Deploy Sentiment Analysis Model in Flask

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Deploy a sentiment analysis machine learning model in Flask, you will need to follow these steps:

  1. Train your sentiment analysis model: This can be done using any machine learning library, such as scikit-learn, TensorFlow, or Keras. You will need to split your data into training and testing sets, and use the training set to fit your model.
  2. Save your trained model: After training your model, you will need to save it to a file so that you can load it later for deployment. You can do this using the joblib library in Python.
  3. Create a Flask application: In your Python script, import the Flask library and create a Flask app. You can then define the routes for your application, which will specify the actions to be taken when a user accesses a particular URL.
  4. Load your trained model: In your Flask app, you will need to load your trained model from the file in which you saved it. This can be done using the joblib library.
  5. Create a form for users to input text: In your HTML template, create a form that includes a text field for users to enter the text they want to analyze.
  6. Process user input and make predictions: When the user submits the form, the Flask app will receive a request containing the user’s input. You can then use your trained model to make a prediction on the sentiment of the input text.
  7. Return the prediction to the user: Finally, you can return the prediction to the user by rendering an HTML template with the prediction included.

You can code a sentiment analysis application using Flask and a pre-trained machine learning model:

from flask import Flask, render_template, request
from sklearn.externals import joblib

app = Flask(__name__)

# Load the trained model
model = joblib.load('sentiment_analysis_model.pkl')

@app.route('/')
def home():
  return render_template('index.html')

@app.route('/predict', methods=['POST'])
def predict():
  # Get the user's input text
  text = request.form['text']

  # Use the model to make a prediction
  prediction = model.predict([text])[0]

  # Render the prediction in an HTML template
  return render_template('prediction.html', prediction=prediction)

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

This code creates a Flask app with a single route, /predict, which accepts POST requests. When a user submits the form on the homepage, the app receives the input text and uses the trained model to make a prediction. The prediction is then rendered in the prediction.html template and returned to the user.