Tutorial Lengkap Streamlit Advanced: Build Production-Ready ML Apps

Streamlit adalah library Python yang powerful untuk membangun aplikasi web interaktif untuk machine learning dan data science. Tutorial advanced ini mencakup production patterns, optimasi performa, dan fitur enterprise.

Mengapa Streamlit untuk Production?

• Rapid development: Build apps dalam hitungan jam
• Pure Python: Tidak perlu frontend knowledge
• Interactive widgets: Rich UI components
• Easy deployment: Streamlit Cloud, Docker, Kubernetes
• Active ecosystem: Community components dan integrasi

• ML model demos dan dashboards
• Data exploration tools
• Internal analytics apps
• Customer-facing applications
• Prototyping dan MVPs

Instalasi

pip install streamlit

Dengan fitur tambahan
pip install streamlit-extras
pip install streamlit-aggrid
pip install plotly

Verify installation
streamlit --version

App Architecture

1. Multi-Page Apps

# pages/1Home.py
import streamlit as st

st.setpageconfig(

    pagetitle="ML Dashboard",
    pageicon="🤖",

    layout="wide",
)

st.title("Welcome to ML Dashboard")
st.write("Navigate menggunakan sidebar")

# pages/2DataExplorer.py
import streamlit as st
import pandas as pd

st.title("Data Explorer")

uploadedfile = st.fileuploader("Upload CSV", type="csv")

if uploadedfile:
    df = pd.readcsv(uploadedfile)
    st.dataframe(df)

# pages/3ModelInference.py
import streamlit as st

st.title("Model Inference")
Model inference code disini

2. Session State Management

import streamlit as st

Initialize session state
if 'counter' not in st.sessionstate:

    st.sessionstate.counter = 0

if 'userdata' not in st.sessionstate:
    st.sessionstate.userdata = {}

Update session state
def increment():
    st.sessionstate.counter += 1


st.button("Increment", onclick=increment)
st.write(f"Counter: {st.sessionstate.counter}")


Store user data
name = st.textinput("Name", key="nameinput")

if name:
    st.sessionstate.userdata['name'] = name


Access across pages
st.write(st.sessionstate.userdata)

import streamlit as st

Callback function
def onsubmit():
    st.sessionstate.submitted = True

    st.sessionstate.result = f"Hello, {st.sessionstate.namefield}!"

Form dengan callback
with st.form("myform"):

    st.textinput("Name", key="namefield")

    submitted = st.formsubmitbutton("Submit", onclick=onsubmit)


if st.sessionstate.get('submitted'):
    st.success(st.sessionstate.result)


Multiple callbacks
def clearform():
    st.sessionstate.namefield = ""
    st.sessionstate.submitted = False


st.button("Clear", onclick=clearform)

import streamlit as st
import pandas as pd

@st.cachedata(ttl=3600)  # Cache selama 1 jam
def loaddata(url):

    """Load dan cache data"""
    return pd.readcsv(url)

@st.cachedata(showspinner="Loading data...")
def expensivecomputation(df):

    """Expensive computation dengan spinner"""
    # Simulate long computation
    import time
    time.sleep(5)
    return df.describe()

Gunakan cached functions
df = loaddata("https://example.com/data.csv")
stats = expensivecomputation(df)

import streamlit as st
from transformers import pipeline
import joblib

@st.cacheresource
def loadmodel():

    """Cache ML model (singleton)"""
    return joblib.load("model.joblib")

@st.cacheresource