🌿 Mental Health Emotion Analysis

🧠 About the Project

In today's digital world, understanding emotions through text can help promote better mental well-being. This project allows users to analyze their emotions simply by pasting text into our site—immediately revealing the emotional tone behind their words.

Built with React.js on the frontend and Flask on the backend, this platform leverages cutting-edge Natural Language Processing (NLP) to provide real-time emotion analysis, making it easier to reflect on and improve mental health.

💡 Why This Matters

• 💙 Empower Self-Awareness: Understand emotions behind text messages.
• 🔍 Real-Time Emotion Detection: Get instant feedback on the emotional impact of your words.
• 🌱 Mental Health Awareness: Recognize emotional patterns and seek support if needed.
• 🖥️ Seamless Experience: Built for accuracy, speed, and ease of use.

🚀 Key Features

✅ Instant Emotion Analysis – Simply paste any text and get real-time emotional insights.
✅ Deep Sentiment Breakdown – Categorizes emotions into happiness, sadness, anger, anxiety, etc.
✅ Intuitive & User-Friendly UI – Clean and engaging interface for seamless interaction.
✅ AI-Powered Analysis – Leveraging NLP & Machine Learning to improve accuracy.
✅ Scalable & Secure – A robust Flask backend ensures data security and performance.

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🖼️ Screenshots

🔹 Homepage

🔹 Emotion Analysis Input

🔹 Detailed Sentiment Breakdown

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🔧 Tech Stack

🎨 Frontend (React.js)

• React.js – Modern frontend library for dynamic UI.
• Tailwind CSS / Styled Components – For a sleek and responsive design.
• Context API / Redux – For efficient state management.

🧠 Backend (Flask & NLP)

• Flask (Python) – Lightweight and powerful backend framework.
• Natural Language Processing (NLP) – Using NLTK, TextBlob, or TensorFlow for sentiment analysis.
• REST API – Seamlessly processes text input and returns analyzed emotions.

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📌 How to Use This Project

💻 Frontend Setup

1. Clone the Frontend Repository:

   git clone https://github.com/sinless_rook/baymax.git
   cd baymax

2. Install Dependencies:

   npm install

3. Run the React App:

   npm start

🧠 Backend Setup

1. Clone the Backend Repository:

   git clone https://github.com/sinless_rook/baymax-backend.git
   cd baymax-backend

2. Create and Activate Virtual Environment:

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # macOS/Linux
   source venv/bin/activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Flask App:

   flask run

The Flask backend will typically run on http://localhost:5000 and the React frontend on http://localhost:3000.

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✅ Verify Installations

To confirm that both frontend and backend are running smoothly:

1. Open the React app in the browser: http://localhost:3000
2. Paste some text and check if the backend returns the correct emotional analysis.

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