AI_Health_Assistant

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