metadata

title: NLP Sentiment Analysis App
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 3.50.0
app_file: app.py
pinned: false

NLP Sentiment Analysis 🚀

This is a sentiment analysis web app built using Hugging Face Transformers and Gradio, deployed on Hugging Face Spaces.

🌟 Features

✅ Uses DistilBERT for sentiment analysis.
✅ Provides positive/negative sentiment classification.
✅ Web-based UI using Gradio.
✅ Easy deployment on Hugging Face Spaces.

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

git clone https://github.com/your-repo/mental-health-assistant.git
cd mental-health-assistant

2. Install Dependencies

pip install -r requirements.txt

3. Download NLTK Resources

import nltk
nltk.download('stopwords')
nltk.download('wordnet')

Training the Model

Step 1: Preprocess and Train

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 test.py

Deployment on AWS

This project can be deployed on AWS Lambda & API Gateway using Flask.

1. Create an AWS Lambda Function

Package your code into a ZIP file:
```
zip -r deployment.zip *
```
Upload the ZIP file to AWS Lambda.
Set the runtime to Python 3.8+.

Create a handler function (lambda_function.py):

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()

Deploy with AWS API Gateway to create an endpoint.

2. Access API

Once deployed, you can access the API via:

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
Open for Contributions 🚀