Codes are generated but not run by jupyter

[DPCau]

I've run this to start jupyter server

(codeact) xxxxxxx@xxx code-act % ./scripts/chat/code_execution/start_jupyter_server.sh 8081
JUPYTER_API_PORT=8081
~/MyProject/code-act/scripts/chat/code_execution ~/MyProject/code-act
INFO:root:Using Docker as the backend for JupyterGateway

And run the ui

(codeact) xxxxxxx@xxx chat-ui % npm run dev -- --open

> chat-ui@0.6.0 dev
> vite dev --open



  VITE v4.3.9  ready in 986 ms

  ➜  Local:   http://localhost:5173/
  ➜  Network: use --host to expose
  ➜  press h to show help
Browserslist: caniuse-lite is outdated. Please run:
  npx update-browserslist-db@latest
  Why you should do it regularly: https://github.com/browserslist/update-db#readme
(node:73526) Warning: Failed to load the ES module: /Users/xxxxxxx/MyProject/code-act/chat-ui/tailwind.config.cjs. Make sure to set "type": "module" in the nearest package.json file or use the .mjs extension.
(Use `node --trace-warnings ...` to show where the warning was created)
(node:73526) Warning: Failed to load the ES module: /Users/xxxxxxx/MyProject/code-act/chat-ui/tailwind.config.cjs. Make sure to set "type": "module" in the nearest package.json file or use the .mjs extension.

Then I could open http://localhost:5173/ to the ui
However when I tried the question Can you create 100 random data points (each with a dimension of 2) and create a scatter plot. Run a k-means to cluster them and visualize it.
The CodeActAgent-Mistral-7b-v0.1 model give response as

To create 100 random data points with 2 dimensions each and visualize a scatter plot, we can use NumPy and Matplotlib.

First, let's import the necessary libraries and create the random data points.
<execute>
import numpy as np
import matplotlib.pyplot as plt

data = np.random.rand(100, 2)

Then the response is over
I guess it's because that the jupyter had something wrong when running
So the code with <execute> were not running correctly
And the following response stop

Is there anyone who has met this problom as well