How to Build a Language Translator Application with Strapi, Streamlit, and Hugging Face Models

If you’re building a cool side project or an MVP, you must store user and application content. This article will teach you about Strapi, a headless CMS you can use as your application backend. You will build a language translator application using Streamlit and a language translation model from Hugging Face that allows users to translate text written in any language to English, using Strapi as a back-end to store user inputs and outputs.

Benefits of a Language Translator Application

A language translator application is handy, especially when you come across texts online that you need help understanding because you are unfamiliar with the language. It can also serve as an educational tool, improving your understanding of foreign languages by learning through provided translations. Here is a demo of the language translator application you will build in this tutorial.

Prerequisites

Before starting, ensure you have;

Installed Python 3.9+ or above.
Installed Node.js
Installed npm
Set up a Hugging Face account.
Set up a Strapi account.
Set up a Streamlit Cloud account.
Set up a Github Account.

Set up the Project Directory

On your terminal, create a new project language_translator.
```
$ mkdir language_translator
```
Inside the project directory, create a Python virtual environment. This environment maintains the library versions used in your code, ensuring your code is reproducible every time it runs.
```
$ python -m venv venv
```
The first venv command creates a virtual environment, while the second venv specifies the name of your virtual environment, which you can give any name you like. After running the above command, a venv folder should be created in your project directory.
Run the following code to activate the virtual environment and start working on it.
```
$ ./venv/Scripts/Activate
```
Upon activation, you should see the name of your virtual environment in green, signifying that you are already in a virtual environment.
Inside the project directory, create a requirements.txt file
```
nano requirements.txt
```
Copy and paste the following Python Libraries into the requirements.txt file.
```
streamlit
python-dotenv
requests
```
- streamlit is a Python UI library for building interactive web applications.
- python-dotenv is a Python library for working with environment variables.
- requests is an HTTP client library that you can use to make requests from an API.
Install the above Python libraries using the following command
```
$ pip install -r requirements.txt
```
Create a .env file to store environment variables such as API keys and secrets.
```
$ touch .env
```
Inside your language_translator directory, create a folder frontend for the application frontend.
```
$ mkdir frontend
```
In the folder frontend, create the files main.py and utilis.py
```
touch main.py utilis.py
```
In the project folder, create a .gitignore file, to ignore the venv and .env path when pushing to git.
```
/venv
.env
```

Build the Front-end

The front end is where users will interact with the application. You will build it using Streamlit and translate user inputs using a language translator model from Hugging Face.

Select a Model from Hugging Face

Log in to Hugging Face, go to the search bar, and search for facebook/mbart-large-50-many-to-one-mmt. This is the model you will use to allow users to give input in any language and translate it into English.
Click on Deploy, then Inference API.

Copy the code displayed to you and paste it into utilis.py.

import requests

API_URL = "<https://api-inference.huggingface.co/models/facebook/mbart-large-50-many-to-one-mmt>"
headers = {"Authorization": "Bearer <your-api-token>"}

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

output = query({
    "inputs": "The answer to the universe is",
})

API_URL is the variable storing the link to the model API.
headers is the variable storing your Hugging Face authorization token to use the API.
The query function makes a POST request to the model API to translate a given input text and return the JSON output.

Copy your API-TOKEN, go to the .env file, and create the variable. HUGGING_FACE_TOKEN.
```
HUGGING_FACE_TOKEN="<your-api-token>"
```

Go back to the utilis.py file, copy and paste the following code to create a function of Step 3 that accepts a text in any language and translates it to English.

import requests
import os
import json
from datetime import datetime
from dotenv import load_dotenv

load_dotenv(".env")

HUGGING_FACE_TOKEN = os.getenv("HUGGING_FACE_TOKEN")

API_URL = "<https://api-inference.huggingface.co/models/facebook/mbart-large-50-many-to-one-mmt>"
headers = {"Authorization": f"Bearer {HUGGING_FACE_TOKEN}"}

def translate(inputs):
    def query(payload):
        response = requests.post(API_URL, headers=headers, json=payload)
        return response.json()

    output = query(
        {
            "inputs": inputs,
        }
    )
    return output[0]["generated_text"]

load_dotenv(".env") loads your .env file, making it visible for os.getenv() to get the environment variable specified.
The translate() function takes input text, while the query function processes it and returns it in JSON. Finally, the translate() function extracts the translated text.
Here is an example of how the translate function works. Copy and run the text below.

text = "संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है"
translate(text)

>>Output: 'The head of the UN says there is no military solution in Syria'

Build the User Interface with Streamlit

Inside your main.py file, import the translate function and streamlit.
```
from utilis import translate
import streamlit as st
```
Create a title and input area.
```
st.title("Language Translator to English")

input_text = st.text_area("Enter the text you want to translate:", height=150)
```
- st.title sets a text in bold and large size.
- st.text_area is a multi-line text input widget that allows users to give input.
Run the code below on your terminal to launch the Streamlit application.
```
streamlit run main.py
```
Create a button to process any input given in the text area.
```
if st.button("Translate"):
    if input_text:
        translated_text = translate(input_text)
        st.write("## Translated Text")
        st.write(translated_text)
    else:
        st.warning("Please enter some text to translate.")
```
- st.button creates a button to translate a text.
- If there is an input text, the translation function is called on the input_text and saved as translated_text; otherwise, a warning is given.
- Save your file and go to the Streamlit application to see the changes.
Paste the Hindi text given earlier into the text area and click on translate.

What is left is to set up a back-end on Strapi to save any translated text.

Build the Back-end

Set up Strapi

In your main directory language_translator, create a new Strapi project called backend.
```
npx create-strapi-app@latest backend --quickstart
```
This will install Strapi and an SQLite database in your project directory.
Run the following code in your terminal to open the admin panel at http://localhost:1337/admin. Fill in your credentials, Sign up, and Log in.
```
npm run develop
```
Go to Content Type Builder, click Create a new collection, create a new collection Translation, then Continue.
Create input_text and translated_text as Text fields while translation_date is a Date field and Save.
Go to Settings on your admin menu, under User & Permissions Plugin, click on Roles, and edit the Public role.
In the Public role, set the Translation permission to create and find, then Save.

Connect Strapi with Streamlit

Copy and paste the following code to create a function save_translation that saves both the input_text and output_text to Strapi.
```
STRAPI_URL = "<http://localhost:1337/api>"

def save_translation(input_text, translated_text):
    data = {
        "data": {
            "input_text": input_text,
            "translated_text": translated_text,
            "translation_date": datetime.now().isoformat(),
        }
    }

    response = requests.post(
        f"{STRAPI_URL}/translations",
        headers={"Content-Type": "application/json"},
        data=json.dumps(data),
    )
    return response.json()
```
- STRAPI_URL is the link to the Strapi backend API. You will use this to send and receive data from Strapi.
- The save_translation() function receives the user input_text, translated_text, and translation_date as JSON in the variable data and sends it for storage in the Strapi back-end.
- datetime.now().isoformat() saves the current date and time an input_text was translated as "translation_date" into data.
- The response variable makes a POST request to save the values into the Translation collection in Strapi.
Go back to main.py and update the code just before the end of the inner if condition. This will ensure that anytime a user clicks the Translate button, the text displays, and the input and output text are saved to Strapi through the save_translation() function.

if st.button("Translate"):
    if input_text:
        #code to display text after input is given
        #....
        #code to update
        #--------------
        save_translation(input_text, translated_text)
    else:
        st.warning("Please enter some text to translate.")

Try to translate a text, and go back to your dashboard under the Content Manager menu to see the saved text.
To output the history from latest to oldest, add the get_history() function to utilis.py.
```
def get_history():
    response = requests.get(f"{STRAPI_URL}/translations")
    if response.status_code == 200:
        return response.json()
    return []
```
- This function gets all the items stored in the Translation collection using a GET request and saves it in the variable response as JSON.
Update main.py with the following code to create a History button where users can view previous translations.
```
if st.button("History"):
    history = get_history()
    if history:
        for item in reversed(history["data"]):
            st.text(
                f"Date: {item['attributes']['translation_date']}\\nInput: {item['attributes']['input_text']}\\nTranslated: {item['attributes']['translated_text']}"
            )
    else:
        st.write("No history found.")
```
- If the History button is clicked, the get_history() is called, fetching and displaying all items in the Translation collection.
- The reversed() function loops the list history[data] in a reverse order to display recently added text first.
- If no history is found, the application returns a warning.
- If you click the history button, you should have something like this.

Deployment

The application consists of two components: the back end and the front end. There are various ways to deploy these components, but for this tutorial, you will use Strapi Cloud to deploy the back end and Streamlit Cloud to deploy the front end.

Strapi Cloud deployment is free for 14 days, but the Streamlit Cloud is completely free. Before proceeding, ensure you have already versioned your project on GitHub.

Deploy the Backend on Strapi Cloud

Go to Deploy on your admin dashboard menu, and click Deploy to Strapi Cloud.
Sign in to Strapi Cloud.
Click on Create Project to create a new project.
Select the Free trial and click on GitHub to permit Strapi to authorize your GitHub account.
After completing the authorization steps, you should have something like this. Select the account you want Strapi to access and the project repository.
Give the application a display name, and leave other options as the default.
Under Show Advanced Settings, type the backend directory, which is /backend, leave other options as default, and click on Create Project.
You can see the application build and deploy logs displayed while the application is being built.
Click on the Visit app to open up the live admin panel.
Like before, when you open the admin panel locally, fill in your credentials and log in.
Since the backend is deployed, change the STRAPI_URL variable in utilis.py to the live URL. Copy the URL of the live dashboard, excluding the part /admin, and include /API at the end.
```
STRAPI_URL="<your-strapi-api-url>/api"
```
Ensure you cross-check your settings to see if all the roles are set. If not, you can set them back using Step 6 in the Set up Strapi section.

That’s it, you now have your back-end live, let’s deploy the frontend and the back-end.

Deploy the Frontend on Streamlit Cloud

Log in to Streamlit Cloud and authorize Streamlit to access your GitHub account.
Click on Create app.
Please complete the form by providing the project repository and the path of the streamlet application, which is /frontend/main.py. If you have authorized Streamlit to access your GitHub repositories, it will list all the repositories in your GitHub account.
Click on Advanced Settings, type in your HUGGING_FACE_TOKEN as it is in your .env file, and Save.
Click on Deploy! to start building the application. This is going to take a while.
Now, you have successfully deployed the Streamlit application.

Conclusion

In this article, you have learned how to use a model from Hugging Face to build a Streamlit application and store your user inputs and outputs on a Strapi backend. You also learned how to deploy the back-end and front-end components on Strapi and Streamlit Cloud. Strapi has a fun and active Discord community to help answer your questions whenever you feel stuck.

Here is the GitHub repository for the language translator application. You can further extend the application by:

Add authorization to your Streamlit front end and set various user roles and permissions for your Strapi backend.
Exploring various language models on Hugging Face, such as English-to-many language translators or audio translators.

How to Build a Language Translator Application with Strapi, Streamlit, and Hugging Face Models

Benefits of a Language Translator Application

Prerequisites

Set up the Project Directory

Build the Front-end

Select a Model from Hugging Face

Build the User Interface with Streamlit

Build the Back-end

Set up Strapi

Connect Strapi with Streamlit

Deployment

Deploy the Backend on Strapi Cloud

Deploy the Frontend on Streamlit Cloud

Conclusion

Recommended Reads

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Benefits of a Language Translator Application

Prerequisites

Set up the Project Directory

Build the Front-end

Select a Model from Hugging Face

Build the User Interface with Streamlit

Build the Back-end

Set up Strapi

Connect Strapi with Streamlit

Deployment

Deploy the Backend on Strapi Cloud

Deploy the Frontend on Streamlit Cloud

Conclusion

Recommended Reads

Need Help with Data? Let’s Make It Simple.

Your next breakthrough could be one email away. Let’s make it happen!