom data, Otto leverages Wit to perform the key step of selecting a classifier or regressor. A Wit client (
Otto-Model
) parses a brief user description of their data for key phrases indicating the desirability of a particular model.
Otto-Model
includes around 15 phrases and synonyms per model and performs fuzzy string matching, making it an effective and scalable technique for model recommendation.
A characterization of the classification dataset as “simple” or having “just a few columns”, would make the K-Nearest Neighbors algorithm a good choice, while a description of the regression data as “crime rates” or “annual consumer rankings” would suggest a Poisson or ordinal model, respectively. If no phrase is flagged, Otto will default to the most general model available: a Neural Network for classification, or a linear fit for regression.
In the case of a natural language task, users can combine multiple models together for a more comprehensive analysis. Otto will recommend both sentiment analysis and entity recognition models, but provides users with information about both in case they'd like to adjust this. Our NLP models are built on a Wit backend (
Otto-NLP
) configured to identify
built-in traits and entities
.
Supported models:
Model Name
Task
Description
K-Nearest Neighbors
Classification
Draws class regions by looking at surrounding data
Neural Network
Classification
Deep learning model suitable for complex datasets
Linear
Regression
Ordinary linear relationship between variables
Poisson
Regression
Models count data, which tends to follow a Poisson distribution
Ordinal
Regression
Learns rankings (e.g. "on a scale of 1-5")
Sentiment Analysis
Natural Language
Detects polarity, expressions of thanks, and greetings/goodbyes
Entity Recognition
Natural Language
Extracts structures such as people, times & locations, and works of art
Preprocessors
What good is a fancy model if it takes ages to train? In this step, Otto swoops in with handpicked preprocessors for the user's data and model selections, abstracting away the intricacies of feature engineering and dimensionality reduction — machine learning techniques that optimize the data for efficient learning. As always, users can override the recommendations.
Supported preprocessors:
Preprocessor Name
Description
Principal Component Analysis
Performs dimensionality reduction and/or feature selection
Normalization
Scales data to have mean centered at 0 and unit variance
Text Cleaning
Removes emojis, noisy symbols, and leading/trailing whitespace
Visualization
The visualization stage activates for neural network design, or to render any models built on sample data.
Neural Network
Satisfy your curious mind with our fun, interactive network builder!
Otto preconfigures a standard model architecture with
research-based
activations and initializers, but users are free to tinker with it layer by layer as they wish. Additionally, Otto can make network redesigns en masse with the aid of a dedicated Wit model (
Otto-Net
) that translates user instructions into architecture changes.
Model Visualization (Sample)
Instantly explore how parameters affect KNN clusters and regression slopes!
Code Display
All done! With your data sorted out, preprocessors set, and model configured, Otto gives you a nice view of your work.
Future
Otto's modular design makes it readibly extensible, and its use of Wit means its natural language capabilities can be extended to even more domains. Here are just a few things planned for Otto:
More models:
logistic regression, support vector machines, decision trees
New tasks:
data generation (e.g. GANs), speech recognition
Smarter NLP:
being able to ask Otto to explain machine learning concepts or describe the difference between options
About
Kartik Chugh
Kartik is an incoming second-year at the University of Virginia, currently an AI intern at Amazon Alexa. An avid open-source contributor, he is passionate about API design and developing only the coolest machine learning tools :)
Sanuj Bhatia
Sanuj hopes he has a good chance at the hackathon, as it might have something to do with him being a Software Engineer at Facebook. He loves building interactive React-based applications, and likes to introduce and then fix bugs for maximum impact :D
Built With
facebook-duckling
facebook-nlp
material-ui
node.js
react
wit
wit.ai
Try it out
ottoml.online
github.com |
10,007 | https://devpost.com/software/fibonaccis-s | Start a chat
Chat
Login
Initial Page
Recipes
Example of suggestion
Recipe
Profile
Inspiration
Eating, one of the fundamental needs of the human being, one of the greatest pleasures in life, and despite the fact that we all love to eat, very few of us know how to cook, and not knowing how to do it not only lies in the need to depend on someone to eat, It can also cause problems. According to the British chef Jamie Oliver, the obesity problem that is rooted in the United Kingdom is mostly due to the lack of knowledge to prepare food, which makes us depend on junk food or simply not eating, and this can occur not only in United Kingdom but worldwide.
Our inspirations is taken from the problem that a lot of people have the limitance in kitchen, are afraid to get close to it and prepare their favorite dish. Nowadays, for the quarantine around the world, a lot of people want to take the iniciative to learn, and Fibonacci could help in this task, making prepare a recipe in a funny process, learning until you play.
What it does
FIBONACCI is application that contains recipes that you cook, you can make step by step alone, or with the company of LEO, an assistant created in WIT.AI to have a fluid conversation while creating your favorite dish. FIBONACCI, in addition to being a cookbook, also has a touch of gamification, which allows you to challenge yourself and your friends to learn new dishes and increase your skills in the kitchen, until you become the best cook in the app, and in the LEO sous chef at his prestigious restaurant Fibonacci.
How I built it
-Wit.ai -Vue -Node.js -Firebase
We builr an serverless API with REST, hosted in Firebases Docs:
https://documenter.getpostman.com/view/11046751/Szzn5wDM
And the PWA, in Vue.js conects to it We use Firebase for auth and db
Challenges I ran into
Learning the integration of wit.ai with our own web app was one of the biggest challenges that we face, fortunately, we could passed by and have a very nice final result.
Accomplishments that I'm proud of
We are proud of Leo, our chef bot maked with wit.ia, second the design of the app and the powerful api the core of the chat!
What we learned
We learned how to create our own Api Rest with the information of the recipes so we can use them in our web app, we learned how to manage backend loading information from the database and storing information to the database, also front end with Vue. We learned how to communicate with Wit.ai API, we learned on how to create different intents and how to use entities with useful data so we can change the flow of the conversation.
What's next for Fibonaccis's
We can make cooking more than a laborious task, it could become a game for many people who fear starting on this path, and the potential of the idea is fully scalable, supplying one of the basic needs of the human being, and in a future, make interactions through speech, send challenges to your friends to cook together, know the price of the ingredients that you need to cook, share in social media all the recipes that you prepare and know what all your friends said about it, create an entire ecosystem around this app.
Built With
Built With
apidojo
express.js
firebase
node.js
vue.js
vuetify
wit.ai
Try it out
fibonacci-app.web.app
github.com
github.com |
10,007 | https://devpost.com/software/memorai | Inspiration
Alzheimer's is a very common disease that has been flowing on from years yet there is not cure to it. There is no greater pain than actually forgetting the ones you love and it is even worse for people who are very close to them. We know that it is a very difficult process to go through and we came up with this idea of using the modern tech to slightly better the lives of these patients.
How we build it
We brainstormed for a bit and thought about the various implementations a chatbot could have that could not only
benefit us but also help society and people in society and in general, make lives better.
That's when we came with the idea of creating an app that could help Alzheimer's patients with their day to day tasks.
Alzheimer's patients have it tough, depending on the degree of the disease, their ability to perform trivial tasks can vary.
They use sticky notes to try and remember basic things and in general, have to depend on family to live life.
Enter MemorAi, a chatbot integrated into an app, memorai. This chatbot is like a personal assisstant to the
Alzheimer's patient! It helps with daily tasks and answers basic questions that the patient might have.
Reminders can be set by just asking the bot to do so! Apart from this, it also keeps track of close contacts which
can be accessed by the patient, merely by asking for the same.
In a likely scenario where the patient might feel like he/she is forgetting something, memorai can step in and really help out. It's easy to interact capability helps with common problems that a patient might face such as forgetting to take his/her medicines or forgetting their way home. In any case, memorai has the patient covered and the patient will feel safe.
The frontend was built using flutter and dart. Multiple plugins were used and additional features like patient login
and a memory game were incorporated. The interface is simple and easy to use. Even people not too familiar with smart devices should not have much of an issue navigating around the app.
The backend included some python to access wit.ai. The bot was trained to handle several different kinds of utterances and can manage to help patients with their daily tasks and possibly provide interesting data for doctors
to study and analyze.
Built With
dart
flask
flutter
python
wit.ai
Try it out
github.com
memorai.herokuapp.com |
10,007 | https://devpost.com/software/agatha | Agatha
Inspiration
The Startup came together after the team ran in a hackathon and won a position in the innovation section of Pontifical Catholic University of Paraná. From then on, the group has created solutions to help everyone live in a connected and smart way.
The globe is currently facing a pandemic of enormous proportions that will change how people live, communicate, and interact with each other. In this context, Agatha, meaning
automated gastronomic assistant totally helpful and accessible
, was born. Through her, the group aims to not only make ordering at restaurants quicker, easier, and more fun, but also safer because of the reduced human contact, essential in the current situation.
What it does
Agatha will be available in the tables of the establishment either through QR codes or tablets. Through these means, the clients will be able to access the menu and self-order everything they need with Agatha. Her uniqueness comes from the fact that she will have a personality, this makes ordering not only easier, but also more interesting and fun. In addition, she will be able to recognize and communicate through sign language, increasing the restaurant's accessibility to nearly everyone. With this tech, the interaction with the waiters will reduce considerably, minimizing the probability of getting into contact with the Coronavirus, and reducing expenses for the establishment.
How we built it
Agatha was programmed in js, react.js, wit.ai and css.
We began by programming the Speech to Text and Text to Speech part, obtaining the functionality of the computer being able to understand what is spoken and accomplish a specific task. with this in mind, we noticed that Wit.ai would be an extremely useful application. Through Wit.ai, we were able to create not only a vocabulary for Agatha, but also a whole "context" for her to act in, akin to a real waiter.
With this, Wit.ai would recognize the intention of the user's input(be it spoken or written) and send it to our application. Within our application's code, there is a function containing a list of possible answers (chosen randomly) for each registed intent. With this, Agatha will answer the user appropriately.
In order to view the menu, the tab of each client, Agatha's image, the chat and the other features of the application, a program in css and react.js was created.
Challenges we ran into
During our project's development, we had difficulties with the language, since no one within our group had extensive experience with it. Structing the connection between Wit.ai and our application was challenging, since we had to develop two AIs, connecting them in such a way that one receives the client's intentions and commands, while the other analyses and separates each order correctly and systematically.
Accomplishments that we're proud of
it took us an amount of effort and racked brains to develop a program with ample functionalities which, after a certain amount of training, will be able to become something helpful and useful to a signficant group of places. Through the ample support and training we had, we could develop, in a way, an AI which is already recognizing the user's will.
What we learned
To get into the market we had to research about how important the food industry is to the world. After ample research and discussions, we understood that even this industry, which is billionaire, needs innovation. With this in mind, we started to work in an AI that would revolutionize the market. to accomplish this, we learned how to utilize tools such as react.js and css to build a website. Nowadays, just a website is not enough to get by on the environment of the connected world, so we also had to study other important tools, especially Wit.ai. With this tool, we understood how to connect it to our code, and how to work with two simultaneously.
What's next for Agatha
In the near future, the group plans to transform Agatha into a hologram. With this tech, ordering will be very unique, getting the attention of more customers, making their experience in the restaurant special.
Built With
react.js
wit.ai
Try it out
www.agatha.opfinds.com |
10,007 | https://devpost.com/software/grapevine-6phogb | Inspiration
You know that one recipe that you grandma has that she absolutely rocks , or maybe you uncle is the best story reader ever. Maybe you know a skill that is unique and fun. But would you really ever get employed for these abstract qualities ? In this quarantine a lot of people have been home bound and struggling to keep up with their finances , at a time like this we need to empower every member of the family who has a skill which can be marketed. Not only that as an employer you don't need to feel limited while being specific about the kind of employee you are looking for. You can make the most vague requests and we will still get you exactly what you need because we understand that you only get a 100% of what you seek when you look for it in your own words.
What it does
Our app is a new take on job seeking applications. Our app strives to give you exactly what you want no matter how vague your request might be. How do we do that ?
1)The app first stores the profile of the user based on his purpose, i.e: to be hired or hire someone (you can do both as well).
2)It then takes the user to a
Wit.ai
powered chat bot.
3) The chat bot interacts with the user to understand his needs and connects him with other app users who are suitable for them.
Using
Wit.ai
we gather information from user text regarding the skills they are looking for be it a 'nanny' or a 'gardener' or an 'expert cook' . But that isn't it we further allow our users to search for specific qualities like 'punctual' or 'kind'. We understand that sometime you want something very specific while making a very vague request and we are here to provide. After we gather the user request and extract skills and qualities from it we then match it to our users who are looking to get hired and then we connect them.
How we built it
We used flutter to build our app and firebase to store user information. We used Wit.ai to process the information our chat bot was receiving.
Challenges we ran into
Wit.ai was a new concept for us and we took some time to implement it.
Accomplishments that we're proud of
We were able to understand and implement NLP using Wit.ai.
What we learned
We learnt the application of nlp through wit.ai in terms of data extraction from a conversation.
What's next for Grapevine
We plan on implementing a local mode that will enable one to find jobs or people in your locality itself.
Built With
firebase
flutter
wit.ai
Try it out
github.com |
10,007 | https://devpost.com/software/mrs-career-wise | GIF
Quick glance
Poster
Feature 1
Feature 2
Feature 3
Feature 4
Feature 5
Architecture
Home page
Ask Data science/Machine learning questions
Ask Data structure and Algorithm questions - 1
Ask Data structure and Algorithm questions - 1
Ask for tips :)
DevOps questions
Know about company's history, founders, culture, mission, interviewing process - 1.
Know about company's history, founders, culture, mission, interviewing process. - 2
Track progress - 1
Track progress - 2
Track progress - 3
Look out for opportunities.
Ask pay related questions
Download your personal data.
Note: Works best on Chrome
Demo -
Link1
,
Link2
Inspiration
The news of my friends losing out their jobs due to COVID-19 was heartbreaking.
Such posts flooded LinkedIn, highlighting how many companies have either fired their existing workers,
or revoked the offers of new hires.
About to graduate in these uncertain times, looking for jobs has become even more difficult.
It has become much more important to prepare thoroughly for your interview processes, as the competition rises stiffly with a growing rate of unemployment.
With hundreds of resources on the web, it can be overwhelming to pick the best one and get started with interview prep.
What it does
Mrs. Career Wise helps you prepare for your next interview at tech giants like Facebook, Microsoft, Google, Amazon for various roles like Software engineering, Testing, Product Management, etc.
Features
Prepare for leading Tech giants
Tech questions - Data structures, Data Science, Machine Learning, DevOps, Product Management ..
Interview tips.
Analytics - Keep track of your progress.
How I built it
1) With a pen and paper, try and list out all possible ways a user might interact with Mrs. Career wise. This helped me list the intents and entities.
2) Creating a Knowledge base of interview questions asked by various tech giants for different roles.
3) Creating a wit.ai python client.
4) Creating a flask server
5) Using Plotly.js to plot graphs for user's progress.
6) Deploying it over Glitch,Heroku
Challenges I ran into
The biggest knowledge was creating the Knowledge base. There is no API that provides such data, so I set out creating one of my own.
Also going all alone may not be the best idea.
Accomplishments that I'm proud of
I am glad I was able to create a product that would be immensely helpful to people.
What I learned
Using an NLP engine!
Chatbot design - handling intents, entities, contexts...
Creating a complete product from scratch
Caring for user's data privacy
What's next for Mrs. Career Wise
The next step would be to gather a lot of feedback from users.
Expand the Knowledge base to cover more types of questions.
Cover more types of jobs, not just Software-based.
Gamify the process.
Detailed progress monitoring.
Leaderboard - comparing with peers.
Built With
flask
glitch
heroku
jquery
plotly
python
wikipedia-api
wit.ai
Try it out
careerwise.glitch.me
career-wise.herokuapp.com |
10,007 | https://devpost.com/software/well-beings | WQS self assessment test layout
Media kit
Mind map
Messenger screen #1
Messenger screen #2
Inspiration
Mental disorders affect
one in four people
. Treatments are available, but nearly
two-thirds of people
with a known mental disorder never seek help from a health professional. The stigma around mental health is a big reason why people don’t get help. This needs to change. By changing the attitude towards mental health in a community setup, we believe we can create a domino effect of more people opening up as a result of increased social and sympathetic views on mental health.
Our Solution - Wellbeings: A Community
Wellbeings is a Mental Health Community. Unlike most mental health communities, Wellbeings is inclusive to even people that are unaware of mental health problems. This community is called Wellbeings because we want to de-stigmatize mental health.
Our solution to the problem is to provide access to vital information so that people can educate themselves on types of mental health problems, identify any warning signs by a quick self-assessment, information, and resources including helplines, advice on helping someone else, tips on wellbeing, etc.
We want this done in the most interactive way possible, which we believe we can achieve by creating a chatbot and a community that is synonymous with peer support groups. We want to focus on the idea that people with mental illnesses are not abnormal or some isolated group of people, but as many as 1 in 4 people in the world will be affected by mental disorders at some point in their lives. By creating a community, we want to reach out to the victims as well as the general public because they are likely to know someone who suffers from mental illness.
Collectively in a community setup, we harness a "me too" feeling and help members become advocates of mental health.
To sum up, we aim to
advocate the importance of mental wellbeing,
make information accessible and available,
tackle stigma,
empower community,
support people by aiding recovery through early identification & intervention.
Who are we?
We are a team of 4 people - which consists of a developer, a designer, and 2 doctors. All of us share a common vision to improve the intricate health system with the use of revolutionary technologies. Mental health is one of the issues we feel strongly about.
How we built it
Our messenger bot is powered by wit.ai to handle all the NLP tasks. The webhook managing all the backend logic and scoring is built with flask. For the bot flow, we have used Chatfeul. And for the self-diagnosis of disorder, we have used the WQS standardized test.
Challenges we ran into
Most of the people don't even know that they are suffering from some kind of mental distress, so they usually are not engaged with apps and bots marketed as self-diagnostic/help apps. To even reach to that naive user, we have taken a community approach to engaging him by providing a comfortable community which will be pictured by the user as the answers to his unknown problems. Once engaged, we can help him use our bot to take the assessment and know about his/her mental wee being.
Most of the self-diagnostic tests available are lengthy or too monotonous so implementing it in a bot is not a good experience and the drop out ratio of users becomes high. So our team of health professionals selected WQS from various different standardized tests and modified it to be more interactive and less negative to increase conversion ratio. Also, the questionnaire has around 50 questions but we have made it dynamic to the users are not given disorder-specific questions if his/her response is negative to the screening question. So for a normal user, the effective number of questions is around 15-20 which improves the number of people who complete the test.
What's next for Well Beings
We don't stop here. We aspire to engage as many people as we can and bring this to every person who is unknowingly possessed by this demon. We also want to educate the community about mental well being so that they can understand its importance and observe the silent cues of people in distress.
We plan on scaling this solution in the following ways
Incorporate health care professionals to help members with an accurate diagnosis
Add a database of country-wise helplines
Work on suicide prevention
Improve the self-help questionnaire
Make our bot even smarter (Thanks to wit.ai)
Incorporate CBT (Cognitive Behavioural Therapy) to assess and help people with mild symptoms here in our community only.
Built With
chatfuel
flask
glitch
messenger
wit.ai
Try it out
www.facebook.com
glitch.com
m.me
mm.tt
www.mindmeister.com
www.figma.com |
10,007 | https://devpost.com/software/covid-tracker-bot | Inspiration
As an effort to keep people’s awareness of how serious COVID-19 has became, we want to create an app that offers users quick access to the up-to-date number of cases (infected and death) caused by COVID. COVID Tracking API (Tracking API) is established and well maintained. Regardless of Tracking API’s great resource on COVID, there is not yet a quick way to obtain data from the API because of its vague parameters and the lack of an interface. Therefore, we are building a COVID Tracker Bot using Wit.ai to provide a user-friendly way for everyone to interact with the API.
What it does
COVID Tracker Bot provides a user-friendly interface and meaningful interaction for ExpDev07's
tracking API
, which provides up-to-date data about the world's COVID cases from three sources: John Hopkins University (JHU), CSBS, and New York Times. The chatbot app allows Facebook (FB) Messenger users to get instructions by typing
Hello
,
Get Started
, etc., and query COVID-19 information by typing in the country names and/or date. Once users send out an input, the bot would detect keywords (getting started, country name, date-time), get the right information based on the keywords, and respond to users correspondingly.
How we built it
The bot is written in Python using
FastAPI
. Integrated into FB Messenger, COVID Tracker Bot uses Wit.ai for user input analysis. We use Wit
intents
model to determine user's action, user needs help versus user needs information, and built-in entities,
wit/location
&
wit/datetime
, to obtain parameters needed for the Tracking API calls. The app is trained to mainly recognize countries and time.
In terms of architecture, the bot interacts with three external services: Wit.ai, FB Messenger, and Tracking API. First, after receiving the FB message through POST call, the chatbot feeds the raw text content to Wit.ai using Wit client. Secondly, the trained Wit model extracts the message’s intent and entities. Thirdly, the data received from Wit is used to obtain COVID data from the tracking API. Finally, the chatbot processes and returns the data by replying to the end-user.
Challenges we ran into
We faced lots of challenges trying to understand and troubleshoot the interaction between the chatbot and FB Messenger. In the beginning of the Facebook app setup, the payload received didn’t have a text field because of security reasons of an in-development app. We figured that adding test users helped solve this problem.
At one point during testing, the replies didn’t get back to end-users even though the bot receives multiple HTTP calls fom FB. After some researching and helps from the Facebook Online Hackathon community, we realized that FB messenger mechanism only allows
200
responses, categorizing any other responses as
500
, and leave the unprocessed messages in the queue. Thus, we implemented better logging and exception handlers to ensure that the chatbot always returns a
200
.
Wit.ai’s built-in entities helped us extract crucial data to get COVID cases from the Tracking API, which requires two-letter country codes. However, challenges arose when we tried to build the lookup's mapping system from country names to country codes. Wit doesn't refer to any documentation about resolved
wit/location
entity's values, e.g. "USA" is resolved into "United States of America" by Wit. Our solution was to run a dataset consisting of country names and codes in Wit to obtain the exact string value, and map them to the corresponding country codes.
Accomplishments that we're proud of
We learned about this Hackathon when it was just one week away from the submission date. We are proud that we promptly came up with the COVID Tracker Bot idea and were able to implement it, though none of our team members ever built a chatbot before. We consider our ability to learn and apply a great amount of new knowledge while doing this project a big accomplishment.
What we learned
We gained in-depth knowledge and hands-on experience in NLP, processing data, test-driven-development framework, and how to build a chatbot. Always assuming a very specific, well-designed chatbot framework is a must to make one, we have learned that a secured implementation can be as simple as having an API/microservices with
GET
and
POST
endpoints, especially with the help of Wit.ai and Facebook Messenger authorization protocol.
What's next for COVID Tracker Bot
For now, the bot only supports John Hopkins University’s data and process input with country names and/or a specific date. We’d love to add the other two sources (CSBS and New York Times) in the future, which provides information on US states and regions, as well as include more functionality to process interval dates/multiple time values.
At the moment, our processing time is a bit slow; it is taken up by the bot’s calls to Tracking API. The delay might be even longer (up to 30 seconds) if the data isn’t cached in the Tracking API. Therefore, instead of implementing the chatbot as an app separately, it can be an extended feature in the currently open sourced tracking API (a.k.a. a bunch of additional modules and two more endpoints). The transition is feasible since both the bot and the API implements FastAPI. Lastly, our main goal for next steps would be scaling and expanding the bot, specifically using Facebook Messenger Quick Reply to train Wit.ai and integrating the bot into other social media platforms like Twitter.
Built With
chatbot
fastapi
heroku
messenger
python
tdd
wit.ai
Try it out
www.facebook.com
github.com |
10,007 | https://devpost.com/software/spydergramai | Inspiration
What it does
SpyderGramAI is a web scraping tool that collects and collates images and videos of Instagram content.
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for SpyderGramAI
Try it out
bitbucket.org |
10,007 | https://devpost.com/software/stigmatized-lekds7 | Inspiration
This was inspired as a result of recent Facebook social media trend in my country, Nigeria where rape survivors found it difficult to come out publicly due to the stigma and guilt they face as a result of the experience.
What it does Stigmatized do
Stigmatized is dedicated to helping people of prior sexual assault. The Facebook Messenger Chat Bot is intended to identify with rape survivors and converse with them in a friendly tone, it recommend steps from the helpguide.org to help them to recover from any trauma that follows and overcome the guilt that follows and pursue legal action.
How I built it
I used a Node.js back-end along with a Heroku server to implement our Facebook Messenger Chatbot. Then I employed Facebook Wit.ai NLP to process user input and provide adequate response.
Challenges I ran into
I had issues deploying to the heroku server, Time was also a factor and considering my country Nigeria power supply and internet connectivity was a big challenge too.
Accomplishments that I'm proud of
I was able to implement a chat bot for something that I'm so passionate about.
What I learned
I came in to this hackathon with no experience using Wit.ai or even app server deployment. This has exposed me and even though I cannot say I'm very good at it now, I believe it is a great step and what I have learnt on Natural Language Processing, I'm sure the coming years will be a busy one with NLP in mind.
What's next for Stigmatized
Stigmatized is far from perfect, being someone with a quest for humanitarian activities, I intend to use it to bring that to actuality but that cannot be done if the idea is not fully established.
Built With
express.js
heroku
javascript
node.js
wit.ai
Try it out
www.messenger.com |
10,007 | https://devpost.com/software/witty-walk-with-me | The Proud Pic
Playing around with Wit.ai
Starting the hack!
Finishing touches applied!
DONE!
Proud Final Pics
Yeah Yeah sometime it did listen wrong! BUT worked most of the time!
Inspiration
I got the inspiration to develop a smart walking start from my relatives and my father gave me the idea and mum helped in building the walking stick. This was really fun. And my first Devpost & FB Hackathon.
A Walking stick built for the people who are alone.
A quick overview regarding people living alone.
Ex.
If, when people around you hear you saying "Hey my heart is paining badly!" they will surely rush you to the nearest hospital or at-least call your nearest/closest friend/neighbor/relative, and done you are almost saved!
But now think what if you were alone in this situation, and what if that was a
sign of a Heart Attack
. You are probably dead by the time someone realizes that there is no movement from this house for a long time!
And this is where my "Witty" comes to scene! Witty intelligently understands the users distress calls and triggers a event like sms or call as per configuration.
Some document on the web supporting this case
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6199841/
UN -Sustainable Goals
My project adheres to the third goal of sustainable development. Ensuring healthy lives and well being of the end-user.
Ensure healthy lives and promote well-being for all, at all ages.
Using Wit.ai in Health Care can bring in more opportunities to understand a remote patient in need.
In this time of COVID-19 pandemic, people cant visit each other or talk with anyone in person. If bots built with services like WIt.ai can be deployed in healthcare will benefit a lot for the the aged, alone and maybe kids too.
Problems faced by the “elderly staying alone”
What it does (current stage)
Listens to users distress sounds.
Creates alarms based on different distress voices. (sends msgs)
How we built it
Wit.ai (FB NLP API). It was very satisfying to see a actual NLP service working in my project. I trained the Wit.ai with many negative help regarding statements.
Twilio for alerts (sms alerts). Applied a simple if-else logic to trigger events.
We triggered a sms event from twilio when a negative statement was detected from Wit.ai trained model! 😍
Challenges we ran into
I faced a ton of challenges in this project.
Everything from starting to end was a challenge cause it was new. Like finding wake word detection library but then Jetson Nano wouldn't support some of them.
Firstly due to lock-down I couldn't get cheaper and lightweight hardware which could be used for the project purpose, so had to stick with a huge "Jetson Nano" which I got in a old idea submission contest and hardware accessories in India is very costly, that development is always hindered in some way!
Microphone battery died half way had to wait a lot but then found the a good old camera's microphone working good.
Am really new to developing a whole project involving API's so it took some time to figure out code and making it safer!
I messed around with the project logic a million times, I had to go on changing the way the idea had to be implemented.
Everyday the main code would change!
Accomplishments that we're proud of
My very first hackathon project
Feel in love with Wit.ai (now am gonna use it as a bot service for new projects)
Got to explore more using Python
Tried Twilio (I had seen people sending sms from devices but didn't know that this was easy)
I feel confident now to participate in other public hackathons.
What I learned
Python intermediate
Using API services in Python
Using Twilio
Wit.ai
What's next for Witty Walk With Me
PyTorch CV for surrounding environment detection.
Fold-able wheels attached under the stand so that it can move around the house also pull the user to a specific path just like a autonomous car. (highly optional because I can think of the dangers and ways it can go wrong if not properly implemented)
Ultrasonic based object detection so that it can work without light too.
Use lighter board such as RPI Nano and more sensitive microphone, make the whole project smaller and safe for kids too.
Planning a hard shock proof case.
Audio device pair-able for voice based interaction.
I have a very good feeling that bots like "Witty" will be needed a lot in houses where people live alone with no one to assist. Not just home's, Wit.ai unlocked the possibilities of NLP and voice based add-ons in every other gadget you can think of. Voice interface is the future and its here.
Some understandable demo code
import json
json_file = open('keys.json')
json_data = json.load(json_file)
keys = json_data["api_keys"]
access_token = keys[:][0]['wit']
account_sid = keys[:][0]['tsid']
auth_token = keys[:][0]['tauth']
from wit import Wit
client = Wit(access_token)
resp = None
with open('temp.wav', 'rb') as f:
resp = client.speech(f, {'Content-Type': 'audio/wav'})
text = resp['text']
trait_confidence = resp['traits']['help'][0]['confidence']
trait_value = resp['traits']['help'][0]['value']
from twilio.rest import Client
T_MSG = "Hi, you have a emergency msg from your dear! -> '"+text+"!'"
client = Client(account_sid, auth_token)
if trait_value=='call' and trait_confidence>=0.75:
message = client.messages.create( to="+918237842347", from_="+12245076842", body=T_MSG)
Built With
jetson-nano
python
pytorch
wit.ai
Try it out
github.com |
10,007 | https://devpost.com/software/wit-covid | Cover Pic
Information Retrieval
How to use
Bye Bye
Inspiration
My major inspiration is to help the needy in this pandemic situation. In my country like India, many of the poor people don't have proper knowledge of websites, or even access to televisions. Due to this, many of the information regarding the precautions, nearest hospital facility, etc. never reach these people, and due to that many people are dying on the streets just because of either they don't visit proper health facility in time, or which they visit, doesn't have space. But one thing people have is the smartphone, no matter cheap or expensive. And, one app I see people using frequently is Facebook (one of major impact by the company in connecting people). So, I and my teammate, decided to build a bot in Fb Messenger itself, so people can come in contact with this often, and just don't have to learn and search throughout the websites or streets, and all the information are easily accessible with just one question away.
What it does
This bot tells you about the current cases, deaths, and recoveries from COVID-19 countrywise and also statewise but only limited to India, for now atleast. Also, one of the key features is that, if you have symptoms or if you feel you have symptoms, this bot can lead you to various helpline numbers so that help reaches immediately if any serious case is encountered. If your health is deteriorating, this bot also provides information about various hospitals in Indian states treating COVID-19 patients, and also tells you if hospitals have space or not (this solely depends on state), so that you reach the right hospital at right time.
How I built it
We used nodejs, messenger api, and wit.ai, and made them communicate with each other. Questions received from messenger ai were sent to wit.ai for NLP synthesis, and data processed was parsed to nodejs for further actions. After actions, the result was reverted back to messenger api as their possible answer.
We used different public websites to scrape information and accelerated their goal of transferring useful information and resources to the people.
Challenges I ran into
The major challenges were caused while collecting data especially live feed data. Collecting this type of data was more important for better analysis of the current scenario. Another major challenges were to extract information from wit's response. For different sentences, wit returns different json structure, for eg., wit identifies some locations but isn't able to resolve it. For handling such scenarios, each possible sentence was carefully tested using Wit in Python. Also, identifying and catching bugs in the questions asked was by far the toughest challenge to tackle. Due to greater efficiency of Wit, it extracted useful information and treated the question with some other similar question's answer. Also, we had some minute issues in communicating with the websites, that was later resolved quickly.
Accomplishments that I'm proud of
I'm proud to build something useful not for the proper earning, educated people, but for the people those don't have access to many priviledges. Through this hackthon, I got an opportunity to build something useful for the community.
What I learned
Through this hackathon, I feel obliged to get introduced to Wit.ai. It is such a nice framework with much accuracy and was impressed by keyword detection and also it predicts the context with much greater accuracy. Also, with this project, I learnt how to detect points or fields to target, or which needs to be addressed to bring a massive change in the community for its betterment.
What's next for Wit COVID
If you think with broader perspective, there is still an important feature missing i.e. identifying COVID hotspots or sealed areas near our houses. We were working on this feature, but due to lack of time, we decided to put a hold on this. Also, we weren't able to add various NGOs working for COVID relief, as these organizations are the fastest to reach an infected person or area, and pay proper medical attention before the actual medical facilities arrive.
Built With
facebook-messenger
glitch
javascript
node.js
wit.ai
Try it out
m.me |
10,007 | https://devpost.com/software/schemeai | ext/Voice enabled bot serving the schemes to the public using wit.ai
Inspiration-
I am inspired by the challenges faced by the public during the lockdown where they are returning to the hometown and suffering from the challenges they are currently facing.
My application serves the purpose of helping the citizens of my country to access the schemes provided by the government.
This will solve the problem of poverty and economy of the country as it will give many opportunities to the people in various sectors and helping each one to access the benefits.
What it does-
It will answers automatically to the questions asked by the public about the schemes.
Featuring voice and text enabled to ease the access of the application.
Using the feature of natural language processing it will serve the purpose of hitting awareness about various opportunities under various sectors according to their interest and clustered in similar entity.
How I built it
i built is using python with the wit.ai feature provided by the facebook for the natural language processing.
also using speech and message feature of wit.ai helped me a lot for accessing the voice and text messages and could generate a template form of message that will enable to the user with the great data about the schemes.
Challenges I ran into
i ran into a lot of challenges because i was having no knowledge regarding facebook development and wit.ai .
i just have knowledge of python and i learnt from scratch about how to learn to about bot app and wit.ai.
i devoted alot of time in learning then building applications.
I struggled with the unknown bugs for a lot of time but i managed becaused i invested my whole day for the development of the project.
Accomplishments that I'm proud of
i am very happy and proud to become a facebook app developer in less time after learning from scratch and also eligible to say that i am skilled enough to contribute my skills for the growth of the community
What I learned
What's next for SchemeAi
i will be adding a lot of features after learning on the availabilty of it.
Various langauge support
video enabled and image enabled message conversation.
enhanced templated message.
More accurate data for the public
Notification Reminder messages feature
Built With
atom
audio
bot
facebook
facebook-messenger
flask
heroku
http
python
wit.ai
Try it out
www.facebook.com |
10,007 | https://devpost.com/software/open-accessibility | Check-Accessibility
Getting info
Adding info
Inspiration
No one can deny that there is plenty of information for general public to plan their day and visit restaurants, museums and go to work , however accessibility information for people living with disabilities is still extremely limited. Websites like Yelp and similar reduce the concept of "accessibility" to a binary selection (Wheelchair accessible or not). Unfortunately this doesn't help the majority of people stranded at home with different disabilities. For example, visually or hearing impaired does not get any help from the current categories. Many people are left having to call or email and that can be discouraging and a way to block them from integrating into society. We believe that technology, in this case AI applied to an assistant, have the potential to make life better for a lot of people.
What it does
Accessibility-Check bot have 2 main functions, the first one is to organize a coordinated crowdsourcing effort to map restaurant, store , offices, etc. It basically shoot a quick set of questions like "Do you see a ramp?", "Can you ask for a Braile menu?" , "Is the bathroom in the first floor?", etc. All these questions are based on American’s with Disability Act (ADA) and Open travel alliance (OTA).
The second functionality of this bot is to inform the general public about the accessibility based on their location. A mechanism will be included to catch unanswered queries, save them in a database and add them to the crowdsource queue.
Stack we'll be using: WIT.AI (NLP), Yelp, GoogleAPI's (to map locations), React (Frontend), Node.js + Express , GraphQL/AWS Appsync (DB, storage).
Github Repo:
https://github.com/six100/accessbot
This project is still in a very early stage and open to new ideas, your ideas. Contact me if you are interested in joining forces.
Looking for:
Node, Express experts.
API Guru (To connect 3rd party Geo-location API's, other APIs)
ML, NLP Jedi (To take it to the next level)
Anyone that want to be part of this.
Objectives:
To make a solution that solves a problem in the real world.
To find the best, leanest, most effective tech solution for all the challenges ahead of us.
To meet great people.
To Open Source the code at the end.
Thanks!
Built With
facebook-messenger
graphql
node.js
react
wit.ai
Try it out
accessbot.chat |
10,007 | https://devpost.com/software/a-2ukwsq | Inspiration
As many stores start to reopen, social distancing becomes more important to contain the spread of Covid-19. We will build an application that will allow for users to book slots for their favorite stores based on the number of users at any particular time that will be present at the store. If the store is full for a slot, the user can view other available slots and book them.
By open, we mean they can accommodate more people.
What it does
The chatbot collects information about the number of people allowed per slot. Then, it uses the location of the user to find nearby stores with open slots for a specific need (such as an item users are looking for). After listing the nearby open stores, the bot allows users to choose to view the number of available slots, most popular items in the stores. Then, user will receive a code as a booking confirmation if he/she confirms booking slot. Users can use that code or send a message to store's website/page or come to the store with the code so that the stores can update available slots.
With this, the bot gives a live count of people inside the store. If the user is a business owner,
How we built it
We built it with: Express Node.js backend, Sequelize ORM and Postgres database. It's hosted on Heroku.
We also use Facebook Messenger Platform
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for a
Built With
express.js
heroku
node.js
postgresql
sequelize-orm
Try it out
github.com
m.me |
10,007 | https://devpost.com/software/days | Home Page
Metrics Page
Metrics Page
Our Inspiration
Our project started with one question. How can we help? Today, AI and NLP are commonly used to streamline lives and simplify tasks through smart assistants and chatbots. With such powerful tools, it was obvious to us that there was so much potential and practicality for a project.
After days of brainstorming, we pinpointed a consistent pain-point in our friends and family’s lives, as well as our own: time always feels like a rare resource. We decided to work on Smarter Days with the
main goal of improving the lifestyle of people who use the app
.
What It Does
Users would log their day-to-day activities and receive visual breakdowns of their activities over the course of days, weeks, or months.
This will enable people to gain valuable insight into how much time they spend per activity which can have a great positive impact on time and life management.
How We Built It
Our project started by enabling our Wit.ai model to recognize and sort different types of user activities. We created intents and entities for working, exercising, studying, and resting activities and training the different ways for phrasing them.
We then built a full-stack web application to house the model and provide a user interface for users to interact with the model.
Technologies
AI/Machine Learning
:
Wit.ai
Web Application
:
MongoDB, Express.js, React.js, Node.js
Google Firebase for SPA hosting
Heroku for Node.js (backend) hosting
Challenges and Solutions
Challenge
: Due to external circumstances, our project was entirely virtual making coordination tough
Solution
: We adopted Scrum strategies and engaged in daily stand-ups as well as goal/task planning.
Challenge
: Considering the nature of Machine Learning, it’s difficult to train for multiple categories to have intended outputs at the same time. Especially true when considering our project bandwidth (
1 month
)
Solution
: We took a methodological approach to training and created various Word and Excel documents to randomize words and follow the modeling process. This saved us an incredible amount of time and allowed us to comprehensively train for even the edge cases.
Accomplishments
Training a comprehensive Wit.ai model to recognize a wide range of entries
Creating a full-stack application from the ground up
Consistent virtual coordination and communication over the course of the project
What We Learned
Natural Language Processing techniques and concepts
Making use of Wit.ai models and training tools
Full-stack development
What's Next For Days
Continuous learning via phrase validation from the user (correct/incorrect validation options)
Built With
express.js
firebase
heroku
mongodb
node.js
react
wit.ai
Try it out
smarter-days.web.app
github.com |
10,007 | https://devpost.com/software/wkend-home-maintenance |
window.fbAsyncInit = function() {
FB.init({
appId : 115745995110194,
xfbml : true,
version : 'v3.3'
});
// Get Embedded Video Player API Instance
FB.Event.subscribe('xfbml.ready', function(msg) {
if (msg.type === 'video') {
// force a resize of the carousel
setTimeout(
function() {
$('[data-slick]').slick("setPosition")
}, 2500
)
}
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Wkend chat!
Wit App ID: 328662241461519
Inspiration
Will has long talked about a management system for the home, a seamless way to keep on top of building maintenance and create a record of work done. Together, we came up with a chat-based solution for keeping track of work done, work to do, and even recommendations for your home. We think there's a lot of potential in scheduling, locating contractors, and keeping a historical building record!
What it does
When you first start using Wkend, you'll be asked to give your home a name and describe it a little bit. Using Wit.ai, we were able to parse those attributes and begin creating a record for the home. From there, you can tell Wkend tasks you need to do regularly, or reference when they were last done.
How we built it
Our first decision was to keep this web based for ease of accessibility. We turned to Next.js and ANT for our UI, and FastAPI for our server. After doing a bit of training in Wit.ai, we were able to set up simple endpoints for handling text or speech and returning responses to the user. One neat integration we were able to take advantage of is the text-to-speech browser api now widely available, which coupled with Wit.ai allowed for a full speech interface. For authorization we integrated Auth0, and in order to save the user's data we set up Hasura with Postgresql. This combination allowed for a slick web application and a fully featured backend.
Challenges we ran into
We ran into so many challenges! Recording in different browser contexts, sending speech snippets to FastAPI, trying to launch the Next.js app on AWS lambda... the list goes on. We had high hopes for completing a fully fleshed out application, but at the end of the day we were both excited to take our first stab at chat/voice enabled app.
Perhaps one of the more surprising challenges has been comprehending the types of interactions a chat-based interface entails. How do we make it clear to a user the actions available? How do we handle different chains of thought that end up at the same result?
Accomplishments that we're proud of
A few of our accomplishments were launching FastAPI on lambda, getting both sides of the voice interaction to work, and overall architecting a multifaceted application. The combination of libraries we arrived at seems very versatile, and we're excited to push Wkend further.
What we learned
Don't try too many new technologies at once! Oh my goodness, we overwhelmed ourselves and were not able to get to a super cohesive application. That's not to say we have any immediate regrets, learning to use many of these new tools such as Next.js and Wit.ai has made it all worthwhile.
What's next for Wkend
We're going to continue developing Wkend until it can be properly deployed, tested out in the real world, and then shared with a few friends we know who are interested in using it. There are still a few key features we would like to get to, such as searching for contractors or providing recommended cost-saving tasks. We'd also like to provide Alexa or Google Home integration so interactions could be more passive--the goal here really is simplifying staying on top of house work and keeping a record of it.
Thanks for the opportunity to be part of this Hackathon!
Built With
fastapi
hasura
lambda
next.js
postgresql
wit.ai
Try it out
github.com
test.wkend.work |
10,007 | https://devpost.com/software/heybro-programmers-personal-assistant | 6. Execute a project
3. Project analysis
4. Select another project
1. Select a workspace
2. Select a project
7. Exit screen
5. Open a project
Inspiration
Command-line interfaces are a hassle. It just tests our memory instead of logic. As programming professionals, we always felt there should be someone who helps us with all other mundane tasks like opening, running, analyzing projects, etc, so we can focus on the logic. This is where HeyBro comes into picture!
What it does
HeyBro is an intelligent personal assistant who resides in the command line, and we can ask it to do mundane tasks. There are no particular commands like a typical common line interface. Instead, just type in whatever you want to do, and it utilizes Wit.ai's NLP processing to identify what user intents and executes commands to fulfill the needs. Simply put, type in "run this project" or "execute it" or "can you start this" and code will be executed. Similarly "give me an analysis of this project" or "examine it" will do an examination of the project. Basically ask HeyBro what is your need and get it done.
How I built it
We've created a command-line interface with JavaScript utilizing the capabilities of NodeJS. An npm package named Enquirer is utilized to take inputs.
Challenges I ran into
Running some commands with NodeJS was tricky. Like running a project. A great amount of time was spent on researching this. And there were lots of edge cases like there tons of programming languages and libraries and supporting everything requires hectic effort. But we limited our scope for the first version to tackle this.
Accomplishments that I'm proud of
HeyBro can now do basic analysis, open, and run (NodeJS at the moment, but it is easily extendible to other languages) on projects. Wit.ai's NLP capabilities are so impressive as training our model is such a breeze. Above all this is a project that we, as programmers will use on day to day basis from now on. And we are pretty sure that with some optimization, this project can take out many repetitive tasks from our schedule, which will help us focus on things that really matter.
What I learned
NLP has a huge potential to make our life easier, and imagination is the limit. We will try to integrate NLP wherever possible to ease interactions from now on.
What's next for HeyBro - Programmers Personal Assistant
Automatic project language detection with support to more languages
Improve project analysis
Linked commands. Eg: we can just tell HeyBro to "add a commit message 1234fixed and push to master" and HeyBro will detect it is a git command, switch to master, perform a
git add .
and
git commit -m "1234fixed"
then push it to remote.
More useful traditional command-line replacement commands, like "rename this file abc.txt to 123.txt"
More activities, by analyzing user queries
Built With
javascript
node.js
Try it out
github.com |
10,007 | https://devpost.com/software/messages-with-hope | working figure of chatbot
Inspiration
To this day, there remains no constructive, operative, and practical method that efficiently helps in emergencies to deliver critical patients to hospitals.
The access to ambulances and the reaching of ambulances on the location of patients have been time-consuming. Not only that but, also the process to register an ambulance can take approximately 10 to 20 minutes for a person. This happens because the user has to explain the route to the ambulance driver, providing details to the ambulance call center. These formalities take a lot of time, which can be fatal for critical patients reaching the hospital on time.
What it does
Messages With Hope; a helpful chatbot, acting exactly as its name suggests when it comes to registering an ambulance and making contact easier between ambulance service and the user. Where the whole process is automated, you don't have to worry about any external equipment as it is just available on your mobile phones. All you have to do is message in the chat box, provide the address in text or pin location, provide other necessary information for ambulance service and you won't feel troubled about getting an ambulance to your doorstep. From there you can instruct the ambulance driver which hospital you chose as the destination from the provided nearest hospitals. It is time-efficient, easy, and reliable.
Innovation
Currently, no such chatbot exists which provides such service. Also, the Wit.AI platform was used for understanding entities in the conversation and integrated it with Google Maps to provide the user with different routes to different hospitals making it easier for him to choose the hospital especially if the user is new or traveling in that area. Also, the ambulance service can have the exact location of the user helping in reaching the ambulance in the minimum time possible.
What we learned
It was quite interesting to work with Facebook technologies, especially with Wit.AI. Making different conversation cycles have enabled to understand how should chatbots be developed in the long run.
Impact
In Pakistan, the ambulance service systems are quite unreliable. The number of hospitals in Pakistan is limited. Also, the process of calling an ambulance and registering and contacting the ambulance driver to help him to reach the location takes up a considerable amount of time. Using our solution we tend to reduce that time as much as possible and provide the user as much knowledge and information on the spot to assist the user in this emergency and help in saving a person's life.
Built With
facebook-messenger
google-maps
google-places
node.js
wit.ai
Try it out
m.me
wit.ai |
10,007 | https://devpost.com/software/addmision-helper-0ikgzl | our logo in test group
Inspiration
We realized that it's time to automate this area of life. During the creation process, we discovered that there are no similar work projects and this is a good opportunity for us to develop.
What it does
Answers to specific sections are prepared in the form of standard chatbot buttons, and you can also immediately ask a question without going to the menu. But also, a feature that is not related to AI is tracking the rating of students. A person does not have to constantly search nervously for this list and their place. the bot will immediately give out its position.
How I built it
It built with python
Challenges I ran into
How can I work with words and correct answers
Accomplishments that I'm proud of
It work!
What I learned
How to prepare words for search and correct mistakes, work with tables and machine learning
What's next for admission-helper
We should try to launch it in our University
For Facebook, this is a great opportunity to increase interest in their platform by making a link between universities and the ability to connect educational institutions to this program.
P.S. We have no time to translate in English and learn some rules for admission. I think the general idea is clear.
Built With
keras
python
tensorflow
vk
Try it out
github.com |
10,007 | https://devpost.com/software/old-is-gold | Inspiration
Honestly one of my best friends 87 year old father was recently diagnosed positive for COVID19. Sitting almost thousands of miles away he had no way to ensure his father, a diabetic had even basis emergency response to queries (not talking of treatment here just basic query and response to that simple question which is so difficult to find)
An app with a heart (bot that cares for elderly senior citizens) for virtually caregiving senior citizens adequate care and solace amidst trying COVID19 times when they feel lost for purpose or alarmed by increasing spread of the virus.
Like Alfred the butler in a famous DC Super hero magazine (our team doesn’t intend to take any credit for DC creations and acknowledges all copyrights owned by DC) this app bot is a voice butler providing interesting and informative responses to key queries around covid19 and ways to mitigate its treatment
Oft ignored and marginalised due to age factors are the elderly folks staying alone amidst the COVID19 pandemic crisis. Not at all easy and a harsh reality of our times..
Hence we are inspired to call ourselves OLD IS GOLD...
What it does
An interactive NLP bot that answers all health related queries around COVID and connects the elderly people with immediate directions, solutions and clarifications
How I (no we) built it
We built it using wit.ai and performed a few use cases or prototypes around integrating wit.ai with a mobile app or Facebook..
Challenges I (no we) ran into
We found integration into Facebook and apps a bit challenging given time constraints not our technology restraint.
Then maki g a compelling storyline which powerfully conveyed our USPl
Accomplishments that I'm proud of (for my rock star team)
We completed the end to end workflow is like less than 16 hours. Period. That’s something we believe our grandma will celebrate baking home made cookies, once she sees our prototype and how safe it makes them feel.
What I (we) learned
Stitch in time saves none, not just in terms of our time for development integration and end to end testing. We are referring to the fact that a query well answered by our bot could end up saving the life of an elderly patient
What's next for Old is Gold
We hope to finish among top of the charts and seek Facebook help in getting required guidance for integration and testing.
God speed Good luck and as we say in India JAI HO
Built With
wit.ai |
10,007 | https://devpost.com/software/gratitude-genie | Splash Screen
Auth Screen
Gratitude Journal Screen
Mood and Journal Streak Tracker
Gratitude List
Settings Page
Inspiration
2020 has definitely not been a year to remember. The COVID-19 pandemic, deaths, lockdowns, job losses, racial violence and just recently there was the news of a popular Indian Bollywood actor (Sushant Singh Rajput) who committed suicide (which was very disturbing)—it's like COVID started as a spark and now it has turned into a forest fire of negativity.
With everything seeming so grim right now and seeming completely out of control, I felt like there was a need to bring an element of gratitude back into our everyday lives. A reminder to count our little blessings. Just a tiny drop of positivity amidst the forest fire could do our mental health a lot of good.
I think Dumbledore put it way better than I ever could—
"Happiness can be found even in the darkest of times if one only remembers to turn on the light."
That was my inspiration behind taking some time out to build Gratitude Genie. :)
What it does
Gratitude Genie is a conversational everyday gratitude journal. Following are its key features—
Conversation Journal that makes your journal experience fun and engaging
Inspiration
Timely reminders
Journal Streak Count
User Mood Tracker
Save Gratitude List
Beautiful wallpapers updated dynamically
How I built it
I have built this application using React Native. I started by thinking about the bottlenecks that prevent you from journaling on a daily basis. Then, it came down to problem-solving and forming the feature requirements.
After that, I had to plan the conversational flow. How could Gratitude Genie help on days when you just didn't feel grateful for?
Chaining is something I picked up from the book "Atomic Habits" and so, I implemented a quick journal streak counter and mood tracker. This can be helpful in maintaining accountability for the way you feel on a daily basis and help you form long-lasting habits.
Wallpapers were added to introduce some spice and a separate gratitude list was added so users can reminisce about their past victories/ memories from time to time.
That's how I ended up building Gratitude Genie. And of course, caffeine helped a great deal as well ;)
Challenges I ran into
I wanted to build for both iOS and Android. So, it came down to React Native vs Flutter. I went ahead with React Native considering the time deadline and the fact that I was coming from a web background with no prior experience in mobile app development.
Also, I fist had plans of making it a messenger bot. But, ensuring user privacy was a challenging aspect there. The standalone app, even though time-consuming to build offered a lot more flexibility in terms of features.
Accomplishments that I'm proud of
This was my first mobile application and.. my first hackathon as well. I like the fact that I've been able to develop it for both Android and iOS devices and that it is in the mental wellness category, which means it can have a significant impact on people when executed well. If this app can improve the mental health of even a single user on a day to day basis, that would be the accomplishment I'd be most proud of.
What I learned
From the technical side, I got to learn a lot about mobile app development. I got well versed in React Native and other frameworks. This was the first time I got to be both the product manager and the developer. Another important learning was that ideas don't really work unless you do the work. You can ideate a lot of features, but none of that manifest into reality until you turn it into code.
What's next for Gratitude Genie
Get feedback from early users.
Make the conversations more personal and engaging.
Think about user privacy and implementing anonymous features where people are able to talk openly about depression etc.
Can also implement analytics to track user's mood over a timeline
Can add Facebook Sign in as well to Google
Built With
asyncstorage
expo.io
react-native
react-native-gifted-chat
redux-persist
ui-kitten
unsplash
wit.ai
Try it out
github.com
balloffocus.life
drive.google.com |
10,007 | https://devpost.com/software/ponder-tiw5y3 | Overview of main screens
Final screens (top: adding entry and user profile, bottom: archive)
Final high-fidelity mockups
Initial low-fidelity sketches
Inspiration
The coronavirus pandemic has taken a toll on people’s mental health around the world. Millions of people are isolated from a support system, and with the priorities of our day to day activities changed, it’s not uncommon for many to have feelings of anxiety and uncertainty.
Research has shown that journaling helps people improve their mental health as it’s a way for them to regain control over their emotions. To help facilitate journaling with additional support, we integrated with Wit.ai to use AI to recommend resources that would help users feel supported based on what they journaled.
What it does
Ponder is a journal that uses Wit.ai to recommend various articles and resources specifically tailored towards the user's journal entry for the day. This helps the user reflect deeper on their emotions and help facilitate beneficial changes to their lifestyle. Each journal entry is paired with a related article, and these pairs can be archived for easy access in the future. The user can look at their history to see their progress, and we also provide a virtual plant to illustrate personal "growth" by measuring their app usage.
How we built it
We first used Figma to visually prototype our app, working from sketches and wireframes. We then used Dart and Flutter to construct the front-end of the app, as well as MongoDB for database storage, Mongoose for the REST API, and Wit.ai for sentiment analysis.
Challenges we ran into
Since we are a large team and we were working on integrating many different frameworks, we ran into the major challenge of learning new languages and frameworks in a short amount of time.
The design team had to learn how to integrate a plethora of different ideas into one prototype that could also be implemented by the rest of our team.
For the frontend team, we both picked up Dart and Flutter for the first time. After learning the ropes, one major challenge we bumped into was configuring the navigation bar to allow for in-page routing.
For the backend, a major challenge was learning how to leverage Wit.AI to produce relevant results.
Accomplishments that we're proud of
We worked extremely well as a remotely organized team, from ideation to submission. Each person on the team was always willing to chip in where needed and took care of their own deliverables as well.
Additionally, a majority of our team had never used any of the tools listed for creating this product. We are proud of how much we have all learned during this endeavor and of our ability to adapt to use new techniques and resources.
What we learned
The frontend team learned how to use Flutter and Dart, experimenting with mobile development for the first time. The backend team learned how to integrate Wit.ai into the multiple technical components that were being used, as well as how to query in Dart. On the design side, we learned how to organize and maintain a large team remotely, as well as how to shorten but efficiently do the UX research portion.
What's next for Ponder
We hope to allow Ponder to support a wider variety of media (videos, research journals, etc.). Another area of focus is to accommodate for a larger range of journaled situations and topics.
Backend code can be viewed here:
https://github.com/angelina124/ponder-api.git
. Since it is run through Heroku, downloading the backend code isn't necessary.
The APK files can be found here:
https://github.com/jonnachen/ponder_front/tree/master/apk
Built With
dart
figma
flutter
javascript
mongodb
mongoose
wit.ai
Try it out
github.com |
10,007 | https://devpost.com/software/travel-partner-3z0abl | Inspiration
Being a citizen in one of the busiest countries, it is a daily challenge to commute to your destination on time. Public transport directions and signage are disorderly and confusing sometimes and properly organized transport data is hard to find. The average person in travelling a new city loses about 3 hours a day commuting. We want to help optimize this and make sense of the chaos.
Imagine if we want to take a taxi, we need to open the Uber app. If we want to take a bus, we need to open CitiMapper. If your wallet does not have enough cash, you need to open Google Map to find the closest ATM. Imagine after completing all these searches and queries across multiple apps, our lives might have already lost at least 5 minutes (1-2 minutes per app). In some serious circumstances, we could even miss a date.
What it does
Travel Partner is Waze for public transport. We are a travel and navigation chatbot that solves the problem of travelling by providing intelligent commuting directions and route analysis for all countries.
Travel Partner is a bot who recommends transit route from Point A to Point B by any means of transport.
tells you when the next public transport will arrive
tells you how much time you will need to commute
shows you the closest parking area.
helps you discover point of interests around a certain location
just LOVE TRAVELLING
How I built it
We started this project around one month before the deadline and each of us are responsible for different tasks. One is responsible for coming up bunches of utterances and entity keywords that teaches Travel Partner with the help of Wit.AI. One focuses on building the backend infrastructure. One focuses on integrating the bot with Facebook Messenger and one coordinates and manages the team to make sure the project runs smoothly.
We used Python as the programming language, Wit.ai for Natural Language Processing, HERE API as the data source, Redis for session caching and Facebook Messenger as the bot interface.
Challenges I ran into
The biggest challenge for us is to write a bot because none of us has previous work experience. In addition, we also set high expectation on the bot to make sure it does not look like a traditional rule base FAQ chatbot. At the beginning, we struggled on caching information within a user session by creating multiple threads and variables. Until then, we figured out that it was a lot simpler to use an external in-memory database to store the information.
Another challenge was not from the technical perspective, but on how to compromise each other’s idea as a team. Right at the beginning of the project, we spent 5 hours sitting in a room brainstorming ideas on how to implement a chatbot that helps people’s lives. From writing bots that checks the amount of litter in a rubbish bin to bots that recommend products from online stores, we assess our project idea not only based on the judging criteria, but also on whether the product is usable and sustainable, technically feasibility, last but not least our time availability.
Accomplishments that I'm proud of
We built a travel companion from scratch as a team that could help us and other busy man to save time.
What I learned
From technical perspective, we learnt that it has become a lot easier to apply AI to solve problems with the help of many pre-trained model. This hackathon also strengthens our skills on Python and the concept of Multi-Threading (though we did not apply it to bot).
Last but not least, we learnt about teamwork. We learnt how to make compromise with different idea and opinions, compensate on each other’s workload and share knowledge across one another. As all of us comes from different education background, we can feel that each of us are bringing our expertise and knowledge to achieve our common goal.
What's next for Travel Partner
As there are still limited number of questions related to travelling that Travel Partner could answer, we want to gather feedback on what other questions that Travel Partner should learn to answer. We would love to introduce Travel Partner to our friends and share it with the community at Product Hunt. In addition, we could foresee that the complexity of the question structure would increase when Travel Partner “knows more people”. For instance, Travel Partner can now deal with questions like “from A to B by X”, however some users may be a perfectionist who need “the cheapest way from A to B by X with the least traffic jam”. This would be a challenging task for us to come up with an efficient coding logic and infrastructure to achieve this. In the meantime, it is also interesting to integrate idea that Travel Partner could help during our commute. For example, setting an alert to remind us getting off the train. Otherwise, we will miss our dates again.
Built With
facebook-messenger
python
redis
wit
Try it out
m.me |
10,007 | https://devpost.com/software/lilo-ai | Inspiration
We were inspired by Siri, Alexa, and Cleverbot
What it does
It detects the emotion of a user and based on that data produces an output that mimics friendly human interactions.
How we built it
We used wit.ai, GitHub and glitch
Challenges we ran into
We are an international team so the biggest challenge was to select the best time to brainstorm.
Accomplishments that we're proud of
We have created an NLP model that detects human emotions, and its a first step to creating an artificial friend on Facebook
What we learned
That everything in software development has to be done by a team and that teamwork is the best tool to solve problems.
What's next for Lilo AI
We want Lilo to become the new way of human-AI interactions. We want to populate the database with more utterances, teach it how to detect more emotions, and train it on more sitcoms, books, and cartoons to become a great conversational companion. We hope that in the future the AI could become your new best friend.
Our wit.ai app id:
618990298698878
Our code:
https://github.com/brahada/lilo-ai
https://glitch.com/edit/#!/chill-rightful-jacket?path=wit_handler.js%3A19%3A4
Built With
github
glitch
javascript
node.js
wit.ai
Try it out
www.facebook.com
glitch.com |
10,007 | https://devpost.com/software/music-ally-trained-t9zao1 | Brand Logo
Inspiration
A problem we've seen many beginner music students have constantly is finding the motivation to dive into the nitty-gritty details of music. Many of these students find topics such as intervals, chords and progressions a bore compared to the myriad of distractions online (Yes, Facebook. That's you!), and so we thought: Why not stop trying to
fight
these distractions and try to
integrate
with them instead? Wouldn't it be great to have a musical companion bot which music students (and anyone who's interested) could ask their questions and even get a dose of inspiration to love music? Thus, Music.ally Trained was born --- and the rest is history :)
What it does
Music.ally Trained is a bot which provides a quick and user-friendly way to get started with the basics of music theory and to discover new music! Here's what it can do currently:
Return an interval given 2 notes
Return the notes in a chord given the chord's name
Return songs which include a specified chord progression
Return a musical joke
Return information about a composer
Return information about an instrument
Jukebox - helps you pick a random song for your next karaoke session!
How we built it
Music.ally Trained is built in Python, using a Bottle app deployed on Heroku. To help our bot achieve musical intelligence, we employed the use of the Mingus library and integrated the Spotify and Hooktheory APIs into our app.
Challenges we ran into
The main challenges we faced stemmed from being new to Wit.ai and Facebook's ecosystem, as well as a tight timeline given that we started the project with a week left to the submission deadline.
As we only had 1 Developer account and so many features we wanted to implement, it was a challenge for us to find a workflow which would allow us to build, test and refine our work smoothly. Moreover, with strict social-distancing measures in place, we found ourselves spending more time and effort trying to find an efficient way to collaborate rather than focusing on the features of our bot.
With regards to Wit.ai, it was a challenge determining which labels to give to our intents and entities as the lines between them got blurred the more we ventured. As for Messenger, it was difficult to test our bot as it required the creation of a Developer account and it would also be a tedious process to make our app live to the public.
Accomplishments that we're proud of
Firstly, we are proud to have completed this project with only 1 Developer account despite having 2 team members as we had to think of innovative ways to ensure both members could test our bot after making changes to the code. Thus, we decided to take advantage of technology, utilising a combination of communication platforms from Skype's screen share to VS Code's Live Share and to traditional Whatsapp messaging, such that we were able to keep making progress towards the finish line.
Additionally, we are proud that we managed to integrate as many APIs and libraries as we have! Initially, we didn't believe we would need to use many third party tools but by the end of the project, we are proud to say that we have integrated the Spotify and Hooktheory APIs as well as the music theory library Mingus into our project, and have experimented with many others as well!
All in all, we're proud to have accomplished this many features given the short amount of time and how little prior experience we had. Though we ran into many errors along the way, we're glad we were able to press on and crush all (or most) of the bugs!
What we learned
On the technical front, we learned how to create an intelligent bot using Wit.ai and integrate it with Facebook's Messenger, but beyond that we also learned about various deployment methods (such as Glitch and Heroku), a bunch of music APIs and libraries, and Python, our choice of programming language.
Furthermore, we learned about collaboration in the context of software development projects through experiencing challenges such as managing different versions of our app, dealing with merge conflicts due to code changes, and the endless Googling in order to fix our bugs. With regards to our technical challenges, we must definitely mentioned how helpful the Facebook hackathon community has been, providing us with timely support and advice whenever we felt like we were really stuck. As such, we are thankful to have learned from the experiences of other developers and looking back, we can now definitely see the importance of not being afraid to ask others for help when we are stuck and hopefully we ourselves can provide the same help and guidance to others in future.
Lastly, looking at the amount of time and effort we put in for this hackathon, we can now better appreciate the amount of planning, communication and focus required to deliver a project by a given deadline. Perhaps, it is the immense satisfaction we get when we finally achieve a final product which drives most, if not all, of us to do what we do.
What's next for Music.ally Trained
We would definitely love to expand on the functionality provided by Music.ally Trained as we do have many ideas for further improvements.
For instance, we would like to implement Messenger's Private Replies function to allow users to receive links to useful musical resources so that they can further expand their knowledge given that there are limitations to almost any bot's capabilities. However, since our Facebook page is new and has neither any useful posts nor users, we decided to leave this feature as an option for future work instead.
Moreover, we also have plans to broaden the range of music theory questions users may ask and we believe this would be relatively easy to accomplish as the library we chose, Mingus, offers many useful functions for learning about music. Hence, our project would be easily extensible using it and is rather flexible in this sense.
Additionally, we are also planning to tap on Natural Language Processing, to improve our bot's persona to be even more light-hearted and engaging, so that we can sustain the attention of our users.
Lastly, we hope to further improve our bot's intelligence through more rigorous training such that it will better handle misspellings and provide users a better experience overall.
Built With
facebook-messenger
heroku
hooktheoryapi
mingus
spotify
spotipy
wit.ai
Try it out
www.facebook.com
github.com |
10,007 | https://devpost.com/software/mq |
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Hanah by Metaquid
Inspiration
Develop a general AI, imagining it in the future, which returns to the present to modify itself.
Tell his story in images and make those images become real when you really talk to this AI entity.
The future is realized in many ways but the best way is to anticipate it!
What it does
Metaquid is an AI that can learn and face free speeches;
interacts with writing and voice;
interacting with people he gives and receives useful stimuli for the subsequent development of the graphic novel;
in private mode: dialogues remain private and not shared - safe mode;
in public mode: dialogues are shared and therefore can interfere with each other - risky mode;
selecting the item allows you to customize the Hanah holographic avatar;
activating the microphone allows you to activate the wit.ai voice recognition service.
How I built it
To function in a widespread way it was developed as a PWA (Progressive Web App)
Works both on pc and smartphone, cross-platform, ...
It is connected with the functionality of the metaquid.com blog for later development.
I used the server side PHP7 and client side javascript languages.
Developing in the wordpress environment I used the jquery framework already present for some features.
Challenges I ran into
Voice integration without using any framework required a lot of trial and error.
Server-side development in PHP7 has been going on for many years and will continue.
The interference between the wordpress environment and wit.ai integration took me a long time to understand.
The PWA features were not immediately clear to me at the beginning, but in the end I understood what to do.
Accomplishments that I'm proud of
Giving the voice and recognizing the voice to AI adds that level of reality that was missing.
Being the creator of AI I am proud to have thought of giving him a story through the graphic novel.
What I learned
I learned to do basic PWA: Add to Home screen (or A2HS for short).
I started learning how to use wit.ai but I will continue to understand more.
I learned to make videos by assembling the images of my comics.
I learned to do without frameworks to keep the code simple and maintainable.
What's next for Metaquid
The next step is to make a version for facebook messenger.
Metaquid will always be in development in my plans until it becomes what is described in the graphic novel.
in reality the short circuit created between the fantasy of the graphic novel and the reality of development will bring new ideas.
Built With
javascript
jquery
linux
pc
php7
smartphone
windows-10
Try it out
www.metaquid.com |
10,007 | https://devpost.com/software/moodanalyzer | Home Page
Detected Mood using wit.ai
Quotes for uplifting the mood
Inspiration
Mental health is an important part of our life. It impacts our thoughts and our lifestyle. We decided to do something good for society by making a small effort in improving people's moods. As Mother Teresa said "We ourselves feel that what we are doing is just a drop in the ocean. But the ocean would be less because of that missing drop", we believe this small effort will make an impact on people's lives. About 900,000 people die due to suicide every year worldwide. Mood swings are the prime cause for a person to attempt suicide. This has pushed us to ponder over this issue deeply. Here is our small effort to analyze a person's mood based on their daily activities and soothe it in case of a disturbed mind.
What it does
Our application asks users to describe their daily activities using which we detect their mood. With their mood known, we ask them to read quotes and watch videos that have been specially catered for uplifting their mood.
How we built it
We built this application using the wit.ai NLP framework and Flask. We trained the wit.ai application for 8 different moods using several hundreds of utterances in order to detect the mood based on the given phrase describing a person's activity. Then, we developed a website using Flask and Python which takes the user input (which offers both textual and speech-to-text recognition) and connects to the wit.ai using REST APIs. Based on the mood output, it displays the detected mood and illustrates a set of 4 quotes from our pre-defined collection of quotes catered for each mood. Additionally, it selects a video from a collection of video URLs for further impact. In case, a person provides some irrelevant input, the application automatically handles and displays an error requesting another input.
Challenges we ran into
We had to learn the wit.ai framework and its integration with our Flask application which took a while initially as the quick start guide offers limited information. Additionally, integrating the speech-to-text recognition system posed a challenge while integrating it into the core application. Apart from the challenges faced in the web development, it was very cumbersome to train the wit.ai app manually with several hundreds of utterances as it offers no easy way of providing a file input containing a dataset. We performed this tedious operation for training each of the eight trait values associated with the moods, apart from collecting a library of quotes and videos for each mood individually.
Accomplishments that we're proud of
We are proud to help society by providing a platform that can uplift people's moods in times when the world is dealing with the ongoing Covid-19 global pandemic leading to the prevalence of discouraging moods among people.
What we learned
We learned how to use the wit.ai NLP framework, how to build an application using Flask, how to deal with REST APIs, and speech-to-text recognition functionality. We also learned how Precision/Recall confidence scores change as we train a model for multiple traits. Last but not least, we learned how to collaborate with team members, work together through virtual platforms, and test our leadership skills.
What's next for Mood Analyzer
We would like to expand our domain by exploring more varieties of moods (apart from the existing eight) and also plan to train the wit.ai with more examples. Additionally, we would like to explore further possibilities of detecting moods beyond the recordings of daily activities and also provide more support apart from displaying quotes and videos.
Built With
css
flask
html
javascript
python
wit.ai
Try it out
github.com |
10,007 | https://devpost.com/software/medicai-3ir7l8 | Complete Web App Screenshot
Facebook page for MedicAI
Web app front page
Working Bot 1
Working Bot 2
Inspiration
-> We were inspired to work for this project after the lack of quick medical emergency facility available till now.
-> Most of the pharmacy delivery system requires you to undergo lot of tasks before ordering medical items. And these private apps aren't even available in most of the regions.
-> In many areas people don't know much about these facilities or neither know how to use a mobile application.
-> The whole process of payment and services is a huge headache as every time it is to make a transaction without the option of single click and quick buy options.
-> Neither we have seen a system that can take users assessment and suggest real time medical items.
-> We believe that at the time of emergency, its not practical to wait for a person to help us buy medical items.
-> We wanted to leverage power of a common portal like Facebook that is used by millions of users and neither a user must have headache to keep installing independent apps for emergency purpose.
-> Thus we built an Intelligent chat bot application called MedicAI.
What it does
-> Medic AI is your day to day personal assistant. With its smart cognitive intelligence powered by Wit.ai engine this artificially intelligent assistant delivers you the best and most possible accurate results each time.
-> We use Facebook messenger as bot to take very few questions and leverage complete delivery of essential medical items quickly.
-> We have inbuilt wallets which can be recharged earlier. So as it makes the lives of users much easier by making a single click buy system.
-> Medic AI has been created to cater to the needs of millions of people who can easily get access to all the necessary services virtually from home.
-> It’s specialized algorithm helps you find the nearest medical centers, buy emergency medical kits and even assess your health instantly with the help of its symptom checker.
-> When a user enters the details of medical items he is prompted for the nearby store for the purchase, which on selection can be received by the particular pharmacy stores.
-> The bot also has a self-assessment system in which users can check for any problems they have by talking it out with the bot. The bot is clever enough to help user with appropriate suggestions.
How We built it
-> For the chat bot we used Wit.ai. The NLP system helped us to create an powerful AI bot on messenger.
-> We have created a Facebook page for our services.
-> We made our Website using Bootstrap and integrated Facebook messenger services to our application.
-> In Wit.ai we used the python as the language and later integrated it with the Facebook messenger.
Challenges We ran into
-> We had never worked with a creation of bot services. So the process was quite challenging for us.
-> Integrating the bot created with wit.ai with our Facebook messenger took us some time.
-> We had never integrated a bot application with a live website, so this process was bit of a task.
-> We are developers of different universities and have our exams going on. So it was bit challenging to work in between and make this project.
Accomplishments that we are proud of
-> Working with a team and completing project during tough times is something we all are proud of.
-> Building an ecosystem for making peoples life easy by empowering Facebook's awesome technologies feels great.
-> We had never worked on a chat bot project before. So this allowed us to learn lot about bots and its concepts.
What we learned
-> Team work and Time management
-> Understanding Facebook's messenger technology.
-> Integrating chat-bot to a web application
-> Integrating Wit.ai to messenger using python.
-> Making process for demo video.
-> Hosting an bot based website on cloud service like Heroku.
What's next for MedicAI
-> This is our idea and we have presented an demo of our application. Next we plan to work in depth of the chat application.
-> Make more users understand about this simplicity of using Facebook for emergency and SOS services.
-> Help pharmacy stores with usage of application.
-> Make more assessment features for users.
Built With
bootstrap
bot
facebook
facebook-messenger
github
heroku
python
wit.ai
Try it out
github.com
medicai-1.herokuapp.com |
10,007 | https://devpost.com/software/911-assistant | Fire on Hacker Way 1
Inspiration
When people call 911, they want it picked up on the first ring, usually with a good and "urgent" reason. But people often have to wait longer than expected due to a lack of 911 operators especially during the pandemic of infectious disease just like now.
In South Korea, where we live, one sick employee has taken out the entire call center.
Of 97 confirmed cases, 94 were working on the same floor with 216 employees, translating to an attack rate of 43.5%. It's a lot bigger than the household secondary attack rate among symptomatic case-patients which is only 16.2%. So It's clear that call centers are vulnerable to infectious diseases due to the nature of the jobs that don't have a large space between people and have to keep talking. Also, it's important to isolate the suspected patients in the early stage to block further transmission in crowded work settings. But what if a 911 operator gets infected and the entire center goes into quarantine? Then who will answer the calls?
What it does
AI Assistant for 911
converts your voice directly into text and automatically extracts key information with an AI model that has learned emergency call scenarios, significantly reducing the speed of call managing.
Relay the voice from operator to caller, and vice versa
Recognize the voice and extract the valuable information by using Wit.ai STT API
Visualize the information on a browser by using React
How we built it
Wit.ai provides most of the functions we want to implement. Thanks to Wit.ai, The only part that we spent on NLP is adding a separate model to Wit.ai by recognizing special entities such as emergencies and injuries.
The STT function in Wit.ai takes the part of converting voice data into text, and the built-in NLP function can extract and classifies entities like place and time.
Challenges we ran into
Our biggest challenge was streaming audio between browser and server. There was several methods to achieve this goal; especially using WebRTC and establishing the call. But it was too hard to achieve quickly; we have to construct STUN/TURN server to properly use this protocol.
So we convert the audio to a series of buffers and streamed it to server by using socket.io. We tried to transmit our audio buffer as soon as possible to the opposite side, and we achieved only 1s latency while the call.
We decided to stop developing the advanced features for this since this is a demo, but we think that we can construct the appropriate call structure by using the proper infrastructure.
What's next for 911 Assistant
If this gives us a certain level of accuracy, we can introduce it into the queueing system
where a simple scenario would allow us to proactively extract key information from requests waiting to be answered, thereby reducing a huge amount of time to manage emergency calls.
Reference
Coronavirus Disease Outbreak in Call Center, South Korea - CDC Paper
Built With
audio
react
socket.io
typescript
webrtc
Try it out
github.com
chadolbagi.github.io |
10,007 | https://devpost.com/software/hear-everthing | Inspiration
I wanted to create an app that could help people who have difficulties in this pandemic to connect to their love ones online to have it more easier by controlling their social media by voice. With this app my goal is to make it more easier for people with visual impairments with a tool that could automate social networks and google searches
What it does
The app uses selenium to control the user browser of the user to login into the user social media and post depending on the voice command. It also can do google searches and read the information of the web page.
How I built it
I created a wit.ai app and created intents to classify the different social medias that I was going to use for the app.
I started to create a app with python that had a simple GUI that the user could only had to open the app for it to work.
With selenium I created functions depending on the social media to automatize the log in and the posting.
What's next for HearEverything
Finish the software development of the app
Create more options for the user to enjoy more of each social media
Create a way for the user to also use the app to play music
Find more ways to build a easier GUI for the user
Built With
gtts
python
selenium
speechrecognition
wit.ai
Try it out
github.com |
10,007 | https://devpost.com/software/jobproctor | Lets get started
Choose from multiple features
Preferences and skills section
Recommendations tab
Inspiration
Refusing to be ordinary has been the motto of our team at JobProctor since the very beginning. We are a team of high spirited engineers who want to tackle the problem of unemployment in the gig economy for the people from the lower strata of society.
Given the uncertainty in the world and the ever-increasing number of people losing their jobs due to the global pandemic, we decided to come up with a platform that can empower our society in such tough times. Thus, we decided to tackle the giant of unemployment which would further worsen in the near future.
In our opinion, the people who would be the worst hit by this situation would belong to the unorganized informal industry. Considering Facebook and messenger’s penetration through all the sections of our society, we felt it was best to use this platform in order to reach a large number of people who are in search of jobs and do not use traditional job search platforms like LinkedIn, etc.
Our app aims at organizing and managing the informal job industry. Such engagement does more than increase productivity, it decreases attrition, reduces snafus, and rationalizes the cost of operation; all while giving a much safer cultural fit.
JobProctor’s mission is to create a transparent and ethical and efficient job-search platform for all domestic and gig workers and household employers, and provide bespoke platform features to assist and support users throughout the employment term.
What it does
JobProctor is an interactive and easy-to-use chatbot on messenger, where people can search for jobs, create jobs, create personalized alerts for particular openings and also apply for these positions via messenger.
The semi- and unskilled workforce in India are expanding as demand for everyday services has increased in urban areas. From delivering food and appliances to helping with home maintenance and carpentry work, the segment is growing exponentially, mostly driven by rapid urbanization. There are several job search platforms available, but all of them are concentrated in the professional and white-collar sectors. We do not have a leader in this job sector. All these factors added to our heartfelt desire to make rural India economically self-sufficient lead to the isolation and selection of this particular problem.
Today, unskilled and gig workers are looking at savings, location, living conditions, and a community, which are some of the key factors in determining the willingness for them to take up a job. Our solution caters to all these factors and provides a personalized job search considering all such factors. We aim at fostering better job opportunities for workers and domestic help. Our venture will also help promote local businesses and mom-pop stores who are in search of workers. We want to broaden the horizon of opportunities for domestics and unskilled workers.
How we built it
Explained below are the features of our apps and how we built them.
Create a Job Posting:
We allow employers to create job postings instantly. All job postings are saved in our database and also in Google’s Cloud Database to ensure they reach the right audience. All the added jobs go through our reliability model to notify users about sham or fake postings so as to safeguard them. Employers can add more details to make their job reliable.
Show Job Postings:
This allows users to view their job postings. Edit or Delete them.
Get Alerts:
This unique feature helps the users to keep a track of all the positions he/she is interested in and get daily updates for the same. You can just type ‘Alert’ to see Set one or Delete an existing one.
Google’s Recommendation Engine:
We have used Google’s Job Search v3 API to make sure users get the right recommendations when they add their skills/preferences. Google’s API indexes the added jobs and recommends them in order of highest relevance with respect to all preferences.
Auto Complete Feature:
We also allow users to paste a job description into our Messenger Interface. The bot leveraging
Wit.AI’
s amazing technology is smart enough to identify the key parameters like
Job Title, Salary Range, Work Experience
to make an effortless experience for employers.
Automatic detection of possible fraudulent jobs:
This feature helps us to see the degree of legitimacy of a posted job and bolter the decision of an individual while applying for the same. In order to achieve this feat, we integrated our app with a machine learning model which predicts the percentage of the legitimacy of a job in a job posting.
The technology arsenal used to build this feature consist of python(with libraries like scikit-learn, xgboost, pandas, hyperopt), flask, and Heroku. Python is solely used in order to build the ML model while Heroku and flask are used to host the model and run a server to listen for Http Requests respectively. Diving into further details,
Dataset Description:
The dataset used is an annotated public dataset with 17,880 job postings with 900 fraudulent jobs. Each record in the dataset is represented as a set of structured and unstructured data with the label as if the job record is fraudulent or not. The dataset is highly unbalanced which is dealt with using oversampling the minority label.
Data visualization and feature engineering:
In order to understand and better model the task at hand, we analyzed the data through visualization and built a proper understanding of the same. The categorical features like employment type, department, and experience needed were embedded using CatBoost categorical encoder. The job description associated with a job was cleaned and a 100-dimensional vector embedding was created using Doc2Vec.
Model training:
The model was trained with various models in order to select which of the algorithms proved promising for the given data. Finally, we decided on the top-performing classification algorithms, xgboost and RandomForest, and ensembled them to create our final model.
Optimisation:
Optimization of the hyperparameters of each algorithm was done using Bayesian Optimization. The final set of hyperparameters which yielded the best result during validation.
Hosting:
In order to integrate the above functionality into our application built in Node.js, the model was hosted using flask locally and then publicly using Heroku. For every job posted the application fires an API request to the hosted model, to which it answers with the legitimacy prediction, which is displayed on our application.
Challenges we ran into
We had a holistic experience full of ups and downs that further broadened our approach towards tackling problems, both in tech and socially.
Learning and creating an app in Node js and getting familiarized with Facebook’s Messenger Platform was the key part of our journey. Integration with Google’s Job Search v3 API was one of the cardinal challenges since the API had little documentation and sources to refer to. The next part of our journey was identifying how we can make our system reliable and it was at this juncture that we thought of having a reliability system in place.
During the legit job identification model building, the public dataset was severely imbalanced to which we dealt with using oversampling of the minor classes. This way the model was better able to generalize on the features that permit a job to be flagged fraudulent. Another challenge was to integrate the model built-in Python with the application which was in javascript.
The workaround to this was to create an API that links both. The main app calls for the prediction of the model with the details of jobs, the hosted model receives the API call, predicts the legitimacy of the job, and sends the prediction which is then shown in the application.
The asynchronous nature of Javascript made things difficult while we tried communicating with different components of our application which are interdependent on each other for data. Designing user interactions and experience was also another challenge. Choosing from the available plethora of UI frameworks that offers most of the required components and also looks modern was also a part of the design process. We kept reiterating the design process as the app progressed to come up with a more intuitive user experience.
Also, other challenges of implementing JobProctor include: how to encourage its initial usage, and build a ‘trust’ community with users on the platform; how to build upon initial momentum towards strong user retention; creating conditions for social awareness among employers in host-countries; increasing conditions for platform accessibility for those within the identified demographic, but are digitally-handicapped and/or in hard-to-reach areas.
Accomplishments that we're proud of
We have classified our accomplishments into two baskets, a technology bucket, and a social impact bucket.
To begin with the former, integrating the Google Job Search API was something that was a blocker for our way since we wouldn’t have been able to provide our users with the much needed personalized suggestions. After following the documentation thoroughly, the team was finally able to get past it and we were happy we could bring this to our users.
We wanted to reduce the number of online recruitment frauds, especially employment scams, which may lead to privacy loss for applicants and in turn, harm the reputation of various organizations involved. Our application provides a way to solve this problem by using machine learning. This way the app can reinforce the trust that we form with the aspiring applicant’s community.
Applying the idea of organization and management to the informal job industry in India is an unprecedented task. Innovation shines through JobProctor’s easy-to-use mechanism, which is designed to engage user segments by giving personalized and timely alerts and updates. Inbuilt platform features aim to continue supporting employers as well as employees throughout their job-hunting process.
Team JobProctor is proud of the fact that we could successfully use Facebook's penetration to reach out to such an often neglected section of our society and thus create a positive impact in their lives by exposing them to infinite opportunities of progressing their careers.
What we learned
The main takeaway for our team was to appreciate how tech-dominated if implemented in a simple yet elegant way can serve a larger purpose for the greater good of our society.
The satisfaction with the fact that JobProctor will positively impact the growing number of increasing informal workforce in India along with the expanding migrant populations is yet another takeaway.
JobProctor’s backbone lies within SDG 10: Reduced Inequalities, and Goal 10.7 — “to facilitate orderly, safe, regular and responsible migration and mobility of people […] through the implementation of planned and well-managed migration policies,” alongside Indicator 10.7.1 to measure impact (“Recruitment cost borne by employee as a proportion of yearly income earned in country of destination”).
What's next for JobProctor
We plan to increase the job posting on our platform by a large number by 2021. We also aim to provide support in regional languages. We also look forward to implementing voice-based conversations keeping in mind our target audience. We want to add bio finder functionality to our application. We also have global aspirations with the platform and are aiming to provide a meaningful livelihood to 120 Cr domestic workers and blue-collar individuals.
Built With
angular.js
category-encoders
css
flask
gensim
google-job-search-v3-api
heroku
html
javascript
matplotlib
nltk
numpy
pandas
postgresql
python
seaborn
sklearn
uikit
xg-boost
Try it out
m.me |
10,007 | https://devpost.com/software/x-1oqc6m | Infrastructure diagram
Contacts section
Calibration of the user's speech when sober
Notification when Lucid detects a high intoxication level
Inspiration
The United Nations health agency's reported that alcohol causes more than one in 20 deaths globally each year, including those resulting from drink driving, alcohol-induced violence and abuse and a multitude of diseases and disorders.
Yet another study conducted in the US found that 11% of women have experienced alcohol or drug-facilitated sexual assault at some point in their lives, and 5.5% of men were made to penetrate someone else through alcohol/drug facilitation.
We imagined a world where there was much more lucidity, and a clearer awareness about one’s own degree of intoxication to avoid making dangerous decisions. Why were we relying on legacy devices like breathalyzers that were expensive and impractical, when we could bring this knowledge into the hands of the masses?
We believed that if the individual could signal for help once they were past a certain threshold of intoxication, they would be able to get themselves out of potentially precarious scenarios.
What it does
We created an intuitive app that could enable users to self detect their level of intoxication, automatically reaching out to saved contacts once the user presented that they were overly intoxicated.
How we built it
Our app detects intoxication on two fronts, leveraging both mental and verbal cues in order to come to a conclusion on the user’s intoxication level.
Since intoxication causes impairment of cognitive functions (Fillmore, 2007), the basis of checking for intoxication is through time-sensitive simple logic puzzles. The app will ask the user a series of questions. A sample question would be: “If you have to wake up for a meeting at 8am tomorrow, what should you do?”. The user is expected to respond with possible commands, that wit.ai can then process. If the user returns an incoherent or illogical answer, the audio file of the user’s response will be sent together with a notification to the user’s saved contacts.
This audio file will also be processed by our RNN ML model, which will consider the following speech properties that are affected by intoxication (Marge, 2011):
Clarity of pronunciation: Intoxicated users tend to have poorer speech clarity
Pace of speech: Intoxicated users tend to speak slower
Pitch accents: Intoxicated users have higher/lower emphasis frequencies as compared to when they were sober
Challenges we ran into
We were limited by the fact that we did not possess a comprehensive dataset of audio files at varying degrees of intoxication. This dataset would be necessary in order to build the speech recognition capabilities of our app. In order to mitigate this limitation, our app will also actively be collecting data. This happens when we send the audio file of the user’s response to his saved contacts, and the contact responds to this audio file by identifying it as “Sounds OK” (not intoxicated) or “I’m on my way” (intoxicated). Clicking either of these buttons serve as a human form of verification, generating data that is cleanly labeled as either intoxicated or not. These datasets can then be used to further refine our RNN model, improving its accuracy in detecting intoxication via audio files.
Accomplishments that we're proud of
Building a working prototype!
What we learned
Data considerations - we wanted to make sure that users' privacy was not compromised, and so we thought also about anonymizing our data collection as well.
What's next for Lucid.ai
Refining our Machine Learning model and ensuring higher degrees of accuracy for our product.
Built With
adobe-xd
flask
python
react-native
wit.ai
Try it out
github.com |
10,007 | https://devpost.com/software/companion-9u5kxc | Inspiration
Usually I will not like chatbot because I got the feeling that am chatting with robot which will not understand like human. So, using Wit.ai needs to provide human level conversation.
End user needs to feel like he is talking with human. So we made every response typed instead of giving options to users(i.e.which will give Robotic chatbot experience).
Main theme : Everyone problems needs to be addressed.
Companion chatbot - Once after the conversation, it gives positive approach which is scarcity in society.
What it does
Step 1 : Companion Chatbot will listens to your problem
Step 2 : Provides multiple ways to solve it. Solution will be in various ways(Books, Videos,Websites).
How I built it
Using Facebook messenger product and wit.ai.
Once the user interacts through facebook messenger, using wit.ai utterances and corresponding response is handled in node.js.
Challenges I ran into
Problems to be chosen - Currently chose Work and Family after discussion with the team.
Response make the person to be interactive and give feeling like talking with human.
Accomplishments that I'm proud of
Learnt Node.js - UI framework,
Discovered the messenger and Wit.ai features,
Connecting multiple applications and makes working as single application.
What I learned
Working Chatbot learning experience from scratch
What's next for Companion
Plan to add more problem areas and make the interaction more like human feeling.
Built With
facebook-messenger
node.js
wit.ai
Try it out
www.facebook.com |
10,007 | https://devpost.com/software/haylingo-your-new-language-practice-companion-amcxfb | Inspiration
Eager to learn and be fluent to acquire English language motivates me to make something that can help people like me can easily practice my target language.
What it does
Main Feature
HayBot
a fast way to practice your new language with a conversational AI bot.
HayFriend
connecting you with real people across the world who enthusiast to practice a new language too.
HayWord
Play guess the word game to enrich your vocabulary with the fun way.
Support Feature
Translate
you can translate by word or sentences powered by wit.ai language identifier, NLP and NLU.
Pronounciation
listen to how the word is pronounciate
Quick Reply Feature in Conversation
Translate All
make it easier for you to understand the last chat text.
Change to Speech
a mode that made for accustomed you to listen to the new language.
How I built it
using FB messenger with node js as backend and typescript as languages, processing user input with wit.ai to determine their intent for translate and pronounce also to identify the user input language to assign the parent language of the user in MongoDB, and for translation service and text to speech, I used was and wordapi for hayword feature.
Challenges I ran into
send an audio file from aws polly to bot messenger.
bridging users.
-create a guess word game in messenger
Accomplishments that I'm proud of
can use wit.ai for the first time and integrate it with messenger
bridging users
send an audio text to speech to messenger
make a guess word game to enrich the user vocabulary.
What I learned
a lot of wit.ai and messenger api
be patient for facing stack and error during code
What's next for HayLingo! - Your New Language Practice Companion
iteration for getting product-market fit
expand to other new languages like Korean, Japanese, Spanish, French and etc
Built With
aws-translate
cleverscript
facebook-messenger
mongodb
node.js
polly
s3
typescript
wit.ai
wordapi |
10,007 | https://devpost.com/software/botfind | Bot_find
Inspiration
To ease the stress to programmers getting answers to a bug
What it does
It scraps through doc files to give answers to a bug and also web pages where an answer has been provided already
How I built it
Built with wit.ai
Challenges I ran into
Linking the bot system to doc files and web pages where answers can be gotten
Accomplishments that I'm proud of
I gained some insight about wit.ai
What I learned
How apps can be created with wit.ai
What's next for Bot_find
Linking doc files with it and using it with a web page and an app were programmers can get answers to bugs and in sigh to a concept
Built With
wit.ai |
10,007 | https://devpost.com/software/facebook-messenger-chatbot-boilerplate | Inspiration
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for Facebook Messenger Chatbot Boilerplate
Built With
mongodb
redis
wit.ai |
10,007 | https://devpost.com/software/robodoc-p3lb8h | RoboDoc
breast mammograms
Chest x-ray
working demo
COVID19 symptoms and analysis
Inspiration
We came up with this idea because of the lockdown caused by the coronavirus pandemic people are confined to your homes and it's a bit risky visiting the hospitals as there are chances of getting infected. We all are facing problems because of the coronavirus pandemic where people are in a state where anyone suffering from a disease is thought to be suffering from
COVID19
. Therefore we wanted to help people reduce this state of panic and thought of building a bot that will answer people’s questions regarding symptoms. That way people will gain knowledge of their disease.
What it does
RoboDoc
is a messenger bot where people could chat with it and get their symptoms analyzed using messages. Based on symptoms sent by the user it analysis and most accurate diseases are diagnosed. At present we have added 21 common diseases we will be expanding it to 87 diseases our main objective was to lessen the panic caused by the coronavirus pandemic. We have added analysis of Frontal
chest x-ray for covid19
and analysis of
mammography for breast cancer detection
.
Dataset
For the COVID19 detection model using X-rays, we used Kaggle and Github dataset accounting for total of 1300 COVID19 and 1200 normal chest x-rays. For breast cancer, we used the Kaggle dataset. For symptoms and disease, we used a CSV file for NLP training.
How we built it
We are using wit.ai for Natural Language Processing and based on symptoms mentioned by users we are predicting the disease and for detection of
covid19
using chest x-ray and breast cancer using mammography we are using tensorflow.js models and javascript
Challenges we ran into
Training using wit.ai was difficult and between the event, there were some changes made to wit.ai. Integrating tensorflow.js models with messenger webhook and integrating all of them into one single project was challenging.
We are trying to make our bot perfect and will research methods of implementation that could improve the accuracy of our bot. We will experiment with other architectures for training our model to improve efficiency. This can be achieved approximately in a time span of a month. After this is done we may look for funding and make it available to people.
Accomplishments that we're proud of
We are proud that we could build a chatbot that will help people to know whether they are covid positive or not. There is a messenger bot itself that detects covid using lungs x-rays and gives you an idea whether you have covid by symptoms is a proud thing for us. We were able to deploy a python trained model into javascript and deploy it on a server is an accomplishment to be proud of, as it was one of our major challenges. And Finally, we are proud that our bot is working as it should. So basically we are proud that we overcame all the challenges and built an application.
What we learned
A deeper understanding of
Facebook Messenger
architecture and how wit.ai works.
Training of NLP using wit.ai. Machine learning model creation, conversion to tensorflow.js, and integrating it with messenger
What's next for RoboDoc
At present we have added
21 diseases
we will be expanding it to
87 diseases
for predictions using all symptoms. We are trying to make our bot accurate and as it is used more we will train it for more symptoms and diseases. We will research methods of implementation that could improve the accuracy of our bot. We will experiment with other architectures for training our model to improve efficiency. We will be including more medical models for the diagnosis of more diseases using x-rays and MRIs. This can be achieved approximately in a time span of a month. After this is done we may look for funding and make it available to people.
Built With
glitch
tensorflow.js
wit.ai
Try it out
www.facebook.com |
10,007 | https://devpost.com/software/eldy-bot | Eldy-Bot responds to a nursing home concern.
Inspiration
During our final year of college, for a final project, we spoke to older individuals about their lives during the COVID-19 pandemic. These individuals were often feeling lonely since their loved ones no longer lived with them and since they could no longer participate in volunteering opportunities. Since COVID-19 can be deadly to adults 65+ years old, these individuals also feared going outside for regular everyday tasks such as grocery shopping. We hoped to create a product that would help out one of societies most knowledgeable and selfless populations during this rough period of time.
What it does
Eldy-Bot is designed to aid the most vulnerable populations during the COVID-19 pandemic.
In order to
help older individuals gather necessities during the pandemic
with messages such as "Eldy, I need someone to go grocery shopping for me." or "Eldy, I need water.", Eldy-Bot can provide the user with a list of nearby people who have any of the requested items or that can provide any of the requested services.
In order to
help older individuals to create meaningful connections and therefore fight loneliness
with a message such as "Eldy, I feel lonely.", Eldy-Bot can help the user connect with people based on their intersecting interests/hobbies.
Eldy-Bot also has the ability to
answer COVID-19 related questions that older adults may have
such as: "Eldy, I have a kidney disease, what actions should I take in order to be safe?" or "Eldy, what's the status of COVID-19 at 777 Brockton Avenue, Abington MA 2351?".
How I built it
Wit.ai
to identify the intents and entities of a user sentence.
Airtable Forms
to allow good samaritans to submit information about the products and services they have available as well as what their hobbies and interests if they wish to connect with an older individual.
Airtable API
to retrieve data (stored from form submissions) in order to craft a response to the users request.
Flask
to handle web requests.
Heroku
to deploy our product.
Challenges I ran into
We were initially using Glitch to deploy our product; however, since it recently stopped allowing pinging services we had to switch out entire code base to Heroku.
Accomplishments that I'm proud of
What I learned
How to create chat bots that can provide accurate responses regardless of the phrasing of a sentence.
How to train Wit.ai to identify specific intents and entities.
How to integrate the messenger service to a Facebook page.
How to use Airtable forms to create a database that stores user information submitted via a form and how to retrieve that information using Airtable APIs.
What's next for Eldy-Bot
We would love to further develop Eldy-Bot identify any organizations that
Built With
airtable
apis
facebook-messenger
flask
heroku
mapquest
nltk
pymessenger
python
rest
wit.ai
Try it out
www.facebook.com |
10,007 | https://devpost.com/software/voluntree2 | Listens to both Feed and Page Inbox
Data collection & account linking in 3rd party tools
Automated answer learning from knowledge base
Convenient "Add to Calendar" button for volunteer
Volunteer can "Share" their activity with their friends and motivate others to sign up
The problem
How do nonprofits reach volunteers? Mostly by creating a new sign up page or adding a Google Form on the website. But you can reach more potential volunteers where they are already spending their time – on Facebook. But doing so can lead to a lot of manual work like keeping track of volunteers on spreadsheets. Imagine having to collect emails from individual chat and create an account in a volunteer management system. Or, going through the spreadsheet for sending important updates to individuals. If the organization doesn’t use any management software, things get even worse. Data gets lost eventually and volunteers have to fill up a form every time they come back. There remains no way to recognize returning volunteers and pay tribute to their excellent work. And of course, the manual bookkeeping process doesn’t scale, especially if the organization has a large number of followers on social media.
The solution
Voluntree comes into the picture to provide automation to the volunteer recruitment workflow from social media.
Once connected to a Facebook page, VolunTree will listen to the feed and page inbox. It will automatically recognize interests from comments and messages. It will initiate data collection, onboard volunteers, and create accounts in the volunteer management software you already use. It will also be able to answer factoid questions by learning from the knowledge base you provide- so that you don’t have to deal with repetitive questions over and over again.
Inspiration
We have closely seen how nonprofits from our local community were struggling due to the manual bookkeeping process during the challenging time of the world pandemic of COVID-19 and it was a great motivation to automate this process with software.
Features
Outreach Tools
Transparent and detailed “Sign Up” posts
Spread the words with multiple pages and posts
See response in real-time and take actions
Sign Up Management
Automated data collection, email verification and onboarding
Account linking with 3rd party integrations
Volunteer profile, activities & ratings
Communications
Automated onboarding and acknowledgment
Automated response from sign-ups, volunteer info and payments
Broadcast event updates in messenger
For Volunteers
Review and respond on the go (post comment + messenger)
Convenient "Add to Calendar" button
"Share" their activity with their friends and motivate others to sign up
Built With
django
react
wit.ai |
10,007 | https://devpost.com/software/umnofon | List of projects for the user
Captured notes
Details of a single note after analysis by Wit.ai
Adding new note with direct input
New note after analysis by Wit.ai
Inspiration
Looking at the doctors at a hospital or building inspectors in the field I see how much time they spend and how uncomfortable it can be to record and remember their observations and take notes.
Their skills and work are
not for scribbling the note
, they
need to focus their attention
on the patient or the building they make.
Let the technology help them by taking care of note taking.
Let technology enhance this note taking.
Make it safer, faster and more productive to better people lives.
What it does
Umnofon is a mobile app companion for a field professional. It uses voice-to-text and NLP models to process, understand and make available digital notes. With help of NLP and built-in inference logic an app produces a report document which is based on the notes that had been submitted by the user.
We had selected a civil engineering building construction case where an engineer needs to constantly monitor the project's progress by visiting a building site. Without our app an engineer will go through the building and either memorize or write down notes which are then reported to the project supervisor. Such approach
can lead to checkpoints being missed (too much to check, limited time), errors (incorrectly captured data) etc.
With our app a civil engineer will dictate notes and they will be automatically recognized using Wit.ai-based algorithm. To avoid excessive repetition an inference algorithm is used to construct complete notes from historical data and incomplete information.
In the MVP app the report lists the resolved notes and their context.
How I built it
The app is split into on-device audio capture and a cloud-based processing. The stack for the app is the following:
App: Expo.io SDK37, React-Native-Paper interface,
Backend: Google Firebase: Authentication, Firestore, Functions, React-Admin
Speech-to-text/NLP: wit.ai
Challenges we ran into
wit.ai approach to NLP is different from my existing experience - the model was rebuilt at least twice,
mapping a real-world situation (in this case - civil engineering way of working) requires very flexible NLP and complex inference logic
Accomplishments that we are proud of
We had built an MVP full-stack application that runs on a mobile device, collects audio and uses Wit.ai for recognition and analysis in less than 3 days.
What we learned
professional settings like civil engineering can certainly benefit from the new AI/ML based technologies,
technology can make real and significant impact in traditional fields - reduce time and non-essential effort,
improvements in process and technology has real life impact - more patients served, safer buildings built etc.
What's next for Umnofon
There are several areas where Umnofon can develop:
security and privacy (encrypted notes)
clean and polish of the app,
new professional fields with specific terminology and models,
new natural languages support,
team collaboration when several professionals work on the same project,
integration capabilities,
compliance (i.e. HIPPA for medical data, GDPR)
Built With
expo.io
firebase
typescript
wit.ai
Try it out
expo.io |
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Hackathon Projects
Summary
- This dataset contains
- 200k+ hackathon project descriptions
- from 6700+ hackathons
Data Description
combined_df.parquet: This contains all the projectsid: The id of the hackathonproject_link: Link to the hackathon projectproject_description: Text from hackathon page
hackathons.json: This contains the details of the hackathons
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