A Machine Learning project that uses Natural Language Processing (NLP) and Logistic Regression to classify text into different emotions.
The trained ML pipeline is integrated with FastAPI and a modern frontend built with HTML, CSS, and JavaScript.
Live demo: https://emotiq-ai.onrender.com
- 🧠 NLP-based emotion classification
- 🔤 Text vectorization using CountVectorizer
- 🤖 Logistic Regression classification
- 🔌 FastAPI REST API
- 🛡️ Pydantic input validation
- 📊 Prediction confidence and probability distribution
- 🎨 Modern responsive frontend
- ⚡ Real-time prediction without page reload
- 🌐 CORS support
- 📱 Responsive UI for desktop and mobile
The model predicts six emotion categories:
- Sadness
- Anger
- Love
- Surprise
- Fear
- Joy
- Python
- Scikit-learn
- CountVectorizer
- Logistic Regression
- Joblib
- FastAPI
- Pydantic
- Uvicorn
- HTML
- CSS
- JavaScript