💭 Emotion Detector Web App 😃😢😡

Welcome to the Emotion Detector Web App! This project is a user-friendly web application that detects emotions from text using a machine learning model trained on the GoEmotions dataset. Whether you're building a chatbot, analyzing social media, or just curious about NLP, this project is for you! 🚀

📦 Features

• Detects a wide range of emotions from text
• Beautiful and interactive web interface (Streamlit)
• Preprocessing, training, and evaluation scripts included
• Uses scikit-learn for easy customization
• Ready-to-use trained model

📁 Project Structure

GALLERY/
├── app.py                # Streamlit web app for emotion detection
├── train.py              # Training and evaluation script
├── model.py              # Model pipeline definition
├── preprocess.py         # Text cleaning utilities
├── goemotions/           # Dataset and label files
│   ├── train.tsv
│   ├── test.tsv
│   ├── dev.tsv
│   ├── emotions.txt
│   └── ekman_mapping.json
├── emotion_model.pkl     # Saved trained model
├── emotion.txt           # (Optional) Additional emotion info
└── README.md             # Project documentation

🛠️ How to Run the Web App

1. Clone the repository

   git clone https://github.com/nithins7676/emotion-classification.git
   cd emotion-classification

2. Install dependencies

   pip install streamlit scikit-learn pandas numpy joblib

3. Train the model (if not already trained)

   python train.py

   This will preprocess the data, train the model, and save it as emotion_model.pkl.

4. Run the web app

   streamlit run app.py

   Then open your browser and go to the local URL shown in the terminal (usually http://localhost:8501).

📝 Example: Predicting Emotions in Python

from joblib import load
model = load('emotion_model.pkl')
text = ["I am so happy to see you!"]
pred = model.predict(text)
print(f"Predicted emotion: {pred[0]}")

🤖 Dataset

• Based on the GoEmotions dataset
• Contains 27 emotion labels + neutral

✨ Contributing

Pull requests are welcome! For major changes, please open an issue first.

📧 Contact

For questions, open an issue or contact the maintainer.

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Made with ❤️, Streamlit, and Python 🐍