Multi line input is not yet supported but server and chat handler, but can be sent by web ui. Either disable or fix multipart form data issue

31c8a57

this commit supports expressions as text and parse it, but still doesn't remove it while giving voice

you'll need to create an endpoint in your website's backend that takes in text input and outputs the synthesized audio. You can use the Tortoise-TTS library in your backend code to generate the audio. Here's a sample code snippet in Python:

python

from tortoiseTTS import Tortoise

def generate_audio(text):
tortoise = Tortoise()
audio = tortoise.say(text)
return audio

You can expose this function as an API endpoint using a Python web framework like Flask or Django. For example, with Flask:

python

from flask import Flask, request

app = Flask(name)

@app.route('/api/synthesize', methods=['POST'])
def synthesize():
text = request.form.get('text')
audio = generate_audio(text)
return audio

if name == 'main':
app.run()

Note that this is just a basic example, and you may need to modify it depending on your specific use case. Also, be aware that generating audio can be a computationally intensive task, so you may want to optimize the code for performance if you expect a high volume of requests.

Add "allow-device-control" in user.json (False)
Toggle switch in webUI
On 1st toggle user needs to agree from commandline running terminal window
Then will verify,
Verify process:

1. Browser request for token
2. Server generate 2 token:
   i. One to use as url query so that browser can go to (probably use uuid.uuid4() library to generate a random token)
   ii. Other in the get request in above link (similarly)
3. Server send 1st token and generate a site with it and put the 2nd there
4. Browser request 127.0.0.1:port/?verifyDevice=token
5. Make the 4 request url Allow all origin header response in server
6. If Server gets request (that means same device, since 127.0.0.1 can only be used from same device), sends 2nd token, browser gets that
7. Browser sends the second token to server (same as step 4). Server approves both in browser and server and saves (that token) in user.json and localstorage. Since its in localstorage, unless malicious intent, it won't be shared
8. Also the request message like "raise volume" will be revarified (using step 4) with the token.

It won't hurt to make some extra requests rather than letting someone control your pc

This is also done in every login or verify user

Check and update from
Wikipedia-API
https://github.com/martin-majlis/Wikipedia-API

Older one is causing dirty issues

Block from both js and py

I've improved the server backbone in https://github.com/RaSan147/py_httpserver_Ult

Planning to migrate

There's a chance replit server may go down in 2024

Check both js and py

To create a chat AI that can respond to user messages and provide appropriate responses, you will need to follow a few basic steps:

Collect and preprocess data: You will need a dataset of conversation examples to train your chat AI. You can either collect this data manually or use an existing dataset such as Cornell Movie Dialogs Corpus, Ubuntu Dialogue Corpus, etc. After collecting the data, you need to preprocess it to remove noise, normalize text, and convert it into a machine-readable format.

Choose a model architecture: There are various types of models that can be used for chat AI, such as sequence-to-sequence models, transformer models, and memory networks. You can choose the model architecture based on your requirements and the size of your dataset.

Train the model: Once you have chosen a model architecture, you need to train the model on your preprocessed dataset. This involves feeding the model with input-output pairs and adjusting the model's parameters to minimize the loss function.

Test and evaluate the model: After training the model, you need to test it on a separate test dataset to evaluate its performance. You can use metrics such as perplexity, BLEU score, and ROUGE score to evaluate the model's performance.

Deploy the model: Once you are satisfied with the model's performance, you can deploy it to a production environment such as a web application or a chatbot platform.

As for using open source machine learning, there are several libraries and frameworks available that you can use, such as TensorFlow, PyTorch, and Keras. These frameworks provide pre-built models, as well as tools for training and evaluating custom models.

get some conversation from here:
https://helenadailyenglish.com/basic-english-conversation-100-daily-topics

add more by editing and comments

Auto catching wrong intent

   Love you >> Love you
   user msg id:  359
   Flags:  {}
   intent: ['say_hello', 'love_you']

send localtime and say it

You can use various video processing libraries like FFmpeg or GStreamer to manipulate the video files and streaming protocols like HLS or MPEG-DASH to serve them over the internet. You can also use third-party APIs like Cloudinary or Amazon S3 to store and retrieve the video files. However, it's important to note that enabling a chat AI to send videos on a self-hosted website could potentially create legal or ethical issues, especially if the videos are not moderated or monitored for inappropriate content. It's important to carefully consider the implications of such a feature before implementing it.

Sure, here's an example of how you could allow a chat AI to send videos on a self-hosted website using HTML and JavaScript:

First, create an HTML form with an input field for the user to upload a video file:

python

Next, create a JavaScript function to handle the form submission and send the video file to the server using AJAX:

javascript

function uploadVideo() {
var file = document.querySelector('input[type="file"]').files[0];
var xhr = new XMLHttpRequest();
xhr.open('POST', 'upload.php', true);
xhr.upload.onprogress = function(e) {
if (e.lengthComputable) {
var percentComplete = (e.loaded / e.total) * 100;
console.log(percentComplete + '% uploaded');
}
};
xhr.onload = function() {
if (this.status === 200) {
console.log('Video uploaded!');
} else {
console.error('Error uploading video');
}
};
var formData = new FormData();
formData.append('video', file);
xhr.send(formData);
}

Finally, create a PHP script to handle the video upload and store the video file on the server:

php

Note that this code is just an example and would need to be customized to fit your specific website and use case.

Here's an alternative implementation of the same functionality using Node.js and the Express framework:

First, install the necessary dependencies by running the following command in your project directory:

npm install express multer

Next, create a server.js file with the following code:

javascript

const express = require('express');
const multer = require('multer');
const app = express();
const upload = multer({ dest: 'uploads/' });

app.post('/upload', upload.single('video'), (req, res) => {
if (!req.file) {
return res.status(400).send('No video uploaded');
}
res.send('Video uploaded successfully');
});

app.listen(3000, () => {
console.log('Server listening on port 3000');
});

Finally, create an HTML form with an input field for the user to upload a video file, and use JavaScript to submit the form to the server:

php

Again, note that this code is just an example and would need to be customized to fit your specific website and

Hello, I'm a not a pro python developer so I've got a question. (maybe stupid)

How can I change the model? I don't need a full explanation just some ideas how to do it.

Thanks for your VoiceAI Asuna project. It's amazing!!!!

Compliments
https://www.happier.com/blog/nice-things-to-say-100-compliments/

title covers it would be sweet to talk and reply in audio