Streamlit: Build and Deploy ML Apps in Pure Python in 10 Minutes
Streamlit turns Python scripts into interactive web apps with no frontend knowledge required - build a working ML demo, RAG chatbot, or data dashboard and deploy it for free.
Streamlit converts a Python script into a web app by running it top-to-bottom every time a user interacts with a widget. No HTML, no JavaScript, no React. Just Python.
This execution model is the key to understanding Streamlit. Every button click, slider move, or text input triggers a full script rerun. This sounds inefficient, but Streamlit optimizes it with caching.
Your First Streamlit App
import streamlit as st
import pandas as pd
st.title("Sales Dashboard")
uploaded = st.file_uploader("Upload CSV", type="csv")
if uploaded:
df = pd.read_csv(uploaded)
region = st.selectbox("Filter by region", df["region"].unique())
filtered = df[df["region"] == region]
st.dataframe(filtered)
st.bar_chart(filtered.groupby("product")["revenue"].sum())
Run with: streamlit run app.py
Team workspace
Ship faster with chat, meetings, and projects in one place — Zlyqor.
import streamlit as st
import pandas as pd
from sentence_transformers import SentenceTransformer
@st.cache_data # cache data loading - reruns only when inputs change
def load_data(path: str) -> pd.DataFrame:
return pd.read_parquet(path)
@st.cache_resource # cache heavy objects - loaded once, shared across sessions
def load_model() -> SentenceTransformer:
return SentenceTransformer("all-MiniLM-L6-v2")
df = load_data("embeddings.parquet") # cached after first run
model = load_model() # loaded once, reused
Use @st.cache_data for data (serializable, per-user). Use @st.cache_resource for models and database connections (shared across all users).
Building an LLM Chatbot with st.chat_message
import streamlit as st
from openai import OpenAI
client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
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.write(msg["content"])
if prompt := st.chat_input("Ask anything..."):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
with st.chat_message("assistant"):
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=st.session_state.messages,
stream=True,
)
content = st.write_stream(response)
st.session_state.messages.append({"role": "assistant", "content": content})
Session State for Persistence
import streamlit as st
if "counter" not in st.session_state:
st.session_state.counter = 0
if st.button("Increment"):
st.session_state.counter += 1
st.write(f"Count: {st.session_state.counter}")
Session state persists across reruns for a single user session.
Practical deep-dives on LLMs, developer tools, and AI engineering. No filler. Unsubscribe any time.
// written byFIG. AUTH-01
538
Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
What Is Alibaba Banning Claude Code Over Backdoor Risks? A Practical Overview
Alibaba is reportedly banning Claude Code from its workplace due to alleged backdoor risks. This post explains the incident, the technical concerns, and the broader implications for AI coding assistants in regulated environments.
