1. Introduction
In this blog, you will learn how to deploy a natural language processing (NLP) model built with PyTorch as a web app using Flask and Heroku. NLP is a branch of artificial intelligence that deals with the interaction between computers and human languages. PyTorch is an open-source framework for deep learning that provides a flexible and easy-to-use way to build and train neural networks. Flask is a lightweight web framework that allows you to create web applications with Python. Heroku is a cloud platform that enables you to deploy and scale web apps without managing servers.
By the end of this blog, you will be able to:
- Build a NLP model with PyTorch that can perform sentiment analysis on movie reviews.
- Create a web app with Flask that can take user input and display the model output.
- Deploy the web app with Heroku and access it from any device.
This blog assumes that you have some basic knowledge of Python, PyTorch, and NLP. You will also need to install some packages and tools to follow along. You can find the complete code and instructions for this blog on this GitHub repository.
Are you ready to deploy your NLP model as a web app? Let’s get started!
2. Building a NLP Model with PyTorch
The first step of deploying a NLP model as a web app is to build the model itself. In this section, you will learn how to use PyTorch to build a NLP model that can perform sentiment analysis on movie reviews. Sentiment analysis is a common NLP task that involves classifying a text into positive or negative based on the emotions or opinions expressed in it. For example, the sentence “I loved this movie, it was so funny and entertaining” would be classified as positive, while the sentence “I hated this movie, it was so boring and stupid” would be classified as negative.
To build a sentiment analysis model, you will need two things: data and architecture. Data is the collection of texts and labels that you will use to train and evaluate your model. Architecture is the design of your neural network that will process the texts and produce the labels. Let’s see how you can obtain and prepare the data and architecture for your sentiment analysis model.
The data that you will use for this tutorial is the Large Movie Review Dataset, which contains 50,000 movie reviews from IMDb, labeled as positive or negative. This dataset is widely used for sentiment analysis research and benchmarking. You can download the dataset from this link and extract it to your working directory.
The architecture that you will use for this tutorial is a recurrent neural network (RNN) with a long short-term memory (LSTM) layer and a linear layer. RNNs are a type of neural network that can handle sequential data, such as texts, by maintaining a hidden state that captures the information from the previous inputs. LSTM is a variant of RNN that can deal with long-term dependencies and avoid the problem of vanishing or exploding gradients. The linear layer is a simple layer that maps the output of the LSTM to the final label.
In the next subsections, you will learn how to load and preprocess the data, define the model architecture, and train and evaluate the model using PyTorch.
2.1. Loading and Preprocessing the Data
Before you can train your NLP model, you need to load and preprocess the data. Loading the data means reading the files that contain the texts and labels and storing them in a suitable data structure. Preprocessing the data means applying some transformations to the texts and labels to make them ready for the model. For example, you might want to remove punctuation, lowercase the words, tokenize the sentences, convert the labels to numbers, etc.
To load and preprocess the data, you will use two PyTorch classes: Dataset and DataLoader. A Dataset is an abstract class that represents a collection of data samples and labels. A DataLoader is a class that wraps a Dataset and provides an easy way to iterate over the data in batches. You can also specify some options for the DataLoader, such as the batch size, the shuffle mode, the collate function, etc.
To create a custom Dataset for your sentiment analysis task, you need to define a subclass of the Dataset class and implement two methods: __len__ and __getitem__. The __len__ method returns the number of samples in the dataset, and the __getitem__ method returns a single sample and label given an index. In your subclass, you will also define the logic for loading and preprocessing the data from the files.
Here is an example of how you can define a custom Dataset class for your sentiment analysis task:
import torch
from torch.utils.data import Dataset
import os
import glob
import re
class SentimentDataset(Dataset):
def __init__(self, data_dir, split):
# data_dir: the directory where the data files are stored
# split: the split of the data, either "train" or "test"
self.data_dir = data_dir
self.split = split
self.label_dict = {"pos": 1, "neg": 0} # a dictionary to map the labels to numbers
self.data = [] # a list to store the data samples and labels
self.load_data() # a method to load the data from the files
self.preprocess_data() # a method to preprocess the data
def load_data(self):
# this method loads the data from the files and stores them in the data list
# each data sample is a tuple of (text, label), where text is a string and label is a number
for label in ["pos", "neg"]:
# iterate over the two labels
files = glob.glob(os.path.join(self.data_dir, self.split, label, "*.txt"))
# get the list of files for each label
for file in files:
# iterate over the files
with open(file, "r", encoding="utf-8") as f:
# open the file and read the text
text = f.read()
# append the text and the label to the data list
self.data.append((text, self.label_dict[label]))
def preprocess_data(self):
# this method preprocesses the data by applying some transformations to the texts and labels
# you can customize this method according to your needs
for i in range(len(self.data)):
# iterate over the data samples
text, label = self.data[i]
# get the text and the label of each sample
text = text.lower() # lowercase the text
text = re.sub(r"[^a-z0-9\s]", "", text) # remove punctuation
text = re.sub(r"\s+", " ", text) # remove extra spaces
text = text.strip() # remove leading and trailing spaces
label = torch.tensor(label, dtype=torch.long) # convert the label to a torch tensor
self.data[i] = (text, label) # update the data sample
def __len__(self):
# this method returns the number of samples in the dataset
return len(self.data)
def __getitem__(self, index):
# this method returns a single sample and label given an index
return self.data[index]
Now that you have defined your custom Dataset class, you can create an instance of it and pass it to a DataLoader. You can also specify some options for the DataLoader, such as the batch size, the shuffle mode, etc. Here is an example of how you can create a DataLoader for your sentiment analysis task:
from torch.utils.data import DataLoader
# create an instance of the Dataset class
dataset = SentimentDataset(data_dir="aclImdb", split="train")
# create an instance of the DataLoader class
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# iterate over the data in batches
for batch in dataloader:
# get the texts and labels for each batch
texts, labels = batch
# do something with the texts and labels
Congratulations, you have successfully loaded and preprocessed the data for your sentiment analysis task. In the next subsection, you will learn how to define the model architecture for your NLP model.
2.2. Defining the Model Architecture
After loading and preprocessing the data, you need to define the model architecture for your NLP model. The model architecture is the design of your neural network that will process the texts and produce the labels. In this subsection, you will learn how to use PyTorch to define a model architecture that consists of a recurrent neural network (RNN) with a long short-term memory (LSTM) layer and a linear layer.
To define a model architecture in PyTorch, you need to create a subclass of the nn.Module class and implement two methods: __init__ and forward. The __init__ method initializes the layers and parameters of the model. The forward method defines the logic for how the model processes the inputs and returns the outputs. You can also define some helper methods for the model, such as the embedding layer, the hidden state initialization, etc.
Here is an example of how you can define a model architecture class for your sentiment analysis task:
import torch
import torch.nn as nn
class SentimentModel(nn.Module):
def __init__(self, vocab_size, embed_dim, hidden_dim, output_dim, num_layers, dropout, pad_idx):
# vocab_size: the size of the vocabulary
# embed_dim: the dimension of the word embeddings
# hidden_dim: the dimension of the hidden state of the LSTM
# output_dim: the dimension of the output (either 1 or 2 for binary classification)
# num_layers: the number of layers in the LSTM
# dropout: the dropout probability for the LSTM and the linear layer
# pad_idx: the index of the padding token in the vocabulary
super().__init__() # initialize the parent class
self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=pad_idx) # define the embedding layer
self.lstm = nn.LSTM(embed_dim, hidden_dim, num_layers, dropout=dropout, batch_first=True) # define the LSTM layer
self.linear = nn.Linear(hidden_dim, output_dim) # define the linear layer
self.dropout = nn.Dropout(dropout) # define the dropout layer
def forward(self, texts, text_lengths):
# texts: a batch of texts, each text is a sequence of token ids (shape: batch_size x max_length)
# text_lengths: a batch of text lengths, each length is the number of tokens in the text (shape: batch_size)
embedded = self.dropout(self.embedding(texts)) # embed the texts (shape: batch_size x max_length x embed_dim)
packed = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths, batch_first=True, enforce_sorted=False) # pack the embedded texts
output, (hidden, cell) = self.lstm(packed) # pass the packed texts through the LSTM (shape: batch_size x max_length x hidden_dim)
hidden = self.dropout(hidden[-1]) # get the last hidden state of the LSTM (shape: batch_size x hidden_dim)
output = self.linear(hidden) # pass the hidden state through the linear layer (shape: batch_size x output_dim)
return output # return the output
Now that you have defined your model architecture class, you can create an instance of it and pass it the parameters that you want. You can also specify some options for the model, such as the optimizer, the loss function, the device, etc. Here is an example of how you can create a model instance for your sentiment analysis task:
# define some hyperparameters
vocab_size = 25000 # the size of the vocabulary
embed_dim = 100 # the dimension of the word embeddings
hidden_dim = 256 # the dimension of the hidden state of the LSTM
output_dim = 1 # the dimension of the output (either 1 or 2 for binary classification)
num_layers = 2 # the number of layers in the LSTM
dropout = 0.5 # the dropout probability for the LSTM and the linear layer
pad_idx = 0 # the index of the padding token in the vocabulary
learning_rate = 0.001 # the learning rate for the optimizer
# create an instance of the model class
model = SentimentModel(vocab_size, embed_dim, hidden_dim, output_dim, num_layers, dropout, pad_idx)
# define the optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# define the loss function
criterion = nn.BCEWithLogitsLoss()
# define the device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# move the model and the loss function to the device
model = model.to(device)
criterion = criterion.to(device)
Congratulations, you have successfully defined the model architecture for your NLP model. In the next subsection, you will learn how to train and evaluate the model using PyTorch.
2.3. Training and Evaluating the Model
Once you have defined the model architecture, you need to train and evaluate the model using the data. Training the model means adjusting the model parameters to minimize the loss function on the training data. Evaluating the model means measuring the performance of the model on the test data. In this subsection, you will learn how to use PyTorch to train and evaluate your NLP model for sentiment analysis.
To train and evaluate the model, you need to define two functions: train and evaluate. The train function takes a model, a dataloader, an optimizer, and a criterion as inputs and returns the average loss and accuracy on the training data. The evaluate function takes a model, a dataloader, and a criterion as inputs and returns the average loss and accuracy on the test data. You can also define some helper functions for calculating the accuracy, printing the progress, saving the model, etc.
Here is an example of how you can define the train and evaluate functions for your sentiment analysis task:
import torch
import numpy as np
def train(model, dataloader, optimizer, criterion):
# this function trains the model for one epoch and returns the average loss and accuracy on the training data
model.train() # set the model to training mode
epoch_loss = 0 # initialize the epoch loss
epoch_acc = 0 # initialize the epoch accuracy
for batch in dataloader:
# iterate over the batches in the dataloader
texts, labels = batch # get the texts and labels for each batch
texts = texts.to(device) # move the texts to the device
labels = labels.to(device) # move the labels to the device
text_lengths = torch.sum(texts != 0, dim=1) # calculate the lengths of the texts
optimizer.zero_grad() # reset the gradients
output = model(texts, text_lengths).squeeze(1) # pass the texts and lengths through the model and squeeze the output
loss = criterion(output, labels) # calculate the loss
acc = binary_accuracy(output, labels) # calculate the accuracy
loss.backward() # compute the gradients
optimizer.step() # update the parameters
epoch_loss += loss.item() # update the epoch loss
epoch_acc += acc.item() # update the epoch accuracy
return epoch_loss / len(dataloader), epoch_acc / len(dataloader) # return the average loss and accuracy
def evaluate(model, dataloader, criterion):
# this function evaluates the model on the test data and returns the average loss and accuracy
model.eval() # set the model to evaluation mode
epoch_loss = 0 # initialize the epoch loss
epoch_acc = 0 # initialize the epoch accuracy
with torch.no_grad():
# disable gradient computation
for batch in dataloader:
# iterate over the batches in the dataloader
texts, labels = batch # get the texts and labels for each batch
texts = texts.to(device) # move the texts to the device
labels = labels.to(device) # move the labels to the device
text_lengths = torch.sum(texts != 0, dim=1) # calculate the lengths of the texts
output = model(texts, text_lengths).squeeze(1) # pass the texts and lengths through the model and squeeze the output
loss = criterion(output, labels) # calculate the loss
acc = binary_accuracy(output, labels) # calculate the accuracy
epoch_loss += loss.item() # update the epoch loss
epoch_acc += acc.item() # update the epoch accuracy
return epoch_loss / len(dataloader), epoch_acc / len(dataloader) # return the average loss and accuracy
def binary_accuracy(output, labels):
# this function calculates the accuracy of the model for binary classification
# output: the output of the model (shape: batch_size)
# labels: the true labels (shape: batch_size)
rounded_output = torch.round(torch.sigmoid(output)) # round the output to 0 or 1
correct = (rounded_output == labels).float() # compare the output with the labels
acc = correct.sum() / len(correct) # calculate the accuracy
return acc
def print_progress(epoch, train_loss, train_acc, valid_loss, valid_acc):
# this function prints the progress of the training and evaluation
# epoch: the current epoch number
# train_loss: the average loss on the training data
# train_acc: the average accuracy on the training data
# valid_loss: the average loss on the test data
# valid_acc: the average accuracy on the test data
print(f"Epoch: {epoch+1}")
print(f"Train Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%")
print(f"Valid Loss: {valid_loss:.3f} | Valid Acc: {valid_acc*100:.2f}%")
def save_model(model, model_path):
# this function saves the model to a file
# model: the model to be saved
# model_path: the path of the file
torch.save(model.state_dict(), model_path)
print(f"Model saved to {model_path}")
Now that you have defined the train and evaluate functions, you can use them to train and evaluate your model for a number of epochs. You can also keep track of the best validation loss and save the model with the lowest validation loss. Here is an example of how you can train and evaluate your model for your sentiment analysis task:
# define some hyperparameters
num_epochs = 10 # the number of epochs to train the model
model_path = "sentiment_model.pt" # the path of the file to save the model
# create the dataloaders for the training and test data
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
# initialize the best validation loss to a large value
best_valid_loss = float("inf")
# iterate over the epochs
for epoch in range(num_epochs):
# train the model on the training data
train_loss, train_acc = train(model, train_dataloader, optimizer, criterion)
# evaluate the model on the test data
valid_loss, valid_acc = evaluate(model, test_dataloader, criterion)
# print the progress
print_progress(epoch, train_loss, train_acc, valid_loss, valid_acc)
# save the model if the validation loss is lower than the best validation loss
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
save_model(model, model_path)
Congratulations, you have successfully trained and evaluated your NLP model for sentiment analysis. In the next section, you will learn how to create a web app with Flask that can take user input and display the model output.
3. Creating a Web App with Flask
Now that you have trained and evaluated your NLP model, you might want to deploy it as a web app that can take user input and display the model output. In this section, you will learn how to use Flask to create a web app for your sentiment analysis model. Flask is a lightweight web framework that allows you to create web applications with Python.
To create a web app with Flask, you need to do three things: write the app logic, design the web interface, and integrate the model with the web app. In the next subsections, you will learn how to do each of these tasks step by step.
Before you start, you need to install Flask and some other packages that you will use for this tutorial. You can use the following command to install them:
pip install flask flask-wtf wtforms
You also need to create a folder for your web app and copy the model file that you saved in the previous section to that folder. You can name the folder anything you want, but for this tutorial, we will call it sentiment_app. You can use the following commands to create the folder and copy the model file:
mkdir sentiment_app cp sentiment_model.pt sentiment_app
Now you are ready to create your web app with Flask. Let's begin with writing the app logic.
3.1. Setting Up the Flask Environment
Before you can create a web app with Flask, you need to set up the Flask environment on your local machine. Flask is a Python package that you can install using pip, the Python package manager. You can also use virtualenv, a tool that creates isolated Python environments, to avoid any conflicts with other packages or versions. Here are the steps to set up the Flask environment:
- Create a new folder for your web app project and navigate to it in your terminal.
- Create a virtual environment using the command
virtualenv venv
This will create a folder called venv that contains the Python interpreter and the packages for your project.
- Activate the virtual environment using the command
source venv/bin/activate
This will change your prompt to indicate that you are in the virtual environment.
- Install Flask using the command
pip install Flask
This will download and install Flask and its dependencies in your virtual environment.
- Verify that Flask is installed correctly by running the command
python -c "import flask; print(flask.__version__)"
This will print the Flask version that you have installed.
Congratulations, you have successfully set up the Flask environment on your local machine. You can now proceed to create your web app with Flask.
3.2. Designing the Web Interface
Now that you have set up the Flask environment, you can start designing the web interface for your web app. The web interface is the part of the web app that the user interacts with, such as the input fields, buttons, and output displays. To design the web interface, you will use HTML, CSS, and JavaScript, which are the standard languages for creating web pages.
HTML stands for HyperText Markup Language, and it defines the structure and content of a web page. CSS stands for Cascading Style Sheets, and it controls the appearance and layout of a web page. JavaScript is a scripting language that enables dynamic and interactive features on a web page, such as validating user input, sending requests to the server, and updating the output.
To create a web interface for your web app, you will need two files: index.html and style.css. The index.html file contains the HTML code that defines the elements of the web page, such as the title, the header, the input form, and the output area. The style.css file contains the CSS code that styles the elements of the web page, such as the colors, fonts, margins, and alignments.
Here is an example of the index.html file for your web app:
PyTorch NLP Web App
PyTorch NLP Web App
This web app allows you to perform sentiment analysis on movie reviews using a PyTorch NLP model.
Here is an example of the style.css file for your web app:
body {
font-family: Arial, sans-serif;
background-color: #f0f0f0;
}
h1 {
text-align: center;
color: #333333;
}
p {
margin: 10px;
}
#input-form {
display: flex;
flex-direction: column;
align-items: center;
}
#input-text {
margin: 10px;
border: 1px solid #cccccc;
padding: 5px;
}
#submit-button {
margin: 10px;
padding: 10px;
border: none;
background-color: #333333;
color: white;
cursor: pointer;
}
#output-area {
display: flex;
flex-direction: column;
align-items: center;
}
#output-text {
margin: 10px;
font-weight: bold;
}
The result of these files is a simple web interface that looks like this:
In the next subsection, you will learn how to integrate the PyTorch NLP model with the web app using Flask and JavaScript.
3.3. Integrating the Model with the Web App
After designing the web interface for your web app, you need to integrate the PyTorch NLP model with the web app using Flask and JavaScript. Flask is a web framework that allows you to create web applications with Python. JavaScript is a scripting language that enables dynamic and interactive features on a web page, such as validating user input, sending requests to the server, and updating the output.
To integrate the model with the web app, you will need two more files: app.py and script.js. The app.py file contains the Flask code that defines the web app logic, such as loading the model, receiving the user input, performing the sentiment analysis, and returning the output. The script.js file contains the JavaScript code that handles the user interaction, such as submitting the input form, sending an AJAX request to the Flask server, and displaying the output on the web page.
Here is an example of the app.py file for your web app:
from flask import Flask, request, jsonify
import torch
import torch.nn as nn
import torchtext
import spacy
# Create a Flask app object
app = Flask(__name__)
# Load the PyTorch NLP model
model = torch.load("model.pth")
model.eval()
# Load the text field and the label field
text_field = torch.load("text_field.pth")
label_field = torch.load("label_field.pth")
# Define a function to predict the sentiment of a review
def predict_sentiment(review):
# Tokenize the review using spaCy
nlp = spacy.load("en_core_web_sm")
tokens = [token.text for token in nlp.tokenizer(review)]
# Convert the tokens to indices using the text field
indices = [text_field.vocab.stoi[token] for token in tokens]
# Convert the indices to a tensor and add a batch dimension
tensor = torch.LongTensor(indices).unsqueeze(0)
# Feed the tensor to the model and get the output
output = model(tensor)
# Convert the output to a probability using softmax
probability = nn.functional.softmax(output, dim=1)
# Get the predicted label and confidence
label = label_field.vocab.itos[probability.argmax(1).item()]
confidence = probability.max(1)[0].item()
# Return the label and confidence as a dictionary
return {"label": label, "confidence": confidence}
# Define a route for the web app
@app.route("/", methods=["POST"])
def analyze():
# Get the review from the request data
review = request.get_json()["review"]
# Predict the sentiment of the review
prediction = predict_sentiment(review)
# Return the prediction as a JSON response
return jsonify(prediction)
Here is an example of the script.js file for your web app:
// Get the input form element
var inputForm = document.getElementById("input-form");
// Add an event listener for the submit event
inputForm.addEventListener("submit", function(event) {
// Prevent the default form submission behavior
event.preventDefault();
// Get the input text element
var inputText = document.getElementById("input-text");
// Get the input text value
var review = inputText.value;
// Validate the input text
if (review.length == 0) {
// If the input text is empty, show an alert message
alert("Please enter a movie review.");
} else {
// If the input text is not empty, send an AJAX request to the Flask server
$.ajax({
url: "/",
type: "POST",
contentType: "application/json",
data: JSON.stringify({"review": review}),
dataType: "json",
success: function(response) {
// If the request is successful, get the output text element
var outputText = document.getElementById("output-text");
// Get the label and confidence from the response
var label = response["label"];
var confidence = response["confidence"];
// Format the output text
var output = "The predicted sentiment is " + label + " with " + confidence.toFixed(2) + " confidence.";
// Display the output text on the web page
outputText.textContent = output;
}
});
}
});
The result of these files is a functional web app that can perform sentiment analysis on movie reviews using the PyTorch NLP model. You can test the web app on your local machine by running the command
flask run
in your terminal and opening the URL http://localhost:5000 in your browser.
In the next section, you will learn how to deploy the web app with Heroku and access it from any device.
4. Deploying the Web App with Heroku
Once you have created a web app with Flask and integrated the PyTorch NLP model with it, you can deploy the web app with Heroku and make it accessible from any device. Heroku is a cloud platform that enables you to deploy and scale web apps without managing servers. Heroku also provides various features and services that make the deployment process easier and faster.
To deploy the web app with Heroku, you will need to do the following steps:
- Create a Heroku account and app. You will need to sign up for a free Heroku account and create a new app on the Heroku dashboard. You will also need to install the Heroku CLI, a tool that allows you to interact with Heroku from your terminal.
- Configure the app dependencies and settings. You will need to create two files in your project folder: requirements.txt and Procfile. The requirements.txt file lists the Python packages that your app depends on, such as Flask and PyTorch. The Procfile specifies the command that Heroku should run to start your app, such as
web: gunicorn app:app
where gunicorn is a web server that Heroku uses to run your app, and app:app refers to the Flask app object in your app.py file.
- Push the app to Heroku and test it. You will need to use the Heroku CLI to log in to your Heroku account, initialize a Git repository in your project folder, add the files to the repository, commit the changes, and push the repository to Heroku. This will trigger the deployment process, where Heroku will install the dependencies, run the command, and launch the app. You can then open the app URL in your browser and test the web app.
Congratulations, you have successfully deployed the web app with Heroku and made it accessible from any device. You can now share the app URL with anyone who wants to try your PyTorch NLP model.
In the next section, you will learn how to conclude the blog and provide some resources for further learning.
4.1. Creating a Heroku Account and App
The first step of deploying the web app with Heroku is to create a Heroku account and app. Heroku is a cloud platform that enables you to deploy and scale web apps without managing servers. Heroku also provides various features and services that make the deployment process easier and faster.
To create a Heroku account and app, you will need to do the following steps:
- Sign up for a free Heroku account on this page. You will need to provide your email address, a password, and a username.
- Verify your email address by clicking on the link that Heroku will send you. You will then be redirected to the Heroku dashboard, where you can manage your apps and settings.
- Create a new app on the Heroku dashboard by clicking on the New button and selecting Create new app. You will need to give your app a name and choose a region. The name of your app will be part of the app URL, so make sure it is unique and descriptive.
- Install the Heroku CLI, a tool that allows you to interact with Heroku from your terminal. You can download and install the Heroku CLI from this page. You will need to follow the instructions for your operating system.
- Log in to your Heroku account from the terminal by running the command
heroku login
You will need to enter your email address and password, or use your web browser to log in.
Congratulations, you have successfully created a Heroku account and app. You can now proceed to configure the app dependencies and settings.
4.2. Configuring the App Dependencies and Settings
The second step of deploying the web app with Heroku is to configure the app dependencies and settings. The app dependencies are the Python packages that your app depends on, such as Flask and PyTorch. The app settings are the commands that Heroku should run to start your app, such as the web server and the Flask app object.
To configure the app dependencies and settings, you will need to create two files in your project folder: requirements.txt and Procfile. The requirements.txt file lists the Python packages that your app depends on, one per line. The Procfile specifies the command that Heroku should run to start your app, in the format
process_type: command
where process_type is the type of process that runs your app, such as web, and command is the command that runs your app, such as gunicorn app:app where gunicorn is a web server that Heroku uses to run your app, and app:app refers to the Flask app object in your app.py file.
Here is an example of the requirements.txt file for your web app:
Flask gunicorn torch torchtext spacy
Here is an example of the Procfile for your web app:
web: gunicorn app:app
These files tell Heroku what packages to install and what command to run to launch your app. You can also specify other settings, such as the Python version, the environment variables, and the buildpacks, in the Heroku dashboard or the Heroku CLI. For more details, you can refer to the Heroku documentation.
In the next subsection, you will learn how to push the app to Heroku and test it.
4.3. Pushing the App to Heroku and Testing It
The third and final step of deploying the web app with Heroku is to push the app to Heroku and test it. Pushing the app to Heroku means uploading the app files and dependencies to the Heroku server, where they will be installed and run. Testing the app means opening the app URL in your browser and checking if the app works as expected.
To push the app to Heroku and test it, you will need to do the following steps:
- Initialize a Git repository in your project folder by running the command
git init
in your terminal. Git is a version control system that tracks the changes in your files and allows you to upload them to Heroku.
- Add the files to the Git repository by running the command
git add .
in your terminal. This will add all the files in your project folder to the Git repository.
- Commit the changes to the Git repository by running the command
git commit -m "Initial commit"
in your terminal. This will save the changes to the Git repository with a message "Initial commit".
- Connect the Git repository to the Heroku app by running the command
heroku git:remote -a your-app-name
in your terminal. This will link the Git repository to the Heroku app that you created earlier. Replace your-app-name with the name of your app.
- Push the Git repository to the Heroku app by running the command
git push heroku master
in your terminal. This will upload the files and dependencies to the Heroku server and trigger the deployment process.
- Open the app URL in your browser by running the command
heroku open
in your terminal. This will open the app URL in your default browser and display the web app.
- Test the web app by entering a movie review in the input text box and clicking on the submit button. You should see the predicted sentiment and confidence on the web page.
Congratulations, you have successfully deployed the web app with Heroku and tested it. You can now share the app URL with anyone who wants to try your PyTorch NLP model.
In the next section, you will learn how to conclude the blog and provide some resources for further learning.
5. Conclusion
In this blog, you have learned how to deploy a NLP model built with PyTorch as a web app using Flask and Heroku. You have gone through the following steps:
- Building a NLP model with PyTorch that can perform sentiment analysis on movie reviews.
- Creating a web app with Flask that can take user input and display the model output.
- Deploying the web app with Heroku and making it accessible from any device.
By following this tutorial, you have gained some practical skills and knowledge in PyTorch, NLP, deployment, web app, Flask, and Heroku. You have also created a useful and interactive web app that showcases your PyTorch NLP model.
If you want to learn more about these topics, you can check out the following resources:
- PyTorch Tutorials: A collection of tutorials that cover various topics and applications of PyTorch.
- spaCy: A popular Python library for NLP that provides various tools and models for text processing and analysis.
- Flask Documentation: The official documentation of Flask that explains how to use the framework and its features.
- Heroku Python Support: The official documentation of Heroku that covers how to deploy and manage Python apps on the platform.
Thank you for reading this blog and I hope you enjoyed it. If you have any questions or feedback, please feel free to leave a comment below. Happy coding!
