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license: mit |
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# AI Health Assistant |
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This project is a Flask-based web application that provides several machine learning-powered features such as: |
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- Counseling Response Generation using a GPT-2 model. |
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- Medication Information Generation using a GPT-2 model. |
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- Diabetes Classification using a Random Forest classifier. |
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- Medicine Classification using a K-Nearest Neighbors (KNN) model. |
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- General Chat powered by LLaMA 3.1 API hosted on Groq Cloud for AI-powered conversations. |
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The project is divided into two main parts: Backend (Flask) and Frontend (HTML, CSS, JavaScript), with a connection to pre-trained machine learning models. |
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### Project Setup |
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- **System Requirements:** |
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- Python 3.8+ |
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- Flask |
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- Transformers library (for GPT-2 models) |
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- Joblib (for loading pre-trained models) |
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- Langchain Groq (for LLaMA integration) |
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- Frontend: HTML, CSS, JavaScript |
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- **Project Structure:** |
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``` |
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AI Health Assistant/ |
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│ |
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├── backend/ |
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│ ├── models/ |
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│ │ ├── mental_health_model/ |
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│ │ ├── medication_info/ |
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│ │ ├── diabetes_model/ |
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│ │ ├── medication_classification_model/ |
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│ ├── utils.py |
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├── frontend/ |
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│ ├── index.html |
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│ ├── styles.css |
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│ ├── script.js |
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├── app.py |
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├── requirements.txt |
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### Backend |
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**Counseling Response Generation:** |
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- Generates counseling-related responses using a GPT-2 mental health model. |
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**Medication Information Generation:** |
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- Provides medication-related responses using a GPT-2 medication model. |
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**Diabetes Classification:** |
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- Classifies users as diabetic or non-diabetic based on glucose, BMI, and age using a Random Forest classifier. |
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**Medicine Classification:** |
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- Predicts suitable medications based on gender, blood type, medical condition, and test results using a K-Nearest Neighbors (KNN) model. |
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**General Chat:** |
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- Offers general chat responses using LLaMA 3.1 API hosted on Groq Cloud for AI-powered conversations. |
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### Frontend |
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**Diabetes Classification Tab:** |
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- Form input for glucose, BMI, and age to classify diabetes risk. |
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**Medicine Classification Tab:** |
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- Input fields for gender, blood type, medical condition, and test results to classify appropriate medications. |
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**Counseling and Medication Tabs:** |
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- Text inputs for receiving AI-generated responses for counseling and medication questions. |
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**General Chat Tab:** |
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- General-purpose chatbot powered by LLaMA 3.1 for natural conversations. |
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**Dark Mode:** |
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- Toggle dark mode for user interface customization. |
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### Usage |
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1. **Access the Application:** Users interact with the web interface, accessible through a browser once the Flask server is running. |
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2. **Input Data:** Users provide medical-related information or general queries depending on the feature they want to use. |
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3. **Receive Responses:** Based on the input, AI models provide responses such as classification results (diabetes, medicine) or generated text (counseling, medication, chat). |
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4. **Interactive Interface:** Users can toggle between different features, making it suitable for general chat, medical insights, or counseling help. |
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### WebApp |
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