Streamlit
1 Introduction to Streamlit
1.1 What is Streamlit?
1.2 Why use Streamlit?
1.3 Setting up the environment
1.4 Creating your first Streamlit app
2 Basic Components
2.1 Text elements
2.1 1 Displaying text
2.1 2 Formatting text
2.2 Data display elements
2.2 1 Displaying data frames
2.2 2 Displaying tables
2.3 Input widgets
2.3 1 Text input
2.3 2 Number input
2.3 3 Date input
2.3 4 Dropdown selection
2.3 5 Slider
2.3 6 Checkbox
2.3 7 Radio buttons
2.3 8 Buttons
3 Advanced Components
3.1 Interactive widgets
3.1 1 Multiselect
3.1 2 File uploader
3.1 3 Color picker
3.2 Media elements
3.2 1 Displaying images
3.2 2 Displaying videos
3.2 3 Displaying audio
3.3 Chart elements
3.3 1 Line chart
3.3 2 Bar chart
3.3 3 Area chart
3.3 4 Scatter chart
3.3 5 Map chart
4 Layout and Styling
4.1 Layout components
4.1 1 Columns
4.1 2 Tabs
4.1 3 Expander
4.2 Styling elements
4.2 1 Custom CSS
4.2 2 Theming
4.2 3 Adding custom fonts
5 State Management
5.1 Session state
5.1 1 Managing state across reruns
5.1 2 Persisting state
5.2 Caching
5.2 1 Caching functions
5.2 2 Caching data
6 Deployment
6.1 Deploying to Streamlit Sharing
6.1 1 Setting up Streamlit Sharing
6.1 2 Deploying your app
6.2 Deploying to other platforms
6.2 1 Deploying to Heroku
6.2 2 Deploying to AWS
6.2 3 Deploying to Google Cloud
7 Best Practices
7.1 Writing clean and maintainable code
7.2 Optimizing performance
7.3 Handling errors and exceptions
7.4 Version control with Git
8 Advanced Topics
8.1 Integrating with other libraries
8.1 1 Integrating with Pandas
8.1 2 Integrating with Plotly
8.1 3 Integrating with TensorFlow
8.2 Building complex apps
8.2 1 Creating multi-page apps
8.2 2 Handling authentication
8.2 3 Building interactive dashboards
8.3 Custom components
8.3 1 Creating custom widgets
8.3 2 Extending Streamlit with custom components
9 Case Studies
9.1 Building a data exploration app
9.2 Building a machine learning model deployment app
9.3 Building a real-time data visualization app
3 1 2 File Uploader Explained

1 2 File Uploader Explained

Key Concepts

st.file_uploader

st.file_uploader is a Streamlit widget that provides a file upload interface. Users can select and upload files from their local system. This widget is particularly useful for applications that require user-provided data, such as CSV files for data analysis or image files for image processing.

File Types

The st.file_uploader function accepts a parameter called type that specifies the types of files that can be uploaded. This parameter can be a string or a list of strings representing file extensions (e.g., "png", "jpg", "csv"). By specifying the file types, you can restrict the upload to only those file types that your application can handle.

File Handling

Once a file is uploaded, it is stored as a BytesIO object in memory. You can then read and process the file using Python's standard file handling methods. For example, if the uploaded file is a CSV, you can use the pandas library to read and manipulate the data.

Examples

Here are some examples to illustrate the use of st.file_uploader:

import streamlit as st
import pandas as pd

st.title("File Uploader Example")

# Basic file uploader
uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    st.write(f"File name: {uploaded_file.name}")
    st.write(f"File size: {uploaded_file.size} bytes")

# File uploader with specified file types
uploaded_csv = st.file_uploader("Upload a CSV file", type=["csv"])
if uploaded_csv is not None:
    df = pd.read_csv(uploaded_csv)
    st.write(df)

# File uploader for image files
uploaded_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
if uploaded_image is not None:
    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
    

Analogies

Think of st.file_uploader as a digital version of a file drop box. Users can drop their files into this box, and your application can then process these files. The file types parameter is like setting rules for what kind of files can be dropped into the box, ensuring that only appropriate files are accepted.

By mastering st.file_uploader, you can enhance your Streamlit applications by allowing users to upload and interact with their own data, making your apps more versatile and user-friendly.