Emotion Classifier App

This is a Streamlit-based web application for detecting emotions in text using a pre-trained PySpark model. It allows users to input text, predicts the emotion associated with it, and provides a confidence score for the prediction. The app also provides monitoring capabilities to track page visits and analyze emotion classifier metrics.

Features

• Emotion detection in text input.
• Real-time prediction of emotions using a pre-trained PySpark model.
• Visualization of prediction results using interactive charts.
• Monitoring capabilities to track page visits and classifier metrics.
• Database integration for storing page visit and prediction details.

Setup

1. Clone the Repository:

   git clone https://github.com/yourusername/Emotion-Classifier-App.git
   cd Emotion-Classifier-App

2. Install Dependencies:

   pip install -r requirements.txt

3. Set Up MySQL Database:

   • Ensure you have MySQL installed and running.
   • Create a database named DB.
   • Update database.py with your MySQL connection details.

4. Run the Application:

   streamlit run app.py

Usage

• Home: Allows users to input text for emotion detection. Predictions and confidence scores are displayed along with interactive charts showing prediction probabilities.
• Monitor: Provides monitoring capabilities to track page visits and analyze emotion classifier metrics. Users can view page visit details, page metrics, and emotion classifier metrics.

Components

1. Backend

• Spark Model Loading: Loads the pre-trained PySpark model for emotion detection.
• Prediction Function: Defines a function to predict emotions using the loaded model.
• Database Integration: Utilizes MySQL for storing page visit and prediction details.
• Database Functions: Includes functions to create tables, add details, and view data from the database.

2. Frontend

• Streamlit Application: Implements the web interface for the emotion classifier app.
• User Input: Provides a text area for users to input text for emotion detection.
• Prediction Display: Shows predicted emotions and confidence scores.
• Interactive Charts: Visualizes prediction results using Altair and Plotly Express charts.
• Monitoring: Enables users to monitor page visits and emotion classifier metrics.

Contributing

Contributions are welcome! Please feel free to open issues or submit pull requests.