MindMate — AI Mental Health Companion | Django + Groq LLaMA Final Year Project with Source Code
MindMate is an AI mental health companion built with Django and Groq's LLaMA 3.3 70B that offers empathetic chat, mood tracking, private journaling, and instant crisis helpline support.
Technology Used
Python | Django 5.2 | Groq API | LLaMA 3.3 70B | VADER Sentiment | Chart.js | SQLite3 | python-decouple | HTML5 | CSS3
MindMate — Your AI Mental Health Companion
MindMate is a warm, calming web application that gives students a private space to talk, reflect, and track how they feel day to day. It pairs an empathetic AI chat experience powered by Groq's LLaMA 3.3 70B model with a clean Django backend, so the conversation feels genuinely supportive instead of robotic. If you are searching for an AI final year project that solves a real problem and still impresses your evaluation panel, this is one of the most meaningful options on the marketplace.
Mental health is something almost every college student quietly deals with, which makes MindMate an easy project to defend in a viva. It is built as a Python final year project with source code that you can run on your own machine in minutes, customise freely, and submit with confidence.
An important note built into the app itself: MindMate is a companion for emotional self-support and reflection. It is not a replacement for professional mental health care. Verified crisis helplines stay one click away from the navbar at all times, and this safety-first design is part of why the project stands out academically.
Key Features
- Companion Chat: A genuinely empathetic AI conversation powered by LLaMA 3.3 70B through the Groq API, tuned to respond with warmth rather than generic replies.
- Crisis Detection: A layered safety system using keyword matching and sentiment analysis that surfaces helpline cards instantly. It keeps working even if the Groq API goes down, so the safety net never disappears.
- Daily Mood Check-in: Quick 1 to 5 mood logging with tags and short notes, so patterns become visible over time.
- Private Journal: A free-writing space with automatic VADER sentiment analysis running locally, so entries stay private and offline.
- Insight Dashboard: A 14-day mood trend chart, a sentiment trend chart, a streak counter, and a weekly insight summary, all rendered with Chart.js.
- Crisis Resources: India's 24/7 mental health helplines are always reachable from the navigation bar.
- Safety-first Design: An onboarding disclaimer, a persistent footer disclaimer, and a CrisisLog audit table that records when help was offered.
How MindMate Works
When a user opens a chat, their message travels to a dedicated Groq service layer that calls LLaMA 3.3 70B for an empathetic reply. Before and after that call, a separate crisis-detection step scans the text using keyword rules and sentiment scoring. If the system senses distress, it immediately shows verified helpline cards, regardless of whether the AI model responded. Journal entries are scored locally with VADER, so emotional trends are tracked without sending personal writing to any external service. Everything a user logs feeds the dashboard charts, turning scattered feelings into a clear, visual picture of their week.
Where It Can Be Used
- College and university student-wellness portals
- Personal mood journaling and self-reflection tools
- Mental health awareness initiatives and campus campaigns
- A foundation for research projects on sentiment analysis and emotional AI
- A practical demo of responsible AI design for academic evaluation
Why This Makes a Strong Final Year Project
MindMate touches almost every concept your panel wants to see in a final year project: API integration with a real large language model, local sentiment analysis, a relational database, data visualisation, and thoughtful safety engineering. The crisis-detection fallback alone gives you a genuine talking point about building software responsibly. Because the topic is socially relevant, you will find it easy to explain the motivation, the architecture, and the impact during your presentation. It suits BCA, MCA, BTech CSE, and BSc IT students who want a project that feels current and human rather than a tired clone.
What You Get
You receive the complete, working source code with a clean folder structure, a local SQLite database, and a Chart.js-based dashboard ready to run. Need it polished for submission? You can add a custom project report, research paper, or PPT and even get publishing guidance. If you would rather have us walk you through the codebase, our project setup and source code explanation service covers installation, a line-by-line walkthrough, and an architecture overview over a screen-share call.
Want something shaped around your own concept instead? Share your idea and our team can build a custom version for you. You can also browse more ready-made final year projects with source code across AI, web, and mobile categories.
Related Projects You May Like
If MindMate fits your interest, you will probably like RecipeGen, which uses a similar Django and Groq LLaMA stack, and PCOS Tracker, another wellness-focused AI final year project.
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- Custom Project Report: ₹1,500
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