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10,009 | https://devpost.com/software/songs-lyrics | Inspiration
Our inspiration was to see applications like "Shazam" and "Letras.com" that are heavy and we thought why not have the same function as them using Facebook Messenger, in the future we intend to make functions more similar to the search form of the application "Shazam ".
What it does
Our project, Songs Lyrics, is an application integrated with facebook messenger to make life easier for music lovers all over the world, making songs and artists much more accessible, just send a message to our facebook page, and our bot te will help you find what you're looking for, simple as that!
How we built it
We used PHP to build the music lyrics search API, the APIs were built by ourselves but were hosted on servers of our friend’s partner site, who already had the site with a large bank of music lyrics and he gave us authorization to use to create this free project.
Already the writing of the messages using the ChatFuel platform connected with the facebook page, and within that platform we chose the free plan, we used it because it was one of the only ones that allowed us to make requests for an API to get the values dynamically and respond to the user the musical lyrics.
Challenges we ran into
The first big difficulty was when we thought "how are we going to get so many musical lyrics?", And then a member of the team said he knew a friend who owns a music lyrics site and he gave us permission to use the database on his site in our application.
The second difficulty was finding a platform to write the messages that the bot had sent, most platforms did not have the option to send requests for APIs in the free plan, but after a few days of researching we found ChatFuel that we attended very well for the creation of this project.
Accomplishments that we're proud of
We are proud to be the first to build an automatic bot on facebook messeger to search for musical lyrics.
What we learned
We had to learn how to edit video to make our bot presentation more professional.
And we learned all about the ChatFuel platform and its API connection documentation.
What's next for Songs Lyrics
We want to look for ways to make our users' lives even easier, so we will invest our time in research to improve the way our users search for their musical lyrics, thus enabling searches by audio, image or text.
And of course we want more than anything to have the option of previewing the words along with the writing of the letters.
Built With
chatfuel
css
css3
git
github
html
html5
javascript
php
php5
phpmyadmin
Try it out
jotinhabr.github.io
github.com
www.facebook.com
www.facebook.com |
10,009 | https://devpost.com/software/refuge-6eumon | Screen shot
Inspiration
The United Nations Population Fund (UNPF) estimates that the incidence of Gender-Based Violence (GBV) is growing astronomical in Nigeria. From forced and early marriages to the physical, mental or sexual assault on a woman, nearly 3 in 10 Nigerian women have experienced physical violence by age 15 (NDHS 2013).
What it does
Refuge is a platform that enables the reporting and tracking of cases of GBV, with focus on providing needed support to victims.
How we built it
We built the backend with C#
Challenges we ran into
Accomplishments that we're proud of
We are proud to provide a platform for the reporting of GBV
What we learned
We learnt the importance of enabling victims to voice their opinions
What's next for Refuge
To make it accessible to all Nigerian victims of GBV.
Built With
c# |
10,009 | https://devpost.com/software/virus-checker-a-secured-covid-19-symptom-triaging-chat-bot | Fig 1: Percentage of most common COVID-19 symptoms (Source: WHO).
Fig. 2: The process of symptom triaging using SymCheck
Fig. 3: A simplified flowchart of symptom triaging of our chat bot
Fig. 4: Main features of Medbot
Fig. 5: Architecture Diagram for the Hospital Search
Fig. 6: Architecture Diagram for the Nearby Cases Search
Inspiration
According to the Center for Disease Control and Prevention, 80 percent of people with COVID-19 are unaware they have the virus. It also shows that infectors are most infectious when they have symptoms. Because of this, many organizations around the world have added urgency to the efforts to develop protocols for hospital and facility entrance triage. Triaging by Chatbot emerges as an effective tool to reduce the workload for the medical system.
Fig 1: Percentage of most common COVID-19 symptoms (Source: WHO).
Inspired by many successful technology-driven solutions for COVID-19, we’ve developed SymCheck for triaging COVID-19 at any building’s entrance with the six main purposes:
Keeping the workplace safe
Speeding up the screening process
Timely alerting and providing CDC based guidance to users who are at risk for Covid-19
Immediately find nearby testing locations for at-risk users
Notifying users of nearby possible Corona cases
Provide further medical guidance through a qualified panel of doctors/occupational health staff to better guide an infected user.
What it does
When the world is ready to open up after a long period of shutdown due to COVID-19, there is a crucial need for a comprehensive, secure, simple and fully automated symptom self-checker for security check-in at the workplace. Powered by developing tools from Facebook Messenger, Medbot is developed to carry out exactly those needs. With this app, no security personnel or medical staff will be required to be on site to screen and triage the workers at the gate. Instead, when a person enters a building, he/she will go through a fully-automated symptom screening for security purposes using Medbot.
Fig. 2: The process of symptom triaging using SymCheck.
The process is as follows:
When Employee A enters the building , he or she will need to pass through a security check-in station.
At this station, the person will log in the SymCheck website to access the symptom screening chat bot.
Employee A will answer all the screening questions.
If Employee A shows no symptoms after the screening session, he or she will be texted a message of screening result to show to the security guard who will allow the person to enter the building.
If Employee A shows symptoms after the screening session, he or she will be directed to talk with occupational health staff available on MedBot. The occupational health staff will do a live chat session with the employee regarding self-quarantine and monitoring their symptoms. The chatbot will eventually suggest the nearby testing centers so the employee can make an appointment. The patients will also be instructed to self-quarantine and informed about testing sites for Covid-19.
With this fully-automated check-in process, the app will minimize the physical human contact and significantly reduce the risk of COVID-19 transmission. We believe that besides security check-in at the workplace, this app can also have many other use cases such as:
Quick patient screening at a local clinic or hospitals.
After screening by the app, the medical assistants or nurses will separate from all the suspected COVID-19 patients and place these patients in a specific designated area. The medical staff can now be more aware and cautiously follow CDC guidelines while caring for those separated patients. This can help minimize the spread of the virus to health individuals at a high-risk area like a clinic or hospital, and, more importantly, this can help protect the nurses and medical staff from contracting the virus from the patients.
As more companies extend their work-from-home period, our application can also be applied to perform scheduled symptom screening for employees at home so the companies can monitor their employees’ health status during the pandemic.
How I built it
Development process:
Fig. 3: A simplified flowchart of symptom triaging of our chat bot.
In order to create the facebook messenger chatbot, we decided to utilize Manychat. This allowed us to integrate a complex flow and have an efficient working solution. Since Manychat doesn't really have any complex built-in NLP engine, we decided to use Dialog Flow for this purpose which allows us to make the bot smarter.
We did this by creating a Python back-end which received webhooks from Manychat every time a user typed something the Manychat bot didn't recognize. This allowed us to add small talk to the chatbot which, although is a nice feature to have, is something that a lot of chatbots seem to be missing. Every user response was then sent to the Dialog Flow API and returned us the appropriate answer.This solution allowed us to use the best of both worlds: the Messenger UI built visually and an advanced NLP.Our back-end is being hosted on AWS Lambda which has many advantages such as low-cost and scalability.
Facebook Messenger components used:
When building the chatbot, many Facebook products were incorporated to optimize user experience. Specifically, the chatbot incorporated a Persistence Menu, Quick Replies, One-Time Notifications, and Handover protocol.
Persistence menu:
By incorporating a persistence menu, we were able to give users an easier way to navigate different features of the chatbot, thus improving the overall user experience.
Quick replies
are very helpful as they show what answer is required or what the user can do. It gives suggestions which the user can act upon. Without them a bot would be a lot less useful as you wouldn't know what to do or what is possible. Quick replies were incorporated in the chatbot to give users different options to efficiently navigate as needed. People often think a chatbot is a tool that 100% automates every single thing. That couldn't be further from the truth. A chatbot works hand in hand with actual humans. The bot takes over a repetitive task that a human often has to do and whenever the bot doesn't understand what the user needs, the option to get a human to intervene is often very helpful.
Handover protocol:
We’ve incorporated Handover protocol by offering the user a chance to switch to a live chat with an actual doctor.
One-time notifications
are especially useful when you want to check upon the user, ask for feedback or really any kind of post-conversation message or notification you want to send to the user. Within Messenger you can only use these once and the user has to explicitly accept that.We incorporated these notifications by checking up on the user after 24 hours of using the chatbot. If their condition is worse after 24 hours we suggest them to go through the flow again as their situation may have become critical.
Fig. 4: Main features of Medbot.
Furthermore, the chatbot collected sensitive data from the user such as his or her address to find nearby testing centers and medical data, thus introducing the need for user privacy.
User Privacy:
Users are made aware that their data is being used by MedBot solely for guidance and medical assistance.
The Health Insurance Portability and Accountability and Act(HIPAA) outlines several rules and regulations to keep patient data confidential. HIPAA’s privacy regulation protects medical records of individuals, with limits and conditions of various uses that can’t be made without patient approval. In order to maintain and respect user privacy, we first provided users with information on how their data would be used. Then, we used quick replies to give users a choice to agree or refuse to proceed accordingly. Second, if users selected the option to talk to a doctor, they were made aware that the doctor will need access to personalised information to provide medical guidance. Then once again, quick replies were for the user to either proceed or skip talking to the doctor on MedBot. In terms of location sharing, we gave users the option to either click “share” to provide their location, or “skip” to prevent doing so.
Challenges I ran into
When developing the chatbot, we wanted to incorporate the feature where patients would get to choose if they wanted to connect with doctors. The challenge associated with this part was finding actual doctors who would serve as the live healthcare assistants. Eventually, we found 1 doctor from the United States who agreed to serve as the live healthcare staff. In the future, we will contact more doctors from different countries to better serve users. Furthermore, Integrating the Dialog Flow API was quite difficult to do since the documentation isn't that great. However, after many hours we managed to get it working! Another issue was generating the messenger rich elements from our back-end which required us to return JSON that contains buttons, text and URLs depending on which element is needed (e.g. a quick reply or a carousel). Due to time limit, we haven’t finished the UI for SymCheck website and integrated the chat bot in. However, the Medbot is completed and fully functional.
Accomplishments that I'm proud of
At first, we started off with a simple symptom triaging bot following CDC guidelines. However, by using more facebook features, we were able to add in additional useful chatbot functionality. Specifically, we were able to incorporate the Google Maps API to find nearby COVID testing locations for users. We were also able to incorporate the “Cases near me” feature which notified users of nearby patients who were at risk of COVID-19 as determined by MedBot. Within a short period of time, we were also able to get in contact with a professional doctor in the United States who served as the live healthcare staff agent to provide personalised telemedicine based guidance to users in need. In order to maintain user privacy and follow HIPAA security protocol, we allowed users to choose if they want to share their location as well as provided them with an option to make a userID to protect their information.
All in all, we’re proud of developing a comprehensive Facebook messenger bot that incorporates many different useful features to assist users amid the pandemic. We’re proud of building a communication messenger tool that integrates several Facebook features and user privacy to mitigate further spread of the virus.
What I learned
By developing this chatbot, we learned how to build interactive and useful messenger bots which can potentially be used by the 1.3 billion messenger users globally. We learned how to optimize chatbot flow and integrate APIs to make the chatbot more useful especially amid the pandemic.
What's next for Virus Checker: A Secured COVID-19 Symptom Triaging Chat Bot :
Incorporating this chat bot in a security check-in system with employee ID badge scanner, mask detection on faces, and finger scan for vital signs. We expect to automate the whole security check-in process post COVID19-lockdown so that no security staff needs to be on site to minize the human contact to 0. Therefore, the spread and new cases of Coronavirus will decrease significantly.
We will reach out to more doctors and public health specialists for (1) providing feedback on improving our chat bot and security system and (2) serving as live healthcare staff to better assist users of the MedBot.
We will also need to do some UX research by testing on small groups of users to get their feedback to improve the application.
Our end goal is making this a well-functioning product by the time countries start opening up. This application has the potential to become an essential part of security check-in as a new normal at corporate buildings, clinics and hospitals all over the world post-COVID-19.
Built With
awslambda
facebook-messenger
manychat
python
Try it out
m.me
github.com |
10,009 | https://devpost.com/software/cheerio-0he2mi | Display daily schedule feature for therapists
One-time notification for appointment reminders
Attachment disabling during P2P chat
Inspiration
Suicide has been claiming millions of lives across the globe, and the evils of depression and anxiety have spared nobody, starting from celebrities to school students. Recently, Indian cinema lost one of its most beloved actors to depression and anxiety which brought up the issue of mental health all around the country and what steps can be taken to fight depression. This led us to build a chatbot that can get the users medical help when required, keep its users entertained and engaged, motivate them towards enjoying life and eradicate loneliness by connecting to others willing to help.
What it does
Our chatbot leverages Messenger's features to provide a unique Messaging experience with the following features:
User interface
User mood detection using Wit.ai
Built-in-NLP and Wit.ai to build a human-like conversation.
Schedule appointments with therapists as per their available slots
Reminder for appointments when the user asks to be reminded using One-Time Notification.
Live conversation with the therapist in the assigned slot to seek professional help
Suicidal response detection and immediate appointment scheduling with a therapist
Sharing of suicide prevention helpline number on detecting suicidal intent
Live conversation between two users maintaining complete anonymity using Personas.
Hate speech detection using user-to-user convo using Wit.ai and immediate reporting to the admins.
Option to report and block a user in case of misbehavior and present the issue via live chat with the admins using the Handover Protocol.
Conversation timeout in case of chat suspension for more than 5 minutes.
Disable sending of email id, phone numbers, and addresses between two users.
Disable attachment sharing to prevent sharing personal images etc.
Point-based system to judge user misbehavior using chats with other users. Users with very high scores get blocked temporarily.
Partner rating after the conversation ends.
Meme suggestions and display in Messenger
Joke suggestions that learn to adapt to user likes and dislikes based on the user's rating on each joke.
Motivational quote suggestions
Motivational video suggestions
Yoga and meditation suggestions in case of depression
Music suggestions from Spotify
We have taken special care to respect user privacy using the following approaches:
Complete anonymity of usernames is maintained between two users when they are communicating with each other.
Complete anonymity of profile pictures is maintained between two users when they are communicating with each other.
Hate speech is detected using Wit.ai during user-to-user conversation and reported to the admins.
Email addresses, phone numbers, and addresses cannot be shared between two users.
Option to report a user and present the issue directly to the admins via live chat.
No personal info is demanded from the user at any point in time.
Psych interface
Display schedule of appointments for the day
Live conversation with the patient in the allotted slot.
How we built it
Messenger Platform, with Flask for backend
MongoDB Atlas to host our Database (stores user states, prefences, appointment slots, blocked users)
Heroku to host our application
Wit.ai and Built-in-NLP to ensure human-like speech
Handover Protocol (live chat to report another user to the admins)
One-time notification (to send reminders for appointments)
Personas (to maintain anonymity of username and profile pictures during conversation)
Quick replies to handle user responses in multiple scenarios(reminder slots, user rating to attachments etc.)
Templates (media, call button, list, button templates)
Built-in NLP to handle greetings as well as sensitive data like emails, phone numbers, location
Wit.ai to detect user mood and understands what the user wants the bot to do
apscheduler library to connect users with therapists at the appointed time, to remind before appointments, and also to disconnect users in the live chat on chat suspension.
Pktriot to test our application locally
Git and GitHub for version control
Challenges we ran into
One of the biggest challenges we faced was using MongoDB Atlas for hosting the database as testing the application locally through Pktriot took a long time to connect to the database after every restart of the server.
Since there were 2 developers collaborating was hard with both developers living miles away as it brought up challenges while testing locally. We used 2 local servers and had to change the callback URL each time for testing the app.
Being beginners in app development we had no experience with hosting applications online and online-hosted database services.
It took time to find out APIs that would respect user privacy and not require paid subscriptions.
Given our application deals with a sensitive issue and involves interaction among multiple users, we had to come up with innovative approaches to respect user privacy.
Accomplishments that we're proud of
We have been able to come up with a chatbot that attacks a burning problem in today's society and can help users from falling prey to the monsters of depression, anxiety, and suicide.
We are happy that our chatbot can target a wide audience.
We have been able to build human-like conversation using Wit.ai and Built-in NLP knowing very little about the Deep Learning framework that went into building the Wit.ai platform.
We have built an anonymous P2P chat within Messenger that respects user privacy, complete with reporting and blocking features as well as hate-speech detection to prevent misbehavior.
We have been able to connect users with therapists within Messenger itself without using any third-party application and enable live conversation between them.
What we learned
Learned to leverage Facebook features to build an interactive Messenger Bot.
Learned to train NLP models using Wit.ai
Learned about different user privacy policies and built innovative solutions to respect user safety.
What's next for Cheerio
Using localization to facilitate offline meetings with therapists and accept payment for them.
Training a better and bigger model on Wit.ai to detect user intents and hate speech in a better manner.
Using Machine Learning to build a recommender system for jokes, music, video suggestions.
Built With
apscheduler
flask
git
heroku
mongodb-atlas
pktriot
python
reddit
wit.ai
youtube-search
Try it out
github.com |
10,009 | https://devpost.com/software/zyx-an-event-entry-management-tool | You can send the token anytime to verify but you can't access files/info without verification.
Inspiration
It was a conversation with one of my college seniors during a different business competition. We were talking about a startup idea that helps people organize events with less efforts and more flexibility. However, during that time I didn't put much effort into that.
What it does
There is a whole bigger picture behind it. The messenger app/bot can be used for a tool for retrieving user event credentials easily.
You get into the app, type in your verification token given to you by the main website, then the app verifies you as an user and binds your
facebook_user_id
to the database and other information.
You can then access your information through it. You can find your events as well as event tickets or passes. Basically the event pass/tickets will be generated digitally in IMAGE format and assigned to your database automatically. All you have to do is tell the app to fetch it for you.
How I built it
At first, I wanted to use Flask for the backend as I had previously worked a little bit with the messenger API. But then due to the flexibility of Django, I switched to it. I used
Heroku
for deployment. The backend uses PostgreSQL database.
The backend receives the JSON sent by Messenger API. Then it classifies the message-type sent by the API. Using iteration and type checking, the app sends different PAYLOAD, REPLY TEXT to the API that the user receives.
I made a menu system using the "Quick Reply" feature. The menu contains 4 options, each of them send respective and unique PAYLOAD value. In the backend, I used if-else to check for the PAYLOAD and reply to them accordingly.
To use the admin panel for my site, use this login-
username: root
password: toor
Dummy User Tokens:
ZYX_TOKEN|456456456
ZYX_TOKEN|999999999
ZYX_TOKEN|45ABCDEFG
Challenges I ran into
Connecting the app to Facebook Messenger API has been a pain. Django was receiving a form reply from the API without a CSRF token. Due to the security I didn't want to remove the CSRFMiddleware. So, I had to recast the method to receive a CSRF less data.
I am thankful to Justin Mitchel for his this blog:
https://www.codingforentrepreneurs.com/blog/facebook-messenger-bot-with-django
. This one blog has saved me at last after hours of debugging.
There were also some JSON and Dictionary related challenges that I faced. Previously in Flask, I worked with pymessenger module. It made work easy and I was also familiar with Flask's
request
object and attribute. Switching to Django, I had to learn the
request
object again and had to implement the response object thoroughly.
Also, the main challenge was to write clean and organized code. Code organizing is a pain when you work with dictionary. There are longer lines of codes. So, cutting them into smaller pieces and connecting them was a challenger, yeah. I wish we had a framework for this, ugh.
Soon I wish to make a python module/library for that. Apart from these, there weren't much challenges.
Accomplishments that I'm proud of
I hadn't heard about the hackathon until 4-5 days ago. Delivering a minimal app with completeness is something that I am proud of.
What I learned
I learned creating a messenger bot using Django. I learned some basic tips for working with Dictionary Objects. I learned about deploying Django into Heroku and the errors that come with it.
What's next for ZYX - an event entry management tool
In my todo list, I wish
to add more "Quick Replies" feature
to add greeting message and basic reply feature without external framework
create a web view for sending messages to users manually (for development)
complete the web interface
learn REST API to add endpoint for mobile applications
Built With
django
heroku
python
Try it out
github.com |
10,009 | https://devpost.com/software/a-aup7sn | 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
m.me
github.com |
10,009 | https://devpost.com/software/bus-info-teller | Inspiration
I am a university student. My university is beautiful, and also has a large transport section. Students avail the transportation system and as my university has a huge number of students, there are a lot of bus going to different places in different schedules and routes. The buses and the routes also changes according to demand! I had a huge problem keeping up with the schedule, so I thought, as we all use Facebook and messenger, I wish it was possible to keep a track of the buses from anywhere! Then came the idea of having a chatbot solving the problem!
What it does
It interacts with users and gives them information about the trips and bus schedules in details.
How I built it
With the help of a basic chatbot handler
Challenges I ran into
The only challenge was to make the chatbot more human-interactive. This is the only challenge I faced and I hope to keep up with it.
Accomplishments that I'm proud of
This chatbot will help any registered institution's students or employees to keep themselves up-to-date with the bus schedules and thus, it solves a problem we all face! and I am proud to be solving it!
What I learned
I learned how to handle chatbots, how to use human-interactive options and more!
What's next for Bus Info-teller
Better user experience
Detailed Information about the buses
Finding buses' real-time location (if the buses have tracker devices)
and more!
Built With
automatton-instant-answers
facebook-chat
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/teach-it-0yl75q | Logo of Teach it Limited
Inspiration
Being teachers in our past and working in software development fields, we always felt that we could do some much more to democratize quality education. When we used to teach in schools, we figured out that there was a lack of quality teachers in Bangladesh and the quality teachers were not interested in the teaching field due to lack of monetary incentives.
We figured out that there was a lack of community-driven learning platforms and we were inspired to build it to make sure the citizens of Bangladesh get quality education.
What it does
The messenger bot provides information about Teach It and provides notification of the Live Classes that we are initiating from our Teach it Page. The messenger bot also provides Quiz for Grade 5 students so that the students can assess themselves and ask questions to teachers on our page based on that.
How I built it
We used Chatfuel to build the messenger bot. We incorporated text, videos, quick replies, and notification system to make sure the users get notification of the Live Classes and enroll themselves in quiz.
Challenges I ran into
We were new to Chatfuel. So we had to learn it for one day and work on it for four days to make sure we provide a standard user experience.
Accomplishments that I'm proud of
We are graduated from the Startup School by Y Combinator. We were on the top 6 startups of the incubation program by Banglalink, one of the top telecommunication companies in Bangladesh. We got a preseed grant from the Queens Commonwealth Trust.
What I learned
We learned that building a messenger bot is easy even if you are not a coder. Moreover, you can build a great experience for learners using a messenger bot.
What's next for Teach it
We are trying to build our platform '
www.teachit.online
'. Also, we are trying to build a great experience in providing Quizzes through the messenger bot. In the future, we will try to incorporate our messenger bot in every form of communication and try to incorporate features to reach our services to many.
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/testcenter-corona | Start
District Wise Government Test Centers
Test Centers in Dhaka District
Start Again
Private Test Center in Chattogram District
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 TestCenter.corona
What inspired us to create this chat-bot is the Facebook Chat-bot making competition. It didn’t just inspire us but also motivated us as we wanted to reach for the top.
Making this Chat-bot was not easy for a few reasons. One reason which would outshine the others would be the findings of all the Hospitals and Medical Centers and Clinics that provide treatment or test for COVID19. Obviously, it took a great deal of time because the number of hospitals were too much in number but the numbers of places which provide the required treatment is very low in comparison.
Because of everything we had input in this Chat-bot, the people would be able to choose their desired district to get treated. This was my biggest accomplishment so far. We are currently working on including more features, like cost of treatment. As you already know that, the cost of treatment at each hospital is different, some expensive, some not so much. So, once we input the cost information, the public can even be cost efficient about their choice as well.
In the beginning, I honestly had no idea about how to make a chat-bot neither any idea about the procedures involving in the test for Covid-19. Thanks to this project, I got to learn a lot and can now easily make a chat-bot. Soon we plan to include greater efficiency in the Chat-bot.
I used Chatfuel to make it. This is my first Chat-bot. I faced my problems in every steps.
In this pandemic, people who are suffering from other diseases are not getting enough treatment. Just like covid-19 helping hand, we plan to create a helping hand in the chat-bot for those people too who are victims of other diseases during this time of crisis.
Built With
chatfuel |
10,009 | https://devpost.com/software/bottle-keeper | Inspiration
We were inspired by the recent events and how it would be nice to be able to send and receive anonymous messages to be encouraging at this time.
What it does
The messenger bot simulates the message in a bottle experience. Users can choose to send 'bottled messages' to the 'sea' or search for bottle messages on the 'beach'.
How we built it
We used the Messenger API and used Heroku as the host for our webhook. We utilized the quick replies to give commands to the bot and used the one-time-notification feature as a way for users to be notified if a 'bottle' was found. We used the sentiment package from Node.js to analyze the messages sent to ensure that no negative messages were being sent.
Challenges we ran into
We had most trouble trying to get started but once started, the process of adding features went smoothly.
Accomplishments that I'm proud of
We are proud that we got a functioning Messenger bot to work.
What We learned
We learned what goes into creating a Messenger bot.
What's next for Bottle Keeper
We would like to add image support (i.e. send images instead of a message) and create a more fluent user experience. We couldn't figure out a way to hold a one-time-notification so that is something to work on in the future.
Built With
facebook-messenger
node.js |
10,009 | https://devpost.com/software/messages-with-hope-3fs5ni | demo figure of chatbot working
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 chatbox, 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 I learned
It was quite interesting to work with Facebook technologies, especially with Messenger. Making different conversation cycles has enabled us 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
node.js
wit.ai
Try it out
m.me |
10,009 | https://devpost.com/software/candour | Candour answering a question with a fact check.
Candour fact checking a false facebook post.
One time notifications on candour.
Inspiration
I think we have all experienced the divide that misinformation has caused in our society.
And misinformation continues to spread despite efforts by fact-checkers. With Candour, we might be able
to reduce the friction from the process of fact-checking and make it more accessible to everyone.
What it does
Acquires data from IFCN (International fact-checker signatories) such as AltNews and Politifact and allows messenger users to cross-check information they find online.
How I built it
Flask mostly, and a lot of coffee.
Challenges I ran into
Not having enough resources, this whole project is dependent on having a really large database of misinformation. I would've loved to do something beyond matching image hashes for comparisons but that would probably require better infrastructure.
Matching text-based queries with content from online articles. I ended using an approach where I extracted keywords from each article and joined them into a string and then used a Sequence Matcher to calculate similarity scores. Although it's not the best solution, It gave me better results than the other approaches I tried.
Accomplishments that I'm proud of
The data acquisition and parsing module is pretty cool in my opinion.
It can parse most types of news into a
specific JSON structure
which makes it easier to query.
I was really dreading making the video, but I actually had a lot of fun doing it.
What I learned
I hadn't really tried full-text search on MongoDB before, I would probably use it more in the future.
Really liked page-scoped user IDs as a system of preserving user privacy
Testing the bot
The FactCheck database won't get updated until the judging period ends to abide by the rules.
Here are some things you can ask candour till then.
Fact checks were obtained mostly from IFCN signatory Altnews (Altnews.in), but the framework can be extended to work with any other fact-checking portal.
Here are some examples of misinformation for you to try out.
Questions
Is the Whatsapp forward about the lockdown protocol true
Does disaster management act prohibit coronavirus posts
Does magnet therapy treat diseases
Facebook Post Links
https://www.facebook.com/manish.madan.142/posts/1428480933990304
https://www.facebook.com/savecowsli/posts/1081522821877858
Tweets
https://twitter.com/muglikar_/status/1252170483568689152
https://twitter.com/incscdept/status/1259360786545291264
https://twitter.com/madhukishwar/status/1253604397969403905
https://twitter.com/djtronixsa/status/1270582653297856512
External links
https://youtu.be/qik38f83yzy?t=432
Page Link
https://www.facebook.com/candour.framework
What's next for Candour
Get more sources, as of now Candours fact checks are a little too India focussed because those are the sources I went with. I think supporting local languages might also be a good idea. Improving the matching algorithm might be something that could make a lot of difference.
Built With
facebook-messenger
flask
mongodb
python
rest
Try it out
m.me |
10,009 | https://devpost.com/software/emergency-help-bot | Emergency help bot
Inspiration
Do something for our society
What it does
It help anyone for any emergency need.
How I built it
Using Chatfuel
Challenges I ran into
Internet problem, browser interruption issue.
Accomplishments that I'm proud of
Now i am feelingf good that i can help a little to our community.
What I learned
I have learned how to make a messenger chatbot.
What's next for Emergency help bot
We will update it to more specific location in the country.
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/break-illuminati-bangladesh |
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1
2
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Inspiration
i am a cse student. doing my thesis on ai. excited to build a chatbot when i started.
What it does
it is educational site . specifically on illuminati. it will submit a noob with basic understanding and resources.
How I built it
i built it with chatfuel.com
Challenges I ran into
i never have a page before . so i have to do some research on chatbot, managing page, target customer etc.
Accomplishments that I'm proud of
i am very happy to pull out everything just as i want to do!
What I learned
managing page, replying to customer , maintenance the post and page etc.
What's next for break illuminati bangladesh
improving chatbot with awesome features to develop my customer services
Built With
bot
chat
chatfuel.com
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/bilhetinho | Inspiration
What inspired us to create the features regarding the public transportation helper chatbot was that this is a very problematic area (in Brazil) where the user does not have minimum information and facilities about. Also, the COVID-19 increased the problem once the bus lines and other service were restricted. So the idea was to create an easy to use service to help the public transport users (buy tickets, check lines, and so on).
What it does
Bilhetinho allows the public transport user to check the lines and its stops (using an open API of SPTrans) and also ask for notification about the line: when the bus is nearby a stop, we ask the user the permission for a one time notification and send a message saying: "the bus is nearby the selected stop..". Also there is the charge ticket integration that allows the user to buy tickets for the public transportation system and also the human part to help the users, passing the thread control to the inbox when the user asks for.
How I built it
We've created the chatbot using the botframework using Node.js. All the infrastructure needed (azure functions to APIs) were also developed using Azure services.
Challenges I ran into
The permissions regarding private-replies and one-time-notifications were really hard to test on development mode on Facebook.
Accomplishments that I'm proud of
We've created features to chatbot that will really be used in real scenarios.
What I learned
We've integrated with a public API that was really nice, and also started using features from Facebook that requires permissions (asking for approval, etc.) and webhooks that are really different from only programming lines of code.
What's next for Bilhetinho
Improve the features, as the lines search, providing more information. Also add new features such as places where the user can complain about the bus, sexual assaults and other problems that we face in public transportation system in Brazil.
Built With
azurefunctions
botframework
node.js
Try it out
m.me |
10,009 | https://devpost.com/software/calendroo-messenger-bot | Inspiration
Getting to know the country holidays where you live in (especially when moving to a new country), so that the one can plan their holidays effectively.
What it does
Calendroo is a messenger bot, that sends you all the public, local & bank holidays in any country. Calendroo supports over 230 countries holidays.
How I built it
We built it by using Nodejs, facebook-messenger and calendarific-api.
Challenges I ran into
Figuring out how the facebook-messenger api works.
Using the one-time-notifications api.
Designing the UX of the bot.
Accomplishments that I'm proud of
Building our first messenger bot.
We believe its usage is very helpful for us.
What I learned
New tech stack such as facebook-messenger bots.
What's next for Calendroo Messenger Bot
It's currently under development mode, we are planning to extend it by adding more features like "Private replies", "Message tags", and make it easier for the users to plan holidays.
Built With
calendarific-api
facebook-messenger
node.js
Try it out
m.me |
10,009 | https://devpost.com/software/startup-creative-studio-manager | Inspiration
So, we started a Digital Creator's Studio Startup, but we only had a Facebook page. That's how broke we were, but that idea of a Digital Creator's Studio in Bangladesh compelled us to open up a page.
Now, we had all our plannings like revenue model, channels, etc. papered out, but we only had a Facebook page to implement them. It was getting hard to get people to register or to note down what they wanted to do like if there were interested in writing or editing or illustration.
There were just so many questions and options that we needed to confirm that only could be done through a thoroughly made website or app, so we made this Chatbot that uses google forms and Quick replies to categorize users and manages their activities.
So, we ended up making this chatbot.
What it does
This chatbot assists by supporting the aspiring Creators by getting them listed and fully working.
The process is simple. Now, the creators might love to write or edit or review or illustrate, and this list goes on. We listed those common categories for the studio and gave them options from where they can register with their Artist name or Facebook name and Email. The time needed for user’s segmentation and their data collection has been reduced to mere minutes, which used to a lot of time before.
Then we confirm loads of other stuff like if they want to freelance or want to build a 'Creator's' team or want to solo.
Based on that we redirect them to another google form where they can submit their works, and if they want to create a team, we confirm which creators they need, and we help them find their dream team.
At present, our freelance option is unavailable, but we have kept the options open, though normal speed art videos, illustrations, and Light Novels submissions are on.
Thus, the chatbot allows us to focus on more important things.
How we built it
After attending a Bootcamp by StartKoro, we were taught about this fantastic solution. After completing those sessions, we directly opened an account on Chatfuel and started building it, immediately.
We used quick replies mostly and kept the conversation very informal to encourage the Young creators.
Challenges we ran into
As we gave a lot of options with quick replies like at the start, maybe, 4 options (Yes, Maybe, Not sure, and No), we were mixing up all the blocks.
But still, we needed to answer all of the options for an interactive conversation, and AI was a bit slow to learn, too. So, it took more time than expected.
Accomplishments that we're proud of
The chatbot, itself is an accomplishment for us. It has helped us to connect with users of different categories. All of the registration and categorizations completed by the bot are something that we are proud of. It might be funny honestly for people from Computer Science or Software background, but as engineering normies, this means a lot.
What we learned
It was fun learning to make a Chatbot with Chatfuel. We also learned about AI learning in the process, and this helped us to create a small MVP for our Company.
What's next for Scratches
Now the main bot is primarily done, but we are planning on making AI learning for the chatbot much stronger. We are also planning on incorporating a fully autonomous third-party payment system for Bangladeshi Citizens. And lastly, we will open more relevant quick replies and other options for a user-friendly informal experience.
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/tk-project-name | Inspiration
When I saw that this hackathon called for "building lasting relationships through conversation," I knew I had to enter. That goal resonated with me immensely, at a time when technology tends to connect us AND alienates us from each other. Many times, unfortunately, technology prevents us from having meaningful moments with the ones we love. And my mission with Jibber Jabber is to use technology to get people talking--to each other IRL.
To create Jibber Jabber, I drew from my experience as a former writer and as a mother and wife trying to constantly find new ways to connect with her son and husband. I'm sure others will find the chatbot helpful, whether it's to inspire conversations with family members or friends.
What it does
Jibber Jabber provides thoughtfully curated questions (and responses) daily so that users can have engaging and fun conversations with each other IRL. What's more, I put a lot of thought into the flow, from using Quick Replies to move the user through the experience to using GIFs to communicate visually to spending a significant amount of time on the script.
How I built it
For this version, I used ManyChat, a third-party tool for building FB Messenger Chatbots. It relies heavily on Quick Replies to get through the journey.
Challenges I ran into
I really wanted to build Jibber Jabber with sophisticated features, but had to scale down in the interest of meeting the hackathon deadline, but also knowing that starting with a simple version and adding features slowly in the future was the better route.
Accomplishments that I'm proud of
I built a chatbot that will hopefully be useful to people, especially during a time we could all use some lighthearted conversations and ways to bond.
What I learned
Some of what I learned while I researched building chatbots won't be used until later versions of Jibber Jabber or other future chatbots. For example, I'd like to make Jibber Jabber more intuitive by adding NLP capabilities with Dialogflow or a similar technology.
I must say, however, I did learn about some best practices on building a chatbot, in addition to the pros and cons that come with technology. I look forward to continuing this journey.
What's next for Jibber Jabber
Incorporate NLP using Wit.ai or Dialogflow
Building a Spanish-language version
Adding an option that's kid-friendly
Built With
manychat
quickreplies |
10,009 | https://devpost.com/software/raido-mentor-bot | Main screen
Carousel with skills
One time notification
Inspiration
The inspiration came from the problem I faced when decided to refresh my knowledge in Kubernetes. There were so many materials, online courses and books that it was hard to choose what to do next and keep my motivation along the way. So I decided to build a bot that would help on picking the best materials from the Internet and control your progress.
What it does
You select the skill you want to master (Photoshop, Docker, Kubernetes, Frontend development and etc.), your level and time you have. After that Raido proposes the path with several tasks that you need to complete in order to master that skill. Alongside the way you will get notification (in a several days - when Raido thinks you should be done with the task and if user agreed to receive such a notification) and get a new task.
How I built it
For the backend I used Flask + Python wrapped in Docker and deployed it via Cloud Build on GCP and hosted with Cloud Run service.
Challenges I ran into
Organising the main logic and deciding on what is the most important information to display.
Accomplishments that I'm proud of
The easy and production ready pipeline for the deployment for the bot. Current setup costs 0.06$ per month in the cloud and could scale to any number of simultaneous users (costs would rise of course with millions of users :)
What I learned
GCP service Cloud Run and the number of possibilities on a messenger platform.
What's next for Raido Mentor Bot
Develop more skills and add more casual interaction (advice on interesting articles), quiz on the material and adapt learning material individually.
Built With
docker
flask
gcp
natural-language-processing
python
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/dkvt | Audio translation working
Text translation working
Inspiration
We noticed that people were using messenger to make new friends from all around the globe, and us being international friends that speak different languages, found that communicating what we wanted to say was very challenging for us.
Not only that, but using voice messages and translating them was also impossible. So we decided to make a change and bring a better way to communicate with international friends on messenger.
What it does
First, users must select what language they speak and the language they wish to translate to.
_ Text: _ Users can send a text message to the bot, and it will reply a translated version so they can forward to the friend they are talking to. All without leaving messenger.
_ Voice messages: _ Users can send voice messages in English, and it will reply the translated version of the audio in text form.
How we built it
We developed it using Node, some Facebook tools, IBM Watson, and a speech to text library.
Challenges we ran into
We only found this hackathon 2 days before the deadline, so coding it all in little time was a challenge
The audio-to-text library we planned to use had bugs, so we had to fix those.
Accomplishments that we're proud of
We're really proud that we could get this to work very well in such little time, also really proud of how easy we made it to use.
What we learned
Learned that messenger can be a great tool for international friends to talk to each other
Learned how to make a messenger bot
What's next for Flexible Translation
Use the power of neural networks to generate real speech synthesis for audio translation, that way users can send voice message and it will return a translated voice message they can forward to friends.
Implement speech recognition with voice messages for more languages
Built With
bottender
ibm-watson
javascript
node.js
Try it out
m.me |
10,009 | https://devpost.com/software/nx-therapy | Inspiration
Motto :
The main idea of the project was aimed at making people feel good / inspired . The world, especially given the current circumstances, seems to be unhappier than usual. The goal of this app is to spread happiness and inspire people.
Current features :
The app currently provides quotes that are funny or inspiring. An individual can also contact a therapist to talk about depression, anxiety or any other issue. The user can also provide feedback on the services provided by the therapist.
How we built :
We brainstormed on the application to build and had a workflow template of the features of the app. Next, we followed the official documentation guide and created an HTTP server and set up the webhook. After setting up the webhook, we integrated our app to the webhook and developed the features.
Challenges we ran into :
The major challenges were in trying to get the webhook to work and in setting up the remote server on heroku.
Take - aways :
We learnt how to leverage messenger experience and create a chatbot. We learnt about webhooks, hand over protocol, quick replies, Send API's and the backend infrastructure to support the messaging experience.
What's next for NX-Therapy :
The goal is to add more features in order to provide an enriching experience that uplifts individuals. We would like to
add personalized upbeat music recommendations, inspiring stories of individuals who broke the status quo and made it to the top, suggest events users can attend based on their locality and work on creating a happy community.
To sum up, we would like to make this app a one stop solution to feeling good.
Built With
natural-language-processing
node.js
web-hooks
Try it out
github.com |
10,009 | https://devpost.com/software/enterprise-fitness-bot | Inspiration
Helping everyone in a company stay fit in an environment that is safe and friendly
What it does
Let's members of a team track their workouts and share them so they can evaluate progress.
How we built it
This app is meant to be deployed in a per organization manner. Each company can have their own collection of fitness tracking that is isolated and not shared with other firms. This allows teammates' stats to be available only within their company.
So, the reporting and tracking part of the solution runs in a Microsoft PowerApp that can be deployed in Office 365 environments.
Logging fitness activity takes place via a Facebook Messenger bot called Eric, the Enterprise Bot.
Challenges I ran into
We used an interesting collection of technologies, from Microsoft PowerApps, to SQL Server, to Microsoft Bot Framework to of course, Facebook Messenger.
All of those pillars had specific challenges that didn't quite work as advertised. From exporting a powerapp to managing the Sql database in Azure and making calls in Azure functions. Even one form of the quick reply refused to work for us, so we had to work around.
Accomplishments that I'm proud of
This is a very simple thing, but I'm glad. To use Facebook's Quick Replies, we built an extension on top of Microsoft's bot framework that we're hoping to contribute to their repository on Github.
A key part of this work is based on ongoing research by my teammate Geon Bell who is looking at the intersection between technology, fitness and community, I'm really glad to see him further his strategies in this space.
What I learned
It's good to have strategies and back up plans in the event that things don't work like you expect.
What's next for Enterprise Fitness Bot
We're curious about how to get companies interested in rolling out this solution in their teams.
Built With
microsoft-bot-framework
microsoft-power-apps
office
office-365
Try it out
www.messenger.com |
10,009 | https://devpost.com/software/miniapp-chat | Inspiration
We all know many of our friends or acquaintances who run shops that sell items exclusively, or primarily through Facebook pages! Interactions with these shops are done via Messenger but humans take time to respond, they could be offline, and when they do they have to manage the whole process of cataloging purchases and update their inventory as they sell out. Inventory is also not very discoverable most of the time making conversion rates go low.
It is time to automate all these inefficiencies! Say hello to Miniapp.chat, your Messenger’s new best friend!
What Can you do with this marketplace
You can add multiple categories and products.
The product can have different variants and each variant can have extra charges and different images.
Add delivery prices, so it's clear what's the total price of the order.
Review orders in the dashboard and notify the user of his order status by a message on messenger.
The user can talk with the bot with different interactions, like he can see his orders.
The bot offers
Quick Replies
for easier engagement with the bot.
How we built it
For the frontend, we used nextjs to build the pages on the server side, so the website is light on the user device. And webpack to minify the bundle so it works fast even on a slow internet. The data fetching happens only on the server side and the page is served as html/css and js necessary for interactions.
For the backend we used expressjs and postgres for the database. The code is deployed on Amazon Lambda so it scales automatically. And by using aws, the api availability is 99.99% so the bot will answer to all users. We also used redis DB to cache the products so we can fetch them without any delay, and we are generating a token for each user to keep the users data private and other users can't access his data.
Challenges we ran into
The most challenging issue was to get the user through the web-view as few steps as possible while still giving him the full experince.
Making the app works for multi-languages, means we have to support right to left and change the styling in every place.
Keeping the app compact.
Accomplishments that we are proud of
The webview is super fast & light, with the pre-fetch pages and server side rendering.
By using facebook services, the user doesn't have to register nor download an app while keeping his privacy.
The application has support for multi-languages (currently English & Arabic).
The backend is serverless, so it scales up & down as needed with the lowest cost.
What we learned
The bot doesn't have to be fully text based, it should include a web view for complex interactions.
Serverless based architecture works very well for bots related applications.
Server side rendering is a must for slow devices. But client side rendering could be better for admin dashboard.
What's next for MiniApp Chat
Integrate
Facebook Pay
, so the experience is complete.
Add coupons for marketing campaigns (In progress).
Add support for
Private Replies
, where the bot sends a direct link to the product page.
Add support for
Handover Protocol
.
More options in the dashboard to customize the webview.
Add customizable workflow for the bot.
Built With
amazon-web-services
express.js
javascript
nextjs
postgresql
react
redis
serverless
typescript
Try it out
dashboard.miniapp.chat |
10,009 | https://devpost.com/software/police-aid-project | Inspiration
Here's our idea: what if... what if... instead of calling 911, you could send them a text directly through messenger. Under stress and pressure, it is often very hard to communicate important information. This is why quick and accurate information is necessary. In fact, messenger's quick replies simplifies the process by a lot while still being accurate. Just tap a few buttons and police will have a great understanding of what's happening.
What it does
So what does our facebook messenger chatbot do?
evaluates your emergency level
establish the necessary information needed to provide help (using facebook quick replies)
We wanted it to be very simple. Being complicated wouldn't make any sense.
How we built it
We built it with blood, sweat and tears. haha just kidding (not really, but let's go to the main points).
Create a facebook page
Facebook for developers tools
Set up a server
Deploy app using node.js/express.js/heroku
Set up webhooks & app
Javascript code and requests
Design and rethink
Fix bugs (throughout 1-7)
Challenges we ran into
We had to learn on everything from scratch. Setting up a server and deploying it was our first challenge. We ran into many problems, but we never gave up (lots of stackoverflow, youtube tutorials and documentation reading). We encountered merge conflicts (you can't run away from that). It was a first for us and we learned a lot. We also wanted to add send live location feature, but it has be discontinued during 2019. Hopefully, this function could be added back as it could be very useful. Because of this, calling 911 in emergency would make more sense, but we did filter out the non-emergencies one. This will attenuate loads calls of 911 as the non-emergencies one could be handled separately.
Accomplishments that we're proud of
We are happy to say that we built it without prior knowledge to facebook bot api, pages, servers, etc.
At the end of this project, we can proudly say that it was challenging, but very fulfilling. Always remember: there's a first for everyone. So definitely never give up even if you don't understand.
Make the unknown known, and make the known into knowledge.
What we learned
So what did we learn at the end of the day? A whole lot about servers, deployment, merge conflicts, requests, webhooks, facebook bot apis, facebook pages
Most importantly, we learned about facebook for developers feature. Before this hackathon, we have never heard of it. Now, we even have our own chatbot on it.
What's next for Police Aid Project
One thing that we didn't do much is using github. We mainly used heroku
What's next could be evaluating usability/compatibility in local police offices/call centers. Of course, there's still a lot of improvements possible: better user experience, setting up contact with a police officer directly or add "send live location" once facebook adds back this feature.
Built With
api
ejs
express.js
facebook-messenger
heroku
javascript
node.js
Try it out
www.facebook.com
github.com |
10,009 | https://devpost.com/software/yellfi | Creatiing a Yell
Pledging for a Yell
Reading a Yell's Details
Inspiration
When we travel to somewhere, we always check various sources to find help or to get a recommendation. We may not always have a connection who knows the area. So we wanted to build a solution to let people find the recommendations they need easily.
What it does
First of all, Yellfi asks the user for a location. This is the area that the recommendation will be based around. Then the user writes what they need. This can be a recommendation for a good restaurant or someone who has an extra voucher that will expire soon. If the others check the Yellfi around the location, they will see the post (Yell) and answer to the Yeller. The Yeller can also pledge for something in return.
How We built it
We used
Microsoft Bot Framework
. For Geosearching, We are using
ArcGIS
(address to GeoLocation) and using PostgreSQL for storing yells and make geospatial queries.
Challenges We ran into
There isn't many examples for Microsoft Bot Framework ( webpack - NodeJS ) so we struggled when we tried to deploy it to production. Getting user's locations is not accurate with
adress to lat,long
, and we are writing a webview screen for it. However, the most difficult part in the future will be sorting through spam posts. For now, a Yell (post) is active for just 12 hours and has 10 km radius.
Accomplishments that I'm proud of
Even if the chatbot environment isn't as big as other mobile apps, especially in the terms of development, we created a location based chatbot. This is a unique solution that we haven't seen a similar app before.
What We learned
We learned so much technical details about Chatbots, Messaging techs, and NodeJS but the important thing is building a chatbot has some challenges that not just in terms of development. Building Chatbot is creating a persona. So this persona has to be able to connect the users to each other using our solution. This was a new challenge that we've taken on.
What's next for Yellfi
While we will be developing the technical side, the main next step for us is on the commercial section. We will be looking for more use cases for Yellfi, especially in the business space where this app can be used in partnership with other companies that may not be able to reach to their client base. A recommendation service and sponsored recommendations can be a pathway we may want to explore.
Built With
awslambda
awsrdspostgresql
microsoftbotframework
node.js
webpack
Try it out
www.messenger.com |
10,009 | https://devpost.com/software/grow-scale | Inspiration
I recently purchased an item from an e-commerce website. But when I looked at the packet which was delivered to my home. it doesn't have the one which I've ordered. It got replaced with something else. So, I went to their Facebook page and commented on my issue there under a post. Then after I looked at some more comments over there. I could see many complaints. Then it just struck my mind that how the business owners can have a look at all these comments. If they can't then how are they gonna improve their business without reaching out to customer problems.
What it does
Our idea innovates the way how business owners can reach their customers. We made use of some recent advancements in Natural language processing which is a chatbot. Now a user who comments their problem on the page will be redirected to the messager with the help of a feature(private replies). Then the chatbot which we had trained and deployed in the messenger interacts with the user and tries to know his issue by providing the top issues faced by many customers in the form of quick replies. Each of these replies is the intent we took from the already available customer data on an Indian e-commerce website(Myntra). Now, these are some of the intents chosen from the problems faced by the customers majorly. If he doesn't find his issue in the list of quick replies then he needs to describe his problem with a short paragraph. This short paragraph is sent to a page that has a trained Wit.ai model running in the background which can find out the intent in the paragraph. Now the intent is stored into the database in its appropriate intent section. To analyze all the data stored in the database and know what's the most common problem faced by the customers, we've built a website(which looks like a Facebook page) with a histogram plot. This histogram is dynamically updated by fetching the data stored in the database. By looking at the plot business owners can solve the issues which customers face the most
How we built it
For the first task to redirect the customer from the business page comments section to his inbox we made use of a website called PostJelly which can send automated replies for negative comments posted by the customers. It also sends a private reply to the customer's inbox redirecting them from the business page to the messenger. Now from there chatbot takes over the control. we've designed the chatbot with the help of Gupshup.io just for the quick replies part. If the user chooses to describe his problem with a paragraph then in background the paragraph is sent to a page which has a wit.ai model trained in background finds out the intent and will be sent to the database.
Challenges we ran into
Accomplishments that we're proud of
We are proud of the idea we've developed which can impact the business on a large scale. This application helps the business owner to find out the areas where his/her customers' facing problems in his business faster, easier and also to reach his customer's problems within no time and no pain
What we learned
In developing up an application for this hackathon, we got good exposure with some tools like Wit.ai, Gupshup. We've
also learned how to manage work among the team members and make things work out well.
What's next for Grow scale
We would like to add some more improvements in the future to make this application much more interactive to the business owners. If we can help find not only the most common problem faced by the customers but also from which section that particular problem is mostly pointed out by the customers then it could be more helpful. To exemplify, the histogram only shows the most general problems like whether it's a refund issue or product quality issue or it's from the customer service end. But what if we take one step ahead to know the exact brand which people are facing the issue when we talk in terms of product quality. With this kind of smart analysis, business owners can have a clear idea about everything and improve their customer's trustworthiness which in return produce better revenues
Built With
express.js
gupshup
mongodb
node.js
react
wit.ai
Try it out
github.com |
10,009 | https://devpost.com/software/music-ally-trained | 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
python
spotipy
wit.ai
Try it out
www.facebook.com
github.com |
10,009 | https://devpost.com/software/atendmaxx-sistema-de-identificacao-de-fake-news |
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FB.init({
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xfbml : true,
version : 'v3.3'
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// Get Embedded Video Player API Instance
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if (msg.type === 'video') {
// force a resize of the carousel
setTimeout(
function() {
$('[data-slick]').slick("setPosition")
}, 2500
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if (d.getElementById(id)) return;
js = d.createElement(s);
js.id = id;
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Com o avanço das fake news no meio digital pelas redes sociais, nos trouxe inspiração a trabalhar no projeto para diminuir a expansão de notícias falsas pelo mundo. Analisamos recursos disponíveis do Facebook para elaborar e fazer um plano de ação para a ideia, o que nos trouxe muitas pesquisas técnicas e jornalísticas para o aperfeiçoamento da solução.
Oferecemos a ideia de captura de mensagens como fake News de forma que o progresso decorre quando o cliente recebe uma mensagem (texto) no aplicativo Messenger, via canal business da maxxmobi que passa por uma pesquisa rápida no Google direcionados a links de jornais parceiros, assim, analisando e fazendo a verificação se a informação é verdadeira ou falsa, que através do protocolo é retornada uma resposta rápida para o cliente sobre o resultado de busca e identificando se é fake ou news.
Built With
angular.js
brasil
comunicacao
fakenews
java/springboot
javascript
mensagem
portugues
websocket
Try it out
www.maxxmobi.com.br |
10,009 | https://devpost.com/software/cheer-up-57ldq3 | Homepage
Chat Begins
Contact details of helpline
View all tasks
Daily task
Uploading an image as proof of task completion
One time notification when a new task arrives
Inspiration
In the fond memory of #SushantSinghRajput
People often wonder, what led a friend, family member, or celebrity to commit suicide. The recent event in the Bollywood industry, the demise of actor Sushant Singh Rajput, has left everyone in a state of shock. This incident proved that no amount of money, fame or recognition can promise you happiness. According to WHO, "more than 800K people commit suicide every year across the globe" and the ongoing pandemic where the world is in a state of lock down is making the situation worse. We want to target this number, and come up with a solution which shall make an impact in lowering down this statistics.
What it does
Our bot understands the user's emotion, the reason behind, and assigns some daily tasks to cheer up user's mood. We believe these tasks will help users to become happy again and come out of any sadness or depression. It will help users to open up and share their feelings. The tasks will be tracked in our database, and user cannot skip any task in between. After completing the task, user has to submit a proof in the form of an image/video. Meanwhile, user may continue with the next task. We have short listed these tasks very carefully, which will help to cheer up someone's mood.
How we built it
Language - Javascript
Backend Framework - Node.js
Server - Heroku
Database - MongoDB Atlas
Frontend - Facebook Messenger bot
Challenges we ran into
One of us didn't had any prior experience in Node.js
Connecting MongoDB database with Heroku server
## Accomplishments that we're proud of
The motivation behind this project was to do something for the society, and we are proud that we were able to build this project with an intent to fight depression and suicidal behavior. If we are able to help at least one person then, that will be our true accomplishment.
## What we learned
We got familiar with the ins and outs of the messenger bot framework
Collaborating efficiently, though working remotely
Mantra - Keep thinking, keep learning
## What's next for Cheer Up
We will make the Cheer Up live for public access. We will familiarize ourselves with Facebook marketing strategies so that our bot reaches the right set of people.
Built With
facebook-messenger
heroku
javascript
messenger
mongodb
node.js
Try it out
github.com |
10,009 | https://devpost.com/software/covid19-help-find-a-doctor | Welcome message
Inspiration
Many of us prefer not to visit crowded hospitals to verify whether or not they are infected with covid-19, so they prefer to stay home and self isolate, but the problem with this is it might be hard to find a doctor to help you remotely during your isolation.
My uncle have recently passed away by COVID-19, he was self isolated in his home, everything was ok until his last hours we started looking and calling around for doctors to help, when we found one it was already too late.
What it does
This is a messenger chat app, its purpose is connecting doctors to patients who need help. Volunteer doctors gets assigned to a few patients (10 max) who need help or advice, and doctors should try and check on their patients everyday and provide medical advice and guidance.
How I built it
Using the package
messenger-bot
which uses the facebook messenger API, backend is using
express.js
on
node.js
Challenges I ran into
Messenger API only allows sending messages to people in a 24 hour window after the last reply from the user, so, i plan to add a feature to notify users that they need to type something to keep the chat alive, this notification should appear before the 24 hour window.
Accomplishments that I'm proud of
Finishing the project in under 24 hours :D
What I learned
You can use messenger bots for anything really, even connecting different people in the same chat window.
What's next for COVID19 Help - Find a doctor
Win this hackathon maybe? :D
Built With
messenger
node.js |
10,009 | https://devpost.com/software/me-omics |
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
)
}
});
};
(function (d, s, id) {
var js, fjs = d.getElementsByTagName(s)[0];
if (d.getElementById(id)) return;
js = d.createElement(s);
js.id = id;
js.src = "https://connect.facebook.net/en_US/sdk.js";
fjs.parentNode.insertBefore(js, fjs);
}(document, 'script', 'facebook-jssdk'));
What it does:
Me-Omics messenger bot helps reduce and maintain the number of customers at a time in a particular shop.
What it Does
Using messenger, Me-Omics allow customers to place orders for your daily household need products separated in different categories.
Customer can select pick-up/visiting date and time.
Notify the user few minutes before his/her selected time slot.
Copy of final order PDF generated accessible to both the customer and shop.
Different shops can be registered through the app, providing each shop with a bot. (Currently only for a shop)
How we built it:
Used the messenger API with node.js express for backend.
Webview was built using HTML, javascript.
Mongodb for database.
Challenges we ran into:
Connecting all APIs and connecting app with the webhook.
Rendering web view template dynamically.
Accomplishments that We're proud of:
We don't have deep experience with node.js and messenger APIs but we tried our best to justify the project.
What We learned
Learned how to make an interactive messenger bot.
Learned how to host web apps and make API calls in and around.
Learned to incorporate webpages in messenger bot.
What's next for Me-omics:
Incorporating handover protocol wherein customers can contact the shopkeeper directly in case of any difficulty.
A registration system for different shops.
Built With
api
javascript.
mongodb
node.js
Try it out
github.com
github.com
www.facebook.com |
10,009 | https://devpost.com/software/work-manship | 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 |
10,009 | 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 |
10,009 | https://devpost.com/software/together-we | Text to Together WE
Inspiration
I wanted to help my fellow friends who feel depressed because of failure,rejection or any bad trauma. So I wanted to have some sort of system where they can receive the mental stress relieve help. I recently saw people comitting suicide and a portion of the suicide happens when people feel lonely. I wanted to reach out to people with the word that you are not alone.
What it does
It lets a user chat with a chatbot and if the user wants,he can call the someone too
How I built it
I built it with chatfuel,an online app that integrates with messenger page chat options
Challenges I ran into
I had to study how I can interact with people,how to handle people with vulnerabilities.
I had to search for proper conversation flow for my chat bot.
Accomplishments that I'm proud of
I got to learn about the plugin using Chat-bot Building and I am excited about it.
What I learned
I learned empathy,how a conversation is automated,how to reach out to people,how to build a chatbot.
What's next for Together WE
This is a knowledge base chatbot and I still dont know how to add ai here. So maybe I want to learn ai and build a better chat bot with NLP. I want to learn more about HCI and HCD then insert those stuffs here
Built With
chatfuel
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/hope-19-chatbot | Inspiration
Here in Bangladesh most of the people are not aware of the situation of their areas .
And most of the time they go out with a very low safety measures that can cause a
huge problem for them as well as their family . So, we have decided to make a chatbot
that can notify them whether they should go out or not.
What it does
Our chatbot is very simple and effective at the same time . It just ask your current
location. Then , if you are in Covid Danger zone, it will recommend you to not go out
but if you are in safe area it will tell you are safe to go outside .
How I built it
It is my very first experience of making ant chatbot and I started making it on June 23.
So, I made it on Dialogflow..
Challenges I ran into
As i said to it is my first chatbot ,I had not any idea how to make a chatbot but i have this
great idea in my head . I researched how to make a chabot and cameacross with a lot of options
but it was very tough for me to impliment one . But somehow i was able to make a chatbot.
Accomplishments that I'm proud of
Yes , i am very mush proud of it . When my chatbot replies me for the first time , it was
something else and I am very proud that within a few hours i was able to produce this
chatbot and the most importantly , it will help people to stay safe , that is the biggest
proud moment for me and for my team.
What I learned
I have learnt how to make a messenger chatbot ,what is Facebook developer account , how to set up an
app , how to link it with dialogflow . It was a great experience.
What's next for Hope-19 Chatbot
Right now our chatbot Hope-19 only cover 2 states , california,USA and Dhaka,Bangladesh.
We are planing to cover the whole world and want to add another feature that will help
you by providing emergency contacts in your locations .
Built With
api.ai
dialogflow |
10,009 | https://devpost.com/software/mood-journal-24nca9 | Mood Journal Icon
Inspiration
As someone that doesn't often have a pen and pad laying around but was always within reach of a laptop or phone and always has Facebook open, I thought that having a way to track my mood and what I've done in my day through Facebook would be a great project. Using some of the criteria from the hackathon also worked in nicely with how I wanted the bot to work in terms of having something remind me to fill in my log. And also to give me easy to use prompts so that I didn't have to worry about putting the effort in and getting demotivated to use the journal.
What it does
The bot will allow a user to store a mood score and as many journal entries as they would like each day.
They can get back information on their current day.
They can get back statistics on their average mood score across a set time period.
They can get back statistics on their most common mood scores over a set time period.
They can be reminded to fill out their journal using one time notifications.
They can unsubscribe from the service to have their data removed.
How I built it
Our team had initial meetings to discuss the project, made a design document to outline our tasks and also the general functionality. We then worked collaboratively to build the system using pair programming, while streaming over discord.
Challenges I ran into
Using languages that are less familiar to some of the team, using facebooks messaging API's as none of us had used it before, using webhooks as we also had very little experience with them.
We also did not have access to the users' timezone which meant that everything was running off of server time and this would not work for people not using GMT. We did apply but were declined.
Accomplishments that I'm proud of
Managing to use the One Time Notifications API call as well as translating the demo projects for webhooks into functioning code for our webhooks.
What I learned
How Facebook messenger works and stores information, as well as how to use the webhooks for it.
What's next for Mood Journal
Publishing it fully and expanding on the project to be more feature full.
Built With
atlas
express.js
heroku
moment.js
mongodb
mongoose
node.js
otn
quick-replies
Try it out
github.com |
10,009 | https://devpost.com/software/memo-dost-ai | Inspiration
Messenger platform has been a great friend for businesses. As the leading chatbot developer company from a developing country like Bangladesh, we have seen how businesses especially SMBs and eCommerce owners use the Messenger and Facebook platform in innovative ways to drive more customers and serve customers better.
For example, we have seen how eCommerce pages don’t add the price of the product in the post and ask the user to comment to know the price so the engagement is higher.
Developing next-generation automation platform for businesses is the main goal of “MeMo” DOST.AI. Focusing on the growth of the business and customer engagement Facebook ‘Private Replies via Messenger’ feature can be an innovative tool for engagement.
With the recent changes in the push messaging strategy, businesses are seeing a downfall in user interactions. ‘Private Replies’ can be a great feature to utilize. We started to think out of the box and came up with ‘Innovation with Private Replies’ using the MeMo.DOST.AI platform.
What it does
Facebook’s ‘Private Replies’ feature offers a convenient and personal way to connect businesses with prospective customers. With this feature, page admins can reply to public comments with a private message. This enables pages to immediately support any customer requests and efficiently collect more leads. It can help the business to grow faster and pages get more user satisfaction.
Innovative Use
Other than providing regular NLP based replies in messenger, the feature can be used to build engaging content.
Create Quizzes
and Ask to answer. Reply to users who commented correct answer with some interesting gifts or appreciation.
Create Magic Offers
: Ask users to comment Magic and send them some offers in Messenger.
Drive Organic Customers
: Send user related replies like the location of the business, price of the product etc via messenger.
Sports Content
: Ask user who will win a game or who they support. Congratulate them if the team wins.
Negative Feedback Handling
: Check negative feedback (sentiment analysis) of users and send them something special to win them back. (Upcoming feature. Just need to connect with an API)
Responding to each comment manually can be a time-consuming task. When a page replies to a comment using a private message, a note appears saying, ‘Page responded privately.’ This response shows the page visitors that you are responsive to any request and helps the customers to be more reliable to the page for information.
Brands can run campaigns effortlessly with the help of MeMo.DOST.AI. More engagement involves more user participation in the campaign and this can be a major tool for the brands. Brands can set up auto-reply comments to any posts on Facebook Page with a private message. Admin can set up the bot with a specific message or can even share important information or ask for specific user data to generate a lead. MeMo.DOST.AI can be configured in various ‘flows’ allowing for personalized replies for every Facebook post. MeMo.DOST.AI is very simple to set up and can be put into action in just a few steps.
How I built it
We have built the whole thing keeping the Messenger platform in mind. The platform is built with the APIs that Facebook provides. We are using Private Replies and Send API. Technologies like PHP, HTML, CSS, Cron, CURL, jQuery and a bit of 'chemical X' have been used.
Challenges I ran into
Designing the carousel was a challenging part of the project.
Accomplishments that I'm proud of
We are proud to learn new technologies and to successfully create a fully functional platform that can become a major tool for business growth. Creating this exciting and innovative solution is a great achievement for us. We hope to make an impact on the Facebook business pages with this platform.
What I learned
Messenger platform is so strong that it is tough to get new amazing ideas.
What's next for MeMo.DOST.AI
We will add sentiment analysis and NLP engine (our own dost.ai and wit.ai) in the keyword matching feature for better results.
Built With
ajax
bootstrap
curl
html
javascript
messenger
php
sendapi
Try it out
m.me |
10,009 | https://devpost.com/software/quick-solver | Information @ Your Fingertips
Inspiration
I love Competition and work for innovation.
What it does
Its give information a user on preset category Medical support, Police help, and Community information
How I built it
https://chatfuel.com
.
Challenges I ran into
I thought Chatbot is a complex for a user but when I use it its very interesting and very helpful for communication
chatfuel.com is easy to build a good chatbot
Accomplishments that I'm proud of
What I learned
I learn how can a chatbot help people also I learn how to make a chatbot
What's next for Quick solver
we are trying to give more specific information with API now we are working on it
in future, we plan to work AI-based massage replay system for public information For support specific specialist Doctor,
Built With
chatfuel
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/farmore-xyz | Our Leaflet
Polythene is easy recycleable!
Current situation! we will improve!
Bangladesh is a country where recycling waste is still informal and not following an environmentally friendly way to recycle wastes. I witnessed people talking about how polluted is our city is! But no one doing their own part of the job.
That's inspired me to build a recycling Chatbot Supported Startup.
What it does:
Digital vangariwala chatbot communicates with people interested in recycling waste. It is promoting and educating people to recycle waste.
Challenges I ran into
Understand basics of Chatbot.
Accomplishments that I'm proud of
I am really proud of my work as this will engage many of my users.
What I learned
Learned how to Engage people with the use of technology and bring the best out of it.
What's next for Digital vangariwala
Make my chatBot the most informative chatbot on Recycling.
Built With
chatfuel
Try it out
bdrecycle.com
play.google.com |
10,009 | https://devpost.com/software/restaurant-bot | Inspiration
What it does
it's a restaurant chatbot
How I built it
I use Manychat and dialogflow
Challenges I ran into
I am struggling with Ai
Accomplishments that I'm proud of
I can easily add google sheet, Artificial intelligence into my chatbot
What I learned
I learned more about AI and machine learning
What's next for Restaurant bot
I will make a service bot for My own startup
Built With
dialogflow
manychat
Try it out
m.me |
10,009 | https://devpost.com/software/storytime-6e3rut | GIF
Inspiration
I was trying to build something that would be fun for any of the users that use the chat bot, a brief whisking away from the mundane world of reality.
What it does
It is a choose your own adventure chat bot that a user can use to play through any story that has been uploaded
How I built it
I built it using javascript and facebook messenger api, using mongo db as a datastore and heroku as the hosting platform. I used messenger nlp to recognize greetings and farewells and quick replies for choices in the stories. One time notifications for asking users to be notified when new stories are added.
Challenges I ran into
Getting one time notifications to work properly, I followed the documentation but I was unable to save the tokens correctly.
Accomplishments that I'm proud of
Finishing the chatbot
What I learned
Messenger NLP, quick replies and one time notifications.
Using Mongo db with an express app in heroku
What's next for Storytime
Adding more stories for users to play through
Adding a way for users to submit their own stories either through the chatbots page or in messenger itself
Built With
facebook-messenger
heroku
javascript
mongodb
natural-language-processing
Try it out
web.facebook.com |
10,009 | https://devpost.com/software/ticketbot | Basic structure
Inspiration
We were so interested to work on chat bots. When we started, we wanted to build something that will be useful and through which we will be able to learn more on FB platform and technologies. We were also looking in to different ways on how we can utilize FB platform for small medium businesses.
What it does
This BOT is build for companies to handle the client support requests easily. Bot can be integrated to the company page and the company can connect their ticketing system to the bot. Once connected, the bot can accept messages from the both registered and non registered users. Registration process is also inbuilt to the conversations. Registered users can create cases, check case status and subscribe for one time notification for the case updates.
How we built it
As we all are working, we had to find time during our free time. Our interest to learn and apply the technologies moved us on the right direction. We used .NET CORE WEB API, C# and MONGO DB. The structure, tasks and plan where identified in the earlier stages and we worked as a team to complete the whole solution.
Challenges we ran into
Free time to work on the project, learning and applying the technologies where we are new to, editing the video etc etc.
Accomplishments that we're proud of
We are really happy to submit a complete solution that have real use cases.
What we learned
All new set of technologies for FB bot platform, mongo DB etc.
What's next for TicketBot
We are planning to actually use this with our support system and add additional integrations scenarios and other ticketing platforms too.
Built With
.net
c#
mongodb
Try it out
www.facebook.com
github.com |
10,009 | https://devpost.com/software/sky-touching-dreams | Inspiration :Sky Touching Dreams
What it does
It will share moral stories to encourage others. It will help those students who are not concern about their aims. We, on the behalf of the bot, will motivate them and provide right information about their aim.
How I built it
I built it with ChatFuel
Challenges I ran into motivate others.
Teach and help students.
Accomplishments that I'm proud of
them because they will learn the right thing and use it in their real life.
What I learned about many things how to work in a group and prove my ability.
What's next for Sky Touching Dreams
We will add more information about aim and also more stories. Many events are coming soon.
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/how-was-your-lunch | Everyone has a logo nowadays
Inspiration
A healthy diet plays the role of one of the most important factors of human health. As proven multiple times by researchers and nutritionists, one of the key ingredients here is to have a sustainable diet. This is all about developing a habit that you can follow every day, through your normal life or lockdown situations as we have now.
The market is full of various assorted apps that aim to help with controlling food consumption and tracking. Users often spend time building trust with them and trying to create a habit, often finding the process impractical or way too artificial.
How Was Your Lunch is designed to help with nutrition tracking in the most natural way -- through your normal communication. Every day we share millions of food photos on Instagram and tell our friends how yummy was that avocado toast from your local cafe that you had for breakfast. And it feels quite natural, doesn't it?
So instead of installing yet-another-diet-tracking-app, why don't we chat about your daily meals to just another contact in your Facebook Messenger?
How Was your lunch will recognize your language and carefully record this for you, thanks to Wit.Ai Natural Language Processing (NLP) platform. Moreover, it will try to estimate the nutrition facts for the dish that you named (as accurate as it can!), so you can check your daily stats. Of course, the text messages aren't just enough, so you can also share images that How Was Your Lunch will try to recognize and find the best match (again, with nutrition facts). To add to your diary, it will ask you for the mealtime, with Messenger's Quick Replies. With new features to come, you could also leave a voice message if you're in a hurry or just don't feel like typing text.
To keep an impression of talking with the real person, the app has been made to reply and answer to your text with the human language as well. No doubt, sticking to the diet is often hard and any good motivation is important. This is why How Was Your Lunch learns to be a good friend as well and show empathy & support to you.
In future releases, it will carefully guide you as you move on during the day with your meals and help to stay on track, whether your goal is to reduce daily calories, eat more vegs & fibre or stay on a high protein or keto diet.
Technology
How Was Your Lunch is completely integrated into Facebook Pages & Messenger, so no installation needed, just subscribe to the How Was Your Lunch page and start chatting!
The complex but amusing technology layer of NLP and imaging AI is kept completely transparent to a user, exposing only a natural language communication.
Messenger API
Facebook Messenger creates the foundation for the How Was Your Lunch app, connecting it with the user that came to the Facebook page of How Was Your Lunch. Apart from text messages processing and a seamlessly integrated NLP Layer (see below), How Was Your Lunch uses template messages with button replies. It also relies on Quick Replies functionality to instantly ask for the mealtime, when user wants to add a dish from the picture.
NLP layer
How Was Your Lunch relies on the Wit.Ai platform to understand users intent, be it saving a meal or asking for summary. It is aware of several common dishes and can grow to learn more.
With Wit.Ai, the app extracts mealtimes (automatic assignment based on the current time -- to come) and creates a structured summary, if asked for today, yesterday or for the entire week.
Image recognition layer
The app integrates with an outstanding image processing service that performs recognition. So when the user shares a photo, it gets forwarded for ML recognition which in turn results in the details about the dish, including calories & nutrition facts. For demo purposes, it temporarily uses
caloriemama.ai sample API
which has certain limitations but can be replaced with the real one.
Server side
Under the hood, this is a NodeJS application that uses ExpressJS to handle requests and stores data in MongoDB. This gets invoked by FB Messenger platform after applying Wit.Ai NLP layer, and replies back after processing commands or images. The app has been deployed to Heroku, but can be also used with Ngrok to serve from the local machine.
Try out
The technology is in the early prototype stage, so access is limited. If you have already got access, you can follow the steps below:
Visit the page and start messaging.
For text commands, use something along these lines: "I made boiled eggs for breakfast" or "Got a bag of crisps for a snack".
For images, please use examples from here:
http://caloriemama.ai/api
To show a summary, you can ask like this: "What's the summary for today?"
Feel free to contact if any questions.
What's next
This all creates a wonderful ensemble of a friendly assistant that would help you to stay on the track with any of your nutrition plans. Chat with it or share photos like with other friends, and How Was Your Lunch will do the rest.
As briefly mentioned above, there's plenty of more ideas that can be incorporated in the application. To name a few possible directions here:
Personal dietary goals: 5 vegs a day, staying low calories or keto etc.
Small talks and chatter: learn a few tricks to amuse users and keep engaged
Broader nutrition facts: not just calories, enhance with proteins/carbs/fats
Extended food base: learn more basic foods and various recipes
Robust image AI: switch to the practical ML model that'd cope with real-life images
Built With
ai
express.js
facebook
facebook-messenger
javascript
mongodb
natural-language-processing
node.js
wit.ai
Try it out
www.facebook.com
github.com |
10,009 | https://devpost.com/software/covidbot-fna0ir | Inspiration
I've seen the damage misinformation about Coronavirus has done to families across the world. Thousands of people have lost their lives and many more are infected because they could not get accurate information about Covid. That was the inspiration behind this chatbot. I also wanted to get hands on experience with building an AI application with the skills I presently have. The prize money was also a good incentive for me as I am saving up for further studies.
What it does
CovidBot is a chatbot built using wit.ai NLP that provides information about Covid19 to users in real time based on their questions and inputs.
How I built it
Initially, I used my local machine, node.js and ngrok(as local webserver host) to build and test the app while integrating it with the wit.ai NLP. It was difficult for me getting the POST and GET requests to be successful as I was using my local PC as web server and it was my first time working on backend programming. Later, I used Glitch as a webhook to tie everything together; the wit.ai NLP, Node Js environment and my Facebook messenger.
Challenges I ran into
First one was setting up a local server on my machine using node.js and getting my GET/POST requests to return back a successful message.
Second one was when I was trying to integrate my webhook into my Facebook app, I didn't get that quite correctly the first time, so I ran into some errors. But thanks to helpful tips from some members of the Hackathon group, I was able to handle it.
I also had challenges with getting people to test the app before it was approved. That was because I wasn't conversant with using app "Roles", but when I read the documentation on it, I was clear.
Finally, having to write a privacy policy for the messenger app as part of the requirements before the app was finally approved was new to me. I took quite some time to research and come up with a pretty good one.
Accomplishments that I'm proud of
I am proud I've been able to create a Facebook app for the first time, though it's just able to answer questions and give information, the challenges which I overcame while trying to tie it all together using a webhook taught me a lot about how web servers and applications really work, so I am proud to have done something in this field.
What I learned
A whole lot of things like I have already mentioned about web apps, application of AI to practical problem solving. I also learnt about some critical thinking and problem solving techniques in the process.
What's next for CovidBot
I'll keep improving it and explore other ways I can integrate much more complex abilities into it. I will also explore other ways I can build a similar chatbot for other use cases and topics applying what I have learnt.
Built With
facebook-messenger
glitch
node.js
wit.ai
Try it out
m.me |
10,009 | https://devpost.com/software/bot-for-nation | BOT FOR NATION !
Start BOT with HI
TAP JOIN US
BOT will ask your desired role ! Tap on Role !
Bot will give form for that Role, Fill it up and submit !
Inspiration
Bangladesh
is collapsing every passing day , not from COVID but more from the fear of Unemployment and hunger. University over here is backdated and is not equipped to train students for earning a living, and the value of degree is declining everyday. Hence we are bridging the gap between Startups and Students with our mission " Let's Heal Bangladesh "
What it does
Our chatbots Takes entry of Students who wants to learn and work with startups , moreover it also collects Plasma Donors so that we can fight as a nation from COVID and From Unemployment !
How I built it
Its built with Chatfuel which allows the chatbot to give quick replies to the most asked query !
Challenges I ran into
This technology is very advanced for Bangladesh, its been only 4 years the reach of internet has taken place at scale. Hence its difficult to educate the people and most importantly to convey the message of " Skills are more important over degree " was the hardest part for the South Asian Parents.
Accomplishments that I'm proud of
With our mission " Let's Heal Bangladesh " we have successfully reached out to 50 + Startup CEO's who were ready to give the university students a chance to work on their projects and also have attracted top Mentors of the country who can make the path easier with Facebook Live session with us.
What I learned
Technology
is like a Wife ! Scary and Beautiful, if you don't sort things correctly it will mess up the entire life like hell, but with the right person(tools) its amazing and the journey becomes less painful and easy.
What's next for BOT for Nation !
Even at this Information age, Education at Bangladesh is a luxury, only the 14% of the Nation can afford the University level of Education ! We want to make the " Skill Based Learning " available for everyone with our BOT which will be just a tap away and in regional Language. Just with a smart phone a child whose father is a day labor would be able to dream to be the next Mark Zuckerberg, and learn the technology from the top startups of Nation for Free!
With BOT FOR NATION ! We Can Heal Bangladesh !
Built With
chatfuel
coffee
passion
Try it out
m.me |
10,009 | https://devpost.com/software/comestiblebot-user-s-indian-groceries-list-generator | Comersible Bot - Introduction Page
Web View to get the grocery Items
Quick Reply Options
Handover protocol to pass control to another app
Getting email of user using Quick reply
Email of grocery List
Inspiration
Whenever we go to supermarket for buying groceries, we either end up buying more or sometimes less. This leads to wastage or an extra trip to supermarket. To avoid this, we need to carry the list of grocery Items that we need to buy.
In the age of technology, we wanted something to help us organize our shopping behaviors and also share the responsibility among joint family members. This app would help us create a detailed list of groceries and also share it with someone who can bring it to us.
What it does
-This bot will help user to compile the list of grocery items that is needed.
-This application is end to end solution for buying groceries.
The user can generate the list and save it for future
Once the list is generated, the user can email the list.
There is also an option to order online. If user chooses that option, the list will be forwarded to the other app.
There is no need to search for items when ordering online as the app will receive the grocery list.
How I built it
I have created a Node server that subscribes to webhooks to receive the messages from messenger.
The app uses Web Views to collect multiple data from user. The user can select all the grocery items at once.
The application uses wit.ai NLP processing if the user wants to Add/Remove any item.
The app provides user with the quick replies to capture user inputs. Quick replies help the bot to serve a purpose and follow its workflow.
The app uses handover protocol to transfer the control to another app. There are 2 applications - one for generating the list and other is order delivery app. When the list is generated, the application passes the control to order delivery app along with the grocery list. The user can now order its items from that app.
The user can choose to save its list. At any point, the user can use the saved list and modify it according to its requirements.
The app provides the option to email the grocery list. The email address of the sender is also retrieved using quick reply.
Challenges I ran into
When the user send multiple messages, the quick reply is trimmed sometimes. This happens even when the quick reply is the last message as well. To avoid this, I have avoided to send multiple messages with quick reply.
Webviews are a great feature. Having no node js background, it took me lot of time to implement them.
While working on handover protocols, it was difficult to understand which thread has control sometimes. Suppose the secondary app has control and it went into some error or was down, the control never came back to primary. After understanding thoroughly the concepts, I had implemented a solution to take the control from secondary.
Accomplishments that I'm proud of
Learning new Technologies is always fun. In this time, I could learn and implement everything that I had never worked before.
Completing the app within limited time is a big achievement for me.
What I learned
Learnt facebook messenger platform concepts. Though I implemented Quick replies and Handover, I did learn the other concepts as well.
Basic of NLP using wit and dialog flow.
What's next for ComestibleBot - User's Indian Groceries List Generator
Completing the order delivery app.
Web views give the option of sharing. This is definitely a good feature for this app as one partner can create the list and share the order delivery webview with the other one to order.
Generating customized grocery list according to the number of people at home.
Implementation of one time notification if any of the grocery item is unavailable in order delivery app.
Built With
express.js
heroku
node.js
nodemailer
react
sqlite
wit.ai
Try it out
github.com
github.com |
10,009 | https://devpost.com/software/willy-your-willpower-coach | Willy - Your will power coach
Inspiration
Whenever I have tried building a good habit (exercise) or quitting a bad habit (smoking) I have always had moments of great motivation. Most of the time these moments of motivation are triggered by something that I read,see or feel. For example when trying to exercise regularly seeing a fab looking six pack or remembering that amazing feeling you get after a good run, is very motivating. Similarly, when trying to quit smoking reading an article/infographic on how smoking affects your body, or watching a video like this makes you resolve to try harder. The problem, however, is not the lack of things to motivate you but not having an easy way to access them when you need them the most. As James Clear (author of Atomic Habits) says:
“People keep reading self-help and revisiting the same ideas because that’s precisely what we need: to be reminded.The problem is not that information is unhelpful, but that attention is fleeting."
I always imagined a friend who would act as a reminder and feed me these motivation triggers when for e.g. ‘I don’t feel like exercising’ or ‘I feel like smoking.’ And hence the idea for ‘Willy - The trainer for your most important muscle- Your Willpower.’
What it does
Willy is a bot on Facebook messenger who helps you build good habits or quite bad habits.You can share your willpower triggers with Willy and Willy will use these to motivate you whenever you are feeling demotivated or tempted. These triggers can be in 4 forms (for now):
(a.) A quote (that you might have read somewhere)
(b.) A self note (that you write to yourself)
(c.) A video (a link to a video) or
(d.) A facebook post (a link to a facebook post) .
At the time of demotivation or temptation when you need a motivation boost, you just say :
(a.) ‘Willy, I don’t feel like ’ - To motivate you to continue a good habit.
(b.) ‘Willy, I feel like ’ - To motivate you to not restart a bad habit.
Willy works as follows:
To choose the habit you want to build or quit you can either:
Use easy quick reply buttons.
or
Use the phrase:
Start (for good habit) e.g start exercising
Stop (for bad habit) e.g. stop smoking
Choose the options from Persistent Menu.
Next choose the trigger you want to enter by either:
Using the phrase: Add e.g. entering:
Add exercising quote “All progress takes place outside the comfort zone.”
Will save the ‘quote’ : “All progress takes place outside the comfort zone.” in your ‘exercise’ motivation list.
Whenever you want to add a new habit or add a new trigger you can go to steps 2 and 3.
When you need the motivation boost you just write the phrase in the format:
Willy I don't feel like (for good habit) or e.g. Willy I don’t feel like exercising.
Willy, I feel like (for bad habit) e.g. Willy I feel like smoking
and Willy will randomly show you one of the motivation triggers for that ‘habit’ that you had entered earlier e.g. in the morning if you are feeling lazy you can write ‘I dont feel like exercising’ and will show you the quote you had fed it earlier.
Willy will ask you whether you are feeling motivated and if not he will randomly show another motivation prompt for you. He does this a total of 3 times for any one instance, after which he suggests that you either take a break or talk to an expert (a service that we can build later where we connect users to motivational coaches, psychologists and other professionals in that domain.)
How I built it
Front end Facebook messenger.
Backend using node.js , and Neo4J.
Used Wit.ai as the NLP engine.
Challenges I ran into
Saving context for the current habit especially when a user has entered multiple habits.
Since we are dealing with free text and the user can enter anything, designing the flow in the way that the user gets only relevant information.
3.Writing the logic for randomly displaying the motivation quote without repeating it too much
4.Using the appropriate phrases for training wit.
Accomplishments that I'm proud of
Combining free text and quick replies to design a neat flow of the conversation.
Using the intents and entities in wit.ai effectively to make it an easy conversation.
Effectively designing the error handling/fallback strategy for gibberish content.
What I learned
Combining all the different pieces together to deliver one cohesive solution. Also the use of graphs effectively to save the context of the user.
What's next for Willy - Your willpower coach.
Launching Willy as a voice bot.
Build a service to connect Willy with motivation experts.
Building intelligence in Willy so that he can understand which motivation prompts are most helpful so that he can show them more and also suggest similar prompts.
Adding support for other motivation prompt types like: ‘Voice Notes’ and Images.
The bot is still in development and the 2 test accounts have been added as testers. ( 499418056,stef.devpost.1)
Built With
facebook-messenger
neo4j
node.js
wit.ai
Try it out
m.me |
10,009 | https://devpost.com/software/controlly | Win a new coin.
Checking for the text on the blockchain.
NFT token leveled up!!
Inspiration
This project was inspired by the latest tragedy of the boys' locker room that happened in India. There was the spread of this information about a boys-only group in which the participants were body-shaming and planning hideous crimes like rape. There were two separate cases, one on Instagram and on Snapchat. The police proved that the Snapchat case was fake. But before even anything was proven, people started to spread hate speeches against the parties involved. This eventually resulted in one of the involved party to commit suicide.
What it does
My solution aims to inform people if the text or message they are gonna put on their stories or send to someone might cause someone's mental harm. This assists people who are unable to analyze if the message they are sending will affect someone mentally and they are not unknowingly leading someone towards a state of depression, anxiety, etc.
The bot awards the person coming to it with a potential hatred speech with an NFT token.
The texts are recorded on the blockchain. The reward is claimed whenever someone gives any new potentially dangerous hatred text to the bot. This way people also get attracted to using the chatbot since they are being rewarded for it.
How I built it
This application is built by leveraging the power of NodeJS, ExpressJS, Ethereum, Solidity, Web3, NFT ( erc721 tokens), and Google sentimental analysis API.
Challenges I ran into
The few stages I got stuck were:
Machine Learning Model integration in my bot.
Designing the architecture for a bot to tackle messages in a sequential manner.
Why would someone use my bot?
. The solution I came up with was NFT tokens. If people get rewarded for using a bot, then they will use it!!. So by giving them NFT token and leveling their tokens with time, this not only attracts customers but also results in more database regarding such negatively influential texts and posts.
Accomplishments that I'm proud of
Proper working backend design with proper tests for the smart contract.
Work Flow design of the chatbot.
Learning about several cloud services related to ML.
Quite a good understanding of interacting with ERC721 smart contracts.
What I learned
Cloud features related to ML, mainly NLP.
Created two chatbots for following the tutorial purposes, resulting in me understanding not only making an interactive chatbot, but also a web view based bot too.
Running a typescript project using PM2.
What's next for CONTROLLY
Integration of a Text Semantic Similarity Model for better record maintenance.
Adding more interaction features if needed.
Built With
babel
facebook-messenger
google-cloud
node.js
solidity
truffle
web3
webpack
Try it out
m.me
github.com |
10,009 | https://devpost.com/software/moment-messenger | Moment Messenger
ConvoVC
settingsVC
ScheduleVC
Inspiration
It started when I moved to Canada and I promised my mom to send her a message everyday to let her know that I am well and safe, with a Good Morning Mom message everyday. Consistent!! The reason I followed my promise to her, because I did not want her to worry about me. I thought how would I be able to not forget to let her know how I am doing and check up of her. That is how Moment Messenger idea came to be. To be able to sent schedule messages at any Time and Date. The problem I faced was that I did not know any programming languages and had zero background knowledge in computer science, and no one to lookUp to as a mentor to guide me on how to start this journey. So I started learning HTML and CSS baby steps, until I became able to add more languages to my stack and understand code.
What it does
It is an innovated Instant Messaging App that provides users with the convenience to schedule messages to be sent at any later Date and Time
How I built it
using IOS, JavaScript, Node.js, SWIFT, Firebase
Challenges I ran into
The Schedule Function was tricky and more advanced for my skill level which lead me to meet Peter Varga to help me make it happen.
Design patters ect
Builder, Delegate
Accomplishments that I'm proud of
Building an Innovative messaging app that will serve people by providing convenience and organization
What I learned
Don't give up, and virtually any challenge you face someone out there would be able to help you get over it; if you find the right person.
What's next for Moment Messenger
Location
Built With
firebase
ios
javascript
node.js
sinch
swift
ui
uikit |
10,009 | https://devpost.com/software/donateus | Inspiration
I always wanna work for people who're suffering for being poor, my dream is to fulfill every basic need of a human being.
What it does
It provides a way to donate money for your desired sector you wanna donate and you can also donate blood through this. It will let you know the emergency nearest patient who needs your blood.
How I built it
with chatfuel.
Challenges I ran into
Not so much experiences in this kinda work. For being the first time, may be I couldn't some works properly or something like that & will try to update this idea in future.
Accomplishments that I'm proud of
With this idea, people can get help of many sectors through one platform.
What I learned
In the working time, I felt the necessity of helping or donating from the capable people for the poor people, deeply.
What's next for DonateUS
It's not fully developed yet. I want, many people will join with this project to help people. The very next target of DonateUs is to open a school for street children & to take some necessary steps to get some proper training of different sectors for rural, unemployed males and females with people's donation. Some day I dream, there'll be no poor people in this country, no street children, no poverty.
Built With
chatfuel
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/test-mv1sz8 | Inspiration
“Are you trying to disappoint me?”; “Why don’t you just focus?”; “What’s wrong with you?” - These are just some of the questions that I and 129 million other children with ADHD grew up hearing. People with ADHD spend their lives attempting to manage a neurodevelopmental disorder that is
stigmatised, undertreated, and underdiagnosed.
We interviewed and surveyed close to 70 parents and teens with ADHD before starting development. We found that some of the biggest issues they faced were accessible information on their disorder and an understanding of how they can manage their symptoms. A lot of the information on how to manage this disorder effectively gets drowned in dozens of unreputable blog posts or $500/USD behavioural treatment programs. Many people with ADHD don't have the time or focus to do the research that is required to manage this disorder effectively. Charly makes this information easily digestible through short and casual instant messages.
What it does
Charly is every ADHD person's personal side-kick that teaches them about how their brain works and helps them manage their symptoms. Charly helps manage symptoms through evidence-backed treatment techniques such as journaling. Many of the world's leading ADHD psychiatrists have said that journalling can be especially helpful for people with ADHD. Journalling can help people with ADHD become aware of their emotional state and the factors in their life that affect their symptoms.
Charly features a growing knowledge-base where people can instantly get evidence-backed information on how to manage ADHD. Every piece of information in the knowledge-base is backed by peer-reviewed research that the person can access for fact-checking purposes.
If a person isn't able to find a solution in our knowledge base, then they can connect with an experienced ADHD advisor. Our advisors provide personalised advice and resources that help people with ADHD manage their symptoms.
Charly also provides data privacy controls that allow the user to download or delete all the data stored about them.
How we built it
We spent three weeks interviewing people in the ADHD community across Reddit, Facebook Groups and in-person. We really wanted to make sure that we were designing a solution that would actually help the ADHD community.
We went back and re-interviewed members from the community at each stage of our product design process. Our constant iteration meant we were able to narrow down the key pieces of information that we needed in this MVP (e.g. how to take your medication).
The Messenger bot was coded in NodeJS (Express) and is hosted on Heroku. We use Firestore (Firebase) for storing data. The pages shown in the Journalling component are created in React using WeUI (WeChat UI) as the design system.
Challenges we ran into
The biggest challenge behind Charly was getting the idea right. We pivoted the feature-set several times. With each interview we conducted, we gained valuable insights that guided our product design process.
Transferring data between postbacks and dialogue waterfalls became quite difficult. We eventually had to resort to saving state about the conversation on the user object in Firestore.
Handling complex conversation flows and a large number of messages can also make the codebase very confusing and makes scaling difficult. Before continuing Charly, we'll need to do a complete recode using a framework like Microsoft's Bot Framework (or a custom one).
Accomplishments that we're proud of
Through our interviews, we had a solid idea of what the ADHD community needed. But we weren't able to start development till June 20th (4 days before the deadline). We were preoccupied with our semester exams and large assignments that we had to finish first. We started on June 20th with an empty folder and a few outdated NodeJS examples from 2017. Now we've built an MVP that we believe will make a massive positive impact on a community that we personally care about.
(and we're proud of everything in the next section)
What we learned
Our team learnt a lot about the product design process, and we learnt how to run effective user interviews. We were initially hesitant to attend in-person events in the ADHD community. However, we learnt that leaving your comfort zone is very important. These in-person events are where we gained the most valuable insights and we realised that people wanted to understand if "what they were doing was working" (journaling/habit tracking) and that they didn't have time to spend days or weeks reading research (knowledge base/advisors).
As first time users, our team became familiar with the capabilities and limitations of the Messenger platform. We're excited to see how we can take advantage of the extensions SDK and the platform during our future development.
We realised that designing a natural-language app is completely different from designing a traditional app. We have several dozen conversations daily. Yet, we really struggled to design conversations that felt even a little
natural
. Even now, we're not completely happy with the way our conversational flows turned out and we'll be redesigning them before releasing to our first users.
Given our short-development time, we didn't have time to integrate a bot development framework such as MS Bot Framework. We were still able to design some complex conversational flows using our own ad-hoc framework. Developing this primitive 'bot framework' really helped us appreciate the technical issues associated with chatbots. We came up with hacks that allowed us to get past the lack of state we had between conversations. Thanks to our ad-hoc framework, we ultimately only had to resort to saving conversation state when eliciting text prompts (e.g: Setting goals for journals)
What's next for Charly
Behavioural treatment programs are considered first-line treatments for people with ADHD (CDC). Unfortunately, these in-person treatment plans can cost almost $500 USD per month (Foster et al, 2007), and they require a great amount of time and dedication. Recent research has shown that these same techniques can be applied effectively through digital means. Charly has the potential to democratise many of these behavioural treatment techniques, all through a messaging app that all of our interviewees used extensively.
Journalling is a component of many of these behavioural treatment programs, and we plan to expand this heavily by offering intelligent analytics. But this is just the beginning. Our interviews led us to design several features that are tailored to the ADHD community. Some examples include persistent and flexible routines, reminders for family members and reward tools for parents of ADHD kids.
After adding some security measures, and additional privacy controls we'll be launching Charly to our first public users in July.
Built With
facebook-messenger
firebase
heroku
node.js
react
weui
Try it out
m.me |
10,009 | https://devpost.com/software/developer-q4dtjw | conversation
conversation
AR projects
one of the AR projects info through github webview
conversation
one of the robotics project web view
Start Conversation
Inspiration
It is widely observed that developers, designers or creators often have a portfolio website to showcase their projects and also a blog to post about their work. It is not easy to get enough audience to those websites therefore many developers have resorted to an option where they share their work on social media platform which provides them good number of traffic. Since facebook page is a great option for sharing and doing this, there's still a gap which connects other developers or consumers to this developer who is running the page, for a proper bilateral communication. So to overcome this, a messenger experience was a good option to provide people who seek for the developer for his work or collaboration in a project or even simply to see the latest projects he's been working on for inspiration.
What it does
The solution provides a facebook messenger experience where when a person initiates a chat, they get options to select like Projects, Services, Contact Us. Selecting an option like projects gives categories to choose from like web projects, augmented reality projects etc. The person can select a category like web projects and they will get relevant information about the project and even repository link to follow which opens up a web view in the messenger itself. The contacting person can also see what services are provided by the developer like if he freelances or is available for a job. The developer can also be contacted which gives a form to fill and on submission of the form a notifying mail is sent to the Developer with the relevant details from the form and then they can indulge in a one to one chat.
How I built it
Setting up a messenger experience was fairly simple where first an App was created in facebook for developers and messenger and webhooks was selected in products. The webhook/bot was made in node.js/express with nodemailer to send notifications to the page owner of contacts and mailtrap as a service for the e-mail notifications. Then, all the communication was done through the webhook via API calls made by the request package for Node.
Challenges I ran into
The first challenge was to provide a https URL for the webhook which for the development phase was handled by Ngrok to provide a tunnel for the localhost. The final deployment is not done due to my inability to provide a SSL certificate and get a host to do it on time fast. This made the learning, development and testing phase hard for me as i am not able to share a working model to my friends and even to this hackathon but i have provided my local testing video and obviously the full code is also there.
Accomplishments that I'm proud of
It is satifying that in the end i managed to complete a project within 2 weeks and learned something.
What I learned
I learned how to make a chatbot for the first time that too of facebook messenger which is quite useful in day to day life owing to huge traffic on the platform. Also i also went through some dev-ops stuff which was quite useful. Learning about what a webhook is also got cleared.
What's next for Developer
I would like to connect it to database and have content management system such that the page owner can easily update the data of the messenger experience without much hassle such that it becomes a complete product to setup a messenger experience fast.
Built With
css3
express.js
html5
node.js
nodemailer
Try it out
github.com
www.facebook.com |
10,009 | https://devpost.com/software/onduka | Onduka.com
Welcome to a shop chatbot / get started
Hi message
New arrivals items
Engagement
Browsing
Browsing
Cart
Thanks service
Publish to facebook page and save your shop item directly
Choose where to Broadcast
Provide Infromation about your broadcast
Share a broadast unsing Facebook One-Time-Notofication feature
Inspiration
We received a request from a shop owner that needed a chatbot for his business / shop due to this covid-19 lockdown period, this is where everything get started and how we get inspired on what if we bring a solution that will enable any shop owner to create chatbot using OnDuka instantly available on facebook messenger by just few clicks ? from the request and the actual problem that businesses are facing and a great opportunity to open for these businesses to the use of chatbot as an extra hand and a way to sell easily online.
What it does
OnDuka is a chatbot that sells goods and services to businesses customers. It is an automated salesperson that never sleeps. While business owners or physical shops/stores are closed, OnDuka will keep interacting with customers, following up and selling items on behalf of the business owner via their Facebook Pages.
How we built it
Shop owners will come to the OnDuka website to connect the OnDuka Chatbot to their shop pages. The chatbot that uses NLP/NLU and fetch products from the store/shop database. The chatbot helps customers navigate the shop/store items, ask questions, subscribe for notification of new arrival, discount and other things that need notification. All these are done using One-Time Notifications, and Quick Replies but we are also working to implement Private Replies that will send the customers to talk directly to the chatbot.
The website helps shop owners to post on their Shop page wall, in their Groups, broadcast to all customers
Challenges we ran into
Time was not enough to have everything we wanted present. We were not able to implement Private Reply but we are already working on it
Accomplishments that we're proud of
We are just starting a lot of work yet to be done but the work we have done so far shows us our strength and tells us that we can do it, that makes us feel proud of.
The other thing that made us more proud is the smile we saw on the face of a shop owner when we invited him for a test and he gave us great feedback. We had a lot to learn but we decided to challenge ourselves to build it because we believe our product is going to impact business positively.
What we learned
We have learnt a lot about retail businesses how it functions but how to use Facebook technologies with almost all the features they offer. Our platform is completely built using Facebook technologies only.
What's next for OnDuka
Covid-19 has come with a need for all businesses either small or big to be online. Having so much interest in AI for good and Chatbot we want to make these accessible to them to help SMBs, Retailers in Increasing Sales, Reducing Costs and Automate Facebook Support.
We are still building and would like to launch it next month, we tried to get some retailer businesses interested and they are eagerly waiting for the product to be ready for use.
Built With
facebook-graph
facebook-login-api
figma
graphlq
node.js
react
tailwindcss
typescript
wit.ai
Try it out
m.me
onduka.com |
10,009 | https://devpost.com/software/t-shirts-bd | Inspiration
I wanted to be rich.
What it does
Assists customers
How I built it
Using chatfuel
Challenges I ran into
None
Accomplishments that I'm proud of
None
What I learned
How to make it
What's next for T Shirts BD
Making better responses
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/alpobots |
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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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var js, fjs = d.getElementsByTagName(s)[0];
if (d.getElementById(id)) return;
js = d.createElement(s);
js.id = id;
js.src = "https://connect.facebook.net/en_US/sdk.js";
fjs.parentNode.insertBefore(js, fjs);
}(document, 'script', 'facebook-jssdk'));
Alpo Bots
One-Time Notifications, patient facing
Quick Replies, patient facing
Consultation Test Flow, patient facing
My Appointment Test Flow, doctor facing
Prescription Test Flow, doctor facing
F.B. Mesnegers Hospital
F.B. Cortphy Hospital
F. Bras Park Hospital
Fishbone Diagram. source: [https://i.pinimg.com/originals/cd/d4/e3/cdd4e3bc23bb7e08b74145e3b5e525a8.png]
Alpo Bots empower the most important time and place of communication, and that is face to face communication when the doctor and patient meet. By facilitating the major administration process upfront with ease, doctors and particularly patients, can focus and get the most out of the examination session.
Inspiration
Learning from the fish bone diagram, every hospital has similar problems. Presented in the diagram is a situation where patients are waiting too long to get into ED, the most crucial rooms to treat patients immediately. Looking at this, we found just the same problem in the usual waiting room for patients who seek medical consultation. We learned that solving the ED problem needs mostly efforts from the hospitals themselves and deeper analysis on the system design, we found opportunity in the usual patient queue which has a lower time urgency but could be solved via Alpo Bots. Why do we let this long process halt us to start a more meaningful conversation with the doctor to get our treatment faster? We think, these administration processes should be able to be sorted out without going through the long and boring queue that requires a lot of time in the hospital, and this is true particularly in Indonesia.
What it does
The main capability is to enable patients to report and submit health symptoms and problems, and then register it into the hospital’s database, then the data can be accessed by the doctors. Alpo Bots is also able to remind users about their upcoming appointments and manages all the schedules and messages between the doctor and the patient. We build Alp oBots to accelerate the administration process, so that patients can access the core and more meaningful communication with the doctor. Alpo Bots is an end to end chatbot system to assist patients who are looking to make doctor appointments using Facebook Messenger as their first touch point. Patients can consult symptoms to get a better doctor recommendation and apply for a queue number before coming to the hospital to save time. Likewise, doctors, nurses and the hospital can get data entry in advance and read through reported symptoms for a clearer understanding, saving time for the actual examination of the patient. This way, we lessen the waiting time and prepare a fixed schedule for the doctors and patients so everyone knows what’s next. With this, we also finished all the administration work upfront and patients do not need to drive to the hospital or call, they just need to chat with Alpo Bots and could continue whenever they have the time.
How we built it
We build our backend services using Python and serve it on Heroku. The backend services consist of API service and Scheduler service. API service is used to handle webhook events from Facebook Messenger and give responses that are in accordance with our bot main function. Scheduler service is used to handle notification for users (patients). From the Facebook Messenger features that are required to be built in our app, we choose to use One-Time Notifications and Quick Replies.
One-Time Notifications are used for reminding patients one day before their appointment (For this competition, at 11:00am GMT+7. Of course in real life usage should be on local time).
Quick Replies are used in cases where we can suggest choices of answers for patients, so they don’t need to type manually.
Challenges we ran into
At first we thought to make an extensive flow, but apparently there are a lot of scenarios to consider, that is why we focus on the completion of the main consultation flow. We need to make our own word detection library for the body parts and symptoms, that is why the bot can only still detect a handful of words for now. We made some quick wins work around for the codes and it is not as clean as we wanted. The other thing is the chat recording, we recorded how it would show the usage on real users, but the video limit is too short, that is why we resort to only screen recording on the chat flow.
Accomplishments that we're proud of
Being able to detect words sparks the opportunity we see this bot can do in the long run.
Alpo Bots can make multiple appointments, and the reminders capability helps users for their upcoming appointments.
We are able to complete the consultation flow and enable direct back and forth from users to the doctor. This reflects the collaboration of back end engineering and design principles.
We are also able to use three features that are required in this competition. Those are One-Time Notifications and Quick Replies.
We also made some mock assets inspired from Facebook's product range (when the engineer is so absorbed in the coding and the designer has nothing to do :D), and of course the Alpo Bots logo.
What we learned
As a proof of concept it is quite smooth, but we tested it out, users’ still expect it to be a smoother experience.
Of course we learned how to use some features from Facebook Messengers (especially One-Time Notifications and Quick Replies), learned a lot about medical concerns and the problem we are trying to solve.
Although doctor referrals are common, we learned that apparently systems in hospitals are different, this gives another layer of problems and opportunity to solve.
What's next for Alpo Bots
We envision that Alpo Bots can become a chat bot framework that can be utilized by different hospitals and then grow as a centralized system to easier schedules and manages patients transfer from one hospital to another, that ultimately will lessen time for all administration work to healthcare.
Users can contact different hospitals and Alpo Bots will handle all the data and schedule transfer of one hospital to another with an integrated central database so that patients can be easier to be referred to another hospital with all the administration done upfront.
Testing Flow
Below are the step by step to test the product.
To test the patient flow:
Search F.B. Mesnegers Hospital (or access this page
https://www.facebook.com/FB-Mesnegers-Hospital-114487943639689
)
Tap get started
Tap Menu
Choose Consultation to test the consultation flow.
To test the doctor flow, first we assume that doctors have to register in order to use Alpo Bots’ service.
Search Alpo Bots (or access this page
https://www.facebook.com/Alpo-Bots-101722244928623
)
Tap get started
Tap Menu and choose Register as Doctor.
In this testing case, we simplify the registration. Testers only need to choose Register as Doctor and they will get notified every time a patient creates an appointment, for all doctors. Of course in real life usage, the registration will be more detailed and doctors will only get notification when a patient creates an appointment with them.
After registering as a doctor and after patient creates an appointment, by accessing the Menu and choose My Appointment, testers can accesses the after-appointment flow (send medication prescription to patient).
Built With
api
facebook-messenger
flask
heroku
python
scheduler
Try it out
www.facebook.com
www.facebook.com |
10,009 | https://devpost.com/software/mesh-messaging-shopping | Buy your dream product in a faster way
Inspiration
I like to participate in various competitions, in this pandemic situation Facebook Hackathon is creating a good opportunity to utilize my free time. This is my first Facebook Hackathon. From this hackathon, I learned a lot of new things, such as how to create a chatbot and how to connect it to a Facebook page.
And also it gives me an opportunity to show my idea on a global platform.
What it does
This is the idea which can change the shopping experience on Facebook.
I think one more option needs in the Facebook nav bar which is “Facebook Shops”(FS). Facebook Shop is a global platform where all shops will register under “Facebook Shops”(means all e-commerce Facebook pages).
And all shops are independent, they can do their work in their own way.
But Facebook shopbot will help the customer who wants to buy their dream product with the best quality and faster way.
The bot connected all the e-commerce pages. Where customers gave rating the product - good product gain good rating and lower quality product gain low rating. So we find which product is good and which is bad.
For example,
If any user comes “Facebook Shops” page and open the message icon and say “buy pencil” or “pencil” then the bot returns the best quality pencil which is produced by different company (e-commerce Facebook pages). From here user can buy their product directly.
It saves our time because we do not need to go on different pages, we can find our all needed product with an assistant in a single bot who assists us with which product is best and which one we want to buy.
How I built it
First I create a Facebook page whose name is “Face_Book Shops”.
Then,
The help of “Chatfuel” I create a demo chatbot. Here I am using the two most interesting features this is “Automate” and “Set Up AI” and also using “Quick Reply”.
Here I set some products in AI chatbot and trained it using different words.
At last,
I connect the chatbot to the page.
Challenges I ran into
The main challenge is to create a chatbot. Because this is the first time where I going to create a chatbot. So, I direct jump on google and make a couple of searches. Then, I find “Chatfuel” and give some time to learn it and how to build a chatbot using it.
And finally, I have done it.
Then, occur another problem, which is my Facebook page chatbot could not work. It takes some time to fix it. Then I find the problem, the funny thing is I do not connect the chatbot with my Facebook page. Then connect it, and it works properly.
Accomplishments that I'm proud of
After creating the bot when I test it I feel excited. All property is warking well.
If the idea will be selected and billion of people will use it then it will make me more proud.
What I learned
I always try to learn something new. Facebook Hackathon gives me an opportunity to learn new things. For this hackathon, I learn a lot. For the first time, I am successful to build a chatbot that helps people.
Then, I became acquainted with a new platform name “Chatfuel” and I also learn how to connect it with my Facebook page after creating my bot.
What's next for MeSh (Messaging + Shopping)
Short-Term target: Connect to bot all the giant e-commerce Facebook pages(Shops).
Long-Term target: Connect to bot all e-commerce Facebook pages.
Built With
automate
chatfuel
coffee
googlesearch
setupai
Try it out
m.me
web.facebook.com
www.loom.com |
10,009 | https://devpost.com/software/dashver | Logo
Delivery Form
Jobs Form
Chat Conversation with Facebook Bot
Inspiration
In the current climate, for certain groups of individuals are less likely to commute around due to safety/health reasons and services like delivery has a much important role in society now. Recession is also happening worldwide, with such a platform it enables an additional source of income to help with those in need to tide them through this difficult time. What is good about this concept is that it requires no technical skill and no delivery vehicle to start, the courier is even able to travel by foot .
What it does
It is a platform that matches a delivery service seeker to a delivery service provider. The provider or seeker can be anyone that has a Facebook messenger. All information is collected via Facebook messenger chatbot and matchmaking happens when the provider and seeker's geographical location are within distance. The delivery seeker will have to pay an amount (calculated by the system considering the distance and type of parcel) to the delivery service provider.
How I built it
I used Laravel as the backend service and using botman to facilitate interactions with the Facebook Messenger APIs/Webhooks. Frontend for webview i'm using VueJS. Geocoding and mapping displays i'm using google maps. Nginx server and DB is powered by DigitalOcean and i'm using Forge to manage the server settings and configs.
Challenges I ran into
The main challenge is how to gather the user's geolocation in an efficient and seamless manner, it was difficult as the sharing of location feature was deprecated in Facebook Messenger.
Accomplishments that I'm proud of
Managed to complete to complete this project in the span of 5 days with no prior experience working with Facebook Messenger API.
What I learned
Learnt how to utilise Facebook Messenger Chatbot to better enhance the customer experience in e-commerce.
What's next for DashVer
Enhanced matchmaking algorithms for the platform and improvements to the UI/Chabot to better simplify the whole delivery process and also a web platform that is integrated with the Facebook Chat.
Built With
digitalocean
google-maps
laravel
vuejs
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/stay-safe-bot-fewd5b | Stay Safe Bot - Facebook page
Bot on Messenger
Bot welcome page. Say hi to bot
Bot is giving you information
All the common diseases
COVID-19 Details by bot
Self test for COVID-19
Bot First Aid section
First Aid training section
Inspiration
Trying to solve a problem inspires us. And it feels good to learn something new.
What it does
This bot will tell you all details about various diseases, risk factors & how to prevent them. It has a self-test feature, emergency services, test-treatment center information, and First Aid section.
How I built it
Build with Love & Chatfuel
Challenges I ran into
We faced a lot of problems since we first created Chatbot. The page was not connecting, the user interaction was not right. We couldn't match the blocks.
But it was not so difficult as we thought. We finally made it.
Accomplishments that I'm proud of
We didn't expect it to work. We are so happy to see it working as it was our first time to build a chatbot.
What I learned
We have learned how to build a ChatBot, how to organize things, teamwork, and the communication between bot & human.
What's next for Stay Safe Bot
We want to add all the disease information, risk factors & prevention details. And a self-test feature for all the diseases. So users can primarily know if they were infected or not.
We want to go
Global
. So that users from any country can get help easily with the test-treatment center details, and emergency services details as well.
Built With
chatfuel
love
Try it out
m.me
www.facebook.com |
10,009 | https://devpost.com/software/stigmatized | introduction
welcoming
Mobile browser user interaction
Bot making recommendation
Facebook messenger user interaction
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
facebook-graph
facebook-messenger
heroku
javascript
node.js
wit.ai
Try it out
www.messenger.com |
10,009 | https://devpost.com/software/fitness-4-today | Inspiration
In out present situation for Covid-19 maximum of all are in hone quarantine and can't go out nornally anytime for non emergency work.But for our long quarantine stay at home causes our helth weighted and fatty not for physical workout and eat not balance food and not for dieting. But to increase better immunity we have to eat balanced diet food and nutrition food.But for Quarantine we cant go out for gymnasium.So we find a plan to encourage people to connect gymnasium via Facebook for any health tios or diet plan or tips caz everybody is using Facebook naw a days.
What it does
Medibot is a messenger chat-bot. When a person knock a gymnasium facebook page it will start conversation with the person if the trainer of the gymnasium is busy or offline. The chat-bot will provide minimum help conversation or tips for a person query to the gymnasium page or diat tips or diat plan or workout tips
How we built it
First of all, we needed to find specific solution video for that specific question. So, youtube.com was the best source for that.After that we created a welcome messege for costomer and then a default messege for that costomer. After that we created some AI messege, which is actually the answers for the costomer. After binding all these together in a algorithm chatfuel makes that easy to connect that bot with our Facebook page. That is how that bot created successfully.
Challenges we ran into
One of the earliest challenges we have faced is to imagine people psychology to get how and which question the ask to the page and how then reply our chat-bot conversation. Another problem we fave to implement the bot but chatfuel.com have done out work easy to implement and deaign the chatBot .
Accomplishments that we're proud of
The main thing we that we have learn from this experience how to work as a team and how to coordinate with each other besides, it in enhanced our knowledge about technology.
What we learned
Our team is consisting of 3 members. And all of us are first-time hackathon-ers. We all know how is Facebook and how Facebook pages work. But before this hackathon, we had no idea that a messenger chatbot can be taken to this level. We learned completely new technologies in a short period. We learned how to use "Chatfuel".
What's next for fitness 4 today
In near future we are planning to include medication and yoga related information that will be provided by the bottchat.
Built With
chatfuel
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/simba-bot | Bot Icon
Welcome screen
Send a greeting message like Hi to get started
Conversations 1
Conversations 2
Conversation 3: Request to handover conversation to real human
Conversation 4: NPS survey after human support marks page inbox message as done
Conversation 5: NPS survey acknowledgement after response data gets logged on Facebook Analytics
Option to call a business representative
Conversation: Request to handover conversation to real human
Inspiration
Over the years, a number of businesses have shut down due to low engagement ratio with their users. Globally, 1 in 10 businesses shut or close up due to issues relating to customer retention and engagement. The tendency to be a player in solving this issue is the key mother to the project idea for this solution, the Simba Bot.
What it does
Using the Messenger handover protocol API and quick replies, this bot is able to provide an unflinching customer support service for businesses (we partnered with DeliveryNow NG to test this beta version) whereby users can request for information about a specific business (DeliveryNow NG in this case), track orders, and reach out to a real human support via inbox (using the handover protocol API), or via phone call. The Simba Bot uses wit.ai to understand conversations, while it spurts out coded-in responses based on the expressions and intents it receives.
Even after taking back the control of a conversation, the bot sends the user a short NPS (Net Promoter Score) survey using quick replies which helps measure customer satisfaction after it takes back a conversation from a real human. The response of this survey is logged on Facebook Analytics, where the business can make meaningful decisions using the provided survey data.
How we built it
Wit.ai is used in powering the bot, it serves as the brain. The bot was setup using the Messenger Node SDK, while the whole project is written in NodeJS.
Challenges we ran into
Time was a major challenge, even though, we still work on this project day in day out, it isn't yet what we envision it to be.
Accomplishments that we're proud of
Being able to write and run many conditions against the wit.ai entities in such a small amount of time, the project's GitHub commit history is a testament to this.
What we learned
It is not enough to write code for a virtual assistant, training the bot on wit.ai felt just as important as writing the code itself. And two members of the team were assigned this responsibility of training the wit.ai app.
What's next for Simba Bot
To integrate a seamless Messenger chat extension using the chat extensions API, this should afford businesses an advantage to process customer orders, monitor inventory, and process payments for customer orders all within the bot.
We do also have plans to reinvent the bot as a SaaS solution that not only houses support for a single business, but also provides real time support for businesses - while continuing to provide powerful analytics.
Built With
facebook-messenger
node.js
wit.ai
Try it out
m.me
github.com |
10,009 | https://devpost.com/software/doctor-ai | Inspiration
My Grand mother was having some kidney problems and i had to get some quick answers. However I couldnt find anything on the internet so I made a bot that can perform these tasks all by itself.
What it does
Talks to patient like a kidney doctor.
How I built it
The bot was built in colab using nltk.
Challenges I ran into
Nothing much to be honest.
Accomplishments that I'm proud of
Of Completing the project.
What I learned
How to make a chatbot.
What's next for Doctor AI
Making better predictions
Built With
nltk
Try it out
github.com |
10,009 | https://devpost.com/software/bright-social-enterprise-commerce-bot | Bright: A social enterprise commerce bot
Ice Breakers: Get the conversation started with prompts
Persistent Menu at the bottom right: Access a quick navigation
Product enquiry: Train the bot to understand questions about your products
General enquiry: Let Bright be your FAQ go-to.
Product recommendation: Suggest products for your customers to try out
Capture the social impact: Indicate a count based on your order value
Seamless in-app shopping experience: Commerce payment and cart within the chat
Smart order status check: Remembers orders based on user id
Bright: Social Enterprise Commerce Bot
Video Link:
https://youtu.be/MO_p3ylFNxU
Inspiration
With the roll-out of Facebook Shops, there can be stronger social connections between customers and the retailers. As we looked into how we can improve the online shopping experience for users, we really wanted to work on a project that is meaningful. In this project, we hope to create a commerce bot for social enterprises to continue to receive income through an online business model. This bot allows customers to have conversational experiences that cannot be found in a web-based application.
What it does
Small businesses often lack resources to expand their ecommerce businesses. Bright, the social enterprise commerce bot, helps small businesses who contribute towards a social good to set up an online customer service chatbot in an easy and intuitive way where the customers could see the impact of their online purchase.
Customers are just one chat away from getting more information about the store products. Bright will provide human-like responses to questions about items and it learns from customers’ responses over time with Wit.ai natural language processing model
Bright comes packed with features to shop in-chat, make payment and check status of orders through integration with enterprise database
What sets Bright apart from any other commerce app is its capability to show the social impact upon checkout (i.e. the number of beneficiaries the customers’ purchase has helped!)
When customers show their interest or gratitude, Bright can also prompt customers to like and follow their Facebook page to subscribe to the latest updates
How we built it
Ideate
In our preliminary brainstorm, we identified conversational commerce as our focus context. We believe we could enhance buyer-seller relationships through Facebook messenger interactions to create trust in online purchase.
There are 3 customer segments: (1) Customers (2) Sellers (3) Middlemen. We are driven to focus on the customers’ experience as they would make up the majority of our end-users.
Through market research, we found that most messenger apps enable ecommerce to build chatbot solutions to reach out to more customers instead of thinking in the customers’ perspective.
We populated our idea board with the greatest pain points for the customers.
We realised that most of the time customers are troubled by (1) difficulty in finding product (2) lack of pricing transparency (3) complicated checkout system.
To validate our hypothesis, we found that Facebook and Boston Consulting Group had surveyed over 8864 individuals in 2019. The findings were congruent with our top pain point which is to enable users to find additional information on products.
Source:
https://e27.co/southeast-asia-emerges-as-leader-in-conversational-commerce-thailand-vietnam-most-advanced-in-adoption-20191031/
Define
With the user pain points in mind, we conceptualised features that could solve customers’ underlying needs through a messenger app. We prioritised and concluded that we should develop a messenger app that automates customer service. There were also other impactful and easy to implement features like loyalty programmes, but these are nice-to-have instead of must-have features.
With the Bright app, we hope to reach the following success.
Business Objective
: Reduce the man hours for customer service
Success metrics
: High conversion rate of chat to sales funnel
Prototype
With 2 weeks to develop our prototype, we outlined our steps and set a timeline to ensure we reach each of the milestones.
Set up business server (Express js): Creating webhook
Create Facebook app
Connect business server to Facebook app through Facebook developer platform
Connect Facebook page to Facebook app through Facebook developer platform
Test conversation between Facebook Messenger and business server
Develop user flow for chat responses and quick replies
Automate Customer Service: Product Enquiry
Create database with user and product information
Create sample question and answers for the product
Question → Wit.ai Natural Language Processing → Context → Retrieve Answer → Send Response
Implement product enquiry Q&A
Automate Customer Service: Order Enquiry
Populate database with order information
Implement shipping and delivery Q&A
Automate Customer Service: Checkout
Populate database (MongoDB) with order information
Implement in chat bot
Social impact notification upon checkout
QA and Iterate
Challenges that we ran into
Copious amounts of data is required in order for Wit.ai natural language processing model to correctly identify the message’s intent, entities and traits
We integrated with Facebook Shop page to automatically store users’ conversations to our Wit.ai app to validate, tag and train the model for better precision and recall
Difficulty in accessing Facebook’s Marketing API when we attempt to add additional labels to product listings which also requires knowledge of the Graph API
As our app was in development mode, we were not able to gain public page content access. Hence, we had to migrate our data to an external database
Integration with MongoDB as a business database
We had to understand how to manage synchronous and asynchronous functions to deliver dynamic responses to the user
Implementing the end-to-end interactions between the user and backend server
As every component is linked from interactions with users to shopping cart and order management, we had to ensure that each component integrates well with each other
We had to do heavy regression testing on our app to check and fix code issues when new features affected our previously implemented features.
Application Security
We had to ensure that important keys and data are not being hard-coded as strings in our application
Implementation of https for secure usage of the webhook, also to allow us to comply with the OAuth2 regulations of using Facebook’s API
Accomplishments that we are proud of
Having ice breakers and a persistent menu to lower the barrier to entry for new and existing users
Our considerations of what the market needs and thinking through the users’ underlying pain points
Planning, managing and navigating the flow of the dialogue with different message intents made possible with natural language processing
Building a chatbot that does not consist of only if-else decisions, we managed to have great interactions with the backend database for the order management and product recommendation feature
What we learned
How to develop Facebook Messenger chatbot with the Messenger Platform
The Facebook Graph API, Catalogue Manager and Messaging Platform - we had to ensure that we utilise the Principle of Least Privilege to only retrieve information about users that are necessary to keep the chatbot running and to preserve our users’ privacy
Tagging, training and evaluating natural language processing models on Wit.ai
What's next for Bright: Social Enterprise Commerce Bot
Expand the used cases to larger organisations
Utilise machine learning to provide personalise product recommendations based on users’ interactions on Facebook and through the chat
Allow beneficiaries to insert their custom thank you note to the customers
Create a web interface for social enterprise to easily set up their own bot
Build a payment integration with Facebook Pay
Integrate with Facebook Marketing Graph API to retrieve products from Facebook Shop to handle interactions with our application
Built With
Facebook Messenger Platform
Facebook Graph API
Facebook Catalogue Manager
Express JS
PM2
Wit.ai
Glitch
AWS EC2
MongoDB on mLab
Python
GitHub Repository
https://github.com/ngrq123/bright-social-enterprise-commerce-bot
Product Images
Chocolate Chip Cookies:
https://unsplash.com/photos/kID9sxbJ3BQ
Coconut Cookies:
https://unsplash.com/photos/YwKgwIiV_F8
Earl Grey Sunflower Seeds Cookies:
https://unsplash.com/photos/WCx938-AvoE
Tumbler:
https://www.pexels.com/photo/brown-tumbler-filled-with-coffee-1862401/
Bottle:
https://www.pexels.com/photo/vacuum-flask-on-brown-wooden-dock-1188649/
Muffin:
https://www.pexels.com/photo/chocolate-muffin-top-with-chocolate-chips-131899/
Built With
amazon-ec2
amazon-web-services
express.js
facebook-graph
glitch
mongodb
pm2
postgresql
python
wit.ai
Try it out
m.me |
10,009 | https://devpost.com/software/facebook-messenger-replies-for-local-news | Facebook Messenger for Local Newsrooms
Private Replies on Messenger from Local Newsrooms
Inspiration
We want to find a way to help local newsrooms grow Messenger subscribers with a personalized, private experience on Facebook.
What it does
Local Newsrooms can now turn on Private Messenger Replies for their Page – they can turn it on for all, or just some posts. When a user comments on a local newsroom's story that they see in News Feed, they will automatically receive a Message from that Page into his/her own Messenger Inbox. The message allows them to handpick topics of interest for future news updates that will be delivered directly through Messenger.
How we built it
We created a way to toggle on/off at the Messenger settings level within our platform. If "Messenger Replies" is ON, anyone who comments on any post made by that Page and/or anyone who posts onto the page's timeline they will automatically receive a message asking if they want to receive more local news in the future. They can decide what types of stories they want to receive.
The local newsroom can turn it off when they do not want comments to trigger messages.
Challenges we ran into
Pretty challenge free! We didn't encounter any major technical roadblocks.
Accomplishments that we're proud of
We are proud of the idea that our team had to bolster local newsrooms in a way that helps them build their Messenger subscribers. Really proud of how quickly we turned it around.
What we learned
In order to test this project, we had to ask Local Newsrooms to turn it on because we needed an active page. Our clients begged us to leave it on. Love at first sight! We learned this is something 'they' never knew they needed.
What's next for Facebook Messenger Replies for Local News
Since this feature is a smash hit with local newsrooms that are looking for more touch points with their audience, we need to spread the word among our clients and show them how to use this to advance their goals and to help train them on Best Practices in regards to this feature. They have an excellent opportunity to further engage an audience that's already engaged in their local news updates.
Built With
aws-lambda
graph-api
messenger
mongodb
node.js
react
webhooks
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/ai-baymax-digital-healthcare-assistant | Landing
Demo Screenshot
Inspiration
Ai Baymax was inspired by a robotic nurse character Baymax from a movie named "Big Hero 6"I thought why not make a digital version of the nurse that can help a lot more people than a physical one.
What it does
Ai Baymax interacts with people, analyse their health symptoms & provides the quick treatment. It also can find you, interested blood donor, with right blood type near your area from the donor database.
How I built it
Ai Baymax is built with Facebook messenger & Chatfuel.
Challenges I ran into
The biggest challenge is to teach the bot about all the diseases and their symptoms. The bot is still learning.
Accomplishments that I'm proud of
The proudest part is now the Ai can help a lot of people out there abut their health condition.
What I learned
Health is a private issue in human life. People are more comfortable to talk to a robot then another human about it.
What's next for Ai Baymax : Digital Healthcare Assistant
With a proper healthcare protocol & self-learning Ai Baymax can be used in a lot of devices & apps can be implemented with current AI's like Google Assistant, Siri or Alexa
Built With
facebook-chat
facebook-messenger
wit.ai
Try it out
m.me |
10,009 | https://devpost.com/software/who-am-i-uidpx3 | page
Inspiration
Who Am I messenger bot is created to facilitate the mental health services, the motivation behind is my mental health teacher experience. Due to the lack of time, I only focus on sex education in this hackathon. This website
https://www.genderbread.org/
shares a great teaching tool to help people understand and tackle this 'tough' subject. A chatbot that provides resources and support in an interactive way can help eliminate the blocks and benefit more people.
What it does
Who Am I messenger bot will:
break the concept into small pieces by providing survey and explainer
connect the user with a professional counselor
How I built it
• built by Messenger API, leveraging three main features
One-Time-Notification: when user wants to talk to a counselor, bot will notify them when there is counselor available
Handover Protocol: when a counselor is available, pass control to another app and when the conversion is over, pass control back to bot
Quick Replies: mainly used to generate answers to the survey
• built the backend with node.js and JavaScript
• deployed the server on Heroku
Challenges I ran into
• I only have 2 days so I do not have time to pass the app review process
• phrasing the interaction to make users feel being supported
• this bot cannot work without professional counselors
Accomplishments that I'm proud of
let messenger bot to convey the teaching material to people
What I learned
make the good use of tech and we can make a difference
What's next for Who Am I
• add more useful resources
• handle user privacy
Built With
express.js
handover-protocal
heroku
javascript
messenger
node.js
one-time-notification
quick-replies
Try it out
m.me |
10,009 | https://devpost.com/software/chatbot-for-facebook-shop | First greeting
Second one
Third one
Inspiration
When I prepare for an F-commerce business, I create a Facebook page and try to ad chatbot tor, my customers. But that was so difficult to create a chatbot manually.
After that when I learn about this hackathon I decided to create a chatbot that will beneficial for all small business owner.
What it does
It does automatic replay to customers when no one present or not present to replay from a business owner.
How we built it
We use an online platform to build it called "Chatfuel" that include automate and set up AI options with also elements like button, typing, text, etc.
Challenges we ran into
Firstly, it was challenging to understand platform systems. And also challenge to understand what is common query may come from customers.
Accomplishments that we're proud of
Now we proud if Facebook lunch it, much small business holder become beneficial and able to save their time. Its enough pride for us.
What we learned
As we had a little previous idea to create a chatbot. Now, we learn every step of creating a chatbot, how it works and how it beneficial for both business holders and customers.
What's next for Chatbot for Facebook shop
We got a little time to create it. Its need to modify and updates for more smooth uses to users. If Facebook wants to lunch it we will modify it.
Built With
chatfual
Try it out
m.me
www.facebook.com |
10,009 | https://devpost.com/software/virtual-psychiatrist-4jb9ei | Get psychological help here.
INSPIRATION-
There are millions of people who are suffering from psychological problems. People like us silently grow depression inside and most of us have no idea about it. To be honest I was suffering from anxiety
but I didn't had any idea about this mental psychological illness.. The anxiety was growing heavy inside me and there was certain symptoms that I couldn't ignore, then I consult with a psychiatrist and after few sessions counseling
I was feeling better. I thought if there was a fast solution that can help people by giving solution about these psychological issues,it can help them to find out the problem easily.
WHAT IT DOES?
The work is simple. If you are feeling low or if you feel like you are having a mental issue but you don't have any clue what is the actual problem, the user can take help by messaging and if I'm not there to help
the AI bot will automatically show options which are help. The AI bot will give him options related to psychological disorders like depressions, anxiety, Bipolar and others and when you press the options it will show you link which refers to symptoms and treatment for particular
psychological problem.
HOW I BUILD IT?
I build it using chatfuel.
CHALLENGES AND DIFFICULTIES I RAN INTO?
There were few diffuseness those I take as a challenges.
I had to think like a user who wants first replies for their problem.
I had to design it a what would be user friendly.
Find the videos what was needed for the purpose of every psychological problem.
Gathering information.
ACCOMPLISHMENT THAT I'M PROUD OF-
Its hard to share your feelings sometimes or sharing your mental or psychological problem, beacuse people judge very easily without knowing what you are actually feeling. Expressing your
psychological problem is kinda taboo in our society. If someone know that you have a mental condition they certailnty thinks you are abnormal or crazy also harmful but this is not true.
So I created a Virtual Psychological treatment page which is helpful for people who needs psychological help. I'll be a grateful if a single person get help by my creation.
What I learned:
I learned many things how I can manually set a bot for quick replies and there are some others stuffs like setting up buttons and had idea about various kind of mental disorders.
AI interaction.
Content making.
Use of chatfuel.
Whats next for my Virtual Psychiatrist:
There are many platforms where people can get medical helps and I just wanted people will get psychological helps virtually and fast solutions to it.
There are few plans ahead.
1.I will increase AI interaction.
2.Increase content.
3.Add more options for the user to know about their problems easily.
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/westrio-chat-bots | The logo of my small Facebook based start-up for which I have made this Chat-bots.
Inspiration
I need someone to reply my customers within minutes.But I have not got the capability yet to pay someone. Bots are the amazing things in that case. Excitement of trying new things ,my needs and also the prizes inspire me to do this.
What it does
It send some quick replies to my customers so that they stay tuned.
How I built it
With Chatfuel.
Challenges I ran int
I am a Non-Tech.This is my first time building a chat-bots.So being a first timer I face problem in linking the replies of the buttons.
Accomplishments that I'm proud of
This is my first time making something like his. I got highly motivated after doing this.I know it is not perfect but after watching that it is working I feel so happy as I learned this by myself.
What I learned
I have learned how to make bots .How to interact with customer without my presence .
What's next for Westrio Chat-bots
I am working on adding pictures of the dresses .I am still learning . I will try the things after getting know.
Built With
chatfuel
coffee |
10,009 | https://devpost.com/software/vu-online-assistant | Inspiration
Facebook Developer Circle: Dhaka inspires me to build the chatbot.
What it does
This is a chatbot that helps university students to get a few things like Notice, Class Routine, Class Notes, Results, Admission, and Support related real-time information very easily and quickly.
How I built it
I build this chatbot with Chatfuel.
Challenges I ran into
I am new I was no idea about a chatbot, that's why it was a challenge for me to build this one.
Accomplishments that I'm proud of
Finally, I completed my chatbot.
What I learned
I learned about chatbot and how it works.
What's next for VU - Online Assistant
I want to propose this chatbot for my varsity.
Built With
chatfuel
fb-messenger
Try it out
m.me |
10,009 | https://devpost.com/software/shareme-ynmf7c | Inspiration
During covid-19 people are suffering from many mental health issues. I want to help them ,want to listen to their problem and give them solutions .
What it does
It provide solutions and chat with the people and help them to overcome mental health issues .
How I built it
With chatfuel.
Challenges I ran into
I actually don't know how to chat with people to help them overcome their issues .
Don't have that much knowledge .
Accomplishments that I'm proud of
It can help people who are in pain .May be in little because yet many things to develop but at least I started it .
What I learned
During these journey I have do a little research about the people who are in pain .I learned a lot about them and how can I help them . Also I learned how to innovate and help people with it through ideas.
What's next for ShareMe
It's not yet fully developed. I want to hire psychologist and therapist to understand people behavior and set an algorithm to help them by understanding their behavior .
I want to this in large scale where people get the feelings that they are not alone some their for them.
I want to work with suicidal people help them to build their life with positive energy .
I want to build my bot that it can help people like human friend .
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/hotspotloc | Inspiration
Ways of reporting events such as accidents and crimes are tough to come by in Nigeria. I felt that using Facebook Messenger and its bot capabilities (quick replies) would enable me to create conversations that could do the work of reporting these events faster.
What it does
Hotspotloc is an artificial intelligent messenger bot that helps people report and find useful information about a particular location.
What the bot supports:
Report Events: Crimes, Accidents, etc
Search location for recent happenings.
Update Default Location.
Report Event -> Report an ongoing/recent event such as highway robbery, gridlock, accident, etc. We collect the event location, category, description, and image (optional).
Search Location -> Search a location for ongoing/recent events before embarking on a journey to that location.
Default Location-> Set a default location to make reports faster.
help -> Show all the bot's menu
How I built it
This application was built using PHP. These are the main tools used:
CodeIgniter (PHP)
MySQL: Store User Data.
Facebook Messenger API: Conversational Interface.
DialogFlow: Natural Language Processing.
Google Maps API: fetch location.
Challenges I ran into
Messenger's location Quick reply was deprecated which made it difficult to collect users' locations (longitude/latitude). Google Maps API was eventually used to search for the location inputted by the user and then the geocoding API was used to fetch the longitude and latitude of the inputted location.
Accomplishments that I'm proud of
Being able to create a very useful simple chatbot within a short period of time to help collect crowdsourced crime reports around a vicinity.
What's next for Hotspotloc
Spam report filter: Users would be able to report a spam event report and we'd act on it accordingly.
Based on the crowdsourced data, we'd generate information of accident prone and crime ridden areas for users.
Built With
codeigniter
dialogflow-api
google-maps
messenger-api
mysql
php
Try it out
m.me |
10,009 | https://devpost.com/software/mindsmeet | Messenger Mobile Demo
Inspiration
During this challenging time, we understand it can be more difficult to meet people, maintain relationships and have conversations with those who users meet daily. We want to help users find a way to connect to their friends on Facebook by helping them discover who share the same interests as theirs or who could support them in certain tasks, because that can help foster meaningful relationships.
What it does
It allows people to ask questions anonymously and get quick answers to them within a message thread through results.When users ask the bot a question, the message will get processed through NLP. If the question is simple, the bot provides Google search options with direct links. Else, it will automatically post it on a page, where other users go to to answer questions. After that, the bot will return a list of people who are related to the questions and provides users options to chat with people in the list, or it will send the answer back to the user who asks the question. In case users have a conversation with those suggested, they will be asked to rate the conversation.
While in other platforms users may have to wait for responses after they post their question to see who can help them, the bot returns the answer immediately. Users can first contact the bot, then the bot sends a link to the Facebook page for users to create a post and then it matches words from other posts and sends people information about the other matching users.
How we built it
We began coding in Node.Js, with the use of predefine scripts and machine learning NLP to determine what people ask and return and an answer using a knowledge database such wolfram alpha.
Challenges we ran into
We ran into multiple challenges such as finding members of the team later in the hackathon, deciding on what idea to build and dealing with a member responsible for the development who fell sick and abandoned the project in the last week. As a result we only have prototype demo. We would need more data and time to train the bot.
One of the challenge was to think of the idea of how conversations could be encouraged and meaningful connections could be improved by Messenger. Then, we had some difficulty figuring out how we can most accurately detect areas of interests and provide best answers to the users. We also ran into challenges with making the bot function and respond to users. For the technical part, we find it difficult to understand the work of Facebook API.
Accomplishments that we're proud of
We are proud of the idea itself, the research it took arrive at this solution and the learnings about how chat bots work. It is an idea that will need more time to execute.
What we learned
I learned how chatbots work by processing human language and data bases and Apis.
I learned how to conduct quick interviews, plan a project and be more mindful about the people you choose to work with.
We have learned about analyzing text to identify potential interests from written posts. We also have done research user research to understand their current problems and optimize what the bot could help them with. While building the bot, we learned how the Facebook API worked and how we could use the APIs.
What's next for MindsMeet
I'd like to keep developing the idea and hopefully add a feature to allow people to chat anonymously.
Built With
facebook-messenger
heroku
node.js
Try it out
github.com
www.facebook.com |
10,009 | https://devpost.com/software/elfbot | Example Conversation 1
Time taken for call to be answered by helpline worker
Inspiration
Even though mental healthcare therapists are needed, many of the healthcare institutions actually suffer and do not have enough professionals to fulfill the needs of the patients quick enough. Demand for mental healthcare far outstrips the supply of therapists available, leading to lengthy delays for treatment as well as longer wait times between sessions.
What it does
ElfBot helps to check whether patients are utilising tasks and strategies from each therapy session, and asks them for simple feedback. It also helps to stand in for helpline workers and keeps patients engaged in between therapy sessions.
How I built it
Build purely on Dialogflow with Facebook Integration.
Challenges I ran into
Intents were difficult to formulate even after talking over the ideas with therapists. Training data was also difficult to come by due to confidentiality clauses.
Accomplishments that I'm proud of
Quick and simple integration with voice enable sentiment analysis.
What I learned
FB Integration is fast and easy with dialogflow
What's next for ElfBot
We plan to expand ElfBot’s repertoire of intents to include various use cases, like the ones often encountered on crisis helplines that one will call a helpline for support. (
https://www.mentalhealth.org.uk/sites/default/files/life_lines.pdf
)
To use FB's NLG and pre-trained models in transfer learning for our specific use case to help in minimising risks during chats or helping with extension of the therapy sessions.
Icons by
Icons made by (
https://www.flaticon.com/authors/freepik
) - Freepik from (
https://www.flaticon.com/
)
Built With
dialogflow
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/franzzbot | Find It
Inspiration
Social distancing has led people to experience a variety of emotions. As we face the COVID-19 pandemic and stay at home orders, the monotony and lack of social interaction get to us. This situation inspired the idea of “Find It” which offers you engagement in various enjoyable activities. Our "Find It" will suggest you where to invest your leisure time according to your choice.
What it does
"Find It" can help people in restoring interest in various enjoyable activities like watching movies, listening to music etc. in this self-quarantine period and afterwards. We hope this measure of suggesting interesting contents can help to remove the monotony this lockdown has brought. The features of the chatbot are as following:
Can suggest different genre wise movies, songs
Can recommend anime
Can give quotes on different topics
Can provide cartoon links
Can show humorous posts
How we built it
We built the chatbot keeping the present situation in mind. This application was made using chatfuel, one of the world's leading chatbot making platforms. We will continue to bring development in our application in coming days.
Challenges we ran into
Communication became a big challenge for the team members amidst the current COVID-19 outbreak. On a conceptual level, ensuring interactivity was a major challenge. Besides the choice of right contents considering the demographics posed as a tough challenge as well. But we believe we have addressed these issues successfully.
Accomplishments that we're proud of
This is our first time making something like this. We're proud to finally be able to present this bot here. It was very exciting for all of us to work on this project.
What we learned
We have learnt the basics of making a chat bot. We have also learnt how to use chatfuel and how user interaction works.
What's next for Find It
We hope to make "Find It" more interactive enabling different layers of conversation. Another goal is to diversify the contents thus broadening the range of the contents which will hopefully intrigue larger user base.
Built With
chatfuel
laptop
mobile
Try it out
www.messenger.com |
10,009 | https://devpost.com/software/business-management-bot | Business Management Bot
Inspiration
In this edge of digitalization where we need fast and smart solutions for anywork there is a thing which was bothering me so much that is we need to search various websites to create one. So I
came up with this idea that will help people to create a website easily.
What it does
The bot will use the AI system to give quixk replies when I'll be unavailable for the user. The bot will automatically show options that will help the user to create or have information about creating
a website.
How I built it
The process was not that complicated, I used the chatfuel to create this message bot.
Challenges I ran into
Gethring information.
making user friendly contents.
Accomplishments that I'm proud of
Users will get fast solutions and their workprogress will be easy. Users will get information about their needs to build a website using the options.
What I learned
Uses of AI.
What's next for Business Management Bot
I want to increase contents.
Make it more user friendly.
Add more AI interactions
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/smart-bot-bje04v | Text Extraction
Second App
Files and Balance
Customer Service
Web Plugin
Persistent Menu
Database
Environment Speed
Load Balancer
Inspiration & What it does
There are many sources of inspiration that came up with this project. First of all, some of my friends ask me a lot to translate a letter or some news for them. This App will let them take a picture of the letter, and they can translate the letter, and even generate audio for the translation. Secondly, I personally having a hard time to read an article or a book in the crowded subway. This App will allow anyone to send an image, then generate audio and listen instead of reading! Moreover, some students struggle with reading long articles or search about the topic. This App will scan a picture of any article, extract English Text, categorize the text, and offer up to 30 resources to read more about this topic. Also, it can provide summary to get a brief idea about the topic. Nevertheless, all of the scanned images will be saved in a personal storage, and the user can bring his files back at any time and do another process! Finally, this App can scan forms or passports and read the information in a .txt format which can be used to fill forms faster than typing.
Handover Protocol, One Time Notification & Quick Replies
I am using Handover Protocol to connect two different Apps together in one conversation. Also, Handover protocol is used it to take control of the conversation by the main bot in some cases. For example, if the user is connected with the inbox and there is no Customer Service Representative available, the user can type a special phrase to go back to the main bot.
Moreover, One Time Notification is being used to notify the users if there is a new App released or before the monthly files deletion and balance reload. The One Time Notification can be sent as a text or a template from the regular messenger conversation and this feature can be triggered only by the Admin. I implemented this by assigning a variable with the OTN token in the user data. When the One Time Notification Function is triggered, it will loop over all all the users PSIDs and if there is a token the App will send the message.
Finally, Quick Replies is being used to display the file names to the user. If the user have about 28 files, it will display the first 10 Files and a View More button. When the user press View More, he will see the next files and another View More and back button. Also, Quick Replies is being used all over the conversation to provide a seamless communication and support the persistent menu.
How I built it
I build this project in JavaScript using Node.js. First, I used DynamoDB for the users table data. I designed the possible table schema with the user data. Then, I implement the File Storage by using Array List for each Category with the Files name and Map that map all names to it's path. In addition, I have used general state variable which will update the user location in the App. This general state is used to limit triggering the One Time Notification function to the Admin, and handling some possible errors as well. Also, I used some variables as counters for each feature limit.
On the other hand, I used AWS Textract and other Machine Learning Modules to extract the text from the image. Moreover, I used AWS Polly to generate Audio based on the text language "Currently only 3". Then, I used APIs to translate, summarize, and categorize the text. Finally, I used an API that will read the content of the text and search for the key words and offer some resources to read more.
What I learned
I have learned that when someone has passion and love something truly from his heart, he will be able to do anything in this world. Few months ago, I wasn't able to build a webhook and didn't know anything about Node.js. Now, I feel satisfied with what I reached, but still knowledge has no limits and I aspire for more and more :)
What's next for Smart Bot
I will keep adding more Apps and translate the conversation to as many languages as I can. Also, I will add a smart helper that can communicate and answer questions using Text and Voice.
Built With
ai
amazon-dynamodb
amazon-web-services
api
aws-load-balancer
aws-polly
aws-textract
ejs
express.js
facebook-messenger
javascript
node.js
Try it out
m.me
github.com
mynameuuy.com |
10,009 | https://devpost.com/software/back-2-stock | Inspiration
We have a friend, who is running a small jewelry store online. She shared a story with us recently, when one of her product got unexpectedly high attention and was gone long in the middle of a quite costly ad campaign. Unfortunately, it happened during nighttime when she was sleeping, so she was still paying for attracting customers, who were unable to purchase the desired item.
She has an amazing taste and knows how to run a jewelry store really well, but she is not a tech person almost at all. She was still able to figure out, how to launch the store on the existing platform though. Therefore, once we learned about a one-time notification feature provided by Facebook Messenger Platform, we immediately identified a great match for our friend needs and decided not only to help her but also millions of other shop owners l
What it does & How we built it
Shopify app installs js event handler, which listens to the product selector and once the one, which is out of stock is selected, it renders fb-send-to-messenger button. If a user clicks the button, the Facebook Platform will call our backend and provide the user ID and selected product variant payload. Those are used to construct One-Time-Notification API call to request a user's permission to send a notification on behalf of Facebook Business Page. Once a user accepts it, Facebook will trigger another callback, so our backend will have an opportunity to store a one-time access token, which will be used sometime later. Shopify Inventory Update webhook is another component, which is installed as a part of the Back 2 Stock application bundle. It will be triggered once Shopify Admin updates inventory level and lets our backend know if a certain product variant is available again. This triggers sending notifications to all subscribed users with a direct link to the product.
Challenges we ran into
From a technical perspective, it is ridiculous how many different technologies we had encountered for a seemingly small idea:
oauth2 authorization between Shopify and our app, and later our between our app and Facebook to get permissions to install a Facebook app to a Facebook business page
Graphql to request inventory data from Shopify
Setting up webhooks to interact with both Shopify and Facebook
Javascript integration with Shopify product page
While each piece seems manageable by itself, combining them together and making sure that workflow works end-to-end with so many small pieces in the middle was quite a challenge
Accomplishments that we're proud of
We are very proud that we could leverage APIs and frameworks provided by the platforms, so we could quickly build a meaningful product, which we expect to be adopted quite soon by many online shops in such a short period of time with very limited resources. With this mindset and obtained experience, there should be an ocean of other opportunities in front of us
What we learned
In the modern world, one has to learn a lot of different technologies and frameworks to build even a small idea, which might be challenging. But at the same time, there are so many things built already, which gives great leverage to build and test in a wild new idea quickly without necessity to reinvent the wheel.
Built With
facebook-messenger
golang
graphql
javascript
oauth2
shopify
Try it out
github.com |
10,009 | https://devpost.com/software/my-pocket-school | Inspration:
Education is the backbone of every human being. To make it more interesting towards childrens I came up with this idea that is fast learning and intersting towadrs the childres.
hat it does?
The bot will use the AI system to give quixk replies when I'll be unavailable for the user. The bot will automatically show options that will help the user find its needs accoding to the created options which will
help the learner to choose where they want to start from.
How I build it ?
The process was not that complicated, I used the chatfuel to create this message bot.
Challanges that I ran into:
Gethring information.
making user friendly contents.
Accomplishment that I proud of:
Childres are more enthusiastic towards learning. When they will find a easy and fast way to learing they will be more intersted.
What i learned:
uses of AI.
Whats next ?
I want to increase contents.
Make it more user friendly.
Add more AI interactions
Built With
chatfuel.com
Try it out
mbasic.facebook.com |
10,009 | https://devpost.com/software/mystery-buff-niarv5 | To start the game
Maintaining user state
Figure out the killer
Inspiration
Inspiration to be a writer takes its roots from my mother who is a renowned writer in my hometown. Thinking of the pivotal point was a challenge. Moreover, the flow of the story should be like a tree structure, giving 2 or 3 options to choose from, at every point. Moreover, the story should have multiple twists intentionally to confuse the intended audience. The story should be short and should fit into the app being designed. So I have to think from every perspective, so as the user would be driven further based on their choice. Moreover revealing the killer without any clue would be pointless. So I made sure that the clues for the cause of the victim's death were placed at every point of the story. The whole point is to make the user, engaged throughout the flow of the game. And I hope we were able to achieve it.
What it does
People like to solve mysteries. And there are only a few games online that are thought-provoking. With the invent of Alexa & google assistant, people enjoy asking questions and getting back the answers. Chatbot works the same way. So we decided to develop a chatbot with more and more mysteries to solve
How we built it
We used Python as a backend server that connects the messenger and Wit.ai. User input from the messenger will be sent to Wit.ai server for the NLP processing. Based on the Wit.ai understanding users will get follow up questions in the game.
Challenges we ran into
1) Making the chatbot interactive at the same time making it descriptive and detailed for mystery solvers.
2) Connecting Wit.ai with Facebook messengers, because example codes which are given in the GitHub was having a bug. It didn't work as excepted.
3) In the quick reply, keeping track of what users have already selected.
4) Finding the right feature engineering grouping for training wit.ai NLP.
Accomplishments that we're proud of
End to end to implementations.
Working knowledge of Wit.ai NLP features.
Quick reply state maintenance.
What we learned
How to create an efficient and interactive chatbot.
What's next for Mystery Buff
Add complex and challenging mysteries.
Built With
amazon-web-services
messanger
python
wit
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/gifshop-wizard | Initial prompt from bot for GIF processing
Sample quick reply options for CycleGAN
Sample quick reply options for style transfer
More sample quick reply options for style transfer
Quick reply options for selecting an effect to apply
The bot processing the GIF as the user waits for a response
Quick reply options for selecting a source image to apply fake motion onto
Finish processing GIF
Quick reply options for next effect to apply after showing results of previous effect being applied
GIF
Our logo was inspired by the very first test image we used with the "tripping" style mask
Inspiration
We felt that modern computer vision techniques such as style transfer and object removal were only accessible to those who are well versed in machine learning and have sufficient computing resources. The average person does not have access to either of these which means that it is difficult for an average user to try out these techniques on their GIFs or images. We want to alleviate both of these problems and provide a platform for users to easily manipulate their GIFs or images using techniques from computer vision and receive instant feedback.
What it does
GIFShop Wizard is a Messenger bot that applies computer vision techniques on GIFs and images sent by users.
The bot receives images or GIFs and prompts users for an image processing technique to apply using Quick Replies. The bot processes the image according to the user's specification and returns the processed image to the user. The image processing techniques currently supported include fake motion, object removal, style transfer, GAN, and segmented style transfer. We drive the dialogue flow with Quick Replies to minimize communication errors and keep the interaction as close to GIF-to-GIF as possible.
Foreground Object Removal: Objects may appear in images that we wish to remove (i.e. photobombs). It takes long enough to photoshop objects out, but this is even more challenging for videos, where a manual process presents itself as a major obstacle. Thus we provide an object removal function, where we first detect what objects are available in the entire GIF, then return a list of detected objects for the user to selectively remove, and consecutively execute the removal of the specified object.
Fake Motion: This vision function enables users to transfer the motion in their GIF into one of our available source images. Motions can be transferred to faces or body postures using the first order of motion model. For example, if a user has a GIF of a person talking or moving their head, this motion can be transferred to images of faces that we provide. The main prerequisite from the user is that their driving image is as closely cropped to the object (e.g. the face).
Fast Style Transfer: When one GIF or meme is not enough, why not make more? To increase variations of the same content GIF, we can apply a style mask via neural style transfer. We trained on several style images to return style mask weights, such that when a user passes a GIF through, they can select from a variety of masks to apply onto their content image. To minimize latency is retrieving a stylized image, we pre-trained models rather than training on a new style each time (and thus this also means a user is not currently permitted from passing a custom style image to train on the spot).
CycleGAN: Though quick to train and perform inference, style transfer applies the style to the whole image and not selectively. Therefore a generative adversarial network comes in handy, selectively applying the style of the target object onto the source object. For example, for the mask
horse2zebra
, if a user passes in an image with a horse in it, CycleGAN would selectively stylize horses to possess stripes of a zebra. It should be noted that
horse2zebra
means that the GAN was trained on a pair of datasets (horse, zebra), but it does not mean that inference is limited to horse GIFs alone. In fact, users can pass in other images (e.g. people) and the stripes of a zebra can often be transferred as well, though just not as accurately as horse images.
Segmented Style Transfer: While CycleGAN is selective to specific objects, we use instance segmentation to target significant scene components and apply style transfer on those segments (i.e. scene-specific, not object-specific). We use FCN to detect instance segments, identify the largest one, extract that segment as an image, perform style transfer upon this image, and stitch it back onto the original image.
How we built it
We interface with the Messenger API and Webhooks using a Flask server and a custom bot interface. The models used in the various computer vision techniques are trained in PyTorch and TensorFlow.
Chatbot server
We built a convenience interface bot that takes in data from the server and automatically builds the correct POST request and sends it to the Messenger API. The actions currently supported include sending text, sending images, sending quick replies, and sending typing sender actions.
Vision functionality
GIF extraction/stitching:
When the user sends a GIF, we first parse GIF into its individual frames. We then apply the vision function selected by the user with its corresponding arguments and perform inference frame by frame. After all the frames are processed, we stitch the frames back together, compress the file to minimize latency, and send it to the user. Images are treated as a GIF with a single frame and are thus compatible with our bot.
Fast style transfer:
Based on the work of
Johnson et al.
, we implemented their real-time style transfer architecture that uses a perceptual loss function to measure model perceptual differences between the content image and the style image. The loss functions are capturing semantic differences between the original image and stylized image through image classification, based on a 16-layer VGG network pretrained on ImageNet. Stylized images are generated from in-network downsampling and upsampling, and the resulting image is passed as an argument to the perceptual loss function. We store pretrained style mask weights, and when a user selects the quick reply button to select a specific mask, we perform inference on each frame.
Segmented style transfer:
For this function, we use fast style transfer as a boilerplate to perform style transfer. The main difference is that we first perform instance segmentation using Fully-Convolutional Networks to compute whether each pixel is semantically different from another, and thus return a list of segment masks. We detect the largest mask, obtain its pixel coordinates in an array, export it as an image with a single color fill background, perform fast style transfer upon this mask image, then transpose the pixel coordinates from this stylized mask image onto the original image, thus selectively performing style transfer onto a specific mask in the image.
CycleGAN:
For this image-to-image translation architecture (
Zhu et al.
), the generator network transcribes perturbations upon the source image with features from the style image. The discriminator network evaluates the class of the stylized image; if the label is identical to the ground truth label, then the image-to-image translation is a success. This is somewhat similar to the perceptual loss function in FST, but we instead use a discriminator network to measure perceptual differences.
First order of motion:
Based on
Siarohin et al.
, our implementation of the First Order Motion architecture enables users to pass their GIF file as the driving image (image that contains the motion), and we provide the source images (images that users would want to transfer motion from their GIF into). The model works by first computing the first order motion representation by using a keypoint detector (identifying points of motion within a face/body). Using a motion network, we generate optical flow from the motion representations, and perform pixel transformations onto the source image based on the calculated flow of each pixel.
Foreground removal:
For this implementation, we remove foreground objects by performing YOLOv3 object detection, and sending the detected objects to users (objects based on those from the MSCOCO dataset). The object selected by the user via quick replies is passed as an argument to the object removal function, where we first apply a bounding box to the objects detected (if the object is equal to the one selected by the user), then remove pixels within the bounding boxes, then passing the resultant image to
pix2pix
to fill the missing pixels (supposedly with a selection of the surrounding background pixels).
Challenges we ran into
Model inference for image processing usually takes a while. Since we are processing each GIF frame by frame, this means model inference for GIFs takes even longer. Because Messenger requires a response within 20 seconds, this meant that we needed to find a way to work around the constraint. We tackle this problem by continuing to process the image on the server and keeping track of the fulfillment status of the request rather than allow Messenger to timeout our process.
Because we implement several external computer vision architectures, we pull source code from multiple different projects. This means that they could potentially use different versions of PyTorch or TensorFlow. PyTorch and TensorFlow 1 turn out to be incompatible due to TensorFlow 1 using outdated libraries. To remedy the situation, we had to migrate all the TensorFlow 1 code to TensorFlow 2 code.
When we tried sending multiple GIFs to the bot, the GPU would sometimes run out of memory. To address this issue, we allocated memory carefully for each vision function and reduced parallelism to decrease the strain on our GPU.
Trying to maintain and update the state of the user is difficult as the Messenger API uses webhooks. This was solved by creating and implementing a clear organization and structure of the user flow.
Since only one of our members had a GPU, we had to distribute tasks carefully and separate our logic accordingly so that certain features could be tested independently.
Accomplishments that we're proud of
Since the Messenger API does not have an official Python API, we had to use a bot interface to send requests to the API from Flask. Since the bot interfaces we found were insufficient for our purposes, we wrote our own.
We were able to aggregate a bunch of different computer vision models from different projects and both make them compatible with each other and integrate them together into one coherent experience. To do this, we had to modify and rewrite a good amount of the source code and train our own models with our own source images.
What we learned
We got to experiment with various Messenger API functions such as Quick Replies and Sender Actions through Flask. We also got to play around with webhooks and use localhost tunneling to test our code. We learned how to modify existing bot interfaces and deprecated wrappers/libraries in order to customize them to our needs.
Furthermore, we got the chance to play around with various different computer vision models and tinker with different image processing techniques. It was a good opportunity to bring computer vision to the chatbot space, which has traditionally been dominated by NLP literature. We explored state-of-the-art models, made modifications to improve them and generate novel functionality, and exercised proper software engineering and documentation practices with the extended time granted by the competition.
What's next for GIFShop Wizard
There are several directions that we could have taken this project if we had more time to work on it.
Additional Techniques
Some additional things we would like to see as features in our bot include super resolution of GIFs and images, increasing the resolution of each image, and frame interpolation for GIFs, creating intermediate frames in between consecutive frames.
Custom Source Images
We would like to allow users to input custom source images for various features such as fake motion and style transfer. The main concern for implementing this feature is that in order to be able to apply an effect with a source image, the model must be trained with the source image which could potentially take a long time.
Video Processing
GIFs are essentially short videos so extending our bot to videos is not too difficult. The only concern with this is that it may take a long time to render on the server. Since Messenger expects a response within 20 seconds, this could be hard to implement depending on the length of the video.
Model Improvements
Even though our features produce pretty good results, they could always be improved. Some of the things we could do include running more iterations, finding other interesting source images, and investigating other state-of-the-art models.
References
First Order of Motion
paper
Foreground Removal
paper
Fast Style Transfer
paper
CycleGAN
paper
Instance Segmentation
paper
Built With
facebook-messenger
flask
opencv
python
pytorch
tensorflow
Try it out
github.com
m.me |
10,009 | https://devpost.com/software/plurid | from a page
to a space
plurid'
explore information in a 3D structure
GitHub Readme
The monorepository contains packages implementing the
plurid'
technology to create a 3D browser-powered view based on the
plurid-specification
.
With
plurid'
, the content of a web page (or any kind of information) can now reside on a
plane of content
(a
Plurid Plane
) in a three-dimensional space (a
Plurid Space
). The space can be transformed, rotated, scaled, translated, in order to get a better grasp of the displayed information (text, images, videos, and so forth).
plurid'
is being used extensively in the
plurid' ∂products
, however, new applications can be
easily created
.
Plurid' Application
To generate a
plurid'
web application use the
CLI
tool
@plurid/generate-plurid-app
by running the command (provided you have
NodeJS
installed on your machine):
npx @plurid/generate-plurid-app
The generated
plurid'
web application, or any other web application, can be easily deployed to
plurid.app
using the [
plurid-cli
][plurid-cli]: after installation and initialization, simply run
plurid deploy
Packages
Generate
[@plurid/plurid-cli][plurid-cli] •
plurid'
application life-cycle management: generation, development, deployment
[plurid-cli]:
https://github.com/plurid/plurid/tree/master/packages/plurid-cli
[@plurid/generate-plurid-app][generate-plurid-app] • generate a
plurid'
application with one command (and some choices)
[generate-plurid-app]:
https://github.com/plurid/plurid/tree/master/packages/generate-plurid-app
Tools
[@plurid/plurid-data][plurid-data] • constants, enumerations, interfaces
[plurid-data]:
https://github.com/plurid/plurid/tree/master/packages/plurid-data
[@plurid/plurid-engine][plurid-engine] • 3D and utility functions
[plurid-engine]:
https://github.com/plurid/plurid/tree/master/packages/plurid-engine
[@plurid/plurid-pubsub][plurid-pubsub] • publish/subscribe message bus
[plurid-pubsub]:
https://github.com/plurid/plurid/tree/master/packages/plurid-pubsub
[@plurid/plurid-specification][plurid-specification] • plurid' specification
[plurid-specification]:
https://github.com/plurid/plurid/tree/master/packages/plurid-specification
Implementations
[@plurid/plurid-html][plurid-html] • implementation for HTML Custom Elements
[plurid-html]:
https://github.com/plurid/plurid/tree/master/packages/plurid-html
[@plurid/plurid-react][plurid-react] • implementation for React
[plurid-react]:
https://github.com/plurid/plurid/tree/master/packages/plurid-react
[@plurid/plurid-react-server][plurid-react-server] • server for the React implementation
[plurid-react-server]:
https://github.com/plurid/plurid/tree/master/packages/plurid-react-server
Built With
react
typescript
Try it out
plurid.com |
10,009 | https://devpost.com/software/hacker-kids | First Message
Start
Outline of the course
User hits Quiz to test its skills
Prompt for the level user choose
On successful answer, user is lead to a new concept. on unsuccessful one user is asked the question again.
1.1 Syntax, user can press arrow keys to jump to next concept or move back to the older one.
When user asks for more info about the concept, it is lead to a detailed section with more examples.
Outline of the current course
Start
Start and concepts. User can use quick reply to navigate.
Beginner Level
If user fails to enter correct option, it will be asked the question again, else it will be taken to the next concept.
Quiz about a particular topic
User can ask for "More" info about a particular topic
Inspiration
Many a time I have been asked by young developers who want to learn programming but gets overwhelmed at how difficult the access to good learning is and how complicated the words and concepts become. I have been asked to recommend Facebook groups to help them take their interests one step further, but individual learning for kids is a difficult task and it helps if you could just teach them about concepts by having a conversation. Integrating that 1to1 conversation style learning through chatbots was this project aims so kids can interact with the bot understand and continue to develop their concepts.
What it does
This chatbot has multiple features but talking about the primary is that it helps kids learn programming concepts in a 1to1 interactive way. It also helps people with limited facilities like slow internet or no internet to still use Facebook for free and have access to programming concepts. Kids also find the tutorials and videos overwhelming and a steep learning curve when they just want to start of easily, this bot helps them grasp the core concepts easily.
It can also be used to increase more activity and user visits to developers blogs/pages where user can learn more about a particular concept in detail, for example I can increase my Medium blog post visits by adding these pages to the bot replies so whenever users on this Facebook page hit "More" on a certain concept, they are provided with a link that lands them on my page where they can learn more.
How we built it
We used ChatterOn and connected with a Facebook page called "Hacker Kids" so that kids and literally anyone who has access to this page can drop a message and start learning.
Challenges we ran into
Mapping out the scope and how much content and of what type should be displayed to a potential learner.
Accomplishments that we're proud of
We are proud of the way it turned out for little kids to tap and learn and retain that learning by visiting more resources that are catered to them without overwhelming them and keeps the flow on information streamlined for them.
What we learned
That making chatbots can be as difficult or easy a task can be, it all depends on the idea and scope of that idea.
What's next for Hacker Kids
We want to be able to add more content so users have multiple languages to choose from. Add more resources that help young programmers take on difficult programming concepts with ease.
Built With
chatteron
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/my-business-manager | Starting small personal conversation .
Giving order including confirmation .
Inspiration
This is very inspiring . I am a beginner never worked with these things .
What it does
This messenger bot will be able to make small conversation with any person and also it will be able to take orders from customers .
How I built it
It was very complicating because i built it for the first time that's why it was hard for me to make it alone .
Challenges I ran into
It wan't running properly . Didn't knew how to make one . I wasn't able to submit it to the Devpost then a brother of DevC Dhaka helped me . I am totally alone none of my friends are interested in the tech field .
Accomplishments that I'm proud of
I am first time participating on a hackathon . In my area no one has ever joined a hackathon . I am really feeling vrey proud .
What I learned
I learned a lot of things . As a beginner everything i did was learning and practicing .
What's next for My Business Manager
I will make it better so, i can be able to support all types of small business and increase their business and sales .
Built With
dialogflow
Try it out
bot.dialogflow.com |
10,009 | https://devpost.com/software/remedi-xdtsuy | Remedi Logo
Remedi Main menu
Remedi Search Options
Show Text Option
Show Text Result
Emergency Feature
Emergency Feature
Emergency Feature
Inspiration :
People of Bangladesh are facing severe healthcare-related problems. COVID-19 is taking a heavy toll on them. Bangladesh's healthcare system is highly vulnerable at the moment. People are facing problems finding proper treatment for them. They are running around from one hospital to another to get treatment. They go to one hospital & then come to know that all the activities of the hospital are suspended. Sometimes the hospital authorities ask for additional documents. But as the patients don't know anything about those requirements, they can't get the treatment. The patients who need ICU treatments are highly affected in this situation. They don't know in which hospital they need to go for getting the ICU facilities. Thus, they post in Facebook groups or in their wall & ask for help. Friends of the patient share the post to get attention from others who might be able to help the one in need. So, judging from all these aspects, we came up with an idea to help our people.
What it does
To use all the services that are currently available in our country, the users need data. Without data, they will not be able to get the information they seek. So, we have come up with a solution in the form of a Chatbot named “Remedi”. We are passing all the information that a user may need in this pandemic period through Facebook Messenger. As Messenger service is free in our country for the mobile sim operators, the users can get the necessary information without buying any data packages. They just need to turn 'On' their data connection & after getting into Messenger, they can get their desired information by tapping on one of the options of our Chatbot. We are also trying to broadcast the queries that a user might face in times of emergency. For example -
If a person wants to know about the available hospitals with ICU facilities or wants a blood donor who can donate B+ blood, in regular times, they post on Facebook & ask for help. Whereas now, with our chatbot, anyone can ask for help in it. Our chatbot will store the input information of our users & broadcast the news with all the subscribers of our page. The chatbot will, therefore, act as a bridge connecting both the parties - who needs help & who wants to help. We are highly concerned about the user’s security & privacy. So we will only provide the contact information of the user when a person tells us that he can help the other user who needed the help. At first, we will only broadcast the message to all users without providing the contact informations.
Our chatbot will also facilitate the users with getting the necessary information in an emergency without having any data packages activated.
For authenticity, we are providing the users with information that we have gathered from reliable sources. We are working with doctors & medical professionals to provide users with valid information as stopping the spread of fake information is our top priority.
We are using Facebook's comment acquisition system to categorize users into different blood groups. If a user needs O+ blood, we are requesting our page users having O+ blood to help the needy person. We are using a menu & a quick reply feature so that the enquirers need not type anything, just pressing the buttons will be sufficient to get their job done. We are building the system in our native language so that people from all aspects of Bangladesh can understand and avail the features of our Bot.
How we built it
We built it with Manychat which is a tool to build messenger bots with various features such as: Quick replies, adding buttons, images etc. We also used OCR-"Optical Character Recognition" to convert our images and posters to texts for the general convenience of people having issues while loading pictures.
Challenges we ran into
The system we have used to build has both free & pro versions. As we’re currently using the free version, we have to save & broadcast the user queries manually which is sometimes troublesome. Also, we are facing problems gathering all the data & integrating those into our system. So, we’ll need volunteers to work for the project in the future to keep it dynamic. It was seen that in free messenger mode, sometimes the users were unable to see the images which were sent from our bot. Thus we have added “Show Text” option along with the images for users convenient.
Accomplishments that we’re proud of
A few days ago, one of my friend’s relatives was sick. He needed ICU related information as to which hospital might have an ICU facility. He posted in some Facebook groups asking for help. But as it was night, he was not getting any information from anywhere. Then I forwarded him our bot link & told him to search in it & he would see some contacts there. He could also call those numbers & ask for help. Therefore, he used our bot & dialed some of the numbers. After a while, he was able to get the information he needed & took his relative to a hospital where indeed there was an ICU available.
Later on, he thanked me for the help. I as well as my teammate felt very delighted at that moment.
What we learned
We learned that the purpose of life is not to be happy alone. It is to be useful & help others. It gives immense pleasure to help people in their need. We also learned various techniques to manage pages and chatbots which might come in handy on our future endeavors.
What's next for “Remedi”
We want to integrate “Remedi” with external APIs with an intent that it can fetch more pieces of information from different sites & the users can see that information without any data connection. For that, we require some financial aid to improve the full system. We are also trying to integrate our chatbot with USSD & SMS features so that users not owning a smartphone don’t get deprived of using our platform.
Try it out
m.me |
10,009 | https://devpost.com/software/spreading-the-movement | Inspiration
We were inspired by the Black Lives Matter movement in recent months and wanted to use this hackathon to help guide people towards the different ways they could make an impact. Rather than count on Facebook users to open a new tab and search for local opportunities themselves, the bot could help them find different opportunities specific to their location all at once.
What it does
The chatbot integrates with Messenger’s private replies and quick replies functionalities to seamlessly refer users to opportunities for participating in the Black Lives Matter movement. After a user interacts with our Page, the bot sends a private reply to the user asking if they want to learn more about the movement. If so, a quick reply is sent to determine exactly what the user would like to do, from donating to emailing local representatives to supporting local businesses and more.
How we built it
My team and I made early decisions to use a simple, lightweight stack with Next.js and Heroku on top of GitHub. We followed a couple tutorials on how to create a Facebook bot from scratch, but we ultimately had to dive into a lot of documentation in order to successfully use the Facebook APIs for our features.
Challenges we ran into
One major challenge we ran into was being able to capture feed events. We spent hours looking over documentation and were almost certain we had programmed the app correctly, but we ultimately discovered that apps in development don’t allow for customizing private reply tests. We were instead supposed to use the test notification in the Webhook page to simulate an outside feed event.
Accomplishments that we're proud of
We’re proud of simply figuring out how to create a messenger bot from scratch. We saw that there were a lot of third-party solutions to doing it, but as software engineers, we were fully committed to implementing the project programmatically.
What we learned
We learned a lot about how to use the awesome Messenger API’s. While thinking about the different functionality we wanted to provide to users, we also learned ways in which we could participate in the movement ourselves.
What's next for Spreading the Movement
We ultimately want to integrate the app with a database to get real, location-specific opportunities, based on the user-provided zip code. (For the purpose of the hackathon, we had just hard coded the responses to the LA zip code 90017.) In addition, we could also generalize the model to any movement. There are definitely opportunities to make this solution even more powerful.
Built With
github
heroku
javascript |
10,009 | https://devpost.com/software/aini-uq4xa1 | Aini in Action
Inspiration
I'm Pedro and my nephew spent too much time playing online games, my Sister in law was concerned about it, I helped her set up digital wellbeing tools so She could manage his time on the phone, She also wanted him to spend more time reading, so the challenge was, how to get him motivated to do so?
I gave her the idea to ask him to read for one hour to get one extra hour of online games, and She gave it a try. He was very motivated and gave her a call the first time, but She was not available, She was at the office, so he needed to wait until She had time to talk about the book, so Aini seemed like a great idea to solve this problem.
I'm Ingo and my son is already using a couple of websites to supplement his learning (reading, math, etc) but they are not connected to any reward system to make it more fun and for them to be truly motivated to accomplish the next set of quizzes.
I see at school that many parents and kids have challenges getting into so many different platforms, it also becomes challenging to keep track of all of them, using something as simple as Facebook Messenger, lowers that barrier, and simplifies the whole process aside of making it extremely easy to use it from any phone, something that these platforms are not good at.
What it does
Aini is an Educational ChatBot that has a database of books and quizzes for kids to see their progress while earning rewards.
How we built it
For this proof of concept, we used "Quick Replies" and we built it using:
NodeJS
Next.js
AuroraDB
Heroku
Challenges we ran into
Our biggest challenge was to find a simple, straight forward and free platform to prototype this app, we started with glitch.com but it had multiple outages during this time, then we moved to Vercel.com but they did not provide data storage so we finally ended up using Heroku.
Time was also a big challenge for us, coordinating considering our daily lives and jobs did not allow us to do even more progress.
One challenge on Facebook Developers is that it was not clear to us that every time that we changed the webhook we needed to get a new Access Token and that only the creator of the app could have access to create a new one.
Due to the size of our team, we were not able to make significant progress on the admin side and to make everything look better, next time we would like to get a Product Designer on our team.
Because this was our first time developing a chatbot and that we are used to working developing either APIs or for the Web it became a challenge to define and design the interactions with Aini.
Accomplishments that we're proud of
Finishing the proof of concept and having Ingo's son tested it successfully. Knowing that it worked despite how simple it is it shows its great potential.
What we learned
Aside from using Heroku, Next.js, and how easy it is to integrate with the Facebook Messenger API, we learned that we can still code and hack together like the old times.
What's next for Aini
We would love to be able to try this bot with Messenger Kids and:
Finish the admin and give access to Parents/Teachers
Add more books and quizzes
Allow Parents/Teachers to log in with Facebook so they can get Aini to tell them when their kids answered questions
Find a way to allow multiple rewards and for Aini to keep track of them
To expand it to more than just books
To see how we could use AI to make the conversations feel more natural
Built With
facebook-chat
facebook-messenger
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/japanese-car-match | Screenshot of the page and messenger chatbot
Inspiration
I thought it would be cool to have a messenger bot that can help you find out what kind of car you're looking for. I wanted something that would allow users to easily and quickly parse through a list of cars.
What it does
Users interact with the chatbot and respond using quick replies that the bot remembers and uses to find a list of matching cars. Users then interact with postbacks to accept the current car or see another.
How I built it
I built it using javascript and used nodejs to create a webhook that would allow it to send http requests to messenger. I used vscode for the text editor, and a free heroku app as the server.
Challenges I ran into
The most difficult part of the development process was the setup. The tutorial provided by facebook did not explain how to set up a server so I had to look up how to do so. After I finished that, I had to figure out how to use the Facebook template system to send different kinds of replies.
Accomplishments that I'm proud of
I am proud of finishing the coding and debugging. I started a week ago without too much knowledge on node.js and messenger for developers and I learned a lot this past week. Seeing the project come together and become operational on other devices and accounts was truly amazing.
What I learned
I gained a better understanding of javascript. I learned how to set up a server and deploy a project on a heroku app and link it to a facebook app.
What's next for Japanese Car Match
After submission I will plan on making a class diagram to place in the github repo so I can easily understand the function of the application in the future. I plan on working on my web design skills to create a sample car website that can link with my car match app so users can "in theory" purchase the cars that they are matched to. I also hope to expand the database and make the bot interface more accessible to users.
Built With
facebook-messenger
javascript
node.js
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/the-safe-store |
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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
)
}
});
};
(function (d, s, id) {
var js, fjs = d.getElementsByTagName(s)[0];
if (d.getElementById(id)) return;
js = d.createElement(s);
js.id = id;
js.src = "https://connect.facebook.net/en_US/sdk.js";
fjs.parentNode.insertBefore(js, fjs);
}(document, 'script', 'facebook-jssdk'));
Conversation flow
Choosing a product
Image that would be displayed at the store
Logo
The Safe Store
The Team:
Nadav (London), Miguel (Mexico City), Alex (Kiev)
The Safe Store is a service that helps retail stores safely interact with their customers. Shoppers use their phones to chat with a bot that acts as a sales representative, without risking them or the staff.
Inspiration
COVID-19 has affected us in many ways, including the way we shop. Before the pandemic hit, we could go to any retail store or mall without worrying about crowded areas. With so much focus on online shopping, we thought it would be great to find a way to support retail stores.
The challenge:
How to create comfortable interactions between a sales representative and a customer, while maintaining a safe social distance.
Our solution:
A Facebook Messenger's chatbot that serves as a sales representative. You only need to scan a QR code, login to Facebook's Messenger, and answer a set of questions related to which product you're looking for. Once you answer the questions, you'll receive all the items that match your query, and you can choose which one you want.
How we built it
The project was built entirely with Javascript. The backend is Node and Express, while the only front-end pages were built with handlebars. We took Cartier's online catalog, exported it as JSON, and uploaded it to the repository to fetch data from.
Challenges we ran into
It was a challenge to find a real database to work with, and to learn a new API and new technologies such as webhooks.
Accomplishments that we're proud of
Finishing the chatbot on time, and being able to customize and polish it.
What we learned
Work with people with different backgrounds, and coordinate meetings while on different sides of the world.
How would it be used?
Sarah enters a Cartier store. She follows the COVID19's best practices: wearing a mask, and not getting too close to anyone, including the staff.
There is a screen with a QR code displayed and some instructions. She scans the QR with her phone, and the link opens Facebook Messenger; a chatbot attends to Sarah, creating a personalized chat.
The chatbot provides a series of questions in the form of quick replies: What type of jewelry are you looking for, Sarah? Rings, necklaces, earrings, or bracelets?
Which metal? White gold, rose gold, or yellow gold? What size? [...] Thanks, Sarah! Here are some items that match those characteristics. See anything you like? Awesome! The item will wait for you to try on.
Built With
express.js
handlebars.js
heroku
node.js
Try it out
github.com
m.me |
10,009 | https://devpost.com/software/en-route-to-safety | Inspiration
Due to current pandemic situation, I would often check COVID-19 stats for the nearby counties just to be safe before running any errands. My inspiration for this bot comes from my personal experience. It was obvious that everyone in the world is going through what I was going through. Modern problems need modern solutions. I hope this bot would help people avoid high risk areas and choose a safer route.
What it does
En Route To Safety!
helps users into choosing a safer county to shop for groceries, visit hospitals or pharmacies. It takes destination inputs from users and provides COVID-19 stats for the county that the location falls into. It also provides a safer alternative by offering an adjacent county with lesser COVID-19 cases. The bot also allows users to subscribe for daily updates on COVID-19 for any chosen county.
How I built it
The app uses Flask in the backend and is built using Python3 and HTML/CSS
Challenges I ran into
My biggest challenge was time constraint and lack of availability of data.
Initially I decided to do this project in Node.js (Express) but I had to denote a lot of time debugging errors so to save me some time, I switched the whole project to
Python3
. But this was not it, I realized that COVID-19 stats for the US were not available/updated as frequently as other countries. So, I had to invest most of the time into researching reliable COVID-19 data
sources
.
What I learned
This project was a great experience since this was my first messenger bot. I got the opportunity to understand Node.js and web technologies on a different level and explore Facebook’s Messenger API. It was fun playing with Facebook’s features like Quick Reply, Handover and One-Time Notification.
What's next for
En Route To Safety!
Currently,
En Route To Safety!
is only supported in the US. My immediate goal is to make it available for every country.
Built With
css
flask
google-maps
html
python
Try it out
github.com
erts.herokuapp.com |
10,009 | https://devpost.com/software/diary-chatbot | Inspiration
We found ourselves spending a lot of time on messenger apps talking to friends. But often we would have thoughts or ideas we wanted to jot down that we would forget in the flow of conversation. We realized that with a messenger bot we could quickly jot down our thoughts and resume our conversation without breaking the flow or having to open a separate app.
What it does
This bot enables you to record your thoughts and later view them. It also lets you analyze and plot out your mood overtime.
How I built it
I made a node.js backend to accept the webhook and send back responses
I stored user diary entries in a MongoDB database
I created webpages to view user entries in ejs using Plotly for the graphs
Challenges I ran into
Familiarizing myself with the various part of the Messenger API proved to be the biggest challenge. Configuring and researching the exact parts that I needed was something that took the longest amount of time.
Another problem was designing the webview UI elements for mobile. Some of the graphs we wanted to show proved to be unfriendly for smaller screens.
Accomplishments that I'm proud of
We were proud that at our fast iteration time and how we were able to write and deploy this with no major problems.
What I learned
The main thing we learned was the messenger api. However working with a templating engine was new for us, as well learning how to design for mobile
What's next for Messenger Diary
The chatbot definitely has room for improvement. The main point of concern is the slightly unintuitive user experience that requires the user to press multiple button to view the webview. The analysis could also be improved with further mood analysis and phrase detection etc.
Built With
chatbot
ejs
facebook-messenger
heroku
mongodb
node.js
sentiment
Try it out
www.messenger.com
github.com |
10,009 | https://devpost.com/software/mood-match | Logo
Sample conversation
Inspiration
Many times, it is very difficult to pick a song that is perfect for your current mood. Especially during such an unprecedented time in our world, it is hard for people to understand how they feel, let alone understand how to feel better. A plethora of research studies have proven that music is truly one of the best ways to brighten someone’s day and MoodMatch aims at doing just that. This issue of not being able to understand one’s true emotions and grasp what is needed to acquire a more positive mindset inspired me to create MoodMatch.
What it does
MoodMatch is a Facebook Messenger ChatBot that recommends songs based on a user’s current mood. MoodMatch understands the user’s current emotions by asking several personalized questions and using novel sentimental analysis algorithms to deliver several songs that will cheer up the mood of the user.
How I built it
Before this project, I was very new to the ideas of creating a project. This project introduced me to the end-to-end programming pipeline. I started by first mapping out what I had to do:
Obtain a list of songs, artists, and lyrics of the songs
Perform sentimental analysis on the songs to understand what kinds of feelings are exhibited by the song
Create a messenger bot
Program the messenger bot to ask the user questions
Process the results (sentimental analysis on the chosen choices)
Recommend songs with similar sentiment levels or perhaps higher sentiment levels to cheer one up
I followed this process thoroughly and used the Microsoft Azure Cognitive Science API to perform sentimental analysis. I also used various APIs including Spotify, Genius Lyrics, and various others.
Originally, I had meant for MoodMatch to pop up a Messenger Webview and encourage users to fill out a form that would then allow for the processing of data, however, Facebook’s innovative Quick Replies module on Messenger allowed me to make MoodMatch more interactive and personalized for users.
Furthermore, all of the data regarding songs and the various components associated with it are stored using AWS S3, making this application a perfect model of cloud-based computing. Through this project and several past instances, I understood the growing power of cloud-based applications in our world and how leveraging the cloud through applications such as AWS and Azure will undoubtedly ameliorate your app.
Challenges I ran into
As I was very new to the end-to-end programming process, I ran into many challenges including not being able to understand what was wrong. My first challenge in this journey of completing MoodMatch was deploying my messenger bot on a Heroku server. Many times, I received an Error 400 or Error 500 stating that the /GET request cannot be found. However, once I got over this hump by fixing a few lines in my code, I was faced with several other tough mountains:
Obtaining results from the HTML form and using ExpressJS to deliver POST requests
Returning a list of songs and executing my algorithm on an ASYNC function
Transitioning from a bland messenger webview to more interactive Quick Replies
I often asked friends or even posted on StackOverflow to get some of my questions answered and get over these challenges. This project truly wouldn’t have been possible without others who helped me get over these challenges.
Accomplishments that I'm proud of
Creating a bot was not something I could've imagined me doing a few months ago, but now I have fully learned one example of the end-to-end programming pipeline. Getting over the aforementioned challenges and not giving up throughout the process has not only given me more technical knowledge, but it has impacted me mentally as I am more confident in my coding ability after this project.
What I learned
I fully learned the end to end programming pipeline of sorts. In particular, I learned the ins and outs of the various APIs I worked with and understanding how to deploy/manage server-based applications like a messenger chatbot.
What's next for MoodMatch
A lot is in store for MoodMatch, ranging from automating the song updates as well as adding more factors that may influence someone's choice of song. I plan on having an autorefresh of sorts for the list of songs already scraped off the web right now so that users will continuously get different songs. Perhaps even employing an account scenario in which users are never recommended the same song on multiple occasions is also one thing I plan to work on. Lastly, a lot of factors go into one’s feelings and current emotions, which definitely cannot be grasped by the current 2 questions asked my MoodMatch. I plan on adding more of these questions and even an open-ended question of sorts, in which users can type in their response, and emotional analysis will be performed on their inputs. In fact, a few of these are already in the works so be on the lookout for upcoming updates ;)
Built With
billboard
facebook
genius
html
javascript
node.js
python
spotify
Try it out
www.messenger.com |
10,009 | https://devpost.com/software/yourcloud | messeging screensort
Inspiration
Though I am a beginner, I am very passionate about messenger bot, AI, Deep learning, NLP. These helps me to begin these journey.
What it does
This is a messaging bot used to provide cloud service to each facebook user effectively. Here, User upload his/her files along with some input tags. Later he can find files effectively with this tag and some sentiment analysis. This bot can be used in self to self messaging in facebook and It will help user finding his file easily.
How we built it
This bot is built with chatfuel, some api and plugins. The bot is still in developing stage and It can be improved more.
Challenges we ran into
Many drawbacks we faced to develop that bot. Sometimes It didn't provide exact file by analysis. But after some effort, It starts working.
Accomplishments that we're proud of
I'm really satisfied with the amount of code I could churn out in a short amount of time. I am also proud by participating a big hackathon like facebook.
What we learned
We have gained a vast knowledge working in this project. we have learned how to make an interactive messenger bot.
What's next for YourCloud
Extend the functionality of this bot. Developing some existing work that still exist.
Built With
chatfuel
natural-language-processing
Try it out
m.me |
10,009 | https://devpost.com/software/mental-heal | No problem too small! Please see our video to see how it works
Inspiration
Wanted to offer a low level support for those struggling with mental health to make it as easy as possible for people to find solutions to their issues. Speaking to external sources such as a GP or referring into a Psychological service can be intimidating for those who need a little advice with day to day things, so our service combats this.
What it does
Offers easy to try solutions to day to day problems associated with mental health concerns such as stress/anxiety, depression.
Acts as a first point of call to reduce the cause of the stressor and improve quality of life
Also offers external help sources such as anxiety support charities for those who need further support and don’t know where to turn.
Provides validation to encourage people ask for help.
How we built it
We used python so we could rapidly prototype our idea, using the fastapi library to handle web requests and wit.ai to understand the users situation. There are 3 NLP models to determine how the user is feeling, the first to see if their initial message is positive or negative then a coarse classifier to determine whether they are stressed / depressed / lonely and then a final model to determine a specific symptom such as tiredness. The project was hosted on Heroku with automatic deployment from GitHub to make server management as easy as possible. Ideas were generated and stored with GitHub projects letting the team sort concepts and track progress. A focus on rapid prototyping was made, changing code and instantly seeing the effect in messenger.
Challenges we ran into
Understanding how the messenger API will interact with our project
Understanding how our users will use the project, how they may use it is different to how we imagine
Wit.ai training struggled with multiple similar intents
Running our code on a remote system due to messenger API requirements meant understanding issues was harder and a very iterative process
Managing all the possible paths the conversation can go down cleanly within our code
Accomplishments that we're proud of
Since the bot interprets natural language it allows an individual to feel like the system is listening to them more so than just going through multiple choice questions.
Good use of multiple talents/backgrounds
People we spoke too regarded the idea highly, especially if they suffered from minor anxiety and felt they wouldn’t seek professional help since it was wasting someone's time
What we learned
Using a large API, how information flow is handled
How to handle sensitive user data
Importance of continually testing the application
Team co-ordination to generate ideas and implement our application, especially in terms of code as none of us have coded in a team before
What's next for no problem too small
Make the user experience more personal
Focus on user experience improvement if they come back, tracking what they're tried and their mood, there's a lot of potential to help the user more if we can build on our previous suggestions.
Potential survey to get a much wider range of answers to questions to improve Wit.ai training.
Notes:
Currently our GitHub project is private as it has wit API keys within the source code. We can any judges access to the repository if wanted and we will shortly publish a clean public repository.
Built With
fastapi
github
heroku
python
wit.ai |
10,009 | https://devpost.com/software/mindpeer | Scenario 1: When you are a help seeker and want support.
If you are comfortable enough, you'll be connected with verified helpers otherwise good consulting suggestions will be provided.
Scenario 2: When you want to provide support.
Basic deatils will be needed for further email communication to review and verify helper request.
Private chatroom of verified helpers and help seekers
Inspiration
It’s no secret that mental health is routinely treated differently than physical health, but sometimes it’s difficult to understand how or why this affects us. It is not the same thing as the absence of mental illness but includes emotional, psychological, and social well-being as well.
Mental health care challenges that inspired our hack were:
Access to care & support
Workforce shortages
Quality of care and variation in practice
Social discrimination
And Lack of funding
So these types of challenges gave us an idea of why not help in bridging this gap by connecting different communities and people so that they can help each other with mental care support by connecting help seekers and helpers in private chatrooms.
What it does
We have built MindPeer in such a way that it bridges the gap between people who need support for mental health care and verified people who can lend them ears and provide support by creating a safe space.
The way it works, you can add yourself as a helper or a person who needs help.
Scenario 1: When you are a help seeker, you can choose for private chatrooms if you are comfortable enough for that otherwise you can choose for good consulting service suggestions which may be helpful for your issue type.
Scenario 2: When you are a helper, you'll be asked different sets of questions to know your expertise and later your request will be reviewed and verified through email communication.
Once the requests have been approved, help seekers and helpers can communicate in private chatrooms to discuss, solve, and support each other issues.
When faced with an emergency, a member can reach out to any of his favorite communities or helper.
How we built it
We have built this bot by connecting the Dialogflow NLU engine with Facebook messenger platform APIs. Further, we have used cloud functions for listening to Messenger platform webhooks and process posts. We have also used Facebook Graph API to pass the control thread and notify the user when a private chatroom has been created with a helper.
Challenges we ran into
We faced many challenges while building this but that only made our journey interesting.
Passing the control thread
Testing and changing the bot quickly
Accomplishments that we're proud of
We were able to come up with a working prototype of the product that effectively matched help-seekers with potential peer-vetted help-givers.
Created a seamless way of providing help for mental care such that any request doesn't go unattended, gets full support, and no spam messages.
Help givers are verified through email so that there could be one to one communication for fair results.
If possible, people committed to making a difference are matched efficiently as per their location.
What's next for MindPeer
Facebook business app review
Phone number helpline
Sentiment analysis
Built With
dialogflow
facebook-graph
facebook-messenger
mongodb
nlu
node.js
Try it out
www.facebook.com |
10,009 | https://devpost.com/software/first-aid-xqc9p7 |
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Inspiration
There is lots of people whom are suffering from many regular deases. Sometimes
many people are dying from this deases
But one first Aid formula can safe their life in this situation.
So our inspiration was safe life.❤️
What it does
It can provide us medical support in emargency situation.Its called first aid.
It will provide us regular deases like diabetes, blood pressure and covid 19 symptoms including first aid.
It also can provide child care information.
It also can reduce depression by giving mental care information.
It also can give you doctors information.❤️
How we built it
It was built by chatfuel.❤️
Challenges we ran into
That was many.But we enjoyed it and tried to overcome this.❤️
Accomplishments that we're proud of
We made something that can provide medical support in emargency situation including covid 19 .❤️
What we learned
We learned many things.
We learned how can a simple things could slove big Problems❤️
What's next for First Aid
1.We will add all countries best doctors information
2.We will add something that can give you first Aid by see your symptoms.
3.You will add a chatbot that will support us in our depression time as a friend.
❤️
Built With
chatfuel
Try it out
m.me |
10,009 | https://devpost.com/software/chat-bot-pwf5le | Inspiration
Chat bots are becoming increasing common. However, building one require technical proficiency that small owners don't have. Creating a solution that helps ease the process will help proliferate the use of chatbot and increase customer service efficiency while reducing operating cost
What it does
Allow shop owner to create a product and the attributes for it e.g price, shipping, material, etc. so that commonly asked questions by user will be automatically handled.
If the chatbot fail, another chat app made for human agent will handle the conversation instead
How I built it
When a webhook is called from Facebook, the content is passed to wit.ai and the intent is retrieved. The item name is identified and the detail is retrieved. Then, based on the type of question (what, how much, how long) the appropriate reply is sent back.
For comment handling, because there is a context of a post, when the user asks about a generic item (one that does not have a full item name), the generic name is compared against a list of user-defined references and the corresponding info is retrieved.
When an item is found, it also sets a context variable so that user can refer to it in the following questions
When the thread is passed onto the 2nd app, it will store the messages and later display as pending conversations. This design allow multiple agents to collaborate and whoever is available can accept the support request.
Challenges I ran into
Facebook didn't allow me to receive webhook for comments, so i had to poll instead.
This is the first time i use sailsjs and node. Also using react with sails js together was a bit difficult at first due to browser security constraint.
Accomplishments that I'm proud of
Everything
What I learned
Sails.js
Facebook graph api
Facebook app review process
Json web token
Wit.ai
What's next for Chat bot
Multiple chat agents
Non-product specific questions
Built With
graphapi
react
sails.js
wit.ai
Try it out
chatbot.mysuperawesomeweb.co.uk
chatbot.mysuperawesomeweb.co.uk
github.com |
10,009 | https://devpost.com/software/plasmin-0-1-plasma-donor-in-your-area | Welcome Message
Need a Donor
Be a Donor
Add New Area
Inspiration
At this Covid-19 Situation, where no effective medicine has yet been discovered, plasma seems to be a name of hope.
But it would have been more helpful if people could easily find out about plasma donors around them through messenger bots. From that thought we plan to make this plasmin 0.1.
What it does
At the beginning we have a welcome message, about us, some instructions and two quick reply buttons.
Need A Donor
Want to be a Donor
If someone wants to be a donor, (for which area does he want to be a donor ?, what is his blood group, name, phone number and how long ago he was recovered from Covid-19) the bot takes this information and saves in a Google form.
Then if someone comes looking for a donor, the bot searches donor by ( donor area + blood group ) from the Google sheet and displays the appropriate donor information.
If no donor is found according to the requirement, it says "Sorry donor not found" and stores the information.
How we built it
Basically we made this bot by using Chatfuel.I dicovered Chatfuel very usefull and easy to use.than we have to use Integromat to connect our bots to Google sheet. we made JSON API , use charts query language to manege data search.
Challenges we ran into
We were brand new to the chatfule. At first we thought we would make a bot by coding but then we decided to starting with chatfuel. The basic tasks were very easy. The problem started when I went to connect with Google Forms. Will we have a separate form for each area or will it all be in the same form? Can data be collected by filtering from the same form with it? Or will we do hand coding without chatfule? We were very confused.
We were able to keep the data in Google Form very easily but we couldn't find a way to read the data, we couldn't do anything even after trying the zipper. Then we started trying at Integromat, there was a lot of trouble with filtering. It takes a little time to master a new technology. So without losing patience, we completed the making of our Plasmin 0.1 bot that night. And the felling of accomplishment was aowsome.
Accomplishments that we're proud of
There are some Android apps where the information of plasma donors is available in the same way but they can't serve as many people as we can with our Facebook Messenger bot. Besides, being free, people feel comfortable finding a solution on Facebook. So we are really proud to think that we can help thousands of people through our bot.
We showed our work to the head of our department, who appreciated it very much and said that he would make arrangements to promote it.
What we learned
This was really an awesome experience for us. new technology , had no idea which can be possible by these.we face many challenges and learned how to handle them. Sharing idea and contributions, learning new thing together.Above all this was a good lesson for us to learn teamwork.
What's next for Plasmin 0.1 - Plasma Donor in your Area.
In next ,We want to add a new donor request function considering the needs of the people. where if anyone who didn't find any donor, can request for a donor according to his need. and our bot will deliver this notification to all the donors in that area. So that they can help us find a donor they know.
We also have plans to work with telemedicine.
Built With
charts-query-language
chatfuel
googlesheet
iintegromat
json
Try it out
m.me |
10,009 | https://devpost.com/software/plus-ultra-care-bot | Inspiration
“A lacking supply of ICU beds is leading to preventable deaths"- this one line is our main inspiration in making this. No matter what happens, we cannot become habituated to this new normal. People who can be saved are dying every day due to Covid-19, even some are taking their lives not being able to cope up with isolation which resulted in various crimes both inside our houses and outside. Thus we were inspired to make our bot to help people in this dire situation.
What it does
Plus Ultra Care is here to aid people in this pandemic. Let our chat bot find you vacant ICU beds in your vicinity and also give information of Covid-19 test centers in your area. Hehe you thought that’s it? Plus Ultra Care is much more. Anxiety is up, depression is up, domestic violence has escalated. So our chat bot is also here to virtually pat your back and give you the support you need in this scary time.
HOSPITALS/ CORONA TEST CENTERS
Patients who become critically ill from COVID-19 require weeks of treatment in intensive care units, and many are intubated and ventilated to have even a chance of survival. However, due to the rise of the affected number, there is great shortage of ICU beds. The solution to the problem is not simple. Not only we need more ICU facilities in hospitals but also a great number of health workers with ventilators are critical equipment to keep patients alive and maintain hope for survival. So what this bot will do is, if a user ask to know vacant hospitals that take corona patients, the bot will give him/her a list of the hospitals. This would be done for non covid-19 patients who need to visit hospitals. Since all the hospitals do not accommodate covid positive patients and also some patients do not like to visit hospitals that take covid positive patients. The given hospital list will be updated every six hours.
Also if a user wants to know about the covid test centers in his/her vicinity, then our bot can also give the list with contact details.
MOTIVATION/HELPLINE
Domestic violence has increased during lockdown as many people are trapped at home with their abuser. If someone thinks their life or their loved ones' lives are at stake or they need counselling, let our bot know and it will direct them to national helplines. This helplines will provide mental and legal help to any victim reaching out.
If we look at the news, at social media, or even out of the window at times, it’s clear that a lot of people are struggling with the whole ‘social isolation’ thing. It is so important to make a conscious effort to stay healthy and positive. So our bot can give a few advices to keep you motivated when you are feeling down.
How I built it
We are both technologically backward weebs but we tried hard to learn to work with Chatfuel and this bot is our end product.
Challenges I ran into
We had to pull an all-nighter because we got to know about this hackathon pretty late. Also coming up with a solution to a real time problem was also challenging.
Accomplishments that I'm proud of
IT IS WORKING! We are proud that we could be productive in this quarantine and got to do something useful.
What I learned
This is our first time taking part in a hackathon where we learnt how to make a chat bot which will be helpful for the general people. Glad that we learned a new skill and got ourselves enlightened with this new world called hackathon. Looking forward to taking part in more of these competitions.
What's next for Plus Ultra Care Bot
We hope that our government takes initiative to execute this Plus Ultra Care Bot. Bangladesh is a small country with a very large population and we are still not close to bringing covid-19 outbreak under control. So we hope that our government will take initiative to implement this bot. This bot will be very helpful to minimize the hassle of the patients who have to move from one hospital to another for their treatments.
Bangladesh being a country with a huge population, most of the people are ignorant about the mental health issues which have escalated during this lockdown. Here we have shown an example how a bot can try to lift one's mood up. We want to feed more data into it by suggesting books, playlists etc. Because, we never know, one word from a bot might brighten one's life.
Built With
chatfuel
gossip
procrastination
Try it out
www.facebook.com |