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Another fresh python dashboard. Really cool. We need more of these for our Python Apps.

by srbhr 3y ago

jairuhme 3y ago
Do we? I struggle to find anything that really differentiates all of these different products. Streamlit, Gradio, etc. are all doing the same thing. What would be the benefit of yet another way to get around writing HTML/CSS/JS? These tools are great for quick POC's, but from what I have seen, none of them are great production ready apps
- Alyx1337 3y ago
  If this is really your sentiment, I strongly invite you to try out Taipy. This was exactly our reaction when we decided to build Taipy. Streamlit was already somewhat popular, but it would always fail at the production stage when we tried using it for consulting missions. Any application in production generally has a significant workload in the back-end, multiple pages, and users. Streamlit's approach of re-running your code outside cached variables limits it to POCs, as you said. That is why we created Taipy. We wanted an easy-to-learn Python library to create front-end for data applications while remaining production-ready: we use callbacks for user interactions to avoid re-running unnecessary code. Front and back-end run on separate threads so your app does not freeze whenever a computation runs. We also focus on providing pre-built components to allow the end-user to play around with data pipelines quickly. These components allow the user to visualize the data pipeline in a DAG, input their data, run pipelines, and visualize results...
- JRoMed 3y ago
  I think Taipy is excellent for getting around HTML/JS; that's true, but it is not only that. Here are a few Taipy functionalities that I found handy for applications to be used in production by end-users: - For example, the scenario and data management feature helps end-users properly manage their various business cases. We can easily configure scenarios to model recurrent business cases. I am thinking of standard industry projects like production planning, demand or inventory forecasting, dynamic pricing, etc. An end-user can easily create and compare KPIs of multiple scenarios over time (e.g., a new demand forecast every week) and multiple scenarios for the same time period for what-if analysis for instance. - The version management is also a good example. Besides a development mode and an experiment mode for testing, debugging, and tuning my pipelines, a specific production mode is designed to easily operate application version upgrades. It helped me deploy a new release of my Taipy application in a production environment including some data compatibility checks and eventually some data migration. I don't know any other system that helps manage application versions, pipeline versions, and data versions in a single tool. Plus it's really easy to use with git releases for instance. - The pipeline orchestration is also very production-oriented for multi-user applications. You have visual elements for submitting pipelines, managing job executions, tracking successes and failures, historizing user changes, etc. Which is more than helpful in a multi-user environment. Everything is built-in Taipy.
- stkiller 3y ago
  Have you ever delivered a Python project (AI or not) with a multi-page GUI, multiple end-users, and dynamic graphics? Well, we failed completely with Streamlit. Gradio is even more limited for this. Streamlit and even more so Gradio are simple tools. They won't make the mark for such projects. They lack so many things: - not really multi-user - event loop is inefficient and creates side effects - no support for large data in graphics - difficult/impossible to call asynchronous functions (u get stuck in the GUI while waiting for the job to complete) - fixed layout / no real way to customize the look&feel - etc. Don't get me wrong: Streamlit has benefits and was actually the first package to offer Python devs a low-code approach for building GUIs (for non-GUI specialists).

Another fresh python dashboard. Really cool. We need more of these for our Python Apps.

by srbhr 3y ago

Another fresh python dashboard. Really cool. We need more of these for our Python Apps.

Do we? I struggle to find anything that really differentiates all of these different products. Streamlit, Gradio, etc. are all doing the same thing. What would be the benefit of yet another way to get around writing HTML/CSS/JS? These tools are great for quick POC's, but from what I have seen, none of them are great production ready apps

Alyx1337 3y ago
If this is really your sentiment, I strongly invite you to try out Taipy. This was exactly our reaction when we decided to build Taipy. Streamlit was already somewhat popular, but it would always fail at the production stage when we tried using it for consulting missions. Any application in production generally has a significant workload in the back-end, multiple pages, and users. Streamlit's approach of re-running your code outside cached variables limits it to POCs, as you said. That is why we created Taipy. We wanted an easy-to-learn Python library to create front-end for data applications while remaining production-ready: we use callbacks for user interactions to avoid re-running unnecessary code. Front and back-end run on separate threads so your app does not freeze whenever a computation runs. We also focus on providing pre-built components to allow the end-user to play around with data pipelines quickly. These components allow the user to visualize the data pipeline in a DAG, input their data, run pipelines, and visualize results...
JRoMed 3y ago
I think Taipy is excellent for getting around HTML/JS; that's true, but it is not only that. Here are a few Taipy functionalities that I found handy for applications to be used in production by end-users: - For example, the scenario and data management feature helps end-users properly manage their various business cases. We can easily configure scenarios to model recurrent business cases. I am thinking of standard industry projects like production planning, demand or inventory forecasting, dynamic pricing, etc. An end-user can easily create and compare KPIs of multiple scenarios over time (e.g., a new demand forecast every week) and multiple scenarios for the same time period for what-if analysis for instance. - The version management is also a good example. Besides a development mode and an experiment mode for testing, debugging, and tuning my pipelines, a specific production mode is designed to easily operate application version upgrades. It helped me deploy a new release of my Taipy application in a production environment including some data compatibility checks and eventually some data migration. I don't know any other system that helps manage application versions, pipeline versions, and data versions in a single tool. Plus it's really easy to use with git releases for instance. - The pipeline orchestration is also very production-oriented for multi-user applications. You have visual elements for submitting pipelines, managing job executions, tracking successes and failures, historizing user changes, etc. Which is more than helpful in a multi-user environment. Everything is built-in Taipy.
stkiller 3y ago
Have you ever delivered a Python project (AI or not) with a multi-page GUI, multiple end-users, and dynamic graphics? Well, we failed completely with Streamlit. Gradio is even more limited for this. Streamlit and even more so Gradio are simple tools. They won't make the mark for such projects. They lack so many things: - not really multi-user - event loop is inefficient and creates side effects - no support for large data in graphics - difficult/impossible to call asynchronous functions (u get stuck in the GUI while waiting for the job to complete) - fixed layout / no real way to customize the look&feel - etc. Don't get me wrong: Streamlit has benefits and was actually the first package to offer Python devs a low-code approach for building GUIs (for non-GUI specialists).