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---
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
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