Chillbert

Overview

This is a voice-powered agent designed to engage users and mental health patients in casual, mood-lifting check-ins throughout the day. The agent provides friendly interactions, helping users pass time while boosting their mood. After each conversation, a summary is automatically shared with the assigned mental health practitioner via WhatsApp. Additionally, users receive personalized, helpful links based on the topics discussed. Practitioners are equipped with a dashboard, allowing them to explore the conversation further and ask follow-up questions, ensuring more tailored and insightful care.

Technologies Used

We used model on HumeAI for creating a conversational voice agent. These converations and their emotions are tracked, stored and analysed with a Gemini RAG model in LangChain. A summary of the conversation is sent to the health practitioner via Whatsapp.

Demo

Setting up a virtual environment

Before you install the dependencies, you might want to create a virtual environment to isolate your package installations. To create a virtual environment, run the following commands in your terminal:

# Create a virtual environment in the directory 'evi-env'
python -m venv venv

# Activate the virtual environment
# On Mac/Linux:
source venv/bin/activate

After activating the virtual environment, you can proceed with the installation of dependencies as described below.

Dependencies

In order to run it, you need to install the requirements.txt using pip:

Mac

pip install -r requirements.txt

Environment variables

Create a .env file or set environment variables. You will need a HUME account, a Google AI Studio account and a Twilio account.

Example .env file:

HUME_API_KEY="<HUME API KEY>"
HUME_SECRET_KEY="<HUME SECRET KEY>"
TWILIO_ACCOUNT_SID="<TWILIO ACCOUNT SID>"
TWILIO_AUTH_TOKEN="<TWILIO AUTH TOKEN>"
TWILIO_RECIPIENT="<TWILIO RECIPIENT>"
GOOGLE_API_KEY="<GOOGLE API KEY>"

Usage

streamlit run chillbert.py