Streamlit Has a Free API — Build Data Apps in Pure Python

#python #opensource #datascience #webdev

Streamlit: Data Apps Without Frontend Skills

Streamlit turns Python scripts into interactive web apps. No HTML, no CSS, no JavaScript. Write Python, get a beautiful dashboard. Used by data scientists, ML engineers, and analysts worldwide.

Why Streamlit

Pure Python — no frontend knowledge needed
Instant reload — save file, app updates
Widgets — sliders, buttons, file uploads, charts
Data-friendly — native pandas, plotly, matplotlib support
Free hosting — Streamlit Community Cloud

The Free API

import streamlit as st
import pandas as pd
import plotly.express as px

st.title("Sales Dashboard")

# File upload
file = st.file_uploader("Upload CSV", type="csv")
if file:
    df = pd.read_csv(file)

    # Filters
    col = st.selectbox("Group by", df.columns)
    metric = st.selectbox("Metric", df.select_dtypes("number").columns)

    # Chart
    fig = px.bar(df.groupby(col)[metric].sum().reset_index(), x=col, y=metric)
    st.plotly_chart(fig)

    # Metrics
    col1, col2, col3 = st.columns(3)
    col1.metric("Total", f"${df[metric].sum():,.0f}")
    col2.metric("Average", f"${df[metric].mean():,.0f}")
    col3.metric("Count", len(df))

    # Data table
    st.dataframe(df)

ML Model Demo

import streamlit as st
from transformers import pipeline

@st.cache_resource
def load_model():
    return pipeline("sentiment-analysis")

model = load_model()

text = st.text_area("Enter text for sentiment analysis")
if st.button("Analyze"):
    result = model(text)
    st.write(f"Sentiment: {result[0][label]}")
    st.progress(result[0]["score"])

Chat Interface

import streamlit as st

if "messages" not in st.session_state:
    st.session_state.messages = []

for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])

if prompt := st.chat_input("Ask something"):
    st.session_state.messages.append({"role": "user", "content": prompt})
    response = get_ai_response(prompt)  # Your LLM call
    st.session_state.messages.append({"role": "assistant", "content": response})
    st.chat_message("assistant").write(response)

Real-World Use Case

A data analyst built sales reports in Excel. Manager wanted interactive dashboards. Streamlit: 50 lines of Python, deployed to Community Cloud. Interactive filters, real-time charts, CSV export. Manager uses it daily.

Quick Start

pip install streamlit
streamlit hello  # Demo app
streamlit run app.py  # Your app

Resources

Need data for your dashboards? Check out my scraping tools on Apify or email spinov001@gmail.com for custom data solutions.

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