Streamlit Implementation

This section provides the steps to implement the Streamlit framework to build a web application using the Python Stack.

Streamlit App

Prerequisites

Before starting to implement the Streamlit framework using Python Stack, ensure you have met the requirements listed in this section.

Step 1. Create/Select a Repository

Create or select any repository in GitHub or Bitbucket where the Python code will be added.

Step 2. Create base directory

Inside that repository, add a folder named app, which will include the Python scripts with the .py extension, a requirements file with a .txt extension, and .streamlit folder with config.toml file, which will include Streamlit-specific configurations.

python-stack/
└── app/                        # baseDir (referenced in service/stack manifest)
    ├── main.py                 # Application entry point (required)
    ├── requirements.txt        # Dependencies (required)
    ├── app_ui.py               # Streamlit UI logic
    └── .streamlit/             # Streamlit-specific configuration folder
        └── config.toml         # Custom Streamlit configuration

Step 3. Add the Python code and dependencies

In the files with the .py extension, add your Python script, in the requirements.txt file, add the requirements to run the Python scripts, and in the config.toml, add streamlit-specific configurations as given in the example below.

Example:

app_ui.pymain.pyrequirements.txtconfig.toml

# app_ui.py
import streamlit as st

def run_app():
    st.set_page_config(page_title="Demo App", layout="centered")

    st.title("🚀 Streamlit App on DataOS Stack")
    st.write("This is a simple Streamlit app deployed using a custom Python 3.12 stack.")

    name = st.text_input("Enter your name:")
    if name:
        st.success(f"Hello, {name}! Welcome to DataOS Streamlit deployments.")

# main.py
import os
from app_ui import run_app  # Import the user's Streamlit app

def main():
    # Check if already running inside Streamlit to avoid recursion
    if not os.getenv("IS_RUNNING_STREAMLIT"):
        os.environ["IS_RUNNING_STREAMLIT"] = "1"
        os.system(f"streamlit run {__file__}")
        return

    # Call the actual app function
    run_app()

if __name__ == "__main__":
    main()

streamlit==1.45.0
pandas==2.2.2
numpy>=1.24.0
requests

# .streamlit/config.toml
[server]
port = 8050
address = "0.0.0.0"
baseUrlPath = "test_sample"

Step 4. Push the changes

Once the repository and the code are ready to be deployed, push the changes by executing the commands below.

git add .
git commit -m "chore: provision repo with baseDir, script, and requirements file"
git push -u origin main

Step 5. Secure repository credentials within DataOS

If the app code is in a private Git repository, credentials must be secured in DataOS using secrets. These secrets are referred to during repository sync:

• GitHub: Create a Personal Access Token (PAT), then store it in DataOS as a secret. Guide | GitHub PAT docs
• Bitbucket: Generate an App Password (API token) with repo permissions, then store it in DataOS as a secret. Guide | Bitbucket docs

Step 6. Create a Python Service manifest file

Create a Service Resource manifest file using the template given below by replacing the placeholders with the actual values.

name: python-service
version: v1
type: service
tags:
  - service
  - python-stack
  - dataos:type:resource
  - dataos:resource:service
  - dataos:layer:user
  - dataos:workspace:public
description: Python Service Sample
owner: iamgroot
workspace: public
service:
  servicePort: 8050
  ingress:
    enabled: true
    path: /test_sample
    noAuthentication: true
  replicas: 1
  stack: python3:1.0
  logLevel: INFO
  dataosSecrets:
    - name: bitbucket-cred
      allKeys: true
      consumptionType: envVars
  compute: runnable-default
  resources:
    requests:
      cpu: 1000m
      memory: 1536Mi
  stackSpec:
    repo:
      baseDir: queries/pystack-streamlit
      syncFlags:
        - '--ref=main'
      url: https://bitbucket.org/queries/

Step 7. Apply the Service manifest file

Apply the Service manifest file by executing the command below.

dataos-ctl resource apply -f ${{path-to-manifest-file}}

Step 8. Verify the Service

Validate the Service by executing the command below.

dataos-ctl resource get -t service -n ${{service-identifier}} -w ${{workspace}}

Example:

dataos-ctl resource get -t service -w public -n my-python-app-test
# Expected output:
INFO[0000] 🔍 get...                                     
INFO[0001] 🔍 get...complete                             

         NAME        | VERSION |  TYPE   | WORKSPACE | STATUS |  RUNTIME  |    OWNER     
---------------------|---------|---------|-----------|--------|-----------|--------------
  my-python-app-test | v1      | service | public    | active | running:1 | iamgroot  

Step 9. Validate the code execution

To validate if the Python script executed without any errors, run the following command.

dataos-ctl resource log -t service -w public -n ${{service-identifier}}

Example

dataos-ctl resource log -t service -w public -n my-python-app-test
INFO[0000] 📃 log(public)...                             
INFO[0001] 📃 log(public)...complete                     

                  NODE NAME                  │        CONTAINER NAME        │ ERROR  
─────────────────────────────────────────────┼──────────────────────────────┼────────
  my-python-app-test-6831-d-57cbf95b7c-zz4j9 │ my-python-app-test-6831-main │        

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Installing collected packages: pytz, watchdog, urllib3, tzdata, typing-extensions, tornado, toml, tenacity, smmap, six, rpds-py, pyarrow, protobuf, pillow, packaging, numpy, narwhals, MarkupSafe, idna, click, charset_normalizer, certifi, cachetools, blinker, attrs, requests, referencing, python-dateutil, jinja2, gitdb, pydeck, pandas, jsonschema-specifications, gitpython, jsonschema, altair, streamlit
Successfully installed MarkupSafe-3.0.2 altair-5.5.0 attrs-25.3.0 blinker-1.9.0 cachetools-5.5.2 certifi-2025.8.3 charset_normalizer-3.4.3 click-8.2.1 gitdb-4.0.12 gitpython-3.1.45 idna-3.10 jinja2-3.1.6 jsonschema-4.25.1 jsonschema-specifications-2025.9.1 narwhals-2.4.0 numpy-2.3.2 packaging-24.2 pandas-2.2.2 pillow-11.3.0 protobuf-6.32.0 pyarrow-21.0.0 pydeck-0.9.1 python-dateutil-2.9.0.post0 pytz-2025.2 referencing-0.36.2 requests-2.32.5 rpds-py-0.27.1 six-1.17.0 smmap-5.0.2 streamlit-1.45.0 tenacity-9.1.2 toml-0.10.2 tornado-6.5.2 typing-extensions-4.15.0 tzdata-2025.2 urllib3-2.5.0 watchdog-6.0.0
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.

[notice] A new release of pip is available: 25.0.1 -> 25.2
[notice] To update, run: pip install --upgrade pip

Collecting usage statistics. To deactivate, set browser.gatherUsageStats to false.

  You can now view your Streamlit app in your browser.

  URL: http://0.0.0.0:8050/test_sample_st01

Step 10. Access the web app

Once the Service starts running, users can access the web app using the base URL configured in config.toml as shown in the example below.

https://${{dataos-fqdn}}/${{baseUrlPath}}/

Example:

https://liberal-katydid.dataos.app/test_sample/