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How to Deploy Models Using Flask?

Deploying machine learning models using Flask is an effective way to create web applications that communicate with the model. Below are the steps to follow:

1. Set Up Your Environment

Ensure you have Python and Flask installed. You can do this using pip:

pip install Flask

2. Create a Flask Application

Create a file named app.py. Import Flask and create an application instance:

from flask import Flask, request, jsonify
app = Flask(__name__)

3. Load Your Model

Use joblib or pickle to load your pre-trained model:

import joblib
model = joblib.load('model.pkl')

4. Create Prediction Endpoint

Define an API endpoint to handle requests. Use POST to accept incoming data:

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json(force=True)
    prediction = model.predict(data['input'])
    return jsonify(prediction.tolist())

5. Run the Application

Finally, run the Flask application:

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

6. Test the API

You can use tools like Postman or curl to send requests to your endpoint and receive predictions.

By following these steps, you can effectively deploy your machine learning models with Flask and make them accessible via a web interface.