forever. Any suggestions or more minimal steps to reproduce?

At this point it is failing because the output from the execution contains some javascript to create the IFrame. & the JS injects the src using window.location instaed of hardcoding it to localhost. This is to ensure they use the same host name where Jupyter UI is hosted.

We will need to put some hacky code in our extension to handle this situation. For now you won't get the desired output.

Once you have got to this stage the tensorboard (web app) server is running. You can view the output (view js/html rendered) & 99% of the time the port is the same.

You can now go into a separate cell & type the following code:

from IPython.display import IFrame
IFrame('http://localhost:6006', width=700, height=350)

This will create the iframe with the right port. FYi - You can confirm this address is accessible from your local browser.

Also, how do I confirm that I'm using the new notebook api for python vs the old one that's based on webviews?

Once you have the extension activated (you will need to wait for extenion to get activated). Right click on a notebook file & click on the Open in Preview Notebook Editor command

@alexr00 Do u still need some information (asking because of the current label needs more info) Thanks

Thats good news, unfortunately the code is private, hence nothing I can share. Should be easy to repro though (whipping up using a sample fs provider).

Here's the code

        const remoteFolders = await window.showOpenDialog({
            canSelectFiles: false,
            canSelectFolders: true,
            canSelectMany: false,
            defaultUri: fileSystem.rootFolder,
            openLabel: 'Folder to create notebook',
            title: `Select folder on Remote Server (${server.label})`
        });

FYI - I am an extension author, hence do not have access to internal VSCode API. availableFileSystem is not in the exposed API (https://github.com/microsoft/vscode/blob/master/src/vs/vscode.d.ts).

Is it possible your files system provider

To my knowledge this is implemented correctly. Agreed it should be cashed, but not, and that's why I posted that stack.

Default uri is localhost888:/ and this is correct and that's why the folders are listed in the drop-down (ie it is display remote folders).

Checked the existing issues, and they do not apply

<!-- ⚠️⚠️ Do Not Delete This! bug_report_template ⚠️⚠️ --> <!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ --> <!-- Please search existing issues to avoid creating duplicates. --> <!-- Also please test using the latest insiders build to make sure your issue has not already been fixed: https://code.visualstudio.com/insiders/ -->

<!-- Use Help > Report Issue to prefill these. -->

• VSCode Version:
• OS Version:

Steps to Reproduce:

1. Create an extension and register a remote file system
2. Use the command window.showOpenDialog with defaultUri pointing to a directory on the remote file system.
3. VS Code will now select the remote file system folder picker (as expected).
4. Selecting a folder from the list results in the display of the message The path does not exist..

VS Code seems to convert the remote path to a local path & perform some local FS operations on the path (to determine its validity, things fall over as the file doesn't exist, hence we get the incorrect message).

Expected behavior

• VS Code will return the selected remote folder.
• Looking at the code,

Note:

• Not using any of the Remote extensions for VSCode
• I am on the local machine
• I have a local workspace folder opened
• I have registered a remote file system,

See below screenshot:

Screenshots of the stack trace (hope this helps)

<!-- Launch with code --disable-extensions to check. --> Does this issue occur when all extensions are disabled?: No

<!-- ⚠️⚠️ Do Not Delete This! feature_request_template ⚠️⚠️ --> <!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ --> <!-- Please search existing issues to avoid creating duplicates. -->

<!-- Describe the feature you'd like. -->

• Assume an extension registers a file system provider abc.
• User is not using remote extension (as installing VSCode & its remote extension host on the remote machine is not possible).
• Assume user creates a file (using File-> New File).
• Clicking Save As, user is not presented with the option of saving the file on a remote file system.

VSCode assumes user is always dealing with local files.

It would be good for user or extension host to intercept this & present the user with the option of saving the file either locally or on the remote file system (if there are more, then user choses). Basically:

• User will have to chose from the list of file system providers:

• local
• remote fs provider 1
• remote fs provider 2 etc..

Originally posted here https://github.com/microsoft/vscode-python/issues/14476#issuecomment-714895448 Splitting up the issue as there are two issues in the original issue.

We get the following text stream output (ansi)

• Foo\n (now output should display foo)

• Hello World (next line we have Hello World).

• \u001B[A (Ansi escape to move cursor Up)

• Bar (as the cursor has moved one line up, we shoudl now get the output

  Bar
  Hello World
  

• Instead what we get is

  Foo
  Hello World
  Bar
  

I.e. the root cause is we do not understand [A (move cursor up)

Here's the real world example (reported in the past as well) `` Epoch 0: 92%|█████████▏| 1718/1874 [00:17<00:01, 98.09it/s, loss=2.047, v_num=19, acc=0.875] Validating: 0it [00:00, ?it/s] Epoch 0: 92%|█████████▏| 1726/1874 [00:18<00:01, 94.79it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 94%|█████████▎| 1753/1874 [00:18<00:01, 95.74it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 95%|█████████▌| 1782/1874 [00:18<00:00, 96.78it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 97%|█████████▋| 1811/1874 [00:18<00:00, 97.83it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 98%|█████████▊| 1842/1874 [00:18<00:00, 98.96it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 100%|██████████| 1874/1874 [00:18<00:00, 99.57it/s, loss=2.047, v_num=19, acc=0.919] Epoch 0: 100%|██████████| 1874/1874 [00:18<00:00, 99.55it/s, loss=2.047, v_num=19, acc=0.919]

**When what we should get is:**
Basically when we see `[A` erase the previous line.

Epoch 0: 100%|██████████| 1874/1874 [00:18<00:00, 99.55it/s, loss=2.047, v_num=19, acc=0.919]

Here's the sample code
```python
import torch
from torch import nn
from torch.nn import functional as F

from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import pytorch_lightning as pl
class LitLeNet(pl.LightningModule):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.classifier = nn.Linear(84, 10)

        self.accuracy = pl.metrics.Accuracy()
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, (2, 2))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.classifier(x)
        x = F.softmax(x, dim=0)

        return x
    
    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters())
    
    def _shared_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('acc', self.accuracy(y_hat, y), prog_bar=True)
        return loss

    def training_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def test_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_epoch_end(self, outputs):
        self.log('acc', self.accuracy.compute(), prog_bar=True)
mnist = MNISTDataModule('./data', num_workers=2)
model = LitLeNet()

trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, datamodule=mnist)

Thanks for reporting this issue. I can replicate this at my end.

Originally posted here https://github.com/microsoft/vscode-python/issues/14476#issuecomment-714895448 Splitting up the issue as there are two issues in the original issue.

We get the following text stream output (ansi)

• Foo\n (now output should display foo)

• Hello World (next line we have Hello World).

• \u001B[A (Ansi escape to move cursor Up)

• Bar (as the cursor has moved one line up, we shoudl now get the output

  Bar
  Hello World
  

• Instead what we get is

  Foo
  Hello World
  Bar
  

I.e. the root cause is we do not understand [A (move cursor up)

Here's the real world example (reported in the past as well) `` Epoch 0: 92%|█████████▏| 1718/1874 [00:17<00:01, 98.09it/s, loss=2.047, v_num=19, acc=0.875] Validating: 0it [00:00, ?it/s] Epoch 0: 92%|█████████▏| 1726/1874 [00:18<00:01, 94.79it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 94%|█████████▎| 1753/1874 [00:18<00:01, 95.74it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 95%|█████████▌| 1782/1874 [00:18<00:00, 96.78it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 97%|█████████▋| 1811/1874 [00:18<00:00, 97.83it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 98%|█████████▊| 1842/1874 [00:18<00:00, 98.96it/s, loss=2.047, v_num=19, acc=0.875] Epoch 0: 100%|██████████| 1874/1874 [00:18<00:00, 99.57it/s, loss=2.047, v_num=19, acc=0.919] Epoch 0: 100%|██████████| 1874/1874 [00:18<00:00, 99.55it/s, loss=2.047, v_num=19, acc=0.919]

**When what we should get is:**
Basically when we see `[A` erase the previous line.

Epoch 0: 100%|██████████| 1874/1874 [00:18<00:00, 99.55it/s, loss=2.047, v_num=19, acc=0.919]

Here's the sample code
```python
import torch
from torch import nn
from torch.nn import functional as F

from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import pytorch_lightning as pl
class LitLeNet(pl.LightningModule):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.classifier = nn.Linear(84, 10)

        self.accuracy = pl.metrics.Accuracy()
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, (2, 2))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.classifier(x)
        x = F.softmax(x, dim=0)

        return x
    
    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters())
    
    def _shared_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('acc', self.accuracy(y_hat, y), prog_bar=True)
        return loss

    def training_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def test_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_epoch_end(self, outputs):
        self.log('acc', self.accuracy.compute(), prog_bar=True)
mnist = MNISTDataModule('./data', num_workers=2)
model = LitLeNet()

trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, datamodule=mnist)

❓ Questions and Help

Before asking:

1. Try to find answers to your questions in the Lightning Forum!
2. Search for similar issues.
3. Search the docs.

<!-- If you still can't find what you need: -->

Hi, one of the maintainers of the Python extension that provides Jupyter functionality in VS Code. Here's the original issue: https://github.com/microsoft/vscode-python/issues/14476#issuecomment-714895448

What is your question?

When running the code in class Jupyter notebook, we get the widget (I think tqdm) progress bar. However, when running the exact same code in VS Code, we get the class text stream based progress bar.

I would like to get the progress bar (tqdm) being rendererd in the output when running cells in VS Code.

Does this package have some auto detection of the host (whether its Jupyter labs/notebook) & then renders the progress bar as widgets & when executed from another front end like VSCode it will render it using text streams.

Please note: we are not using Jupyter to run the code, we're starting the Python kernel & communicating with the kernel over ZQM directly. Hence Jupyter is not avaiable. Finally, VS Code does support IPyWidgets functionality.

Thanks for your help.

Code

import torch
from torch import nn
from torch.nn import functional as F

from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import pytorch_lightning as pl
class LitLeNet(pl.LightningModule):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.classifier = nn.Linear(84, 10)

        self.accuracy = pl.metrics.Accuracy()
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, (2, 2))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.classifier(x)
        x = F.softmax(x, dim=0)

        return x
    
    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters())
    
    def _shared_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('acc', self.accuracy(y_hat, y), prog_bar=True)
        return loss

    def training_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def test_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_epoch_end(self, outputs):
        self.log('acc', self.accuracy.compute(), prog_bar=True)
mnist = MNISTDataModule('./data', num_workers=2)
model = LitLeNet()

trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, datamodule=mnist)

Here's the output in VS Code

Epoch 0:  92%|█████████▏| 1718/1874 [00:17<00:01, 98.09it/s, loss=2.047, v_num=19, acc=0.875]
Validating: 0it [00:00, ?it/s]

Here's the output in Jupyter Notebooks

<!-- Please paste a code snippet if your question requires it! -->

What have you tried?

What's your environment?

• OS: [e.g. iOS, Linux, Win] - OSX
• Packaging [e.g. pip, conda] - Pip
• Version [e.g. 0.5.2.1]
  • Python 3.8.2
  • pytorch_lightning 1.0.3
  • pytorch-lightning-bolts 0.2.5
  • torch 1.6.0
  • torchvision 0.7.0

Hi, one of the maintainers of the VS Code extension that supports Jupyter functionality. When I run the following code in a Jupyter notebook, I get pretty progress bars (I get the ipywidgets). & I can see that the ipywidgest data comes through in the output as well & saved in ipynb. However, when I run the same code in VSCode, we get plain ansi text based (terminal) progress bars.

Is there something (some initialization code) we need to setup in VS Code to tell Jupyter to return output for progress bar as IPyWidgets?

Original issue https://github.com/microsoft/vscode-python/issues/14476#issuecomment-714895448

Thanks,

Here's the output we get when running the same code against the kernel from VS Code As you can see its just a stream of text

  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:  92%|█████████▏| 1718/1874 [00:29<00:02, 58.67it/s, loss=2.044, v_num=17, acc=0.875]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 0:  92%|█████████▏| 1721/1874 [00:30<00:02, 56.62it/s, loss=2.044, v_num=17, acc=0.875]\n",

Here's the output we get when running the same code against classic Jupyter Notebooks

  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
      ]
     },

Here's the Python code

import torch
from torch import nn
from torch.nn import functional as F

from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import pytorch_lightning as pl
class LitLeNet(pl.LightningModule):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.classifier = nn.Linear(84, 10)

        self.accuracy = pl.metrics.Accuracy()
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, (2, 2))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.classifier(x)
        x = F.softmax(x, dim=0)

        return x
    
    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters())
    
    def _shared_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('acc', self.accuracy(y_hat, y), prog_bar=True)
        return loss

    def training_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def test_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_epoch_end(self, outputs):
        self.log('acc', self.accuracy.compute(), prog_bar=True)
mnist = MNISTDataModule('./data', num_workers=2)
model = LitLeNet()

trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, datamodule=mnist)

Thanks, will close this.

Hi, one of the maintainers of the VS Code extension that supports Jupyter functionality. When I run the following code in a Jupyter notebook, I get pretty progress bars (I get the ipywidgets). & I can see that the ipywidgest data comes through in the output as well & saved in ipynb. However, when I run the same code in VSCode, we get plain ansi text based (terminal) progress bars.

Is there something (some initialization code) we need to setup in VS Code to tell Jupyter to return output for progress bar as IPyWidgets?

Here's the output we get when running the same code against the kernel from VS Code As you can see its just a stream of text

  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0:  92%|█████████▏| 1718/1874 [00:29<00:02, 58.67it/s, loss=2.044, v_num=17, acc=0.875]\n",
      "Validating: 0it [00:00, ?it/s]\u001b[A\n",
      "Epoch 0:  92%|█████████▏| 1721/1874 [00:30<00:02, 56.62it/s, loss=2.044, v_num=17, acc=0.875]\n",

Here's the output we get when running the same code against classic Jupyter Notebooks

  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Validation sanity check', layout=Layout…"
      ]
     },

Here's the Python code

import torch
from torch import nn
from torch.nn import functional as F

from pl_bolts.datamodules.mnist_datamodule import MNISTDataModule
import pytorch_lightning as pl
class LitLeNet(pl.LightningModule):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.conv1 = nn.Conv2d(1, 6, 5, padding=2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.classifier = nn.Linear(84, 10)

        self.accuracy = pl.metrics.Accuracy()
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, (2, 2))
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, (2, 2))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.classifier(x)
        x = F.softmax(x, dim=0)

        return x
    
    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters())
    
    def _shared_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = F.cross_entropy(y_hat, y)
        self.log('acc', self.accuracy(y_hat, y), prog_bar=True)
        return loss

    def training_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def test_step(self, batch, batch_idx):
        return self._shared_step(batch, batch_idx)
    
    def validation_epoch_end(self, outputs):
        self.log('acc', self.accuracy.compute(), prog_bar=True)
mnist = MNISTDataModule('./data', num_workers=2)
model = LitLeNet()

trainer = pl.Trainer(max_epochs=1)
trainer.fit(model, datamodule=mnist)

Yes, from DS we need ability to install python packages. Python knows how best to install extensions & the like, hence would like to leave that to python, let python do what it does best.

Yes, all of these are DS specific & belong elsehwere.

Thanks for filing this issue, please could you provide the entire sample code to replicate this.

@vherasme

Please could you open a new issue and provide the information on the issue template (logs, etc)

import os os.add_dll_directory("F:\movie") import speech_recognition as sr from google import google import pyttsx3 answers = { 'name': 'You should already know that, because you called me just now', 'yourself':'i am a virtual friend designed by ekansh jain ', 'marry':'sorry i am a virtual assistant', 'love':'i love someone else', 'hate':'Imao look at your face', 'children':'oh please', 'chat':'oh please i ignore you do not understand that', 'manu':'manu is a idiot person in this world take some distance from her its is better than you and me', 'wit':'it is better than we do not talk on this topic', 'google':'Google, LLC is an American multinational technology company that specializes in Internet-related services and products, which include online advertising technologies, a search engine, cloud computing, software, and hardware', 'corona':'''COVID-19 affects different people in different ways. Most infected people will develop mild to moderate illness and recover without hospitalization. Most common symptoms: fever dry cough tiredness Less common symptoms: aches and pains sore throat diarrhoea conjunctivitis headache loss of taste or smell a rash on skin, or discolouration of fingers or toes Serious symptoms: difficulty breathing or shortness of breath chest pain or pressure loss of speech or movement Seek immediate medical attention if you have serious symptoms. Always call before visiting your doctor or health facility. People with mild symptoms who are otherwise healthy should manage their symptoms at home. On average it takes 5–6 days from when someone is infected with the virus for symptoms to show, however it can take up to 14 days.''', 'shubh':'Ab ish chutiye ke bare me puch rhe ho toh suno itna dushro ki fikar krta he ki tujhe pta he yeh hua tujhe pta he voh hua etc etc', 'bye':'Do not disturb me again bye!'

} r=sr.Recognizer() r1=sr.Recognizer()

engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices')

engine.setProperty('voice', voices[1].id)

def SpeakText(command): engine=pyttsx3.init() engine.say(command) engine.runAndWait()

def solutionFinder(incomingText): answer=''

if 'your name' in incomingText:
        answer = answers['name']
    
elif 'yourself' in incomingText:
        answer = answers['yourself']

elif 'marry' in incomingText:
        answer = answers['marry']

elif 'love' in incomingText:
        answer = answers['love']

elif 'hate' in incomingText:
        answer = answers['hate']
    
elif 'children' in incomingText:
        answer = answers['children']
    
elif 'chat' in incomingText:
        answer = answers['chat']
    
    
elif 'manu' in incomingText:
        answer = answers['manu']
    
    
    
elif 'w i t' in incomingText:
        answer = answers['wit']

    
    
elif 'google' in incomingText:
        answer = answers['google']

    
    
elif 'corona' in incomingText:
        answer= answers['corona']
    
    
elif 'shubh' in incomingText:
        answer= answers['shubh']
    
    
elif 'bye' in incomingText:
        answer= answers['bye']
    
    
    
SpeakText(answer)

while(1): try:

    with sr.Microphone() as source2:
        r.adjust_for_ambient_noise(source2,duration=0.2) 

        print("Call me alex") 
        audio2 = r.listen(source2,phrase_time_limit=4)

        MyText =''
        MyText = r.recognize_google(audio2)
        MyText = MyText.lower()
        print(MyText)

        if 'alex' in MyText:
            SpeakText('tell me')
            r1.adjust_for_ambient_noise(source2,duration=0.2)
            print('Listening.....')
            audio3 = r1.listen(source2,phrase_time_limit=4)

            MyNewText = ''
            MyNewText = r1.recognize_google(audio3)
            MyNewText = MyNewText.lower()
            print("you said " + MyNewText)
            solutionFinder(MyNewText)

except sr.RequestError as e:
    print("could not request results : {0}".format(e))

except sr.UnknownValueError:
    pass

except sr.WaitTimeoutError:
    pass

For #

<!-- If an item below does not apply to you, then go ahead and check it off as "done" and strikethrough the text, e.g.: - [x] ~Has unit tests & system/integration tests~ -->

• [ ] Pull request represents a single change (i.e. not fixing disparate/unrelated things in a single PR).
• [ ] Title summarizes what is changing.
• [ ] Has a news entry file (remember to thank yourself!).
• [ ] Appropriate comments and documentation strings in the code.
• [ ] Has sufficient logging.
• [ ] Has telemetry for enhancements.
• [ ] Unit tests & system/integration tests are added/updated.
• [ ] Test plan is updated as appropriate.
• [ ] package-lock.json has been regenerated by running npm install (if dependencies have changed).
• [ ] The wiki is updated with any design decisions/details.

For #12253

• All changes are related to DS except the following
  • Moved core extension dependencies from devDependencies into dependencies. (sourcemap-support & @enonic/fnv-plus)
• Changed version of some packages (we were shipping one version, but package.json had another, shipping later versions due to other dependencies pulling later versions)
• Updated some type definitions (for some reason compiler would complain, as it was a noop i didn't dig into the reason)
• Removed some unnecessary type definitions

<!-- If an item below does not apply to you, then go ahead and check it off as "done" and strikethrough the text, e.g.: - [x] ~Has unit tests & system/integration tests~ -->

• [ ] Pull request represents a single change (i.e. not fixing disparate/unrelated things in a single PR).
• [ ] Title summarizes what is changing.
• [ ] Has a news entry file (remember to thank yourself!).
• [ ] Appropriate comments and documentation strings in the code.
• [ ] Has sufficient logging.
• [ ] Has telemetry for enhancements.
• [ ] Unit tests & system/integration tests are added/updated.
• [ ] Test plan is updated as appropriate.
• [ ] package-lock.json has been regenerated by running npm install (if dependencies have changed).
• [ ] The wiki is updated with any design decisions/details.

You might want to run this in the background. I.e.

public async activate(){
	this.activeInternal().catch(noop);
}

private async activateInternal(){
	....
}

Else this code blocks the startup perf of the extension. I.e. any code in IExtnesionSingleActivation.active method ends up running in the startup process of extension & as they are promises they are awaited on, meaning it slows down extension. We need to do this only where necessary.

Request for ability to hide output from notebooks.

Currently in the Python implementation of Notebooks, we have output that are hidden.

• Today these are neither displayed in our old Python Notebooks nor in Jupyter
• As far as I can tell, most of these outputs end up as Rich Displays, containing an object with a single key. However no real data for display purposes.
• The outputs are not displayed, however they are persisted on disck (i.e. they are part of the ipynb content).

Adding an issue for what was discussed (forgot to add this issue last week). @rebornix /cc

Why not 1.50

here so that the extension is up on notebook open.

Wouldn't the other extension activate Python extension instead? I.e. Python will get activated due to DS code getting activated when Notebook is open.

This is using the VS Code notebook APIs and not custom editors or webview directly, correct

Yes (used in output using VSCode notebooks API)

oes the python extension set any sort of content security polity in its content that could be blocking this?

This isn't a web server started by Python extension. When running %load_ext tensorboard, tensor flow starts the local web server.

With the Python extension:

• Assume you open a notebook
• Assume python extension is slow to return the list of kernels (hence slow to return the preferred kernel to VS Code).
• Next, click on the Run cell icon.
• VS Code now displays the kernel picker

It seems there's a race condition:

• When Run is clicked, VS Code doesn't yet have the preferred kernel from extension
• VSCode then calls extension to get the list of kernels & displays the kernel picker

Suggestion

• When user clicks Run, wait for first call to extension to complete before checking if extension provides a preferred kernel.

@rebornix /cc

<!-- ⚠️⚠️ Do Not Delete This! feature_request_template ⚠️⚠️ --> <!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ --> <!-- Please search existing issues to avoid creating duplicates. -->

<!-- Describe the feature you'd like. --> In the Python extension when enabling IPyWidgets:

• Some widgest such as beakerx (have calendar controls)
• These controls are floating HTML elements, however when they overflow the bounds of the widget, they end up getting displayed behind the monaco editor.
• Seems like a zindex issue (monaco editors) are dislayed over the HTML content, hence get displayed over outputs.

@rebornix /cc

Creating an issue to capture what was discussed last week with VS Code: Python extension would like the following capabilities:

• Hide output when notebook is not trusted (based on some metadata)
• Trusting a ntoebook (updating this metadata) should not mark the notebook as dirty
• Disable saving/editing notebooks when a notebook is not trusted
  • This is required, because of todays implementation of trusted notebooks in python extension)
  • Today if a user attempts to save an untrusted notebook, Python extension would have to handle this and swallow this method call (i.e. noop).

@rebornix /cc

Wrong container. The container is extension-builds-jupyter

Why not initialize env var for smoke tests too in ci job or is code that launch smoke tests instead of hardcoding the file extension. Ie have one way to go this.

Not a mandatory change.

We can't hardcode this. As discussed recently, this must be done only at build time. Else anyone working on this extension (dev time) will need to install the Jupyter extension. Ie core devs will always have to install this extension to do their dev work, when it's not necessary. We need to update package.json only when building the vsix

Probably good to discuss the requirements and the api, on teams

interface IKernelSpec { // Fields of a jupyter kernelspec.json // Maybe live id for an already running kernel? }

Doesn't the kernel model contain information about a running kernel. Is that what you mean by live Id? I'd prefer to use the existing Jupyter types where possible.

Let an extension send code to this interactive window (This would likely be useful for other language extensions)

Wouldn't it be better for other languages to provide the cell locstions instead? This way we can add the code lenses, and execute things ourselves and support all of the shortcuts. That's how I did it in the past. Is easier for other languages to integrate. And we won't have to expose execution of code.

Woulf be good to know their requirements here as well.

Lol, Remove a lot of unnecessary dependencies, and there was just one depednency that had been removed in the original PR... Fixed now.

made a mistake with npm uninstall, used commas. Submitted a fix.

Wait, i thought i removed all of this, Thanks for commenting on this.

We need to bring back the installers. Python extension is responsible for installing python packages (these were deleted, but required).

Damn it.

We cannot do this, Python extension will not load at all. None of the smoke tests, vscode tests will work. We need to do what we have done to ds, where we install the other extension..