🌿 EcoAyur: Herbal Medicine Recommendation System

EcoAyur is a web application designed to recommend herbal remedies based on user-input symptoms. The application combines machine learning, web scraping, and data visualization to provide personalized herbal recommendations. It also supports multilingual capabilities to cater to a wide audience.

Disclaimer: The information provided by EcoAyur may not be 100% accurate as it is generated by a machine learning model and may contain errors. Always consult a professional before use.

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⚖️ Key Technologies and Libraries

🔢 Frontend

• React.js: Framework for building the user interface using functional components and React Hooks (e.g., useState).
• Recharts: Data visualization library for creating interactive charts.
• CSS: Custom styling with responsive design using Flexbox and Grid layouts.
• React Router: Navigation management.
• Axios: HTTP client for making requests to the backend API.

🔧 Backend

• Flask: Python web framework for creating the backend API.
• NumPy & Pandas: Libraries for data manipulation and numerical computation.
• scikit-learn: Machine learning library for training models.
  • RandomForestClassifier: Used for recommending herbal remedies and crop prediction.
• Flask-CORS: Cross-Origin Resource Sharing management.
• Joblib: For serializing machine learning models.

🤖 Machine Learning

• Random Forest: Model used for multi-label classification in herbal remedy recommendations.
• Web Scraping: Collects herbal data from external sources like BeautifulSoup and requests.
• Text Vectorization (TF-IDF): For analyzing and processing symptom data.

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🌟 Features

🌿 Herbal Recommendation System

• Machine Learning Recommendations: Suggests the most suitable herbal remedies based on symptoms.
• Web Scraping: Retrieves herbal data from external trusted sources.
• Multilingual Support: Available in English, Hindi, and Telugu.

🖼 User Interface

• Interactive Forms: Collects symptom data using dropdowns and dynamic forms.
• Data Visualization: Displays interactive charts (e.g., pie charts, bar graphs) for market trends and herb effectiveness.
• Responsive Design: Adapts to both mobile and desktop devices using Flexbox and Grid layouts.

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🏰 Project Structure

frontend/                   # React.js frontend
    public/
    src/
    package.json
    index.html
backend/                    # Flask backend
    app.py
    data/
        herbal_remedies.csv
    ml_model.py
    requirements.txt
    render.yaml
    __pycache__/

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⚙️ Installation and Setup

🔧 Backend Setup (Flask)

1. Clone the repository:

   git clone https://github.com/haniyakonain/datathon.git
   cd ecoayur/backend

2. Create and activate a Python virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required dependencies:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

🔄 Frontend Setup (React)

1. Navigate to the frontend directory:

   cd frontend

2. Install the required dependencies:

   npm install

3. Run the frontend in development mode:

   npm run dev

🚀 Running the Full Application

1. Ensure both frontend and backend servers are running.
2. Open a browser and navigate to http://localhost:3000 to view the application.

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✨ Key Features

🌿 Herbal Recommendation System

• Machine Learning-Based Recommendations: The system uses a trained model to suggest herbal remedies based on input symptoms.
• Web Scraping: Retrieves herbal data from external sources like NCCIH, BSI, and NGDC.
• Multilingual Support: Supports English, Hindi, and Telugu.

🖼 User Interface

• Interactive Forms: Dropdown selectors and dynamic forms collect user symptoms.
• Data Visualization: Displays charts such as pie charts and bar graphs for crop suitability and herb effectiveness.
• Responsive Design: Fully mobile and desktop-friendly.

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📊 Example Outputs

🌿 Ayurvedic Recommendations:

• Input: Symptoms: Headache, Seasonal Allergies
• Output:
  • Recommendation 1: 25% Match
    • Herbs: Butterbur, Nettle Leaf, Elderflower
    • Ingredients: Butterbur extract, Nettle leaves, Elderflower
    • Instructions: Boil Butterbur and Elderflower in water.
    • Recipe: 1 cup water, 1 tsp each herb
    • Dosage: Twice daily

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🌱 Health Benefits of Herbs

Amla (Indian Gooseberry)

• Other Names: आंवला (Amla), ఉసిరి (Usiri)
• Benefits:
  • Rich in Vitamin C
  • Boosts immunity
  • Improves digestion
  • Enhances hair health

Arjuna

• Other Names: अर्जुन (Arjun), మద్ది చెట్టు (Maddi Chettu)
• Benefits:
  • Supports heart health
  • Maintains healthy blood pressure
  • Strengthens cardiac muscles

Ashwagandha

• Other Names: अश्वगंधा (Ashwagandha), అశ్వగంధ (Ashwagandha)
• Benefits:
  • Reduces stress and anxiety
  • Improves sleep quality
  • Boosts immunity

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🌱 Crop Growing Recommendation System

Input:

• Soil Type: Clay Soil
• Climate Zone: Temperate Climate
• Water Resources: Medium Water Availability
• Farm Location: Hyderabad

Output:

• Recommended Crops: Wheat, Barley, Oats
• Weather Data: Temperature: 23.23°C, Weather: Haze
• Market Trends: Price and demand for Wheat, Barley, Oats

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👨‍🌿 Explore Medicinal Herbs

Example Herb: Arjuna

Arjuna is a herb used for heart health, with the bark containing antioxidants and minerals that support cardiovascular health.

• Benefits: Strengthens heart muscles, reduces chest pain, helps manage blood pressure.
• Market Price: ₹200-300 per 100g (Powder), ₹400-600 per bottle (Capsules)
• How to Use: Typically taken as powder with warm water or milk.

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🌳 Additional Features

Healthcare Services:

• Find Ayurvedic doctors and medical facilities based on location.
  • Example Output:
    • Location: Hyderabad
    • Specialty: Ayurvedic Doctor
    • Results:
      • Ayurvedic College, Kalyan Nagar
      • Government Ayurvedic Dispensary, Golconda Rd

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Made with ❤️ by Haniya Konain and team