Update README.md

README.md

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-license: mit
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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.