https://github.com/alro10/streamlit-time-series
API using streamlit for Time Series Analysis
https://github.com/alro10/streamlit-time-series
cloud cloud-api deployment docker-app docker-deployment ibm-cloud machine-learning-deploy streamlit streamlit-dashboard streamlit-docker time-series-analysis
Last synced: 3 months ago
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API using streamlit for Time Series Analysis
- Host: GitHub
- URL: https://github.com/alro10/streamlit-time-series
- Owner: Alro10
- License: apache-2.0
- Created: 2020-07-09T01:39:54.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-26T20:57:25.000Z (almost 6 years ago)
- Last Synced: 2025-10-25T06:29:10.127Z (7 months ago)
- Topics: cloud, cloud-api, deployment, docker-app, docker-deployment, ibm-cloud, machine-learning-deploy, streamlit, streamlit-dashboard, streamlit-docker, time-series-analysis
- Language: Python
- Homepage:
- Size: 1.04 MB
- Stars: 11
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# streamlit time series API
This is a basic API made using streamlit to analyze time series. You can see the deployment, hosted on IBM cloud - Kubernetes Cluster, here: **http://159.122.181.167:30498/**
## Quick run
- ```virtualenv venv -p python3.7```
- ```source venv/bin/activate```
- ```pip install -r requirements.txt```
Go to src directory
- ```streamlit run app.py```
## Docker
- ```docker build -t streamlit-ts-ml:0.1.0 -f Dockerfile .```
- ```docker run -p 8501:8501 streamlit-ts-ml:0.1.0```
Then stop the process with the following command.
```shell
$ docker kill
$
```
## IBM Cloud - Deploy
- If you need help for deploying on Kubernetes:
- For tag and push (docker image) to container registry:
- ```docker tag streamlit-ts-ml:0.2.0 us.icr.io/ml-api/streamlit-ts-ml:0.2.0```
- ```docker push us.icr.io/ml-api/streamlit-ts-ml:0.2.0```
Go to deploy directory and run the following commands:
- For deployment:
- ```kubectl apply -f deployment.yaml```
- For service:
- ```kubectl apply -f service.yaml```
Do not feel angry if the first try does not work, kubernetes and cloud is not too easy...but the result is really awesome!