Gain insights into building robust front-end Python web applications with Streamlit. Explore integrating Python libraries, creating visualizations, interactive widgets, and deploying on Streamlit cloud.

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Python is the premier programming language for data science and machine learning, but its native strengths for back-end development can leave developers scrambling for a front-end solution. Enter Streamlit, a robust open-source library for developing front-end applications with Python.

This course is a comprehensive introduction to building Streamlit applications integrated with different Python libraries. You’ll be walked through successive projects to create visualizations, display interactive widgets, and customize layouts. You won’t just display data but work on data transformations using Streamlit interfaces and become familiar with basic functionality for native Python applications like caching and placeholders. You’ll wrap up with deploying your application using the Streamlit cloud.

By the end of this course, you’ll be ready to give your Python-based data science and machine learning applications robust user interfaces using Streamlit.

Building the Frontend of Python Web Applications with Streamlit

# Taking input from the user
Here we’ll allow the user to input data and display the prediction.

We’ll use the following methods:

* **`text_input(str)`:** This method takes a string as a parameter and creates a text input field with the input parameter as its label.

* **`st.selectbox(str, options = )`:** This method creates a drop-down menu. It takes in two parameters, the string to use as the label and the list of options. The options need to be passed as a list of string values.
* **`st.slider(str, start, end, step)`:** This creates a slider with the given parameters. The `step` parameter is considered as the step size of the slider.


The following code is used to take input from user:
```python
name = st.text_input("Name of Passenger ")
sex = st.selectbox("Sex",options=['Male' , 'Female'])
age = st.slider("Age", 1, 100,1)
p_class = st.selectbox("Passenger Class",options=['First Class' , 'Second Class' , 'Third Class'])
```
Every time the user gives an input, the script is re-run, and the respective variables will store the input values.

Before we can use these values for prediction, we need to scale and modify them.
```python
sex = 0 if sex == 'Male' else 1
f_class , s_class , t_class = 0,0,0
if p_class == 'First Class':
	f_class = 1
elif p_class == 'Second Class':
	s_class = 1
else:
	t_class = 1
input_data = scaler.transform([[sex , age, f_class , s_class, t_class]])
prediction = model.predict(input_data)
predict_probability = model.predict_proba(input_data)
```
* First, we set the value of `sex` to either `0` or `1`.
* Then, we use one-hot encoding on the passenger class.
* Finally, we scale the input and calculate the prediction and probability.


Learn how to take user input in Streamlit.

Build User Input UI with Streamlit

Introduction

Build a UI for a Model Using Streamlit

Build a Classification Web App using Streamlit and Sklearn

Build a Streamlit App to Scrape Github Profiles

Build a Web App to Transcribe Audio using Python and AssemblyAI

Build a Web App to Generate and Display a Word Cloud of Trending

Build a Web App to Download YouTube Videos in 30 Lines of Code

Deploy Streamlit Apps

Wrapping Up

Build User Input UI with Streamlit

Taking input from the user