---
title: How Am I Feeling? - Emotion Classifier
emoji: 🎭
colorFrom: purple
colorTo: blue
sdk: docker
pinned: false
license: mit
---

# 🎭 How Am I Feeling? - AI Emotion Classifier

An AI-powered emotion detection system that analyzes text and identifies 10 different emotions with 88.6% accuracy.

## 🌟 Features

- **10 Emotion Classes**: happiness, sadness, fear, embarrassment, disgust, drive, surprise, loneliness, love, excitement
- **Beautiful Web UI**: Modern, responsive interface with real-time analysis
- **High Accuracy**: 88.6% validation accuracy
- **Fast Inference**: <10ms per sentence
- **Word2Vec + Neural Network**: 300-dim embeddings → 128→64→10 network

## 🚀 Try It Out

Simply type or paste any text to see what emotions it conveys!

**Example sentences:**
- "I'm so grateful for this beautiful day!" → happiness
- "I miss the way things used to be" → sadness
- "I can't wait for the concert tomorrow!" → excitement
- "Every moment with you makes my heart complete" → love
- "I'm terrified of what might happen next" → fear

## 🧠 Technical Details

### Architecture
```
Input Text → Preprocessing → Word2Vec (300-dim) → Neural Network (128→64→10) → Top-5 Predictions
```

### Dataset
- **Size**: 100,000 sentences (10,000 per emotion)
- **Source**: Generated using LLaMA 3.1 70B via Deepinfra
- **Quality**: Diverse, natural language examples

### Model
- **Embeddings**: Word2Vec (Skip-gram, 300 dimensions)
- **Classifier**: Fully-connected neural network
- **Parameters**: 34,634 trainable parameters
- **Training**: 50 epochs with early stopping
- **Validation Accuracy**: 88.6%

## 📊 Performance

Per-emotion accuracy:
- Best: happiness, love, excitement (~92%)
- Good: sadness, fear, surprise (~88%)
- Moderate: embarrassment, drive, disgust (~84%)

## 💻 API Usage

```bash
curl -X POST http://your-space-url/analyze \
  -H "Content-Type: application/json" \
  -d '{"text": "I am so excited about this!"}'
```

Response:
```json
{
  "success": true,
  "predictions": [
    {"emotion": "excitement", "confidence": 0.92, "percentage": 92.0},
    {"emotion": "happiness", "confidence": 0.85, "percentage": 85.0},
    ...
  ]
}
```

## 🛠️ Built With

- **TensorFlow/Keras** - Deep learning
- **Gensim** - Word2Vec embeddings
- **Flask** - Web framework
- **NLTK** - Text processing

## 📝 License

MIT License - Free to use for personal or commercial projects!

## 🔗 Links

- **GitHub**: [emotion-classifier](https://github.com/yourusername/emotion-classifier)
- **Dataset**: Coming soon to Hugging Face Datasets

---

Built with ❤️ and Python