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Chatbots have become increasingly popular in the healthcare industry as they provide an efficient and cost-effective way to interact with patients, customers, and other stakeholders. With the advent of artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) technologies, healthcare chatbots are now more capable of understanding complex information and providing accurate responses.
Dialogflow is a popular platform for developing chatbot applications. It is an AI-based platform that allows users to create chatbots that understand natural language queries and provide accurate and personalized responses. Dialogflow uses a combination of AI and ML models to understand natural language and provide accurate responses. By connecting to various databases, Dialogflow can also provide contextual data that can be used to provide more customized responses.
Creating a healthcare chatbot using Dialogflow and machine learning models can be a complex process, but it offers many advantages. Such chatbots can be used to provide information about medical conditions, medications, and treatment options. They can also be used to help with diagnosis, provide patient education, and provide reminders and alerts to patients.
The process of creating a healthcare chatbot using Dialogflow and ML models involves several steps. First, the user must define the problem they are trying to solve and create a detailed plan for the chatbot. This plan should include a list of conversations the chatbot should be able to handle and the type of data it needs to process.
Next, the user can create a Dialogflow agent, which will act as the main interface between the user and the chatbot. The user must then create an ML model by training it on a dataset of conversations and keywords that it should understand. Once the model is trained, it can be connected to the Dialogflow agent and integrated with other databases.
Finally, the user can test and deploy the chatbot and then use it to interact with users. By collect feedback from users, the user can refine the model and improve the accuracy and effectiveness of the chatbot.
Creating a healthcare chatbot using Dialogflow and ML models can be a complex process but it offers many advantages for users. By leveraging AI and ML technologies, healthcare chatbots can provide accurate responses to user queries and help improve patient experience.
I want to make this in such a way that person enter there symptoms in dialogflow generated chatbot and machine learning model is return disease name with medicines.
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I am working on developing a chatbot to support students who want to learn programming (Python, for example). I have developed many examples but they all have an intents.json file in which I have to configure questions and answers.
Is there another way to find the data to train the chatbot?
Anyone have any other ideas?
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as stated in the title of the question - What technologies I may use to write drum-pattern audio signal based recognition program? I want to create a tool for me as a drummer musician to transcribe a drum-part from a record. I imagine this as similiar technology to speech-recog but made especially for drum patterns previously defined in some kind of a drum pattern base.
The problem is Im a very beginner in programming. For half year i was interested in microcontrollers with basic c++, not even OOP. Currently im trying out python and this is my final programming knowledge/experience background. Now because of that poor level of know-how in IT, I dont really know what technology, frameworks etc I should get interested in with this kind of a project. It may be obvious that I should look for speech-recognition technologies and learn that and then apply that knowledge to build my own program, but Im not really sure where is the best place to start and if im ready for reading heavy walls of professsional open source projects code. Maybe there is some kind of a friendly python framework to get me started in that topic? I found Python Librosa framework in my research but it seems really advanced and it looks like i should learn signal-theory to get fluent in using that. Let me know what do you think and what kind of tactic should i aim to in your opinion.
The task of transcribing music automatically, from audio into notes (typically MIDI), is known in the research community as Automatic Music Transcription. The specialized task of doing it on drums only is known as Automatic Drum Transcription (ADT).
ADT is widely researched, and both open-source and commercial solutions are available. One open-source software can be found in ADTLib. It provides a very simple Python API that takes a WAV file and returns transcribed drum track. There are papers linked in the README file, describing how it is put together.
A web-based tool called ADTWeb allows to try out transcribing drums without installing any software.
Note that ADT usually assumes an input that is only/predominantly drums. If you want to extract drum patterns from a mixed song containing also other instruments, you mayb need some kind of Source Separation step as well.
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I want to build REST API (or GraphQL API) which I want to connect with Angular (2+) to build some nice web application.
Which framework should I choose to get started with and why? DjangoREST or ExpressJS ?
Which one of them is easier to get connected with front-end frameworks?
I know python as well as JavaScript. I also have experience with django as well as some JavaScript front-end frameworks like Angular. So, it wouldn't be that difficult to get started with nodejs or either of them.
Which of then is easier and better?
In short, Django for quick development and Express for full-stack,
scalable solutions.
Django provides better, easier security without as much experience. In the case of Express, it takes so much experience and confidence to get the same security from Node.JS
Django uses the MVT (Model View Template) design pattern and Express uses event-driven programming in which the entire architecture is driven by “events” or user choices. MVT is excellent for server-side development, on the other hand, event-driven programming is rather than separating client and server sides.
Django uses an in-house template system, so you won't hang to choosing the right one. With Express, a fully open system you may spend a lot amount of time to choose the perfect one for you.
Django is more structured to work around. It tells you how to do whereas express gives you the flexibility to do it in your way. For example, you need to use Django's ORM to interact with the database, but in the case of Express, you can choose any tool you like.
Express gives you freedom. So, if you’re experienced, there is a space to create whatever you want from scratch. Django gives you a lot of solutions with a vast template and library system, which is great for productivity but not enough for the vision.
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I want to develop a conversational chatbot for chinese language in python like the user says "你好" which means "hello" in chinese and chatbot respond. i think it will be very difficult to build from scratch and write every expected response for chatbot. i want to find an opensource library to connect with my API which is able to respond to user and do the conservation with the user. i already know about a chatbot developed by Microsoft called "Xiaobing" which is especially developed for chinese people but i don't know if they provide an API for developers or not.There is also another github project called brobot(https://github.com/lizadaly/brobot/)
but i don't know if it provide chinese language support. Any suggestions or guidance from anyone here would be appreciated.
Have a look at ChatterBot Python module. It is language independent. Means you can train it with any language.
sample snippet to train your bot.
from chatterbot import ChatBot
from chatterbot.trainers import ListTrainer
chatbot = ChatBot("bot") # create an instance of ChatBot and name it.
chatbot.set_trainer(ListTrainer)
data = ["你好",
"我很高兴认识你"] # add data for training
chatbot.train(data) # train the bot
while True:
try:
user_input = input("you - ") # ask something to bot
bot_input = chatbot.get_response(user_input) # get curresponding output from bot
print("bot - ",bot_input)
except(KeyboardInterrupt, EOFError, SystemExit):
break
output:
List Trainer: [####################] 100%
you - 你好
bot - 我很高兴认识你
You can train it with more and more conversations. Just add those conversation in a text file and train with it.
Refer ChatterBot docs for further information on training the data.
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Let's say you have a universal college application. The user inputs their information and the universal application submits the correct subset of information to each school.
What is a good approach to organizing the data validation?
For example, when a user decides to apply to school A you must validate that the user has input into the universal application all of the information required for school A.
This process is repeated for school B, school C, etc. Most of the information that must be validated is probably the same for each application, with some small differences.
Is there a library or architecture that would be optimal for setting this up? I've looked into solutions like Cerberus or Py-Good which help with the validation part, but not really the high-level approach.
In Rails, I have seen a solution called validation contexts. I am trying to implement this in Python (Django).
If you are working with Django, it has everything you will need to validate incoming data. The standard way to validate data with Django is through forms (and more specifically, form validation.)
From the docs:
Django’s role in forms
Handling forms is a complex business. Consider Django’s admin, where
numerous items of data of several different types may need to be
prepared for display in a form, rendered as HTML, edited using a
convenient interface, returned to the server, validated and cleaned
up, and then saved or passed on for further processing.
Django’s form functionality can simplify and automate vast portions of
this work, and can also do it more securely than most programmers
would be able to do in code they wrote themselves.
You will find some helpful examples in the links that I've posted. I would recommend looking at the clean method.
If the logic is mostly the same for each scenario, but with slight differences, you will likely be able to create a single form that can process the data appropriately based on what the user has selected.
Hope this helps.