--- name: streamlit-f3d88d12669c description: >- Streamlit is a Python framework that turns a script into an interactive web app. Use it to build data dashboards, ML model demos, chat interfaces and internal tools in pure Python, with no frontend code. Triggers: "build a Streamlit app", "dashboard in Python", "st.cache_data", "streamlit run", "deploy a Streamlit app". license: Apache-2.0 compatibility: "Python 3.11+ (Streamlit 1.66), pip" metadata: author: terminal-skills version: "1.1.0" repository: https://github.com/streamlit/streamlit category: data-ai tags: "dashboards, python, data-apps, visualization, prototyping" --- # Streamlit — Python Data Apps ## Overview Streamlit reruns your Python script top to bottom on every widget interaction and renders the elements it calls (`st.title`, `st.dataframe`, `st.plotly_chart`, ...). State that must survive a rerun lives in `st.session_state`; expensive work is cached with `st.cache_data` and `st.cache_resource`. Current release: 1.66.x (Python 3.11 or newer). ## Instructions Install and run (use a virtual environment): ```bash python3 -m venv .venv && .venv/bin/pip install streamlit pandas plotly pyarrow .venv/bin/streamlit run app.py # serves on http://localhost:8501 ``` ### Basic App Structure ```python # app.py: sales dashboard over data/sales.parquet import streamlit as st import pandas as pd import plotly.express as px st.set_page_config(page_title="Sales Dashboard", page_icon="📊", layout="wide") # first st call st.title("Sales Dashboard") with st.sidebar: start = st.date_input("From", value=pd.Timestamp("2026-01-01")) end = st.date_input("To", value=pd.Timestamp("2026-03-31")) regions = st.multiselect( "Regions", ["North America", "Europe", "Asia Pacific", "Latin America"], default=["North America", "Europe"], ) min_revenue = st.slider("Minimum revenue", 0, 10000, 0, step=100) @st.cache_data(ttl=600) # cache for 10 minutes def load_data() -> pd.DataFrame: return pd.read_parquet("data/sales.parquet") df = load_data() filtered = df[ df["date"].between(pd.Timestamp(start), pd.Timestamp(end)) # date_input returns date, not Timestamp & df["region"].isin(regions) & (df["revenue"] >= min_revenue) ] c1, c2, c3 = st.columns(3) c1.metric("Total revenue", f"${filtered['revenue'].sum():,.0f}") c2.metric("Orders", f"{len(filtered):,}") c3.metric("Unique customers", f"{filtered['customer_id'].nunique():,}") trends, data = st.tabs(["Trends", "Data"]) with trends: monthly = ( filtered.groupby(filtered["date"].dt.to_period("M").dt.to_timestamp())["revenue"] .sum().reset_index() ) st.plotly_chart(px.line(monthly, x="date", y="revenue"), width="stretch") with data: st.dataframe( filtered.head(100), width="stretch", column_config={ "revenue": st.column_config.NumberColumn("Revenue", format="$%.2f"), "date": st.column_config.DateColumn("Date"), }, ) ``` `width="stretch"` replaces the old `use_container_width=True`, which is deprecated and will be removed (use `width="content"` for the opposite). ### Forms and User Input Widgets outside a form rerun the script on every change; `st.form` batches them until the submit button is pressed. ```python # pages/feedback.py import streamlit as st st.title("Customer Feedback") with st.form("feedback_form", clear_on_submit=True): name = st.text_input("Your name") email = st.text_input("Email") category = st.selectbox("Category", ["Bug", "Feature request", "General"]) rating = st.slider("Satisfaction (1-10)", 1, 10, 5) feedback = st.text_area("Your feedback", height=150) screenshot = st.file_uploader("Screenshot", type=["png", "jpg"]) submitted = st.form_submit_button("Submit") if submitted: if not name or not email: st.error("Name and email are required.") else: save_feedback(name, email, category, rating, feedback, screenshot) # your own function st.success("Thank you for your feedback!") ``` ### Caching and Session State ```python import joblib import pandas as pd import streamlit as st from sqlalchemy import create_engine @st.cache_data(ttl=3600) # return values are pickled; each caller gets a copy def load_large_dataset(path: str) -> pd.DataFrame: df = pd.read_parquet(path) df["month"] = df["date"].dt.to_period("M") return df @st.cache_resource # one shared object for all sessions, not copied def get_engine(): return create_engine(st.secrets["database"]["url"]) @st.cache_resource def load_model(): return joblib.load("models/classifier.joblib") # only load files you produced yourself if "cart" not in st.session_state: # per-user, per-browser-tab state st.session_state.cart = [] ``` Pass hash-unfriendly arguments (connections, clients) with a leading underscore (`_engine`) so `st.cache_data` skips hashing them. ### Chat Interface ```python # pages/chat.py: any model provider works; answer_stream yields text chunks import streamlit as st st.title("Support assistant") if "messages" not in st.session_state: st.session_state.messages = [] for msg in st.session_state.messages: with st.chat_message(msg["role"]): st.markdown(msg["content"]) if prompt := st.chat_input("Ask about your order"): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) with st.chat_message("assistant"): reply = st.write_stream(answer_stream(st.session_state.messages)) # generator of str st.session_state.messages.append({"role": "assistant", "content": reply}) ``` `st.write_stream` accepts a generator, or an OpenAI/LangChain stream object, and returns the full text. Read API keys from `st.secrets` (`.streamlit/secrets.toml`, never committed) and pick the model name from the provider's current model list. ### Multi-page apps Either put files in a `pages/` directory next to the entry script, or define navigation explicitly: ```python # app.py import streamlit as st pg = st.navigation([ st.Page("dashboard.py", title="Dashboard", icon="📊"), st.Page("chat.py", title="Assistant", icon="💬"), ]) pg.run() ``` ### Testing without a browser ```python from streamlit.testing.v1 import AppTest at = AppTest.from_file("app.py", default_timeout=30).run() assert not at.exception at.slider[0].set_value(500).run() print([m.value for m in at.metric]) ``` ### Deployment ```bash streamlit run app.py --server.port 8501 # local ``` Streamlit Community Cloud: push the repo to GitHub, then connect it at share.streamlit.io and pick the entry file. Docker: ```dockerfile FROM python:3.12-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . EXPOSE 8501 CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"] ``` Binding to `0.0.0.0` is needed inside a container; publish the port to the outside only behind an authenticating reverse proxy, because Streamlit has no built-in login for self-hosted apps (`st.login` exists for OIDC providers). ```toml # .streamlit/config.toml [theme] primaryColor = "#6366f1" backgroundColor = "#0e1117" secondaryBackgroundColor = "#1e1e2e" textColor = "#ffffff" [server] maxUploadSize = 200 # MB, default 200 ``` Leave `server.enableXsrfProtection` and `server.enableCORS` at their defaults (both true). ## Examples ### Example 1: Dashboard from a CSV **User request:** ``` I have orders.csv with date, region and revenue columns. Make a Streamlit dashboard with region filters and a monthly revenue chart. ``` The agent creates `app.py` as above with `pd.read_csv("orders.csv", parse_dates=["date"])` inside a `@st.cache_data` loader, a sidebar multiselect for regions, three `st.metric` tiles and a Plotly line chart, then runs `streamlit run app.py`. Result: the app opens at http://localhost:8501, and changing a filter updates the tiles and chart without a page reload. ### Example 2: Chatbot with kept history **User request:** ``` Add a chat page to my app that streams answers from our model and remembers the conversation. ``` The agent adds `pages/chat.py` using `st.chat_input`, `st.chat_message`, `st.write_stream` and `st.session_state.messages`, with the provider key read from `st.secrets["LLM_API_KEY"]`. Result: each reply streams token by token and the history survives reruns within the same browser session (a page refresh starts a new session). ### Example 3: Checking a change without a browser **User request:** ``` Make sure the dashboard still renders after my edit. ``` The agent runs the `AppTest` snippet above in the project's virtualenv, asserts `at.exception` is empty, and prints the metric values. ## Guidelines 1. **Cache expensive work**: `@st.cache_data` for dataframes and query results, `@st.cache_resource` for connections and models; set a `ttl` for data that changes. 2. **Never put per-user data in a cached function's closure or in `cache_resource` objects**: they are shared across all sessions. 3. **`st.set_page_config` must be the first Streamlit call** in the entry script. 4. **Keep the script fast**: it reruns on every interaction; use `st.form` or `@st.fragment` to limit reruns. 5. **Use `width="stretch"`** on charts and dataframes, not the deprecated `use_container_width`. 6. **Secrets** go in `.streamlit/secrets.toml` (add it to `.gitignore`) or the Community Cloud secrets panel; read them with `st.secrets`. 7. **Unpickling is code execution**: do not `joblib.load` or `pickle.load` files from untrusted users. 8. **Not for**: multi-user apps with complex auth, or UIs needing fine-grained layout control; use a web framework there. --- Source attribution: TerminalSkills Original project: https://github.com/TerminalSkills/skills/blob/3bfbf7f76459d89422eddc0e5f572ad0e6c393c9/skills/streamlit/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: TerminalSkills Original project: https://github.com/TerminalSkills/skills/blob/3bfbf7f76459d89422eddc0e5f572ad0e6c393c9/skills/streamlit/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Design, Development & Engineering, Product Management Roles: Data Scientist, Frontend Developer, ML / AI Engineer, Product Manager, UX Designer Specialties: API, Backend, Cross Platform, Frontend Tools: Streamlit Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 62ea82d63829ad77f7c23247e5a44549733531e9089dd7059b1032853ad6d144