Mental_Health_analysis

An AI-powered chatbot that detects mental health conditions and provides supportive interactions in real-time

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Mental Health Analysis Chatbot

Inspiration

Mental health issues are often overlooked, and many individuals struggle to express their emotions or seek professional help. Our goal was to create an AI-powered chatbot that can detect emotions and mental health conditions through text-based conversations, providing users with supportive interactions and guidance.

What it does

The chatbot analyzes user messages and predicts their emotional state using a fine-tuned RoBERTa model. It detects emotions such as anxiety, depression, stress, and more. Based on the detected state, the chatbot adjusts its tone and responses to provide a more empathetic conversation.

How we built it

  • We fine-tuned a RoBERTa model on a mental health dataset to classify emotions and mental health conditions.
  • Used Hugging Face Transformers for text classification.
  • Integrated the model into a Streamlit web application for a user-friendly interface.
  • Used Google Drive for model storage to avoid GitHub LFS limitations.
  • Hosted the application on Streamlit Cloud for accessibility.

Challenges we ran into

  • Managing large model files with GitHub's LFS quota limits.
  • Ensuring the chatbot's responses were empathetic and contextually relevant.
  • Handling real-time model inference efficiently within the Streamlit app.
  • Overcoming deployment issues related to repository access and model hosting.

Accomplishments that we're proud of

  • Successfully fine-tuned a deep learning model for mental health analysis.
  • Built a working chatbot that detects emotional states with high accuracy.
  • Deployed the application, making it accessible for users to interact with.

What we learned

  • Fine-tuning transformer models for sentiment and emotion analysis.
  • Handling model storage and deployment challenges.
  • Implementing AI-driven conversations with an empathetic approach.
  • Using Streamlit for building interactive AI applications.

What's next for Mental Health Analysis

  • Enhancing AI responses with a more sophisticated dialogue system.
  • Integrating voice support for a more interactive experience.
  • Adding support for multiple languages to increase accessibility.
  • Connecting with mental health professionals for guidance and validation.
  • Improving real-time analytics to provide better insights into user emotions.

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