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You can build a small AI app in the Linux development environment on a supported Chromebook and connect it to a model running on that same device. The simplest route is to install a local model runtime, then write a short program that sends prompts to its local HTTP API. “Locally” here means your code and model runtime run in ChromeOS’s Linux container; it does not guarantee cloud-free operation for every setup, or that a particular Chromebook can run a model at a useful speed. The original query says “Googlebook”; this guide treats that as a typo for Chromebook.
What you will build—and what “local” means
This guide builds a command-line app in Python. It accepts a prompt, sends it to Ollama at http://localhost:11434/api/generate, and prints the model’s response. The API call is local when Ollama is running on the same Chromebook, so the app does not need a cloud model provider to generate a response. Your initial software downloads and model download still need internet access.
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There are three separate pieces: ChromeOS hosts the Linux development environment; Ollama runs the model and exposes an API; your Python program is the app that sends requests to that API. Keeping them separate helps diagnose problems: a missing model is different from a stopped runtime, and either is different from a bug in your app.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problems| Approach | Where the prompt is processed | Internet dependence | Main trade-off |
|---|---|---|---|
| Local model through Ollama | On the Chromebook, if the model and runtime are installed and running locally. | Needed to install software and obtain a model; the local request itself goes to localhost. | Uses the Chromebook’s available resources. Google says hardware acceleration, including GPU and video decode, is unsupported in Linux on ChromeOS, so performance and feasibility vary by device and model. |
| Cloud model API | At the provider’s service, rather than on the Chromebook. | Required for requests to the provider. | Can avoid running a model locally, but the app depends on the service and its API configuration. This is not an all-local app. |
The API route is useful for learning how an app and a model communicate. It is not by itself a production service: it has no user authentication, deployment setup, or operational safeguards for serving other people.
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Check Chromebook support and enable Linux
Google’s Chromebook Help describes Linux as a feature for software development and says setup opens a Debian environment. Linux is off by default. Google’s setup estimate is 10 minutes or more, and a work- or school-managed Chromebook may have Linux disabled by its administrator.
- Open Settings → About ChromeOS → Developers.
- Choose Linux development environment and follow the setup prompts.
- When setup completes, open the Terminal app. The terminal is the shell inside the Linux environment, not a shell with unrestricted access to all ChromeOS files.
If the Linux option is missing or blocked, check with the device administrator if the Chromebook is managed. Do not assume that a newer Chromebook or a particular product tier supports Linux: verify the setting on your own device.
Prepare a project directory and update Debian packages
Linux runs in a container. Use a folder in the Linux files area for the project, or share a folder with Linux using the ChromeOS Files app. Google documents shared folders as available inside Linux under /mnt/chromeos. A directory shared with Linux is accessible to Linux apps, so choose a project folder rather than sharing more personal files than the app needs.
- In Terminal, refresh Debian’s package list with
sudo apt update. - Create and enter a project directory in Linux’s home area:
mkdir -p ~/local-ai-app && cd ~/local-ai-app. - Install Python 3 if it is not available:
sudo apt install python3. APT is the package manager Google documents for installing Linux tools. Follow any confirmation prompt shown in Terminal.
If you prefer to edit files in the ChromeOS Files app, share the containing folder with Linux first; then locate it under /mnt/chromeos. For routine development, keeping the project in Linux files avoids sharing a broader ChromeOS folder than necessary. Back up work that matters independently of the container; the setup guidance does not promise that a container is a backup system.
Install Ollama and obtain a model
Ollama provides Linux installation instructions. Its documented install command is:
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curl -fsSL https://ollama.com/install.sh | shThis downloads and runs a shell script from the vendor. The command is shown in Ollama’s current Linux instructions; it has not been tested on every Chromebook or ChromeOS Linux configuration. If you are unsure about running a downloaded script, review the vendor’s instructions and script before proceeding. A successful installation on one Linux system does not establish that every Chromebook has enough resources or identical service behavior.
After installation, use the current Ollama model library to choose a model and confirm its exact identifier. Then, in Terminal, run ollama pull followed by that identifier, for example by typing ollama pull and appending the model name you selected. No particular model size or identifier is prescribed here: the available resources and results depend on the Chromebook and model, and Google’s documentation does not establish a minimum specification or speed threshold for this workflow.
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Keep the exact identifier for the next step. A display name, an outdated name, or a spelling difference may not resolve to the model you downloaded. If Ollama reports that the model is unavailable, check the current library entry and use its exact identifier rather than guessing.
Check the local generation API before writing the app
Ollama documents a generation endpoint at http://localhost:11434/api/generate. Its API streams results by default. Set "stream": false to request one response object, which is easier for a beginner’s command-line app to parse.
Once Ollama is installed and the model has been pulled, test the endpoint from Terminal. Replace MODEL_ID in this example with the exact identifier you chose:
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curl http://localhost:11434/api/generate -d '{
"model": "MODEL_ID",
"prompt": "Explain what a local API is in one sentence.",
"stream": false
}'A successful request returns JSON containing the generated response. If the request cannot connect, the runtime may not be running or listening at that address. If it connects but reports a model problem, verify that the identifier matches a model you have pulled. These checks isolate the runtime and model from your own code.
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Create app.py in your project directory. You can use a terminal editor, or create the file in a text editor and save it in the Linux project folder. This example uses Python’s standard library, accepts the model identifier through an environment variable, sends one non-streaming request, and prints the returned response.
import json
import os
import sys
import urllib.error
import urllib.request
MODEL = os.environ.get("OLLAMA_MODEL")
ENDPOINT = "http://localhost:11434/api/generate"
if not MODEL:
sys.exit("Set OLLAMA_MODEL to the exact model identifier you pulled.")
prompt = input("Prompt: ").strip()
if not prompt:
sys.exit("Enter a prompt and try again.")
payload = json.dumps({
"model": MODEL,
"prompt": prompt,
"stream": False,
}).encode("utf-8")
request = urllib.request.Request(
ENDPOINT,
data=payload,
headers={"Content-Type": "application/json"},
method="POST",
)
try:
with urllib.request.urlopen(request, timeout=120) as response:
result = json.load(response)
except urllib.error.URLError as error:
sys.exit(f"Could not reach Ollama at {ENDPOINT}: {error}")
except json.JSONDecodeError:
sys.exit("Ollama returned a response that was not valid JSON.")
answer = result.get("response")
if not isinstance(answer, str):
sys.exit("The response did not contain the expected 'response' text.")
print("n" + answer)Set the environment variable to your actual model identifier, then run the app from the same Linux terminal:
export OLLAMA_MODEL='your-exact-model-identifier'
python3 app.pyAt the Prompt: line, enter a question and press Enter. The code sends a JSON POST request with the model, prompt, and non-streaming setting; it then reads the JSON response and displays its response field. The timeout is a wait limit in this example, not a claim about how quickly a Chromebook or model will answer. If the model takes longer, you can adjust the timeout in the code, but a longer wait does not make a device capable of running a model it cannot handle.
Choose a ready-made interface or keep your own app
If you want a browser interface instead of writing a frontend, Open WebUI describes itself as a self-hosted platform that supports Ollama and OpenAI-compatible APIs. Its documented Python path is pip install open-webui followed by open-webui serve; the main page lists localhost:8080 for that path. Its Docker examples use localhost:3000. The address depends on the installation mode.
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| Choice | Best fit | Customization and provider choice |
|---|---|---|
| Your small Python app | Learning the request/response flow, building a narrow tool, or making a custom interface. | You control the app code. The example calls Ollama’s local API; using a cloud API instead requires that provider’s configuration and means prompts are processed by that provider. |
| Open WebUI | Trying a ready-made self-hosted interface without building the interface yourself. | Its documentation says it supports Ollama and OpenAI-compatible APIs. Installation and browser address differ by method. |
These are different layers: Open WebUI is an interface, while Ollama supplies a model runtime and API. A ready-made interface does not remove the Chromebook’s Linux hardware limits. Open WebUI’s documentation includes Python and Docker paths, but its install examples are not evidence that Docker or GPU acceleration will work well on every Chromebook. For production deployments, Open WebUI advises pinning a specific image version instead of relying on a floating tag.
Understand ChromeOS Linux limits and permissions
Google states that hardware acceleration in Linux on ChromeOS is unsupported, including GPU acceleration and video decode. A local model may therefore be slow, impractical, or unable to run on a particular device. Small models may be more practical than larger ones, but the documentation cited here does not establish a model-size threshold or guarantee performance. Try a model only after checking its current requirements, and judge it on your own device rather than extrapolating from another Chromebook.
Google describes Linux apps as sharing the Linux container’s sandbox and permissions. Files and folders shared with Linux are available to Linux apps. USB or microphone access may require enabling access in device settings; Google lists cameras and Android emulators as unsupported in this environment. If your app needs a peripheral, check the relevant ChromeOS device setting and support status before designing the workflow around it.
- Keep access narrow: share only the folders the project needs, and consider what data the app can read from them.
- Separate development from deployment: a program running in your Linux container is not automatically a public service. Exposing an app to other users requires separate decisions about access control, updates, network configuration, and the data it handles.
- Protect project data: keep an independent backup of source files or other important work. A shared folder improves access between ChromeOS and Linux; it does not itself provide backup or recovery.
Troubleshoot the common failure points
Linux setup is missing or cannot be enabled
Check whether the Chromebook is managed by a work or school administrator, since the feature may be restricted. Confirm that you are in Settings → About ChromeOS → Developers. If the option remains unavailable, the device’s eligibility or administrative policy is the blocker; commands in Terminal cannot enable a feature that the administrator has disabled.
APT cannot update or install a package
Confirm that the Linux environment has internet access, then rerun sudo apt update before installing packages. Read the complete error message: a network or repository failure differs from a package-name error. Google documents APT as the method for package installation in the Linux environment.
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The local API request cannot connect
Check that Ollama installed successfully and is running in the Linux environment, then retry the non-streaming curl request. Make sure the address is exactly http://localhost:11434/api/generate. The endpoint is local to the environment; this example is not a request to a hosted URL.
The API responds but says the model is unavailable
Check that the model has finished downloading and that the name in the request exactly matches the identifier you pulled. Update OLLAMA_MODEL in the current terminal with export OLLAMA_MODEL='exact-id', then run the app again.
The program receives an error or waits a long time
First run the API test independently to see whether the issue is in the runtime or Python code. If the test works, check that Python is running the saved file from the intended directory and that the model environment variable is set. A long response time may reflect device or model limits; the timeout in the sample only controls how long the program waits for a response.
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For a project stored outside Linux files, share its folder with Linux in the ChromeOS Files app and use its path under /mnt/chromeos. For USB or microphone access, check ChromeOS device settings. Do not design around camera access or Android emulators in Linux on ChromeOS, which Google lists as unsupported.
What to do before other people rely on the app
The example is a local learning app, not a ready-to-deploy service. Before allowing others to use an application, decide how users reach it, what data it can access, how requests are controlled, and how code and dependencies are updated. Do not assume that localhost access means the app is reachable from other devices, or that a development container supplies authentication or production operations.
If you deploy Open WebUI with Docker, follow its current deployment guidance; its quick-start documentation recommends pinning a specific image version for production rather than using a floating tag. More generally, verify current vendor install instructions and model identifiers at the time you set up the software because commands, catalogs, UI locations, and support can change.
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