Writing UI is too hard? Streamlit to the rescue!
Brothers and sisters, are you often tortured by front-end development?
Building AI models is easy, but creating a beautiful display interface is a challenge?
Don’t worry! Today I want to recommend an amazing tool — Streamlit!
What is Streamlit?
In simple terms, it is a UI development tool specifically designed for AI applications!
Do you know what the best part is? Writing an interface is as easy as writing a Python script!
Don’t believe it? Take a look at this:
import streamlit as st
st.title("My First AI Application")
name = st.text_input("Please enter your name")
if st.button("Say Hello"):
st.write(f"Hello, {name}!")With just a few lines of code, a webpage with an input box and a button is ready!
What can we do with it?
Oh, there are so many things we can do:
Image recognition applications? Done in no time!
Text analysis tools? Easy peasy!
Showcasing machine learning models? A piece of cake!
Let’s look at a real case:
import streamlit as st
from PIL import Image
import numpy as np
st.title("AI Image Filter")
uploaded_file = st.file_uploader("Upload an image", type=['png', 'jpg'])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption="Original Image")
if st.button("Add Effect"):
img_array = np.array(image)
st.image(255-img_array, caption="Inverted Effect")See? This is a simple image processing application that can be written easily!
How powerful is the component system?
The component system of Streamlit is simply a treasure trove for developers!
Want charts? One line of code does the trick:
st.line_chart(data)Need file uploads? No problem:
st.file_uploader("Choose a file")Want to add a progress bar? Here you go:
st.progress(66)How cool is data presentation?
When it comes to displaying data, Streamlit definitely knows how to do it right!
Pandas DataFrame? Just display it directly!
Matplotlib charts? Seamlessly integrated!
Interactive charts? One function to handle it!
import pandas as pd
import streamlit as st
data = pd.DataFrame({
'Name': ['Zhang San', 'Li Si', 'Wang Wu'],
'Score': [88, 95, 78]
})
st.dataframe(data) # Display table
st.bar_chart(data.set_index('Name')) # Display bar chartPractical Tips
Having written so many Streamlit applications, I’ve summarized a few key points:
Use st.columns and st.beta_container for page layout, keeping the interface neat and beautiful.
For large file processing, remember to add the caching decorator @st.cache for speed.
Manage session state with st.session_state for a great user experience.
How easy is deployment?
In the past, deploying web applications could be a headache.
Now with Streamlit? One command does it all:
streamlit run app.pyWant to share it with others? Check out Streamlit Cloud!
One-click deployment, generates a URL directly, online in no time!
Pitfall Guide
Writing in Streamlit also has some pitfalls, and I’ve cleared the way for you:
Page state management must be done correctly, or data will be lost.
For large file processing, use caching, or it will be as slow as a snail.
Be cautious about compatibility when creating custom components, or you’ll encounter various errors.
Getting Started Suggestions
Want to quickly get the hang of Streamlit? Trust me, you won’t go wrong:
First, follow the official demo to familiarize yourself with the basic components.
Write a small project, and you’ll understand everything.
Join the Streamlit community, where experts gather and problems are easily solved.
Practical Case Sharing
Let me share a project I recently worked on:
An AI image generation application, where users input descriptions and images are generated directly.
Only 200 lines of code, with a beautiful interface and powerful functionality.
User feedback has been overwhelmingly positive!
In Conclusion
Streamlit truly brings a blessing to Python developers!
No need to learn front-end, no need to set up environments, just focus on writing business code!
What are you waiting for? Come and give it a try!
If you have any questions, feel free to ask me, and let’s explore Streamlit together!