Serving Models Using Web Frameworks (Flask, Django)

Artificial intelligence and machine learning models have become an integral part of many applications today. These models are used for various purposes, such as image recognition, natural language processing, and recommendation systems. However, deploying these models and making predictions in real-time can be challenging. This is where web frameworks like Flask and Django come into play.

Web frameworks provide a convenient way to serve machine learning models as APIs, making them accessible over the internet. In this article, we will explore how Flask and Django can be used to serve models, specifically focusing on the popular deep learning library, Keras.

Flask

Flask is a lightweight web framework for Python, known for its simplicity and ease of use. It can be used to build RESTful APIs that serve machine learning models. Here's how you can serve a Keras model using Flask:

1. Install Flask: Begin by installing Flask using the pip package manager.

pip install flask

2. Create an API: Create a new Python file, let's say app.py, and import the necessary libraries.

from flask import Flask, request, jsonify
import keras
...

3. Load the Model: Load your Keras model in the Flask app.

model = keras.models.load_model('path_to_model.h5')

4. Define an API route: Define a route for making predictions using your model.

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    # Preprocess input data
    ...
    # Make predictions using the loaded model
    predictions = model.predict(data)
    ...
    # Return predictions as a JSON response
    return jsonify({'predictions': predictions.tolist()})

5. Run the Flask app: Finally, run your Flask app.

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Your Flask app is now prepared to serve predictions based on the Keras model. You can send a POST request to http://localhost:5000/predict with the required input data, and it will return the predictions as a JSON response.

Django

Django is a high-level web framework for Python, widely used for developing robust web applications. While it may have more complex features compared to Flask, it provides a solid foundation for serving machine learning models. Let's see how to serve a Keras model using Django:

1. Install Django: Begin by installing Django using the pip package manager.

pip install django

2. Create a Django project: Create a new Django project.

django-admin startproject project_name

3. Create an app: Create a new Django app within your project.

cd project_name
python manage.py startapp app_name

4. Load the Model: Copy your Keras model files into the Django app directory.

5. Define a View: In the Django app's views.py file, load and use the Keras model.

from django.http import JsonResponse
import keras
...

def predict(request):
    data = request.POST.get('data')
    # Preprocess input data
    ...
    # Load the Keras model
    model = keras.models.load_model('path_to_model.h5')
    # Make predictions using the loaded model
    predictions = model.predict(data)
    ...
    # Return predictions as a JSON response
    return JsonResponse({'predictions': predictions.tolist()})

6. Define a URL route: In the Django app's urls.py file, define a URL route for the predict view.

from django.urls import path
from app_name import views

urlpatterns = [
    path('predict/', views.predict, name='predict'),
    ...
]

7. Run the Django server: Finally, run the Django server.

python manage.py runserver

Your Django app is now prepared to serve predictions based on the Keras model. You can send a POST request to http://localhost:8000/predict/ with the required input data, and it will return the predictions as a JSON response.

Conclusion

Serving machine learning models using web frameworks like Flask and Django enables us to leverage the power of AI in real-time applications without worrying about the complexities of deployment. In this article, we saw how we can use Flask and Django to serve Keras models as APIs. With these tools at hand, you can easily deploy your machine learning models and make them accessible to the world.