The Emotion Detection System is an advanced interactive chatbot engineered to understand and respond to user emotions through cutting-edge technology. By harnessing the power of computer vision and natural language processing, this system effectively analyzes facial expressions and voice inputs to deliver a personalized conversational experience. The application is designed to enhance user interaction by accurately recognizing emotions and providing empathetic responses.
| Feature | Description |
|---|---|
| Emotion Detection | Utilizes the FER library to analyze users' facial expressions in real time. |
| Voice Interaction | Enables users to communicate naturally through speech using the SpeechRecognition library. |
| Text-to-Speech | Converts chatbot responses into spoken words utilizing the pyttsx3 library for better engagement. |
| Conversation Logging | Automatically records user interactions in a diary log for future reference and self-reflection. |
| Emotion Logging | Tracks and logs detected emotions over time to provide insights into emotional trends. |
| Mood Trend Visualization | Presents a graphical representation of logged emotions using Streamlit, making it easy to interpret trends. |
To run the Emotion Detection System, ensure you have Python 3.x installed, along with the following libraries:
| Library | Description |
|---|---|
openai |
Integrates OpenAI's API for natural language processing capabilities. |
opencv-python |
Provides functionalities for real-time computer vision tasks. |
fer |
Implements facial expression recognition for emotion detection. |
SpeechRecognition |
Facilitates voice command input for user interaction. |
pyttsx3 |
Converts text responses into speech for a more engaging experience. |
streamlit |
Enables the creation of a user-friendly web interface for interaction. |
pandas |
Supports data manipulation and analysis for emotion tracking. |
python-dotenv |
Loads environment variables from a .env file for configuration. |
You can easily install the necessary libraries using pip. Run the following command in your terminal:
pip install openai opencv-python fer SpeechRecognition pyttsx3 streamlit pandas python-dotenv
Follow these steps to set up the Emotion Detection System:
-
Clone the Repository:
git clone <repository-url> cd <repository-directory>
-
Environment Configuration: Create a
.envfile in the project root and add your OpenAI API key:OPENAI_API_KEY=your_api_key_here -
Running the Application:
- To launch the chatbot, execute:
python chat.py
- To access the Streamlit interface, run:
streamlit run app.py
- Initiate the Chatbot: Start the chatbot to detect your initial emotional state through webcam input.
- Interact with the Chatbot: Use voice commands to express your thoughts and feelings; the chatbot will respond accordingly.
- Review Logs: Access the Streamlit interface to view logged conversations and emotional trends, facilitating self-reflection.
We aim to continuously improve the Emotion Detection System with the following features:
| Future Feature | Description |
|---|---|
| Advanced Emotion Recognition | Incorporate state-of-the-art machine learning models for more accurate emotion detection. |
| Multi-language Support | Enable the chatbot to understand and respond in multiple languages, expanding accessibility. |
| User Profiles | Allow users to create personal profiles to store their interaction history for customized responses. |
| Sentiment Analysis | Implement sentiment analysis techniques to enhance context understanding and improve response quality. |
| Data Export Functionality | Enable users to export their logs (conversations and emotions) in various formats (CSV, JSON). |
| Mood Tracking Notifications | Send periodic notifications to users to check their mood and recommend mindfulness exercises or coping strategies. |
We welcome contributions from the community! If you have suggestions for improvements or would like to report a bug, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Commit your changes and push them to your fork.
- Open a pull request detailing your changes.
- OpenAI: For providing access to the GPT model that powers the conversational capabilities.
- FER: For its robust emotion detection framework.
- Streamlit: For facilitating the development of an interactive web interface.
- Community Contributors: For their valuable feedback and contributions to the project.
For any inquiries or support, please contact the project maintainers via GitHub.