Flask / Streamlit for web deployment

Flask and Streamlit are popular Python-based frameworks used to deploy machine learning models to the web.

Flask is a lightweight and flexible web framework that allows for building custom web servers and APIs. It is suitable for integrating ML models with fully customizable front-ends or third-party services.

Streamlit is a more high-level framework tailored for data scientists and ML developers. It simplifies creating interactive and visually appealing apps for model demos with minimal code.

Example: Deploying a model using Flask

# filename: iris_streamlit_app.py

import streamlit as st
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

# ---------------------------------------
# MODEL BUILDING
# ---------------------------------------

@st.cache_data
def load_data():
    data = load_iris()
    df = pd.DataFrame(data.data, columns=data.feature_names)
    df['target'] = data.target
    return df, data

@st.cache_resource
def train_model(df):
    X = df.drop('target', axis=1)
    y = df['target']
    model = RandomForestClassifier()
    model.fit(X, y)
    return model

# ---------------------------------------
# WEB INTERFACE WITH STREAMLIT
# ---------------------------------------

st.set_page_config(page_title="Iris Flower Classifier", layout="centered")

st.title("🌸 Iris Flower Classification App")
st.markdown("This app uses a **Random Forest Classifier** to predict the type of Iris flower.")

# Load data and train model
df, iris_data = load_data()
model = train_model(df)

# Input sliders
st.sidebar.header("Input Features")

sepal_length = st.sidebar.slider("Sepal Length (cm)", 4.3, 7.9, 5.4)
sepal_width  = st.sidebar.slider("Sepal Width (cm)", 2.0, 4.4, 3.4)
petal_length = st.sidebar.slider("Petal Length (cm)", 1.0, 6.9, 1.3)
petal_width  = st.sidebar.slider("Petal Width (cm)", 0.1, 2.5, 0.2)

input_features = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
                              columns=iris_data.feature_names)

# Prediction
if st.button("Predict Flower Type"):
    prediction = model.predict(input_features)[0]
    predicted_class = iris_data.target_names[prediction]
    st.success(f"🌼 Predicted Iris type: **{predicted_class}**")

# Show raw data (optional)
with st.expander("Show Iris Dataset"):
    st.dataframe(df)