Streamlit vs Django and Flask

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When it comes to creating lightweight, interactive data apps, Streamlit outperforms traditional and more common frameworks like Django and Flask with its developer-friendly design and the extremely simple ways to get things done. Django and Flask cater to a broader range of web applications, but their abstractions often require complex configurations which are really not needed by a beginner developer, such as convoluted routing, detailed front-end integrations, and having to create several views for each function you require, all of which can seem like a tall task for developers looking for prototypes or data centric web apps.
Streamlit discards languages traditionally required by Django and Flask, such as HTML, CSS, or Javascript, and functions only using python. Methods like st.write(), st.plotly_chart(), and st.dataframe(), help in making user-friendly and interactive data visualizations effortless. Devs can make entire web apps in the amount of time it takes to set up the homepage.html in Flask, using built-in widgets like sliders, text inputs, and interfaces to upload files, without the need for external dependencies. For example, rendering a Pandas DataFrame or integrating Matplotlib charts require only a couple lines of code in Streamlit, whereas in Flask or Django, similar functionality demands a combination of templating in HTML, beautification through CSS, and JavaScript for the interactive experiences.
The Streamlit Community Cloud helps to take this ease of use to another level. Publishing apps is as simple as linking a GitHub repository, whether that was made in codespaces or uploaded from a local IDE—no need for finding an appropriate publishing platform, manual server configurations, or complex deployment pipelines. With the cloud's automated setup, sharing apps around becomes painless, allowing creators to focus on improving the app rather than spending countless hours trying to host it on a third-party server.
However, a limitation of Streamlit’s Community Cloud is its strict reliance on either the requirements.txt (for pip) or environment.yml (for conda) files for dependency management. If a necessary package is unsupported or improperly configured, developers cannot manually intervene via console access, potentially blocking deployment. For example, let’s say you want to use the dlib dependency in your program. Dlib through pip is not supported in the Streamlit Community Cloud. So you switch to using conda for dlib. Now, numpy through conda isn’t supported in the Streamlit Community Cloud. This dependency rigidity emphasizes Streamlit’s trade-off between simplicity and control.

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