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license: mit

AI Health Assistant

This project is a Flask-based web application that provides several machine learning-powered features such as:

  • Counseling Response Generation using a GPT-2 model.
  • Medication Information Generation using a GPT-2 model.
  • Diabetes Classification using a Random Forest classifier.
  • Medicine Classification using a K-Nearest Neighbors (KNN) model.
  • General Chat powered by LLaMA 3.1 API hosted on Groq Cloud for AI-powered conversations.

The project is divided into two main parts: Backend (Flask) and Frontend (HTML, CSS, JavaScript), with a connection to pre-trained machine learning models.

Project Setup

  • System Requirements:

    • Python 3.8+
    • Flask
    • Transformers library (for GPT-2 models)
    • Joblib (for loading pre-trained models)
    • Langchain Groq (for LLaMA integration)
    • Frontend: HTML, CSS, JavaScript

  • Project Structure:

    AI Health Assistant/
│
├── backend/
│   ├── models/
│   │   ├── mental_health_model/
│   │   ├── medication_info/
│   │   ├── diabetes_model/
│   │   ├── medication_classification_model/
│   ├── utils.py
├── frontend/
│   ├── index.html
│   ├── styles.css
│   ├── script.js
├── app.py
├── requirements.txt

Backend

Counseling Response Generation:

  • Generates counseling-related responses using a GPT-2 mental health model.

Medication Information Generation:

  • Provides medication-related responses using a GPT-2 medication model.

Diabetes Classification:

  • Classifies users as diabetic or non-diabetic based on glucose, BMI, and age using a Random Forest classifier.

Medicine Classification:

  • Predicts suitable medications based on gender, blood type, medical condition, and test results using a K-Nearest Neighbors (KNN) model.

General Chat:

  • Offers general chat responses using LLaMA 3.1 API hosted on Groq Cloud for AI-powered conversations.

Frontend

Diabetes Classification Tab:

  • Form input for glucose, BMI, and age to classify diabetes risk.

Medicine Classification Tab:

  • Input fields for gender, blood type, medical condition, and test results to classify appropriate medications.

Counseling and Medication Tabs:

  • Text inputs for receiving AI-generated responses for counseling and medication questions.

General Chat Tab:

  • General-purpose chatbot powered by LLaMA 3.1 for natural conversations.

Dark Mode:

  • Toggle dark mode for user interface customization.

Usage

  1. Access the Application: Users interact with the web interface, accessible through a browser once the Flask server is running.

  2. Input Data: Users provide medical-related information or general queries depending on the feature they want to use.

  3. Receive Responses: Based on the input, AI models provide responses such as classification results (diabetes, medicine) or generated text (counseling, medication, chat).

  4. Interactive Interface: Users can toggle between different features, making it suitable for general chat, medical insights, or counseling help.

WebApp

App Screenshot