Streamlit Core Knowledge
SkillCloud & infraStreamlit Python web application framework. Covers session state, caching, layouts, widgets, multipage apps, and deployment. Use when building interactive Python data apps or dashboards.
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What this skill tells your AI
The instructions your AI receives, as published by claude-dev-suite/claude-dev-suite in skills/backend-frameworks/streamlit/SKILL.md and read by ahel’s review.
Installation & Run
pip install streamlit
streamlit run app.py
streamlit run app.py --server.port 8080
Core Concepts
App Execution Model
Streamlit re-runs the entire script top-to-bottom on every user interaction. Use caching and session_state to avoid redundant work.
Session State
import streamlit as st
# Initialize (always check first)
if "data" not in st.session_state:
st.session_state.data = []
# Read and write
st.session_state.data.append(item)
st.write(st.session_state.data)
# Callback pattern (preferred for widget interactions)
def on_submit():
st.session_state.result = process(st.session_state.input_val)
st.text_input("Input", key="input_val")
st.button("Submit", on_click=on_submit)
Caching
# @st.cache_data — serializable return values (DataFrames, dicts, lists)
@st.cache_data(ttl=600) # cache expires in 10 min
def load_dataset(path: str) -> pd.DataFrame:
return pd.read_csv(path)
# @st.cache_resource — non-serializable (DB connections, ML models)
@st.cache_resource
def get_model():
return load_ml_model("model.pkl")
# Clear cache programmatically
load_dataset.clear()
Layout & Components
Columns
col1, col2 = st.columns(2) # equal width
col1, col2, col3 = st.columns([3, 1, 1]) # weighted
with col1:
st.metric("Revenue", "$12,345", delta="+5%")
with col2:
st.image("logo.png")
Tabs
tab1, tab2, tab3 = st.tabs(["Overview", "Details", "Export"])
with tab1:
show_overview()
with tab2:
show_details()
Sidebar
with st.sidebar:
selected = st.selectbox("Area", options=["11301", "11090", "27301"])
date_range = st.date_input("Date range", value=(start, end))
Expander
with st.expander("Advanced Options", expanded=False):
threshold = st.slider("Threshold", 0.0, 1.0, 0.5)
Input Widgets
# Text
name = st.text_input("Name", placeholder="Enter tag name")
text = st.text_area("Description", height=100)
# Numbers
n = st.number_input("Count", min_value=0, max_value=1000, value=10, step=1)
ratio = st.slider("Ratio", 0.0, 1.0, 0.5)
# Selection
choice = st.selectbox("Type", ["Motor", "Valve", "Analog"])
choices = st.multiselect("Areas", ["11301", "11090"])
flag = st.checkbox("Include alarms", value=True)
option = st.radio("Export format", ["CSV", "Excel", "JSON"])
# File upload
uploaded = st.file_uploader("Upload Excel", type=["xlsx", "xls"])
if uploaded:
df = pd.read_excel(uploaded)
Data Display
# DataFrame with config
st.dataframe(
df,
column_config={
"tag": st.column_config.TextColumn("Tag", width="medium"),
"value": st.column_config.NumberColumn("Value", format="%.2f"),
},
hide_index=True,
use_container_width=True,
)
# Metrics
st.metric(label="Total Tags", value=len(df), delta=f"+{new_count} new")
# Charts
st.line_chart(df.set_index("timestamp")[["value"]])
st.bar_chart(df.groupby("area")["count"].sum())
# Download button
csv = df.to_csv(index=False).encode("utf-8")
st.download_button("Download CSV", csv, "export.csv", "text/csv")
Forms (batch input — single rerun on submit)
with st.form("generate_form"):
area = st.text_input("Area code")
count = st.number_input("Instance count", min_value=1, value=1)
submitted = st.form_submit_button("Generate")
if submitted:
if not area:
st.error("Area code is required")
else:
with st.spinner("Generating..."):
result = generate_files(area, count)
st.success(f"Generated {result.count} files")
Progress & Status
# Spinner
with st.spinner("Loading data..."):
data = fetch_data()
# Progress bar
progress = st.progress(0, text="Starting...")
for i, item in enumerate(items):
process(item)
progress.progress((i + 1) / len(items), text=f"Processing {i+1}/{len(items)}")
progress.empty()
# Status messages
st.success("Operation completed")
st.error("Something went wrong")
st.warning("Check your input")
st.info("Processing in background")
Multipage Apps
# app.py (Streamlit 1.36+)
import streamlit as st
pg = st.navigation([
st.Page("pages/overview.py", title="Overview", icon="📊"),
st.Page("pages/generator.py", title="Generator", icon="⚙️"),
st.Page("pages/export.py", title="Export", icon="📁"),
])
pg.run()
Configuration
# .streamlit/config.toml
[server]
port = 8501
headless = true
maxUploadSize = 200 # MB
[theme]
primaryColor = "#1f77b4"
backgroundColor = "#ffffff"
secondaryBackgroundColor = "#f0f2f6"
textColor = "#31333f"
font = "sans serif"
[client]
toolbarMode = "viewer"
Secrets
# .streamlit/secrets.toml (gitignored)
[api]
key = "..."
key = st.secrets["api"]["key"]
# Or flat access
key = st.secrets.api.key
Deployment (Docker)
FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8501
CMD ["streamlit", "run", "app.py", "--server.headless=true", "--server.port=8501"]
Testing Streamlit Apps
# Use streamlit.testing.v1 (Streamlit >= 1.18)
from streamlit.testing.v1 import AppTest
def test_app():
at = AppTest.from_file("app.py").run()
assert not at.exception
assert at.title[0].value == "My App"
def test_form_submission():
at = AppTest.from_file("app.py").run()
at.text_input[0].set_value("11301").run()
at.button[0].click().run()
assert at.success[0].value == "Generated 1 files"
Anti-Patterns
| Anti-Pattern | Fix |
|---|---|
| Heavy I/O on every rerun | @st.cache_data |
| Global mutable state | st.session_state |
| Business logic in UI script | Separate services/ module |
No key= on dynamic widgets | Always set key= |
st.rerun() in loop | Use fragments or callbacks |
Signals
- GitHub stars
- 33
- Forks
- 6
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
streamlit- Source
- github.com/claude-dev-suite/claude-dev-suite