Upload README.md

README.md

@@ -0,0 +1,125 @@
+# Personalized Mental Health Assistant
+
+## Overview
+This project is an AI-powered **Mental Health Assistant** that analyzes user queries, detects emotions, and provides relevant responses and resources. It leverages **NLP (Natural Language Processing)** techniques and **Machine Learning (ML)** models for sentiment analysis, emotion classification, and response generation.
+
+## Features
+- **Sentiment & Emotion Detection**: Classifies user queries based on emotions (Happy, Sad, Anxious, Overwhelmed, etc.).
+- **Preprocessing Pipeline**: Cleans and preprocesses text data using **NLTK**.
+- **TF-IDF Vectorization**: Converts user queries into numerical representations.
+- **ML Classification**: Uses a **Random Forest Classifier** to predict emotions.
+- **Response Generation**: Suggests appropriate responses based on detected emotions.
+- **AWS Deployment**: Deploys as an API using **Flask & AWS Lambda**.
+
+## Dataset Columns
+The dataset includes the following columns:
+| Column | Description |
+|--------|-------------|
+| ID | Unique identifier for each query |
+| User Query | Raw text input from the user |
+| Preprocessed Text | Cleaned version of the user query |
+| Sentiment | Sentiment label (Positive, Neutral, Negative) |
+| Emotion | Emotion label (e.g., Happy, Sad, Anxious) |
+| Severity | Severity level of distress |
+| Intent | Intent classification for responses |
+| Suggested Response | AI-generated response for user support |
+| Recommended Action | Suggested action based on the query |
+| Related Resources | External resources for mental health support |
+| Time of Interaction | Timestamp of the query |
+
+---
+## Installation
+### **1. Clone Repository**
+```bash
+git clone https://github.com/your-repo/mental-health-assistant.git
+cd mental-health-assistant
+```
+
+### **2. Install Dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+### **3. Download NLTK Resources**
+```python
+import nltk
+nltk.download('stopwords')
+nltk.download('wordnet')
+```
+
+---
+## Training the Model
+### **Step 1: Preprocess and Train**
+```python
+python train.py
+```
+This script:
+- Loads the dataset.
+- Preprocesses text using **NLTK**.
+- Extracts features using **TF-IDF**.
+- Trains a **Random Forest Classifier**.
+- Saves the trained model and vectorizer.
+
+### **Step 2: Test Model with Example Queries**
+```python
+python test.py
+```
+---
+## Deployment on AWS
+This project can be deployed on **AWS Lambda & API Gateway** using Flask.
+
+### **1. Create an AWS Lambda Function**
+1. Package your code into a **ZIP file**:
+   ```bash
+   zip -r deployment.zip *
+   ```
+2. Upload the ZIP file to AWS Lambda.
+3. Set the runtime to **Python 3.8+**.
+4. Create a handler function (`lambda_function.py`):
+
+   ```python
+   from flask import Flask, request, jsonify
+   import joblib
+
+   app = Flask(__name__)
+   model = joblib.load("mental_health_model.pkl")
+   vectorizer = joblib.load("tfidf_vectorizer.pkl")
+   label_encoder = joblib.load("label_encoder.pkl")
+
+   def predict_emotion(query):
+       vectorized_input = vectorizer.transform([query]).toarray()
+       prediction = model.predict(vectorized_input)
+       return label_encoder.inverse_transform(prediction)[0]
+
+   @app.route("/predict", methods=["POST"])
+   def predict():
+       data = request.get_json()
+       query = data.get("query", "")
+       response = predict_emotion(query)
+       return jsonify({"emotion": response})
+
+   if __name__ == "__main__":
+       app.run()
+   ```
+5. Deploy with **AWS API Gateway** to create an endpoint.
+
+### **2. Access API**
+Once deployed, you can access the API via:
+```bash
+curl -X POST "https://your-api-gateway-url.amazonaws.com/prod/predict" -H "Content-Type: application/json" -d '{"query": "I feel stressed and anxious."}'
+```
+
+---
+## Future Improvements
+- **Use Deep Learning (LSTMs, Transformers)** for better accuracy.
+- **Integrate GPT-based models** for advanced response generation.
+- **Expand dataset** to include more diverse mental health situations.
+
+---
+## License
+MIT License. Free to use and modify.
+
+---
+## Contributors
+- **Your Name** – [GitHub Profile](https://github.com/your-github)
+- **Open for Contributions** 🚀