Build a Streamlit App with Cohere's Rerank Model and ElevenLabs Integration

Introduction

Cohere is a leading AI company that has developed a suite of powerful language models aimed at revolutionizing the way text data is processed. This tutorial will provide a comprehensive guide on how to utilize Cohere's Rerank (Beta) model to enhance search algorithms and build a Streamlit application. Additionally, for those interested, I highly recommend checking out our tutorial on How to Get Started with Cohere LLMs.

Understanding ElevenLabs

ElevenLabs is a voice AI research and deployment company focused on making content accessible in various languages and voices. Specializing in producing highly realistic, contextually-aware AI-generated audio, ElevenLabs allows users to create speech in multiple diverse voices and languages. Founded in 2022 by Piotr and Mati, this company emerged from a desire to address issues related to insufficient dubbing in films, particularly in Hollywood.

Introduction to Streamlit

Streamlit is a pure Python framework designed for building web applications effortlessly. For more information about Streamlit and its applications, visit the official Streamlit website.

Prerequisites

• Download and install Visual Studio Code or any other compatible code editor like IntelliJ IDEA or PyCharm.
• Obtain a Cohere API key by signing up on Cohere's website.
• Also, get an ElevenLabs API key by registering on ElevenLabs and copying your key from the Profile section.
• Create a new account on Streamlit to easily deploy your application, preferably using your GitHub account.
• Make sure you have a cup of coffee and a laptop ready to go!

Learning Outcomes

• Understand how to use the Cohere Rerank (Beta) model via API.
• Learn how to build web applications using Streamlit.
• Create an application utilizing Cohere's Rerank model.
• Deploy the application on Streamlit Sharing Cloud.

Let's Get Started!

1. Create a New Project

Start by opening Visual Studio Code and creating a new folder named cohere-rerank-tutorial.

2. Set Up a Virtual Environment

Open your terminal and run the following command to create and activate a Python Virtual Environment:

python -m venv env
source env/bin/activate  # for macOS/Linux
# or
env\Scripts\activate  # for Windows

3. Install Required Dependencies

Next, install all necessary dependencies by running:

pip install cohere streamlit elevenlabs

4. Create the Streamlit App

Create a new file named app.py in your project folder. At the top of the file, import the required libraries:

import streamlit as st
import cohere
import elevenlabs

5. Configure the Logger (Optional)

This step is optional, but it can significantly ease the debugging process.

6. Configure the Streamlit Page

Provide a title, favicon, and description for your app so users can easily understand its functionalities:

st.set_page_config(page_title='Cohere Rerank App', page_icon=':sparkles:', layout='wide')
st.title('Cohere Rerank Application')
st.write('A web app that uses Cohere Rerank for enhanced search results.')

7. Create a Sidebar

Add a sidebar to handle API keys and uploaded files:

st.sidebar.title('API Configuration')
c_interface = st.sidebar.text_input('Cohere API Key')
el_interface = st.sidebar.text_input('ElevenLabs API Key')

8. Initialize Messages State

Implement logic to initialize the message states for the users.

9. Create Input Form

Create a form to accept user inputs and process the queries:

with st.form(key='my_form'):
    user_input = st.text_area('Enter your query:')
    submit_button = st.form_submit_button(label='Send')

10. Generate Voiceover

Implement functionality to generate a voiceover for each message:

# Sample Voiceover Function
voiceover = elevenlabs.Voiceover(user_input)

11. Display API Response

Fetch the response from the API and display it:

response = cohere.rerank(user_input)
st.write('Response from API:', response)

12. Clear Chat History (Optional)

You can also implement a function to clear the chat history as desired.

13. Run the Application

Run the application by executing:

streamlit run app.py

Visit http://localhost:8501 to view your app.

14. Install Watchdog Module

For enhanced performance, consider installing the Watchdog module:

pip install watchdog

15. Push Code to GitHub

Create a new repository on GitHub and push your code, ensuring that you include requirements.txt.

16. Deploy App on Streamlit Sharing Cloud

Log in to Streamlit Sharing Cloud with your GitHub account. Click on "New app" and fill in the details to deploy:

• Select your repository.
• Choose the branch and set app.py as the main file.
• Click on "Deploy!" and wait for the process to complete.

Upon completion, your app will be ready to use. You can also update your app by pushing new commits to GitHub, and Streamlit will pull the latest changes.

17. Testing the Application

To test your app, enter your Cohere API key, upload your desired file, enter a query, and hit the "Send" button.

Conclusion

Thank you for following along with this tutorial. You now have the tools to build and deploy a powerful application using Cohere’s Rerank model and ElevenLabs voice AI!