What is Streamlit? A Beginner's Guide to Build and Share Apps

What is Streamlit​?

Streamlit is a tool that allows you to quickly turn Python scripts into interactive web apps, cutting down development time from weeks to just minutes. You can create dashboards, generate reports, or build chat apps with ease. After developing your app, Streamlit's Community Cloud platform enables you to deploy, manage, and share your app seamlessly.

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Core Features of Streamlit

Now, let’s take a closer look at some of the core features of Streamlit that make it a useful tool for developers and data scientists.

• Simple and Intuitive API

Streamlit is designed to be easy to use, especially for Python users. You don’t need to be a web developer to build an app. With simple Python syntax, you can add interactive elements like sliders, buttons, and plots, allowing you to focus more on your data and models instead of learning complicated web frameworks.

• Live Code Editing

Streamlit’s live code editing feature lets you see your changes instantly. As you modify your code, the app updates automatically, saving you time and eliminating the need to refresh or restart the application. This real-time feedback helps you quickly experiment and test ideas.

• Built-in Widgets and Components

Streamlit comes with a number of pre-installed widgets and components that facilitate the addition of interactive elements to your application. Without requiring a lot of coding, these tools enable you to develop more engaging user interfaces with input forms, custom HTML elements, and visualizations.

• Seamless Deployment

Once your app is ready, deploying it is simple. Streamlit supports popular hosting platforms like Heroku, AWS, and Google Cloud. You can deploy your app with just a few commands, allowing you to share it easily with colleagues, clients, or the public.

• Community and Ecosystem

Streamlit has an active community of users and contributors, offering plenty of resources and support. The ecosystem around Streamlit continues to grow, with third-party libraries and templates available to extend its functionality and help you build better apps.

How to Use Streamlit?

Apart from the key features, here’s how you can easily get started with Streamlit and begin building your interactive apps:

1. Install Streamlit

To start, you need to install Streamlit on your system. The installation process is straightforward and works across different operating systems.

• Windows: You can use Anaconda or Python’s package manager, pip, to install Streamlit.
• macOS: You’ll use pip to install Streamlit and may also need Pipenv to manage dependencies.
• Linux: Similarly, you’ll install pip first and then use it to install Streamlit.

The installation commands are simple and can be executed through the terminal or command prompt.

2. Create a New Project

Once Streamlit is installed, you can start a new project:

• Set up a folder: Create a folder on your computer where you’ll store the files for your app.
• Create a new file: Inside the folder, create a new Python file (usually with a .py extension). This file will contain the structure of your app.

All you need to do is define your app's components in this file.

3. Add Elements to Your App

Streamlit is highly flexible, allowing you to include various types of content in your app. Some of the most common elements you can add are:

• Text: You can add different text elements to your app:
• Titles: Add a main title to your app using simple commands.
• Subtitles: Use headers and subheaders to organize sections of your app.
• Formatted text: You can use Markdown for richer text formatting such as bold, italic, and bullet points.
• Media: Streamlit makes it easy to display images, videos, and audio. You can either display static images or video content that users can play directly from your app.
• Graphs and Charts: For data-driven apps, Streamlit allows you to display interactive charts like line graphs, bar charts, and even area charts. This is ideal for visualizing data and making it more engaging for users.

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4. Use Interactive Widgets

Streamlit's standout feature is its wide variety of interactive widgets. These allow you to add interactivity to your app. You can interact with the app using:

• Buttons: Let users click a button to trigger actions or changes in the app.
• Checkboxes: Enable or disable features based on user selection.
• Sliders: Users can slide to select a value, ideal for setting a range.
• Input fields: Allow users to enter text or numbers. This can be useful for search bars, form submissions, or data input.
• Radio buttons and select boxes: These are used to select a single item from a list of options.

These widgets make your app dynamic, allowing the content to change based on user input.

5. Run the App

Once you’ve added all the components, you can run your app with a simple command. After running the app, Streamlit will open it in a web browser.

• Local testing: The app will run locally on your computer, and you can see how it functions in real-time. You can interact with the widgets and see how they affect the content of the app.

Streamlit will automatically update the app as you make changes to the code, so you don't have to worry about restarting the app manually. This is great for rapid testing and development.

6. Organize the Layout

Streamlit provides tools to organize your app’s layout effectively. You can use:

• Sidebars: A sidebar is a vertical panel that stays on the side of your app. It’s useful for displaying controls, settings, or additional information that doesn’t need to take up space on the main screen.
• Containers and Columns: You can use containers to group related content together and columns to divide your page into different sections. This helps keep the user interface organized and easy to navigate.
• Tabs and Expander Widgets: For even better organization, you can use tabs or expandable sections to hide or show content based on user interaction.

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7. Customize the Look and Feel

While Streamlit’s default look is simple and clean, you can customize the appearance of your app to suit your style. You can:

• Use themes: Streamlit offers a range of built-in themes to help you style your app. You can adjust the color scheme, fonts, and layout to match your brand or project theme.
• Custom styling: For more advanced styling, you can use custom CSS. This allows you to tweak every aspect of your app’s appearance.

Streamlit also supports dark mode, which you can toggle if you prefer a darker interface.

Steps to Build a Machine Learning Application

Let's take an example of building a loan prediction app to predict loan approval based on factors like income, loan amount, and credit history. Here are the steps:

• Understand the Problem

The goal of this project is to predict whether a loan application will be approved or rejected based on the applicant’s financial and personal details. You'll develop a machine learning model to make this prediction.

• Data Collection

Begin by gathering relevant data. For loan prediction, data such as applicant income, loan amount, marital status, education, and credit history will be required. The data should also include a target variable indicating whether the loan was approved or not.

• Data Preprocessing

Clean the data by handling missing values, converting categorical variables into numerical ones (e.g., encoding marital status or education), and scaling numerical variables if necessary.

Perform feature engineering, such as one-hot encoding for categorical features with more than two categories (e.g., property area or number of dependents).

• Model Selection

Choose an appropriate machine learning algorithm for the task. In this case, a Random Forest Classifier can be a good choice because it handles both numerical and categorical data well. Train the model using your cleaned and preprocessed data.

• Model Evaluation

Split the data into training and test sets. After training the model, evaluate its performance using the test set. Metrics like accuracy, precision, recall, and F1 score can help assess the model's effectiveness. If necessary, fine-tune the model to improve its performance.

• Web Application Setup

Set up a web application using a framework like Streamlit. The app should have a user-friendly interface for inputting the necessary data for prediction (such as income, loan amount, and credit history). Design the app to include various widgets like sliders for numerical values and dropdown menus for categorical inputs.

• Prediction Functionality

Once the user inputs their data, the app should preprocess the input in the same way as the training data (e.g., encoding categorical features) and pass it through the trained model.

The model will then return a prediction (approved or rejected), which should be displayed to the user in a clear and informative manner.

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How to Deploy a Streamlit App?

Deploying a Streamlit app involves making your application accessible to users over the web. Here are the detailed steps for deploying a Streamlit app:

1. Create a GitHub Repository for Your App

You must save the code for your application in a GitHub repository prior to deployment. To begin, create a new GitHub repository. The relevant files, such as your Streamlit app and associated libraries, will be stored in this repository.

2. Upload Your App Code and Dependencies

After creating the repository, upload your Streamlit app code and any relevant files (like images or datasets) to it. The most important part of this is ensuring that you have a requirements.txt file, which lists all the libraries your app depends on (e.g., streamlit, pandas, numpy). To do this:

• Click on “Create New File” in your repository.
• Name the file requirements.txt.
• Add all the necessary libraries you used in your app, one per line.

3. Set Up Deployment Platform

Next, head over to a deployment platform like Streamlit Community Cloud or Heroku, which can host your app. Here, we will assume you are using Streamlit Community Cloud.

• Log into your Streamlit Community Cloud account.
• Click on the “New App” button to start the deployment process.
• Link your GitHub repository to Streamlit Community Cloud. It will prompt you to select the repository you created earlier.

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4. Deploy the App

Once your GitHub repository is linked, Streamlit Community Cloud will automatically pull your code and dependencies.

• Click on the Deploy button.
• Streamlit will begin the process of staging and activating your app. This may take a few moments.
• After successful deployment, a unique URL will be provided where your app is accessible.

5. Access Your Deployed App

Once the deployment is complete, the platform will open the app in your default web browser. You’ll be able to interact with your Streamlit app publicly, as it is now hosted and accessible through the provided URL.

6. Troubleshooting and Documentation

If you run into issues during deployment, make sure your requirements.txt file is correctly formatted and that all dependencies are properly listed. 

7 Streamlit Applications

Here are some incredible ways you can use Streamlit to create interactive and impactful applications across different domains:

Data Exploration and Visualization

Streamlit makes it easy to create interactive dashboards for exploring datasets. You can visualize trends, analyze data, and uncover insights in a dynamic and engaging way.

Machine Learning Model Deployment

You can use Streamlit to implement machine learning models for problems involving regression, classification, and prediction. It enables real-time output visualization and user interaction with the model.

Custom Dashboards

Streamlit makes it easier to create dashboards that track business performance, KPIs, and indicators. The most pertinent data is always available due to this flexibility.

Text Analysis and Natural Language Processing

From sentiment analysis to text classification, Streamlit enables the development of applications that process and analyze text efficiently, making it ideal for natural language processing projects.

Image Processing and Computer Vision

You can use Streamlit to create applications for image classification, object detection, and other computer vision tasks, making these technologies more accessible and interactive.

Financial Data Analysis

Streamlit helps in building applications to analyze financial data, track stock prices, study market trends, and evaluate portfolio performance with clear visualizations.

Streamlit Components

Streamlit makes it possible for developers to create reusable user interface elements, which can help them optimize operations. These can expedite development for upcoming projects while improving the user experience.

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Streamlit Examples​

Some examples of interesting Streamlit projects include the Sentiment Analyzer Tool, which analyzes user emotions to improve customer experiences, and Visualize Pathologies in Ultrasound Images, a project that helps detect and highlight tumors in medical images. Another example is the FastAPI Model Serving app, which uses Streamlit and FastAPI to serve machine learning models.

Conclusion

In conclusion, Streamlit proves to be a fantastic tool for building interactive applications across various fields, including sentiment analysis, machine learning model deployment, image processing, and more. Whether you are creating custom dashboards or developing real-time applications, Streamlit’s simplicity and flexibility offer endless possibilities for innovation.

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FAQs

1. Is Streamlit free to use?

Yes, Streamlit is free to use for individual developers and small-scale projects. However, Streamlit also offers paid plans for more advanced features.

2. Is Streamlit a frontend or backend?

Streamlit is primarily a frontend framework, though it can integrate with backend services to build full-stack applications.

3. Can Streamlit replace Django?

Streamlit is not designed to replace Django. While Streamlit is great for data-driven apps, Django is a full-stack web framework suitable for building complex, production-grade applications.

4. Is Streamlit better than React?

Streamlit and React serve different purposes. Streamlit is easier for building data-centric applications, while React is a more versatile frontend library for building interactive UIs.

5. Is Streamlit better than Flask?

Streamlit is ideal for rapid prototyping and data visualization, while Flask offers more flexibility for building custom backend services and web applications.

6. Can Streamlit be used for production-level apps?

Yes, Streamlit can be used for production-level apps, though it is more suitable for smaller-scale or internal applications that prioritize quick development and interactivity.

7. What are some real-world use cases of Streamlit?

Streamlit is used for creating dashboards, machine learning model deployment, sentiment analysis tools, image processing apps, and financial data visualization, among other data-driven applications.