ReVerbal

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💡 Inspiration

We were inspired by the real communication challenges faced by neurodiverse individuals—especially those with autism, ADHD, or social anxiety—who often struggle with interpreting emotions and responding appropriately during conversations. Our goal was to create a supportive tool that helps them feel more confident and included in both virtual and in-person communication.

⚙️ What it does

ReVerbal is a real-time, AI-powered conversation assistant that:

Detects emotions from user input using tone and text analysis

Provides contextual response suggestions

Translates complex expressions into simple cues

Acts as a “social guide” by displaying friendly, visual prompts during conversations

It helps users navigate communication with ease—especially in classrooms, meetings, or social discussions.

🏗️ How we built it

Frontend: Built for a clean, intuitive UI/UX

Backend: Developed with FastAPI and Python for scalable API endpoints

Emotion Detection: Simulated with basic logic for now, but extensible to integrate NLP and emotion AI models

Routing: Clean folder structure with modular API routes

Local Testing: Used Uvicorn to host the backend server and test API functionality

🚧 Challenges we ran into

Integrating the backend with the frontend and verifying real-time flow

Structuring the backend correctly for scalability

Designing a user-friendly interface that doesn't overwhelm the user

Creating meaningful mock responses for emotion detection in the absence of a full model

🏅 Accomplishments that we're proud of

Successfully built and deployed a full working prototype

Created a welcoming UI tailored for neurodiverse users

Designed a backend that’s easily extendable with real AI/ML models

Learned how to bridge communication design with tech for accessibility

📚 What we learned

How to use FastAPI for rapid backend development

Structuring a project for real-time interactive applications

Importance of empathy in UI/UX design, especially for diverse user groups

Balancing simplicity with effectiveness in emotional communication

🚀 What's next for ReVerbal

Integrating actual emotion detection via NLP and facial recognition (like Affectiva or Microsoft Emotion API)

Adding real-time speech-to-text and audio analysis

Creating a personalized learning model for user-specific response guidance

Partnering with schools or therapy centers to test ReVerbal in real-life settings

Exploring voice-based interface and wearable integrations for accessibility

Built With

• fastapi
• python
• react
• tailwind
• typescript