SymptoCare

SymptoCare is a smart AI assistant for symptom analysis and mental wellness. The project combines machine learning for health risk prediction with conversational AI for mental health support.

Tech Stack:

• Frontend: React.js
• Backend: Flask (Python)
• Machine Learning: scikit-learn
• AI Integrations: OpenAI GPT, Gemini
• Database: Firebase Firestore

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Features

• Symptom Checker: Users enter symptoms and receive AI-driven risk insights.
• Mental Wellness Chat: AI-powered conversational support (choose GPT or Gemini).
• Doctor Consultation Module: Browse doctors, book appointments, and manage schedules.
• Secure and Private: No sensitive data is stored without user consent.

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Project Structure

symptocare/
  backend/    # Flask API and ML models
  frontend/   # React app

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Getting Started

1. Clone the Repo

git clone https://github.com/yourusername/symptocare.git
cd symptocare

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2. Setup & Run the Backend (Flask)

cd backend
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt
python app.py  # or flask run

By default, the backend runs on http://localhost:5000

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3. Setup & Run the Frontend (React)

cd ../frontend
npm install
npm start

By default, the frontend runs on http://localhost:3000

Note: The React app should call the Flask backend (e.g., via /api endpoints). Update proxy settings in frontend/package.json if needed:

"proxy": "http://localhost:5000"

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Machine Learning Models

• Data cleaning and feature engineering on clinical symptom data.
• Trained using logistic regression and other classifiers (accuracy: ~91%).
• Models serialized with pickle and served as API endpoints.

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Configuration

• API Keys:

  • Place your OpenAI/Gemini API keys in the backend’s .env file or directly in config.py (never commit secrets to source control).

• Firebase:

  • Update Firebase config in the frontend for Firestore integration.

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Deployment

• Frontend: Can be deployed to Netlify, Vercel, or any static hosting.
• Backend: Deploy Flask to Heroku, Render, or AWS (set CORS appropriately).

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Future Improvements

• Real-time doctor chat
• More symptom and condition coverage
• Multilingual chatbot

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License

MIT License. See LICENSE.

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Acknowledgments

Thanks to all contributors and the original inspiration from this Medium article.

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Feel free to copy, edit, or expand this README as you develop the project!