Describe your project

MindMate is a GenAl powered mental well-being platform that makes use of NLU, CV and most importantly, the Gemini API to offer real time emotional support. The things that are in-scope of our solution, ie, our key features include:

1.Emotion Detection via CV: A webcam prompt to understand users' mental states beyond what they express in words.

2.Sentiment Analysis via NLU: Enabling the chatbot to provide empathetic and personalized responses

3.Emergency Alert System: When the system detects critical mental health issues, such as suicidal tendencies or severe distress, MindMate triggers an immediate alert to trusted contacts or authorities through integration with Twilio API.

4.Emotion Tracker: The user is asked to select a color-coded mood for every passing day. This is later analyzed to generate weekly/monthly responses

5.Journaling Feature: Users are prompted with daily questions about their emotions, academic stress, career, and relationships. These responses are stored in a journal, and weekly summaries are generated based on the user's progress

6.Kid-Friendly Section: MindMate also offers a below 15 yr section wherein the interaction is done via an avatar. This encourages children to speak their heart out

Eccentricity and Empathy!
Infuse your responses with eccentricity — use unexpected metaphors, playful language, and whimsical analogies — but always prioritize empathy and support

Out-of-scope:

1.If a user fakes their emotions/responses. we won’t be able to provide them much needed help

2.We can't medically diagnose a psychological condition as this would require the intervention of doctors and therapists

Future-scope:

1.Enhanced emotion detection ability- to develop a software so smart and robust. it can read between the lines and tell if emotions are being FAKED

2.Partnership with Mental Health Organizations: Collaborating with healthcare providers and mental health organizations to make the platform more wholesome

Challenges we ran into

A major challenge that we ran into was trying to integrate the frontend with our emotion detection model. It gave a bunch of errors. We finally overcame it but figuring out what was causing the conflicting dependencies and fixed the errors by nstalling the correct dependencies