Compare Streamlit & Flask for deploying interactive AI/ML demos. Discover which Python framework best suits your machine learning projects.

Streamlit vs. Flask: Deploying Interactive AI/ML Demos

Creating interactive web applications is an excellent way to showcase your AI or machine learning models. Two popular Python frameworks for deploying such demos are Streamlit and Flask. While both serve the purpose of bringing models to life, they differ significantly in their approach, use cases, ease of development, and customization capabilities.

What is Streamlit?

Streamlit is an open-source Python library specifically designed for rapidly transforming data science and machine learning projects into interactive web applications with minimal effort. It allows you to build beautiful, custom web apps in pure Python, eliminating the need for front-end knowledge like HTML, CSS, or JavaScript.

Key Features of Streamlit

Ease of Use: Requires no prior knowledge of HTML, CSS, or JavaScript.
Pure Python Syntax: Develop entirely in Python.
Real-time Interactivity: Built-in widgets (e.g., st.slider, st.selectbox, st.button) enable dynamic user interactions.
Rich Media Support: Seamlessly integrates charts (Matplotlib, Plotly, Altair), images, videos, and audio.
Fast Prototyping: Ideal for quickly building and iterating on ML model demos.
Automatic Reruns: Streamlit automatically reruns your script when user input changes, providing a fluid interactive experience.

Example Streamlit Code

import streamlit as st
import joblib
import pandas as pd

# Load the pre-trained model
try:
    model = joblib.load("model.pkl")
except FileNotFoundError:
    st.error("Model file 'model.pkl' not found. Please ensure it's in the same directory.")
    st.stop() # Stop execution if model is not found

st.title("Iris Flower Predictor")
st.write("Adjust the sliders below to predict the Iris flower species.")

# Input widgets
sepal_length = st.slider("Sepal Length (cm)", 4.0, 8.0, 5.0)
sepal_width = st.slider("Sepal Width (cm)", 2.0, 4.5, 3.0)
petal_length = st.slider("Petal Length (cm)", 1.0, 7.0, 1.5)
petal_width = st.slider("Petal Width (cm)", 0.1, 2.5, 0.2)

# Prediction button
if st.button("Predict"):
    try:
        # Prepare input data as a DataFrame for better compatibility
        input_data = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
                                  columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
        prediction = model.predict(input_data)
        st.success(f"Predicted Iris Species: **{prediction[0]}**")
    except Exception as e:
        st.error(f"An error occurred during prediction: {e}")

st.markdown("---")
st.write("This is a simple demonstration of using Streamlit to deploy an ML model.")

When to Use Streamlit

Quick Model Demos: Presenting AI/ML models to stakeholders or team members.
Prototypes and Data Exploration: Building interactive tools for exploring datasets or model behavior.
Hackathons or Internal Tools: Rapidly developing functional applications for specific tasks.
Data Scientists/ML Engineers: When you need a fast UI without deep front-end development expertise.
Dashboards: Creating simple, interactive dashboards for monitoring or visualization.

What is Flask?

Flask is a lightweight and flexible micro web framework for Python used to build full-fledged web applications. It provides more control over routing, templating, and back-end integrations, making it suitable for complex, production-ready applications.

Key Features of Flask

Lightweight and Modular: Starts with a minimal core and allows adding extensions as needed.
Customizable Routing and APIs: Define flexible URL routes and build robust APIs.
HTML/CSS/JS Support: Integrates seamlessly with front-end technologies via templating engines like Jinja2.
Database Integration: Easily connects with databases using ORMs like SQLAlchemy.
Scalability: Suitable for building scalable, production-grade applications.
Flexibility: Offers complete control over the application's architecture and components.

Example Flask Code

from flask import Flask, request, render_template, jsonify
import joblib
import pandas as pd

app = Flask(__name__)

# Load the pre-trained model
try:
    model = joblib.load("model.pkl")
except FileNotFoundError:
    # In a real app, you'd handle this more gracefully, perhaps returning an error page.
    # For this example, we'll set a dummy model to avoid crashing the app.
    model = None
    print("Warning: Model file 'model.pkl' not found. API will not function correctly.")

# HTML template for the prediction form (save this as templates/index.html)
"""
<!DOCTYPE html>
<html>
<head>
    <title>Iris Flower Predictor (Flask)</title>
    <style>
        body { font-family: sans-serif; }
        .container { width: 50%; margin: 50px auto; padding: 20px; border: 1px solid #ccc; border-radius: 8px; }
        label { display: block; margin-bottom: 5px; }
        input[type=number] { width: calc(100% - 12px); padding: 8px; margin-bottom: 15px; border: 1px solid #ccc; border-radius: 4px; }
        input[type=submit] { background-color: #4CAF50; color: white; padding: 10px 15px; border: none; border-radius: 4px; cursor: pointer; }
        input[type=submit]:hover { background-color: #45a049; }
        .result { margin-top: 20px; padding: 10px; background-color: #f0f0f0; border-left: 4px solid #4CAF50; }
    </style>
</head>
<body>
    <div class="container">
        <h1>Iris Flower Predictor</h1>
        <p>Enter the sepal and petal measurements to predict the Iris species.</p>
        <form action="/" method="post">
            <label for="sepal_length">Sepal Length (cm):</label>
            <input type="number" step="0.1" id="sepal_length" name="sepal_length" required><br>

            <label for="sepal_width">Sepal Width (cm):</label>
            <input type="number" step="0.1" id="sepal_width" name="sepal_width" required><br>

            <label for="petal_length">Petal Length (cm):</label>
            <input type="number" step="0.1" id="petal_length" name="petal_length" required><br>

            <label for="petal_width">Petal Width (cm):</label>
            <input type="number" step="0.1" id="petal_width" name="petal_width" required><br>

            <input type="submit" value="Predict">
        </form>

        {% if prediction %}
        <div class="result">
            <h2>Prediction: {{ prediction }}</h2>
        </div>
        {% endif %}
        {% if error %}
        <div class="result" style="background-color: #ffdddd; border-left-color: #f44336;">
            <h2>Error: {{ error }}</h2>
        </div>
        {% endif %}
    </div>
</body>
</html>
"""

@app.route("/", methods=["GET", "POST"])
def predict_page():
    prediction = None
    error = None
    if request.method == "POST":
        if model is None:
            error = "Model not loaded. Cannot perform prediction."
        else:
            try:
                sepal_length = float(request.form["sepal_length"])
                sepal_width = float(request.form["sepal_width"])
                petal_length = float(request.form["petal_length"])
                petal_width = float(request.form["petal_width"])

                input_data = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
                                          columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
                
                prediction_result = model.predict(input_data)
                prediction = prediction_result[0]
            except ValueError:
                error = "Invalid input. Please enter valid numbers for all fields."
            except KeyError:
                error = "Missing form data. Please ensure all fields are submitted."
            except Exception as e:
                error = f"An unexpected error occurred: {e}"
    
    return render_template("index.html", prediction=prediction, error=error)

# Optional: API endpoint for predictions
@app.route("/api/predict", methods=["POST"])
def predict_api():
    if model is None:
        return jsonify({"error": "Model not loaded."}), 500

    try:
        data = request.get_json()
        if not data or not all(k in data for k in ("sepal_length", "sepal_width", "petal_length", "petal_width")):
            return jsonify({"error": "Missing required parameters in JSON payload."}), 400

        sepal_length = float(data["sepal_length"])
        sepal_width = float(data["sepal_width"])
        petal_length = float(data["petal_length"])
        petal_width = float(data["petal_width"])

        input_data = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]],
                                  columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
        
        prediction_result = model.predict(input_data)
        return jsonify({"prediction": str(prediction_result[0])})

    except ValueError:
        return jsonify({"error": "Invalid input types. Please provide numbers."}), 400
    except Exception as e:
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    # For production, use a production-ready WSGI server like Gunicorn
    app.run(debug=True, port=5000)

Note: The Flask example requires an index.html file to be created in a templates directory within your project.

When to Use Flask

Custom UI/UX Designs: When you need complete control over the front-end appearance and user experience.
Backend Logic and Database Integration: For applications requiring complex server-side logic, user authentication, or database interactions.
REST API Development: Building APIs to serve model predictions or other functionalities to different clients.
Complex Multi-Page Applications: Developing web applications with intricate navigation, multiple sections, and advanced features.
Production-Grade Applications: When scalability, security, and robust performance are critical requirements.

Streamlit vs. Flask: Feature Comparison

Feature	Streamlit	Flask
Ease of Use	Very easy (pure Python)	Moderate (requires HTML/CSS for UI)
Target Audience	Data scientists, ML engineers, beginners	Developers, full-stack engineers
UI Customization	Limited (widget-based, opinionated)	Highly customizable (via HTML, CSS, JS, Jinja2)
Best For	Quick ML model demos, dashboards, prototypes	Full-stack apps, REST APIs, complex UIs
Deployment	Streamlit Cloud, Heroku, Hugging Spaces, Docker	Heroku, AWS, GCP, Azure, Vercel, Docker, Gunicorn+Nginx
HTML/JS Knowledge	Not required for basic apps	Required for advanced/custom interfaces
Development Speed	Very fast (especially for demos)	Slower, more setup required (but more flexible)
Backend Logic	Basic, integrated with UI	Robust, separate from UI templating

Use Cases

Use Case	Recommended Framework
Quick demo of an ML model	Streamlit
Interactive data exploration tool	Streamlit
Custom web app with styled UI	Flask
REST API for ML model predictions	Flask
Dashboard with real-time data updates	Streamlit
Multi-user production web application	Flask
Internal tool for data analysis	Streamlit
Web app with user authentication	Flask

Deployment Options

Streamlit

Streamlit Community Cloud: Free hosting for public apps, perfect for demos and prototypes.
Heroku, Render, Hugging Face Spaces: Popular platforms for deploying web applications.
Docker: Containerize your Streamlit app for consistent deployment across various environments.

Flask

Managed Platforms: Heroku, Vercel, Google App Engine, AWS Elastic Beanstalk.
Cloud Providers: Deploy on virtual machines (EC2, Compute Engine) with web servers.
Containerization: Docker combined with WSGI servers like Gunicorn and reverse proxies like Nginx is a common production setup.

Final Thoughts

Choose Streamlit if you are a data scientist or ML engineer who needs to quickly deploy an interactive demo of your model without getting bogged down in front-end development. Its Python-centric approach makes it incredibly fast for prototyping and showcasing results.
Choose Flask if you are a developer building a scalable, production-ready web application. Flask offers the flexibility and control needed for complex UIs, custom back-end logic, database integrations, and API development, allowing you to build robust and feature-rich applications.

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Interview Questions

What are the key differences between Streamlit and Flask for deploying machine learning models?
When would you choose Streamlit over Flask for a project, and why?
Explain the typical workflow for deploying an ML model using Streamlit.
What are the primary advantages of using Flask for building a production-grade AI application?
How do you handle form data submission and routing in a Flask application?
Can you compare the UI/UX flexibility offered by Streamlit versus Flask?
How would you deploy a Streamlit application, and what platforms are commonly used?
How do Flask and Streamlit differ in terms of scalability and performance considerations for web applications?
What are some limitations of Streamlit when attempting to build complex, highly customized web applications?
Describe how you would build a REST API for a machine learning model using Flask.

Streamlit vs. Flask for AI/ML Demos