Deploying Your First ML Model with Flask: A No-Jargon Guide

So you've trained your first machine learning model and it’s chilling in your Jupyter Notebook. But now what? How do you actually use it in real life?

Don’t worry—this post will walk you through the simplest way to turn your ML model into something the world can use using Flask, a lightweight web framework for Python. No buzzwords. No corporate lingo. Just pure magic (and a bit of code).

What We’re Building

We’ll create a mini web app that:

Takes input from a user
Uses your trained ML model to make a prediction
Shows the result back to the user

Step 1: Train a Simple ML Model

Let’s use a quick model with sklearn to keep things smooth. You can replace this later with your own model.

# train_model.py
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
import pickle

# Load sample data
iris = load_iris()
X, y = iris.data, iris.target

# Train a simple model
model = LogisticRegression()
model.fit(X, y)

# Save it to disk
with open('model.pkl', 'wb') as file:
    pickle.dump(model, file)

✅ Run this once. It saves model.pkl, which we’ll use in our Flask app.

🌐 Step 2: Create a Flask App

Now, let's bring Flask into the picture.

# app.py
from flask import Flask, request, jsonify, render_template
import pickle
import numpy as np

# Load model
model = pickle.load(open('model.pkl', 'rb'))

# Create app
app = Flask(__name__)

@app.route('/')
def home():
    return render_template('index.html')  # Simple form to take input

@app.route('/predict', methods=['POST'])
def predict():
    try:
        # Get input from form
        input_values = [float(x) for x in request.form.values()]
        final_input = [np.array(input_values)]

        # Make prediction
        prediction = model.predict(final_input)
        return render_template('index.html', prediction_text=f'Predicted Class: {prediction[0]}')
    except:
        return render_template('index.html', prediction_text="⚠️ Invalid input. Please try again.")

if __name__ == "__main__":
    app.run(debug=True)

🧾 Step 3: Add an HTML Frontend

Create a folder named templates and add this file inside:

`templates/index.html`

<!DOCTYPE html>
<html>
<head>
  <title>ML Predictor</title>
  <style>
    body {
      font-family: Arial;
      background-color: #f0f2f5;
      padding: 50px;
    }
    .container {
      background: white;
      padding: 20px;
      border-radius: 10px;
      width: 300px;
      margin: auto;
      box-shadow: 0 0 10px rgba(0,0,0,0.1);
    }
    input {
      width: 100%;
      margin: 8px 0;
      padding: 8px;
    }
    button {
      background: #007bff;
      color: white;
      border: none;
      padding: 10px;
      width: 100%;
      border-radius: 5px;
      cursor: pointer;
    }
  </style>
</head>
<body>
  <div class="container">
    <h2>Enter Features</h2>
    <form action="/predict" method="POST">
      <input type="text" name="feature1" placeholder="Feature 1" required>
      <input type="text" name="feature2" placeholder="Feature 2" required>
      <input type="text" name="feature3" placeholder="Feature 3" required>
      <input type="text" name="feature4" placeholder="Feature 4" required>
      <button type="submit">Predict</button>
    </form>
    <h3>{{ prediction_text }}</h3>
  </div>
</body>
</html>

⚙️ Step 4: Run the App

n your terminal, run:

python app.py

Open http://127.0.0.1:5000/ in your browser, enter your inputs, and BOOM 💥—you’ll see your model's prediction!

Example Test Inputs

Try these:

5.1, 3.5, 1.4, 0.2 → Likely Class 0
6.7, 3.1, 4.4, 1.4 → Likely Class 1
7.2, 3.6, 6.1, 2.5 → Likely Class 2

Bonus: Convert it to an API

Want to use it from JavaScript or another app? Just add this route:

@app.route('/api', methods=['POST'])
def api():
    data = request.get_json(force=True)
    prediction = model.predict([np.array(list(data.values()))])
    return jsonify({'prediction': int(prediction[0])})

Then you can do:

curl -X POST http://127.0.0.1:5000/api -H "Content-Type: application/json" -d '{"feature1":5.1,"feature2":3.5,"feature3":1.4,"feature4":0.2}'

Next Steps

You’ve now deployed your first ML model locally. You can:

🚀 Push it to Render or Replit
💻 Turn it into a full React+Flask app
🔐 Add authentication or logging

💬 Final Thoughts

This isn’t just about ML. It’s about shipping your ideas.

You trained a model. You wrapped it in Flask. You made it usable.

Stay curious. Stay shipping. 🚀