writing-skills
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
streamlitStreamlit Python web application framework. Covers session state, caching, layouts, widgets, multipage apps, and deployment. Use when building interactive Python data apps or dashboards. USE WHEN: user mentions "streamlit", "st.session_state", "st.cache_data", "streamlit app", "python dashboard", "python web app", "streamlit deploy", "streamlit form", "str…
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name: streamlit description: | Streamlit Python web application framework. Covers session state, caching, layouts, widgets, multipage apps, and deployment. Use when building interactive Python data apps or dashboards.
USE WHEN: user mentions "streamlit", "st.session_state", "st.cache_data", "streamlit app", "python dashboard", "python web app", "streamlit deploy", "streamlit form", "streamlit component"
pip install streamlit
streamlit run app.py
streamlit run app.py --server.port 8080
Streamlit re-runs the entire script top-to-bottom on every user interaction. Use caching and session_state to avoid redundant work.
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)
# @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()
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")
tab1, tab2, tab3 = st.tabs(["Overview", "Details", "Export"])
with tab1:
show_overview()
with tab2:
show_details()
with st.sidebar:
selected = st.selectbox("Area", options=["11301", "11090", "27301"])
date_range = st.date_input("Date range", value=(start, end))
with st.expander("Advanced Options", expanded=False):
threshold = st.slider("Threshold", 0.0, 1.0, 0.5)
# 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)
# 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")
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")
# 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")
# 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()
# .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"
# .streamlit/secrets.toml (gitignored)
[api]
key = "..."
key = st.secrets["api"]["key"]
# Or flat access
key = st.secrets.api.key
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"]
# 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-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 |